Capacity configuration optimization method for composite compressed air energy storage systems

Abstract

This application relates to the field of compressed air energy storage technology, and particularly to a method for optimizing the capacity configuration of a composite compressed air energy storage system. The method includes: determining a first capacity configuration optimization problem and a second capacity configuration optimization problem for the composite compressed air energy storage system; determining a first preset objective for the first capacity configuration optimization problem and a second preset objective for the second capacity configuration optimization problem; generating a solution strategy for the first capacity configuration optimization problem based on the first preset objective; generating a solution strategy for the second capacity configuration optimization problem based on the second preset objective; and optimizing the capacity configuration of the composite compressed air energy storage system based on the solution strategies for the first and second capacity configuration optimization problems. This solves the problems of related energy storage methods, which are subject to significant limitations, resulting in uncertain and fluctuating power output, which can adversely affect power quality and the stability of the power system.

Description

Technical Field

[0001] This application relates to the field of compressed air energy storage technology, and in particular to a method for optimizing the capacity configuration of a composite compressed air energy storage system. Background Technology

[0002] With the increasing severity of global environmental and climate change issues, countries and regions are intensifying their efforts to develop and efficiently utilize renewable energy, and a high proportion of renewable energy power supply has become a consensus. However, renewable energy sources such as wind power and photovoltaics are subject to limitations imposed by natural conditions, resulting in uncertain and fluctuating power output, which can adversely affect power quality and pose challenges to the power system and the absorption of new energy sources.

[0003] In power systems with a high proportion of renewable energy, the rational configuration of energy storage systems, such as using energy storage for peak shaving and valley filling, can effectively smooth the fluctuations in renewable energy output, improve the average utilization rate of transmission channels, provide flexible peak-shaving capacity, and realize the coordinated regulation of renewable energy power and thermal power. This is of great significance for improving the operating efficiency of the power system, improving the energy structure, and reducing carbon emissions.

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

[0005] However, among the commonly used energy storage methods mentioned above, there are the following drawbacks: (1) Pumped hydro storage is subject to geographical limitations, 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, certain safety risks, and a limited service life; (3) Traditional compressed air energy storage has low efficiency, usually requires additional fuel to heat the compressed air, and has a slow response speed, making it difficult to quickly respond to instantaneous changes in the power system, which urgently needs to be addressed. Summary of the Invention

[0006] This application provides a capacity configuration optimization method for a composite compressed air energy storage system to solve the problems that the energy storage methods of related technologies are subject to great limitations, resulting in uncertain and fluctuating output, which can easily have an adverse impact on power quality and the stability of the power system.

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

[0008] Determine the first capacity configuration optimization problem 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, a first preset objective for the first capacity configuration optimization problem and a second preset objective for the second capacity configuration optimization problem are determined. A solution strategy for the first capacity configuration optimization problem is generated based on the first preset objective, and a solution strategy for the second capacity configuration optimization problem is generated based on the second preset objective.

[0010] The capacity configuration of the composite compressed air energy storage system is optimized based on the solution strategies for the first capacity configuration optimization problem and the second capacity configuration optimization problem.

[0011] According to one embodiment of this application, before generating a solution strategy for the first capacity configuration optimization problem based on the first preset objective and generating a solution strategy for the second capacity configuration optimization problem based on the second preset objective, the method further includes:

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

[0013] Based on the input parameters, an initial feasible solution to the first capacity configuration optimization problem is determined, and based on the initial feasible solution and the second capacity configuration optimization problem, an initial optimal solution to the second capacity configuration optimization problem is obtained.

[0014] Based on the initial optimal solution, set the first initial upper bound target value of the first capacity configuration optimization problem, and determine the first initial lower bound target value of the first capacity configuration optimization problem based on the initial feasible solution, while determining the first initial iteration number of the first capacity configuration optimization problem.

[0015] According to one embodiment of this application, the step of generating a solution strategy for the first capacity configuration optimization problem based on the first preset target includes:

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

[0017] Based on the first objective variable, a first objective constraint of the first objective variable is added to the first capacity configuration optimization problem, and the initial optimal solution and the first capacity configuration optimization problem are combined to obtain the first iterative optimal solution of the first capacity configuration optimization problem.

[0018] The first capacity configuration optimization problem is solved based on the first iterative optimal solution.

[0019] According to one embodiment of this application, solving the first capacity configuration optimization problem based on the first iterative optimal solution includes:

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

[0021] Based on the first iterative optimal solution and the second capacity configuration optimization problem, the second iterative optimal solution of the second capacity configuration optimization problem is obtained;

[0022] The first initial upper bound objective value of the first capacity configuration optimization problem is updated based on the second iterative optimal solution to obtain the first iterative upper bound objective value of the first capacity configuration optimization problem.

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

[0024] 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;

[0025] 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, then the iteration stops and the target optimal solution is obtained;

[0026] 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, then a second target variable corresponding to the first iterative optimal solution is constructed, and a second target constraint of the second target variable is determined. Based on the second target constraint, iterative calculations are 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.

[0027] According to one embodiment of this application, the above-described capacity configuration optimization method for a composite compressed air energy storage system further includes:

[0028] Determine whether the second capacity configuration optimization problem is unsolvable;

[0029] If the second capacity configuration optimization problem has no solution, then a third objective variable for the first capacity configuration optimization problem is constructed, and a third objective constraint for the third objective variable is determined. Based on the third objective constraint, the second iterative optimal solution and the second capacity configuration optimization problem are iteratively calculated until the second capacity configuration optimization problem has a solution.

[0030] According to one embodiment of this application, the strategy for generating the solution to the second capacity configuration optimization problem based on the second preset objective includes:

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

[0032] Obtain initial values ​​for 0-1 variables, and use the pre-defined strong duality theory to transform the second capacity allocation optimization problem into an upper bound second capacity allocation optimization problem and a lower bound second capacity allocation optimization problem. Solve the upper bound second capacity allocation optimization problem to obtain the third iterative optimal solution of the upper bound second capacity allocation 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 allocation optimization problem.

[0033] Based on the third iterative optimal solution and the lower bound second capacity configuration optimization problem, the 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 using the fourth iterative optimal solution to obtain the second iterative lower bound target value of the second capacity configuration optimization problem.

[0034] 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;

[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, then the iteration stops, and the first iterative upper bound target value of the first capacity configuration optimization problem is updated using the first iterative optimal solution. Otherwise, the fourth objective variable and dual variable corresponding to the fourth iterative optimal solution are constructed, and the second initial iteration number is updated 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 one embodiment of this application, solving the upper bound second capacity configuration optimization problem includes:

[0037] 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.

[0038] According to an embodiment of the present invention, a capacity configuration optimization method for a composite compressed air energy storage system is proposed. This method determines a first capacity configuration optimization problem and a second capacity configuration optimization problem for the composite compressed air energy storage system. It also determines a first preset objective for the first capacity configuration optimization problem and a second preset objective for the second capacity configuration optimization problem. Based on the first preset objective, a solution strategy for the first capacity configuration optimization problem is generated. Based on the second preset objective, a solution strategy for the second capacity configuration optimization problem is generated. Finally, the capacity configuration of the composite compressed air energy storage system is optimized based on the solution strategies for the first and second capacity configuration optimization problems. This solves the problems of related energy storage methods, which are subject to significant limitations, resulting in uncertain and fluctuating power output and potentially adversely affecting power quality and power system stability. The method establishes an optimization configuration model for the composite compressed air energy storage system based on a compressed air energy storage energy hub architecture. It decomposes the configuration problem into a main problem and sub-problems. Based on fuzzy sets for source-load power prediction, an affine rule-based solution method for the capacity configuration model of the composite compressed air energy storage system is established, thereby ensuring efficient operation of the composite compressed air energy storage system under various operating conditions.

[0039] A second aspect of this application provides a capacity configuration optimization device for a composite compressed air energy storage system, comprising:

[0040] A determination module is used to determine the first capacity configuration optimization problem and the second capacity configuration optimization problem of the composite compressed air energy storage system.

[0041] The generation module is used 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, and to generate a solution strategy for the first capacity configuration optimization problem based on the first preset objective and a solution strategy for the second capacity configuration optimization problem based on the second preset objective.

[0042] An optimization module is used to optimize the capacity configuration of the composite compressed air energy storage system based on the solution strategies for the first capacity configuration optimization problem and the second capacity configuration optimization problem.

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

[0044] The first acquisition unit is used to acquire the input parameters of the composite compressed air energy storage system;

[0045] The first determining unit is configured to determine an initial feasible solution to the first capacity configuration optimization problem based on the input parameters, and to obtain an initial optimal solution to the second capacity configuration optimization problem based on the initial feasible solution and the second capacity configuration optimization problem.

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

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

[0048] The building unit is used to construct the first objective variable corresponding to the initial feasible solution;

[0049] The second acquisition unit is used 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 the first iterative optimal solution of the first capacity configuration optimization problem by combining the initial optimal solution and the first capacity configuration optimization problem.

[0050] The solution unit is used to solve the first capacity configuration optimization problem based on the first iterative optimal solution.

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

[0052] The first update subunit is used to update the first initial lower bound objective value of the first capacity configuration optimization problem using the first iterative optimal solution, so as to obtain the first iterative lower bound objective value of the first capacity configuration optimization problem.

[0053] Obtain a sub-unit, used to obtain the second iterative optimal solution of the second capacity configuration optimization problem based on the first iterative optimal solution and the second capacity configuration optimization problem;

[0054] The second update subunit is used to update the first initial upper bound objective value of the first capacity configuration optimization problem according to the second iterative optimal solution, so as to obtain the first iterative upper bound objective value of the first capacity configuration optimization problem.

[0055] According to an embodiment of the present invention, after obtaining the first iterative lower bound objective value 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:

[0056] The judgment sub-component is used to 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.

[0057] The first iteration 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 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 objective value and the first iterative lower bound objective value is greater than the preset convergence value, and to determine the second objective constraint of the second objective variable. Based on the second objective constraint, iterative calculations are 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 objective value and the iterative lower bound objective 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 for the above-mentioned composite compressed air energy storage system further includes:

[0060] The judgment module is used to determine whether the second capacity configuration optimization problem is unsolvable;

[0061] The iterative module is used to construct a third objective variable for the first capacity configuration optimization problem if the second capacity configuration optimization problem has no solution, determine the third objective constraint of the third objective variable, and iteratively calculate 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.

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

[0063] The third determining unit is used to determine 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.

[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 using a 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.

[0065] The second update unit is used to obtain the fourth iterative optimal solution of the lower bound second capacity configuration optimization problem based on the third iterative optimal solution and the lower bound second capacity configuration optimization problem, and to update the second initial lower bound target value using the fourth iterative optimal solution to obtain the second iterative lower bound target value of the second capacity configuration optimization problem.

[0066] The judgment unit is used to 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.

[0067] The third update unit is used to stop iterating 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, and update the first iterative upper bound target value of the first capacity configuration optimization problem using the first iterative optimal solution; otherwise, construct the fourth objective variable and 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.

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

[0069] The fourth determining unit is used 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] According to an embodiment of the present invention, a capacity configuration optimization device for a composite compressed air energy storage system determines a first capacity configuration optimization problem and a second capacity configuration optimization problem for the composite compressed air energy storage system. It also determines a first preset objective for the first capacity configuration optimization problem and a second preset objective for the second capacity configuration optimization problem. Based on the first preset objective, a solution strategy for the first capacity configuration optimization problem is generated; based on the second preset objective, a solution strategy for the second capacity configuration optimization problem is generated; and based on the solution strategies for the first and second capacity configuration optimization problems, the capacity configuration of the composite compressed air energy storage system is optimized. This solves the problems of related technologies where energy storage methods are subject to significant limitations, leading to uncertainties and fluctuations in power output, which can adversely affect power quality and the stability of the power system. The device establishes an optimization configuration model for the composite compressed air energy storage system based on a compressed air energy storage energy hub architecture, decomposes the configuration problem into a main problem and sub-problems, and establishes a solution method for the capacity configuration model of the composite compressed air energy storage system using affine rules based on source-load power prediction fuzzy sets, thereby ensuring that the composite compressed air energy storage system can operate efficiently under various operating conditions.

[0071] A third aspect of this application provides an electronic device, including: 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 for a composite compressed air energy storage system as described in the above embodiments.

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

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

[0074] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

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

[0076] Figure 1 This is a flowchart illustrating a capacity configuration optimization method for a composite compressed air energy storage system according to an embodiment of this application.

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

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

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

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

[0081] Explanation of reference numerals in the attached drawings: 10 - Capacity configuration optimization device for composite compressed air energy storage system; 100 - Determining module; 200 - Generating module; 300 - Optimizing module. Detailed Implementation

[0082] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0083] The capacity configuration optimization method for a composite compressed air energy storage system according to embodiments of this application is described below with reference to the accompanying drawings. Addressing the problem mentioned in the background art that the energy storage methods of related technologies are subject to significant limitations, resulting in uncertain and fluctuating power output, which can adversely affect power quality and the stability of the power system, this application provides a capacity configuration optimization method for a composite compressed air energy storage system. In this method, a first capacity configuration optimization problem and a second capacity configuration optimization problem of the composite compressed air energy storage system are determined; a first preset objective for the first capacity configuration optimization problem and a second preset objective for the second capacity configuration optimization problem are determined; a solution strategy for the first capacity configuration optimization problem is generated based on 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 based on the solution strategies for the first and second capacity configuration optimization problems. This addresses the problems of uncertain and fluctuating power output caused by the limitations of related energy storage technologies, which can adversely affect power quality and the stability of the power system. Based on the compressed air energy storage hub architecture, an optimal configuration model for a composite compressed air energy storage system is established. The configuration problem is decomposed into a main problem and sub-problems. Based on the fuzzy set of source-load power prediction, an affine rule is used to establish a solution method for the capacity configuration model of composite compressed air energy storage, thereby ensuring that the composite compressed air energy storage system can operate efficiently under various operating conditions.

[0084] Specifically, Figure 1 This is a flowchart illustrating a capacity configuration optimization method for a composite compressed air energy storage system provided in an embodiment of this application.

[0085] like Figure 1 As shown, the capacity configuration optimization method for this composite compressed air energy storage system includes the following steps:

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

[0087] Specifically, based on the shortcomings of related technologies in terms of geographical conditions, cost, flexibility, environmental impact, and multi-energy complementarity, this application proposes a composite compressed air energy storage hub architecture. Based on this composite compressed air energy storage hub architecture, capacity configuration is optimized to solve a series of challenges faced by high-proportion renewable energy access to the power system. This not only improves the flexibility and stability of the power system but also realizes multi-energy complementarity and efficient utilization, thereby optimizing system configuration, reducing costs, enhancing environmental friendliness, and improving system robustness.

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

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

[0090] Specifically, the composite compressed air energy storage hub architecture adopted in this application embodiment introduces a ground source heat pump 7, an electric chiller 6, and a solar thermal collector system 5 to provide additional heat supply to the energy flow hub based on the classic AA-CAES (Advanced Adiabatic Compressed Air Energy Storage) structure. This enables flexible combined cooling, heating, and power (CCHP) and combined cooling, heating, and power (CCHP) storage. The composite system can supply electricity and heat (cooling) externally. Cooling is provided by turbine exhaust and the electric chiller, while the heat source for external heating can be obtained from the heat pump, the solar thermal collector system, and the compressed heat collection system. Generally, a thermoelectric energy hub needs to have the buffering capacity for thermal and electrical energy. The electrical and thermal energy buffering functions of the energy hub are both realized by the built-in AA-CAES system, that is, the thermal energy buffering is achieved by the thermal storage tank, and the air storage tank and the thermal storage tank jointly provide the electrical energy buffering function.

[0091] Furthermore, based on the aforementioned composite compressed air energy storage energy hub architecture, this 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: a first capacity configuration optimization problem of the composite compressed air energy storage system, i.e., the main problem (min problem), and a second capacity configuration optimization problem of the composite compressed air energy storage system, i.e., the sub-problem (max-min problem). Based on the fuzzy set of source-load power prediction, an affine rule is used to establish a method for solving the capacity configuration model of composite compressed air energy storage, and the main problem and sub-problem are solved iteratively, providing an effective solution for 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, a first preset objective of the first capacity configuration optimization problem and a second preset objective of the second capacity configuration optimization problem are determined. A solution strategy for the first capacity configuration optimization problem is generated based on the first preset objective, and a solution strategy for the second capacity configuration optimization problem is generated based on the second preset objective.

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

[0094] According to one embodiment of this application, a solution strategy for a first capacity configuration optimization problem is generated based on a first preset objective, including: constructing a first objective variable corresponding to an initial feasible solution; adding a first objective constraint to the first objective variable in the first capacity configuration optimization problem based on the first objective variable; 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; and solving the first capacity configuration optimization problem based on the first iterative optimal solution.

[0095] According to one embodiment of this application, solving a first capacity configuration optimization problem based on a first iterative optimal solution includes: updating a first initial lower bound objective value of the first capacity configuration optimization problem using the first iterative optimal solution to obtain a first iterative lower bound objective value of the first capacity configuration optimization problem; obtaining a second iterative optimal solution of the second capacity configuration optimization problem based on the first iterative optimal solution and the second capacity configuration optimization problem; and updating a first initial upper bound objective value of the first capacity configuration optimization problem based on the second iterative optimal solution to obtain a first iterative upper bound objective value of the first capacity configuration optimization problem.

[0096] The first and second preset targets can both be set by those skilled in the art according to actual configuration requirements, and are not specifically limited here.

[0097] Specifically, in this embodiment, 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 based on the requirements of the composite compressed air energy storage system. Since the first capacity configuration optimization problem is usually a large-scale linear programming problem, it can be solved using a column and constraint generation algorithm. The first preset objective of the first capacity configuration optimization problem is to find a set of capacity configurations that minimizes 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 based on the predicted information. The second capacity configuration optimization problem is usually a mixed integer linear programming problem, which can be transformed into a maximization problem (Max problem) using strong duality theory. Therefore, the objective of the second capacity configuration optimization problem is to maximize the worst-case performance index of the system under a given capacity configuration. That is, when dealing with uncertain source load power, an affine strategy is used to adjust the model so that the system satisfies the worst-case scenario within the fuzzy set.

[0098] Furthermore, such as Figure 3 As shown, in this embodiment of the application, a solution strategy for the first capacity configuration optimization problem is generated based on a first preset objective, i.e., the solution strategy for the main problem. Its expression can be:

[0099]

[0100] Here, η is the newly introduced variable; l and l′ are the current iteration number and total iteration number of the main problem, respectively. In the main problem and subproblems, the variable marked with an asterisk in the upper right corner represents the optimal value of the current variable in the solution of another problem. Since matrix F has a variable n, the fifth constraint form has a nonlinear form involving the multiplication of two variables. To facilitate the solution, the main problem F(n) is split into F2(n) and F1, where F1 is multiplied by j, instead of multiplying the values ​​of the variables in the subproblems. Substituting y into the equation, we can directly multiply it with F2(n) as a known parameter, which transforms the main problem into a linear optimization problem, making it easier to solve. In the subproblem, the variable n is a parameter, so this constraint form does not involve the multiplication of two variables.

[0101] Therefore, based on the solution strategy for the main problem, an iterative solution using a column and constraint generation algorithm can be employed. First, the input parameters of the composite compressed air energy storage system are obtained. Based on these parameters, an initial feasible solution to the main problem is determined. Substituting the initial feasible solution into the subproblem yields the initial optimal solution to the subproblem. The first initial upper bound objective value UB of the main problem is set based on the initial optimal solutions of the subproblems. out = +∞, and determine the first initial lower bound objective 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 objective variable (y) corresponding to the initial feasible solution of the main problem. l ,z l Based on the first objective variable, add the first objective constraint η≥c to the main problem, which is related to the first objective variable. T yl, and the initial optimal solution Substituting into the main problem, we obtain the first iterative optimal solution to the main problem. And solve the first capacity allocation optimization problem based on the optimal solution of the first iteration, where (n l ,x l The current system configuration capacity scheme is used as a sub-problem to solve with known parameters.

[0103] Next, the first initial lower bound objective value of the main problem is updated using the optimal solution of the first iteration, thus obtaining the first iterative lower bound objective value LB of the main problem. out =a T nl+b T Substituting the optimal solution from the first iteration into the subproblem, we obtain the optimal solution from the second iteration of the subproblem, xl+ηl. Update the first initial upper bound objective value UB of the main problem based on the optimal solution of the second iteration. out The first iterative upper bound objective value UB of the main problem is obtained. out =a T n l +b T x / +UB in .

[0104] According to one embodiment of this application, after obtaining the first iterative lower bound objective value and the first iterative upper bound objective value of the first capacity configuration optimization problem, the method 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, then stopping the iteration and obtaining 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, then constructing a second objective variable corresponding to the first iterative optimal solution, determining the second objective constraint of the second objective variable, and performing iterative calculations 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.

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

[0106] Specifically, in this embodiment of the application, after obtaining the first iterative upper bound objective value and the first iterative lower bound objective value of the main problem, 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 a 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, i.e., |UB out -LB out If |≤ε, then stop the iteration and obtain the objective optimal solution of the second capacity allocation optimization problem. This is the objective optimal solution for the subproblem.

[0107] Furthermore, if the absolute value of the difference between the upper bound target value and the lower bound target value of the first iteration is greater than the preset convergence value, then let And construct the second objective variable (y) corresponding to the optimal solution of the first iteration. l+1 ,z l+1 ), and determine the second objective constraint η≥c for the second objective variable. T y l+1 Based on the second objective constraint, iterative calculations are performed according to the first iterative optimal solution and the first capacity configuration optimization problem. The current iteration number is updated to l = l + 1, and then the above-mentioned first iterative optimal solution based on the main problem is continued. The steps for solving the first capacity configuration optimization problem based on the first iteration optimal solution are repeated until the absolute value of the difference between the upper and lower bound objective values ​​of the iteration is less than or equal to the preset convergence value.

[0108] According to an embodiment of this application, the above-mentioned capacity configuration optimization method for a 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 for the first capacity configuration optimization problem, determining a third objective constraint for 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.

[0109] Specifically, if, based on the above solution, the subproblem is found to have no solution, then a third objective variable (y) is constructed for the main problem. l+1 ,z l+1 The third objective constraint of the third objective variable is determined, and based on the third objective constraint, the optimal solution of the second iteration is obtained. Substitute the main problem into the iterative calculation and update the current iteration number, i.e., the iteration number is l = l + 1. Then continue to execute the first iteration optimal solution based on the main problem until the subproblem has a solution.

[0110] According to one embodiment of this application, a solution strategy for generating a second capacity configuration optimization problem based on a second preset objective includes: determining a second initial upper bound objective value, a second initial lower bound objective value, and a second initial iteration number for the second capacity configuration optimization problem; obtaining initial values ​​for 0-1 variables; 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 using a preset strong duality theory; solving the upper bound second capacity configuration optimization problem to obtain the third iteration optimal solution; updating the second initial upper bound objective value based on the third iteration optimal solution to obtain the second iteration upper bound objective value for the second capacity configuration optimization problem; and then, based on the third iteration optimal solution and... The second capacity allocation optimization problem is lowered, and the fourth iteration optimal solution of the second capacity allocation optimization problem is obtained. The second initial lower bound objective value is updated using the fourth iteration optimal solution to obtain the second iteration lower bound objective value of the second capacity allocation optimization problem. It is then determined whether the absolute value of the difference between the second iteration upper bound objective value and the second iteration lower bound objective value is less than or equal to the preset convergence value. If the absolute value of the difference between the second iteration upper bound objective value and the second iteration lower bound objective value is less than or equal to the preset convergence value, the iteration is stopped, and the first iteration upper bound objective value of the first capacity allocation optimization problem is updated using the first iteration optimal solution. Otherwise, the fourth objective variable and dual variable corresponding to the fourth iteration optimal solution are constructed until the absolute value of the difference between the second iteration upper bound objective value and the second iteration lower bound objective value is less than or equal to the preset convergence value.

[0111] The pre-defined strong duality theory can be selected by those skilled in the art based on actual configuration requirements, and no specific limitations are imposed here.

[0112] Specifically, the embodiments of this application include a 0-1 variable z, so the subproblem is a mixed integer linear problem. Therefore, the embodiments of this application use strong duality theory to transform the Max-Min problem into a Max problem, and then use a linear programming or mixed integer linear programming solver to solve the subproblem.

[0113] Specifically, such as Figure 3 As shown, the second initial upper bound objective value UB of the subproblem is determined. in =+∞, the second initial lower bound objective value LB of the subproblem in = -∞ and the second initial iteration number of the subproblem, for example, o = 1, and obtain the initial value of the 0-1 variable z from the main problem. make Since the KKT (Karush-Kuhn-Tucker Conditions) introduce more large-M constraints than strong duality theory, this embodiment utilizes a pre-defined strong duality theory to transform the subproblem into a linear problem, specifically into an upper-bound second-capacity allocation optimization problem (upper-bound subproblem) and a lower-bound second-capacity allocation optimization problem (lower-bound subproblem), the expression of which can be:

[0114]

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

[0116] Furthermore, due to the introduction of the bilinear term u T π c The solution becomes complex, 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 uncertain set U. In the integrated energy system proposed in the application embodiment, when the uncertain output u of a single renewable energy device... re,s,h To achieve the minimum value, while the uncertainty of the load u l,s,h When the maximum value is taken, PESR, CO2ERR, and ACTSR will all obtain relatively small values, which can reflect the "worst-case" scenario. Therefore, the uncertain set is rewritten as:

[0117]

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

[0119] Furthermore, in this embodiment of the application, the upper bound subproblem is solved to obtain the third iterative optimal solution u of the upper bound subproblem. * The second initial upper bound objective value UB is updated based on the optimal solution of the third iteration. in = +∞, thus obtaining the second iterative upper bound objective value UB of the subproblem. in =τ, the optimal solution u of the third iteration * Substituting into the lower bound subproblem, we obtain the fourth iterative optimal solution to the lower bound subproblem. The current stage of the system energy scheduling scheme is proposed, and the second initial lower bound target value is updated using the fourth iteration optimal solution to obtain the second iteration lower bound target value LB. in =max{LB in ,c T The expression for y} can be:

[0120]

[0121] Furthermore, in this embodiment of the application, after obtaining the second iteration upper bound target value and the second iteration lower bound target value, it is necessary to further determine the convergence of the absolute value of the difference between the second iteration upper bound target value and the second iteration lower bound target value, that is, 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, that is, |UB in -LB in If |≤ε, then stop the iteration and set u * Return to the main problem and update the first-iteration upper bound objective value UB of the main problem using the optimal solution from the first iteration. out =a T n l +b T x / +UB in Otherwise, return The upper bound subproblem is used to construct the fourth objective variable y corresponding to the optimal solution of the fourth iteration. o+1 And the dual variable, and update o = o + 1, continue to execute the above solution process for the upper bound subproblem until the absolute value of the difference between the upper bound objective value of the second iteration and the lower bound objective value of the second iteration is less than or equal to the preset convergence value.

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

[0123] Specifically, if the upper bound subproblem has no solution, then the uncertain parameters in the input parameters of the composite compressed air energy storage system are determined, that is, a u-to-main problem is directly returned, the expression of which can be:

[0124]

[0125] In step S103, 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.

[0126] Specifically, in the embodiments of this application, the solution strategies for the first capacity configuration optimization problem and the second capacity configuration optimization problem are iteratively calculated based on the above-mentioned solution strategies, and the upper and lower bounds are gradually updated in each iteration, so that a near-optimal solution can be found within a limited number of iterations, thereby optimizing the capacity configuration of the composite compressed air energy storage system to ensure the economy and robustness of the system.

[0127] In summary, based on the detailed description of the above specific embodiments, this application can achieve significant improvements in the overall performance and economic benefits of the system through the capacity configuration optimization method of the composite compressed air energy storage system, which has advantages in many aspects such as precise configuration, reduced costs, enhanced reliability, improved solution efficiency, enhanced flexibility and adaptability, ease of implementation, and data-driven optimization.

[0128] According to an embodiment of the present invention, a capacity configuration optimization method for a composite compressed air energy storage system is proposed. This method determines a first capacity configuration optimization problem and a second capacity configuration optimization problem for the composite compressed air energy storage system. It also determines a first preset objective for the first capacity configuration optimization problem and a second preset objective for the second capacity configuration optimization problem. Based on the first preset objective, a solution strategy for the first capacity configuration optimization problem is generated. Based on the second preset objective, a solution strategy for the second capacity configuration optimization problem is generated. Finally, the capacity configuration of the composite compressed air energy storage system is optimized based on the solution strategies for the first and second capacity configuration optimization problems. This solves the problems of related energy storage methods, which are subject to significant limitations, resulting in uncertain and fluctuating power output and potentially adversely affecting power quality and power system stability. The method establishes an optimization configuration model for the composite compressed air energy storage system based on a compressed air energy storage energy hub architecture. It decomposes the configuration problem into a main problem and sub-problems. Based on fuzzy sets for source-load power prediction, an affine rule-based solution method for the capacity configuration model of the composite compressed air energy storage system is established, thereby ensuring efficient operation of the composite compressed air energy storage system under various operating conditions.

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

[0130] Figure 4 This is a block diagram of a capacity configuration optimization device for a composite compressed air energy storage system according to an embodiment of this application.

[0131] like Figure 4 As shown, the capacity configuration optimization device 10 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 determining module 100 is used to determine the first capacity configuration optimization problem and the second capacity configuration optimization problem of the composite compressed air energy storage system.

[0133] The generation module 200 is used 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, and to generate a solution strategy for the first capacity configuration optimization problem based on the first preset objective and a solution strategy for the second capacity configuration optimization problem based on the second preset objective.

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

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

[0136] The first acquisition unit is used to acquire the input parameters of the composite compressed air energy storage system;

[0137] The first determining unit is used to determine the initial feasible solution of the first capacity configuration optimization problem based on the input parameters, and to obtain the initial optimal solution of the second capacity configuration optimization problem based on the initial feasible solution and the second capacity configuration optimization problem.

[0138] The second determining unit is used to set the first initial upper bound target value of the first capacity configuration optimization problem based on the initial optimal solution, and to determine the first initial lower bound target value of the first capacity configuration optimization problem based on the initial feasible solution, while determining the first initial iteration number of the first capacity configuration optimization problem.

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

[0140] The building unit is used to construct the first objective variable corresponding to the initial feasible solution;

[0141] The second acquisition unit is used to add a first objective constraint to the first objective variable in the first capacity configuration optimization problem according to the first objective variable, and to 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.

[0142] The solution unit is used to solve the first capacity configuration optimization problem based on the optimal solution of the first iteration.

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

[0144] The first update subunit is used to update the first initial lower bound objective value of the first capacity configuration optimization problem using the first iterative optimal solution, so as to obtain the first iterative lower bound objective value of the first capacity configuration optimization problem.

[0145] Obtain sub-units to obtain the second iteration optimal solution of the second capacity configuration optimization problem based on the first iteration optimal solution and the second capacity configuration optimization problem;

[0146] The second update subunit is used to update the first initial upper bound objective value of the first capacity configuration optimization problem according to the second iterative optimal solution, so as to obtain the 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 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] The judgment sub-component is used to 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 the preset convergence value.

[0149] The first iteration component is used to stop the iteration and obtain the target optimal solution if the absolute value of the difference between the target value of the upper bound of the first iteration and the target value of the lower bound of the first iteration is less than or equal to the preset convergence value.

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

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

[0152] The judgment module is used to determine whether the second capacity configuration optimization problem is unsolvable;

[0153] The iterative module is used to construct a third objective variable for the first capacity configuration optimization problem if the second capacity configuration optimization problem has no solution, determine the third objective constraint of the third objective variable, and iteratively calculate 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.

[0154] According to one embodiment of the present invention, the generation module 200 includes:

[0155] The third determining unit is used to determine the second initial upper bound objective value, the second initial lower bound objective value, and the second initial iteration number of the second capacity configuration optimization problem;

[0156] The first update unit is used to obtain the initial values ​​of the 0-1 variables. It uses the pre-set strong duality theory to transform the second capacity allocation optimization problem into an upper bound second capacity allocation optimization problem and a lower bound second capacity allocation optimization problem. It solves the upper bound second capacity allocation optimization problem to obtain the third iteration optimal solution of the upper bound second capacity allocation optimization problem. Based on the third iteration optimal solution, it updates the second initial upper bound target value to obtain the second iteration upper bound target value of the second capacity allocation optimization problem.

[0157] The second update unit is used to obtain the fourth iteration optimal solution of the lower bound second capacity configuration optimization problem based on the third iteration optimal solution and the lower bound second capacity configuration optimization problem, and to update the second initial lower bound objective value using the fourth iteration optimal solution to obtain the second iteration lower bound objective value of the second capacity configuration optimization problem.

[0158] The judgment unit is used to determine whether the absolute value of the difference between the upper bound target value of the second iteration and the lower bound target value of the second iteration is less than or equal to the preset convergence value.

[0159] The third update unit is used to stop the iteration if the absolute value of the difference between the upper bound target value of the second iteration and the lower bound target value of the second iteration is less than or equal to the preset convergence value, and update the upper bound target value of the first iteration using the optimal solution of the first iteration. Otherwise, it constructs the fourth objective variable and dual variable corresponding to the optimal solution of the fourth iteration, and updates the second initial iteration number until the absolute value of the difference between the upper bound target value of the second iteration and the lower bound target value of the second iteration is less than or equal to the preset convergence value.

[0160] According to one embodiment of the present invention, the first updating unit includes:

[0161] The fourth determining unit is used 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.

[0162] According to an embodiment of the present invention, a capacity configuration optimization device for a composite compressed air energy storage system determines a first capacity configuration optimization problem and a second capacity configuration optimization problem for the composite compressed air energy storage system. It also determines a first preset objective for the first capacity configuration optimization problem and a second preset objective for the second capacity configuration optimization problem. Based on the first preset objective, a solution strategy for the first capacity configuration optimization problem is generated; based on the second preset objective, a solution strategy for the second capacity configuration optimization problem is generated; and based on the solution strategies for the first and second capacity configuration optimization problems, the capacity configuration of the composite compressed air energy storage system is optimized. This solves the problems of related technologies where energy storage methods are subject to significant limitations, leading to uncertainties and fluctuations in power output, which can adversely affect power quality and the stability of the power system. The device establishes an optimization configuration model for the composite compressed air energy storage system based on a compressed air energy storage energy hub architecture, decomposes the configuration problem into a main problem and sub-problems, and establishes a solution method for the capacity configuration model of the composite compressed air energy storage system using affine rules based on source-load power prediction fuzzy sets, thereby ensuring that the composite compressed air energy storage system can operate efficiently under various operating conditions.

[0163] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0164] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

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

[0166] Furthermore, electronic devices also include:

[0167] Communication interface 503 is used for communication between memory 501 and processor 502.

[0168] The memory 501 is used to store computer programs that can run on the processor 502.

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

[0170] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

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

[0172] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0173] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the capacity configuration optimization method for the composite compressed air energy storage system described above.

[0174] This invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method as described in the above embodiments.

[0175] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0176] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0177] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0178] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

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

[0180] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0181] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0182] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing the capacity configuration of a composite compressed air energy storage system, characterized in that, Includes the following steps: Determine the first capacity configuration optimization problem and the second capacity configuration optimization problem of the composite compressed air energy storage system; Based on the requirements of the composite compressed air energy storage system, a first preset objective for the first capacity configuration optimization problem and a second preset objective for the second capacity configuration optimization problem are determined. A solution strategy for the first capacity configuration optimization problem is generated based on the first preset objective, and a solution strategy for the second capacity configuration optimization problem is generated based on the second preset objective. The capacity configuration of the composite compressed air energy storage system is optimized based on the solution strategies for the first capacity configuration optimization problem and the second capacity configuration optimization problem. The step of generating a solution strategy for the first capacity configuration optimization problem based on the first preset objective includes: constructing a first objective variable corresponding to an initial feasible solution; adding a first objective constraint to the first capacity configuration optimization problem based on the first objective variable; obtaining a first iterative optimal solution to the first capacity configuration optimization problem by combining the initial optimal solution and the first capacity configuration optimization problem; and solving the first capacity configuration optimization problem based on the first iterative optimal solution, wherein the initial optimal solution is calculated from the input parameters of the composite compressed air energy storage system, the first capacity configuration optimization problem, and the second capacity configuration optimization problem. The strategy for generating the solution to the second capacity configuration optimization problem based on the second preset objective includes: determining the second initial upper bound objective value, the second initial lower bound objective value, and the second initial iteration number of the second capacity configuration optimization problem; obtaining initial values ​​for 0-1 variables; using a 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 iteration optimal solution; updating the second initial upper bound objective value based on the third iteration optimal solution to obtain the second iteration upper bound objective value of the second capacity configuration optimization problem; and, based on the third iteration optimal solution and the lower bound second capacity configuration optimization problem... The fourth iterative optimal solution of the second capacity configuration optimization problem is obtained, and the second initial lower bound objective value is updated using the fourth iterative optimal solution to obtain the second iterative lower bound objective value of the second capacity configuration optimization problem. It is then determined 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, the iteration is stopped, and the first iterative upper bound objective value of the first capacity configuration optimization problem is updated using the first iterative optimal solution. Otherwise, the fourth objective variable and dual variable corresponding to the fourth iterative optimal solution are constructed, and the second initial iteration count is updated 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.

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

3. The method according to claim 1, characterized in that, Solving the first capacity configuration optimization problem based on the first iterative optimal solution includes: The first initial lower bound objective value of the first capacity configuration optimization problem is updated using the first iterative optimal solution to obtain the first iterative lower bound objective value of the first capacity configuration optimization problem; Based on the first iterative optimal solution and the second capacity configuration optimization problem, the second iterative optimal solution of the second capacity configuration optimization problem is obtained; The first initial upper bound objective value of the first capacity configuration optimization problem is updated based on the second iterative optimal solution to obtain the first iterative upper bound objective value of the first capacity configuration optimization problem.

4. The method according to claim 3, characterized in that, After obtaining the first iterative lower bound objective value and the first iterative upper bound objective 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, then the iteration stops and the target optimal solution is obtained; 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, then a second target variable corresponding to the first iterative optimal solution is constructed, and a second target constraint of the second target variable is determined. Based on the second target constraint, iterative calculations are 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.

5. The method according to claim 2 or 3, characterized in that, The aforementioned method for optimizing the capacity configuration of a composite compressed air energy storage system also includes: Determine whether the second capacity configuration optimization problem is unsolvable; If the second capacity configuration optimization problem has no solution, then a third objective variable for the first capacity configuration optimization problem is constructed, and a third objective constraint for the third objective variable is determined. Based on the third objective constraint, the second iterative optimal solution and the second capacity configuration optimization problem are iteratively calculated until the second capacity configuration optimization problem has a solution.

6. The method according to claim 1, characterized in that, The solution to 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.

7. A capacity configuration optimization device for a composite compressed air energy storage system, characterized in that, include: A determination module is used to determine the first capacity configuration optimization problem and the second capacity configuration optimization problem of the composite compressed air energy storage system. The generation module is used 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, and to generate a solution strategy for the first capacity configuration optimization problem based on the first preset objective and a solution strategy for the second capacity configuration optimization problem based on the second preset objective. The optimization module is used 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. The generation module includes: a construction unit for constructing a first objective variable corresponding to an initial feasible solution; a second acquisition unit for adding a first objective constraint of the first objective variable to the first capacity configuration optimization problem based on the first objective variable, and obtaining a first iterative optimal solution of the first capacity configuration optimization problem from the initial optimal solution and the first capacity configuration optimization problem; and a solution unit for solving the first capacity configuration optimization problem based on the first iterative optimal solution, wherein the initial optimal solution is calculated from the input parameters of the composite compressed air energy storage system, the first capacity configuration optimization problem, and the second capacity configuration optimization problem. The generation module includes: a third determining unit, configured to determine the second initial upper bound objective value, the second initial lower bound objective value, and the second initial iteration number of the second capacity configuration optimization problem; a first updating unit, configured to obtain the initial values ​​of 0-1 variables, 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 using a 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 objective value based on the third iteration optimal solution to obtain the second iteration upper bound objective value of the second capacity configuration optimization problem; and a second updating unit, configured to obtain the lower bound second iteration objective value based on the third iteration optimal solution and the lower bound second capacity configuration optimization problem. The process involves: a fourth iterative optimal solution to the capacity allocation optimization problem, and updating the second initial lower bound objective value using the fourth iterative optimal solution to obtain the second iterative lower bound objective value of the second capacity allocation optimization problem; a judgment unit for judging 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; and a third update unit for stopping 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 updating the first iterative upper bound objective value of the first capacity allocation optimization problem using the first iterative optimal solution; otherwise, constructing the fourth objective variable and dual variable corresponding to the fourth iterative optimal solution, and updating the second initial iteration number 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.

8. The apparatus according to claim 7, characterized in that, The generation module includes: The first acquisition unit is used to acquire the input parameters of the composite compressed air energy storage system; The first determining unit is configured to determine an initial feasible solution to the first capacity configuration optimization problem based on the input parameters, and to obtain an initial optimal solution to the second capacity configuration optimization problem based on the initial feasible solution and the second capacity configuration optimization problem. The second determining unit is configured to set a first initial upper bound target value for the first capacity configuration optimization problem based on the initial optimal solution, determine a first initial lower bound target value for the first capacity configuration optimization problem based on the initial feasible solution, and simultaneously determine a first initial iteration number for the first capacity configuration optimization problem.

9. The apparatus according to claim 7, characterized in that, The solution unit includes: The first update subunit is used to update the first initial lower bound objective value of the first capacity configuration optimization problem using the first iterative optimal solution, so as to obtain the first iterative lower bound objective value of the first capacity configuration optimization problem. Obtain a sub-unit, used to obtain the second iterative optimal solution of the second capacity configuration optimization problem based on the first iterative optimal solution and the second capacity configuration optimization problem; The second update subunit is used to update the first initial upper bound objective value of the first capacity configuration optimization problem according to the second iterative optimal solution, so as to obtain the first iterative upper bound objective value of the first capacity configuration optimization problem.

10. The apparatus according to claim 9, characterized in that, After obtaining the first iterative lower bound objective value 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: The judgment sub-component is used to 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. The first iteration 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. The second iteration 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 objective value and the first iterative lower bound objective value is greater than the preset convergence value, and to determine the second objective constraint of the second objective variable. Based on the second objective constraint, iterative calculations are 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 objective value and the iterative lower bound objective value is less than or equal to the preset convergence value.

11. The apparatus according to claim 8 or 9, characterized in that, The aforementioned capacity configuration optimization device for the composite compressed air energy storage system also includes: The judgment module is used to determine whether the second capacity configuration optimization problem is unsolvable; The iterative module is used to construct a third objective variable for the first capacity configuration optimization problem if the second capacity configuration optimization problem has no solution, determine the third objective constraint of the third objective variable, and iteratively calculate 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.

12. The apparatus according to claim 7, characterized in that, The first update unit includes: The fourth determining unit is used 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.

13. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the capacity configuration optimization method for a composite compressed air energy storage system as described in any one of claims 1-6.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the capacity configuration optimization method for the composite compressed air energy storage system as described in any one of claims 1-6.

15. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the capacity configuration optimization method for the composite compressed air energy storage system as described in any one of claims 1-6.

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