A method and device for optimizing energy storage configuration in an integrated energy system

Abstract

The present invention provides a method for optimizing energy storage configuration of an integrated energy system, comprising obtaining and preprocessing multi-energy load data, energy storage device status data, and external environment data of the integrated energy system; using an improved variational mode decomposition method to decompose the preprocessed multi-energy load data to obtain the frequency components of the multi-energy load; constructing a configuration scheme for the energy storage device of the integrated energy system based on the obtained frequency components of the multi-energy load; determining constraints, constructing an energy storage optimization objective function, and combining the preprocessed energy storage device status data and external environment data, using an improved multi-objective particle swarm optimization algorithm to find the optimal solution for the energy storage optimization objective function, and further optimizing the configuration scheme of the energy storage device of the integrated energy system based on the found optimal solution. Implementation of the present invention can effectively cope with the complex coupled fluctuations of multi-energy loads in the integrated energy system, significantly improve the energy supply reliability of the system, and optimize the energy storage configuration effect.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data processing, and in particular to a method and device for optimizing energy storage configuration in an integrated energy system. Background Art

[0002] With the continuous development of integrated energy systems, the volatility of multi-energy loads (including electricity, heat, gas, etc.) has become increasingly significant, which has had a great impact on the stable energy supply and reliability of the system.

[0003] However, existing energy storage optimization methods mostly focus on smoothing fluctuations in a single energy source, making them incapable of adapting to the complex coupling relationships and fluctuation characteristics of multiple energy loads. For example, when addressing electrical load fluctuations, traditional methods ignore the impact of thermal and gas load variations on the overall energy supply reliability of the system. This results in inadequate energy storage configuration and an inability to effectively address the complex fluctuations of multiple energy loads. Furthermore, they also have shortcomings in comprehensively optimizing economic costs and carbon emissions.

[0004] Therefore, it is necessary to provide a new energy storage optimization configuration method that can effectively cope with the complex coupling fluctuations of multi-energy loads in the integrated energy system, significantly improve the system's energy supply reliability, and optimize the energy storage configuration effect. Summary of the Invention

[0005] The technical problem to be solved by the embodiments of the present invention is to provide a method and device for optimizing the configuration of energy storage in an integrated energy system, which can effectively cope with the complex coupling fluctuations of multiple energy loads in the integrated energy system, significantly improve the energy supply reliability of the system, and optimize the energy storage configuration effect.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for optimizing energy storage configuration of an integrated energy system, the method comprising the following steps:

[0007] Acquire multi-energy load data, energy storage device status data, and external environment data of the integrated energy system and perform preprocessing; wherein the multi-energy load data includes electric energy load data, gas energy load data, and thermal energy load data;

[0008] The improved variational mode decomposition method is used to decompose the preprocessed multi-energy load data to obtain the frequency components of the multi-energy load; wherein the frequency components include low-frequency components, medium-frequency components and high-frequency components;

[0009] Constructing a configuration scheme for energy storage devices of the integrated energy system based on the frequency components of the obtained multi-energy load; wherein the configuration scheme includes a combination of the capacity and type of the energy storage devices;

[0010] Determine the constraints to construct the energy storage optimization objective function, and combine the preprocessed energy storage device status data and external environment data, use the improved multi-objective particle swarm optimization algorithm to find the optimal solution for the energy storage optimization objective function, and further optimize the configuration scheme of the energy storage equipment of the integrated energy system based on the optimal solution.

[0011] The specific steps of using the improved variational mode decomposition method to decompose the preprocessed multi-energy load data to obtain the frequency components of the multi-energy load include:

[0012] Decompose the preprocessed multi-energy load data into K A linear combination of modal functions is formed, and each modal function is subjected to Hilbert transform to obtain an analytical signal of each modal function, and further the center frequency of each modal function is determined according to the analytical signal of each modal function; K is a positive integer;

[0013] Based on the pre-processed multi-energy load data and its corresponding decomposition of the analytical signal and center frequency of each modal function, and introducing an adaptive penalty factor adjustment mechanism, a fluctuation characteristic target analysis function is constructed;

[0014] Using the variational mode decomposition method, each mode function and its center frequency are alternately optimized to minimize the wave characteristic target analysis function until the change of the mode function and the center frequency is less than a set threshold or the number of iterative calculations reaches a maximum number of iterations, then the optimization is stopped;

[0015] The frequency components of the electric energy load, gas energy load and thermal energy load are determined according to the fluctuation amplitude and center frequency of each modal function when the fluctuation characteristic target analysis function is minimized.

[0016] Among them, the expression of the wave characteristic target analysis function is: ;in,

[0017] It is the pre-processed multi-energy load data, including electric energy load data, gas energy load data and thermal energy load data, and ; For the modal functions; is the center frequency, and ; For the modal function The analytical signal of ; For the modal function The function after Hilbert transform is the analytical signal The imaginary part of is the penalty factor, which is used to control the bandwidth of the modal function; is the quadratic norm calculation; .

[0018] The specific steps of determining the constraint conditions, constructing the energy storage optimization objective function, and using an improved multi-objective particle swarm optimization algorithm to find the optimal solution for the energy storage optimization objective function in combination with the preprocessed energy storage device status data and external environment data, and further optimizing the configuration scheme of the energy storage equipment of the integrated energy system based on the found optimal solution include:

[0019] Determine the constraints and construct an energy storage optimization objective function that minimizes economic costs, carbon emissions, and energy supply reliability;

[0020] Based on the pre-processed energy storage device status data and external environment data, an improved multi-objective particle swarm optimization algorithm is used to find the optimal solution for the energy storage optimization objective function; wherein the improved multi-objective particle swarm optimization algorithm is constructed by introducing adaptive weight adjustment, elite retention strategy and simulated annealing into the multi-objective particle swarm optimization algorithm;

[0021] According to the desired optimal solution, the configuration scheme of the energy storage equipment of the integrated energy system is optimized.

[0022] Among them, the expression of the energy storage optimization objective function is: ;in, Economic cost, including the investment cost of energy storage equipment , operation and maintenance costs And the charging and discharging costs based on electricity and gas prices ; E is carbon emissions, including carbon dioxide emissions during the operation of energy storage equipment and methane emissions ; R is the energy supply reliability index, including the inverse of the power supply reliability rate , the inverse of the heating reliability rate and the inverse of the gas supply reliability rate

[0023] The constraints include charge and discharge power constraints, capacity constraints, life constraints and energy supply reliability constraints; wherein,

[0024] The expression of the charge and discharge power constraint is: ;in, is the maximum charging power of the energy storage device, is the maximum discharge power of the energy storage device, is the charging power of the energy storage device at time t;

[0025] The expression of the capacity constraint is ; Where S is the total capacity of the energy storage device, is the maximum permissible capacity of the energy storage device;

[0026] The expression of the life constraint is ; Where T is the operating cycle of the system, C is the rated capacity of the energy storage device, and L is the cycle life of the energy storage device;

[0027] The expression of the energy supply reliability constraint is: ;in, These are the minimum reliability requirements for the system's power supply, heat supply, and gas supply respectively.

[0028] An embodiment of the present invention further provides a device for optimizing energy storage configuration in an integrated energy system, comprising:

[0029] A data acquisition and preprocessing module is used to obtain and preprocess the multi-energy load data, energy storage device status data, and external environment data of the integrated energy system; wherein the multi-energy load data includes electrical energy load data, gas energy load data, and thermal energy load data;

[0030] A multi-energy load fluctuation analysis module is used to decompose the pre-processed multi-energy load data using an improved variational modal decomposition method to obtain frequency components of the multi-energy load; wherein the frequency components include low-frequency components, medium-frequency components, and high-frequency components;

[0031] An energy storage configuration generation module is used to construct a configuration scheme for energy storage devices of the integrated energy system based on the frequency components of the obtained multi-energy load; wherein the configuration scheme includes the capacity size and type combination of the energy storage devices;

[0032] The energy storage configuration optimization module is used to determine the constraints to construct the energy storage optimization objective function. In combination with the preprocessed energy storage device status data and external environment data, an improved multi-objective particle swarm optimization algorithm is used to find the optimal solution for the energy storage optimization objective function. Furthermore, based on the optimal solution, the configuration scheme of the energy storage equipment of the integrated energy system is optimized.

[0033] The multi-energy load fluctuation analysis module includes:

[0034] Decomposition modal function submodule is used to decompose the preprocessed multi-energy load data into K A linear combination of modal functions is formed, and each modal function is subjected to Hilbert transform to obtain an analytical signal of each modal function, and further the center frequency of each modal function is determined according to the analytical signal of each modal function; K is a positive integer;

[0035] The objective function construction submodule is used to construct the fluctuation characteristic objective analysis function based on the analytical signal and center frequency of each modal function obtained by the preprocessed multi-energy load data and its corresponding decomposition, and introduces an adaptive penalty factor adjustment mechanism;

[0036] An objective function optimization submodule is used to alternately optimize each modal function and its center frequency using a variational mode decomposition method to minimize the wave characteristic target analysis function until the change in the modal function and the center frequency is less than a set threshold or the number of iterative calculations reaches a maximum number of iterations, at which point the optimization is stopped;

[0037] The frequency component extraction submodule is used to determine the frequency components of the electric energy load, gas energy load and thermal energy load according to the fluctuation amplitude and center frequency of each modal function when the fluctuation characteristic target analysis function is minimized.

[0038] The implementation of the embodiments of the present invention has the following beneficial effects:

[0039] 1. The present invention introduces an improved multi-objective particle swarm optimization algorithm, which can effectively cope with the complex coupling fluctuations of multiple energy loads in the integrated energy system, significantly improve the energy supply reliability of the system, and optimize the energy storage configuration effect;

[0040] 2. This invention comprehensively considers multiple objectives, including economic costs, carbon emissions, and energy supply reliability, and achieves a multi-objective optimization balance through an improved multi-objective particle swarm optimization algorithm. While ensuring stable energy supply for the system, it reduces operating costs and carbon emissions, meeting the sustainable development needs of the current energy system.

[0041] 3. The present invention dynamically adjusts the charging and discharging strategies of energy storage equipment in combination with external factors such as electricity prices and gas prices, maximizes the energy storage configuration scheme, and improves the application value and economic feasibility of energy storage equipment in the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, other drawings obtained based on these drawings still fall within the scope of the present invention.

[0043] Figure 1 A flowchart of a method for optimizing energy storage configuration in an integrated energy system provided by an embodiment of the present invention;

[0044] Figure 2A schematic structural diagram of an energy storage optimization configuration device for an integrated energy system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings.

[0046] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for optimizing energy storage configuration of an integrated energy system, the method comprising the following steps:

[0047] Step S1, obtaining multi-energy load data, energy storage device status data and external environment data of the integrated energy system and performing pre-processing; wherein the multi-energy load data includes electric energy load data, gas energy load data and thermal energy load data;

[0048] The specific process is as follows: first, high-precision sensors are installed at each key node of the integrated energy system to collect real-time data on multiple energy loads such as electricity, heat, and gas, as well as status data such as the power, charge and discharge power, and temperature of energy storage equipment, and at the same time obtain external environmental data, such as electricity prices and gas prices in different time periods.

[0049] Secondly, the collected data is cleaned to remove obvious errors and abnormal data points; the data is filtered using the median filter method to eliminate the interference of random noise; finally, normalization is performed to unify the data into the range of [0,1] to facilitate subsequent analysis and processing.

[0050] For example, for power load data , and its normalization formula is:

[0051]

[0052] in, and are the minimum and maximum values of the electric energy load data, is the normalized electric energy load data.

[0053] Step S2: Decomposing the preprocessed multi-energy load data using an improved variational modal decomposition method to obtain frequency components of the multi-energy load; wherein the frequency components include low-frequency components, medium-frequency components, and high-frequency components;

[0054] The specific process is to introduce an adaptive penalty factor adjustment mechanism based on the traditional variational mode decomposition method (VMD) to automatically adjust the penalty factor according to the characteristics of the load data. , in order to improve the accuracy and stability of decomposition. At this time, the specific decomposition process is as follows

[0055] First, the preprocessed multi-energy load data is decomposed into K A linear combination of modal functions is formed, and each modal function is subjected to Hilbert transform to obtain an analytical signal of each modal function, and further the center frequency of each modal function is determined according to the analytical signal of each modal function; K is a positive integer;

[0056] Secondly, based on the preprocessed multi-energy load data and the analytical signals and center frequencies of each modal function obtained by its corresponding decomposition, an adaptive penalty factor adjustment mechanism is introduced to construct a target analysis function for fluctuation characteristics.

[0057] Then, using the variational mode decomposition method, each mode function and its center frequency are alternately optimized to minimize the target analysis function of the wave characteristics, until the change of the mode function and the center frequency is less than the set threshold or the number of iterative calculations reaches the maximum number of iterations, then the optimization is stopped;

[0058] Finally, the fluctuation amplitude and center frequency of each modal function when the fluctuation characteristic objective analysis function is minimized are analyzed to determine the frequency components of the electric energy load, gas energy load and thermal energy load.

[0059] Among them, the expression of the wave characteristic target analysis function is: ;in,

[0060] It is the pre-processed multi-energy load data, including electric energy load data, gas energy load data and thermal energy load data, and ; For the modal functions; is the center frequency, and ; For the modal function The analytical signal of ; For the modal function The function after Hilbert transform is the analytical signal The imaginary part of is the penalty factor, which is used to control the bandwidth of the modal function; is the quadratic norm calculation; .

[0061] In one example, the power load data Take this as an example to illustrate:

[0062] Multi-energy load signal Can be decomposed into Kmodal function The linear combination of ;

[0063] For each modal function , and its corresponding Hilbert transform is , then its analytical signal is

[0064] Modal Function Center frequency By formula calculate;

[0065] In the VMD framework, by solving The optimization problem is used to obtain the modal function and center frequency , specifically

[0066] (1) Initialization: Setting the modal function and center frequency The initial value of

[0067] (2) Alternating optimization: By alternatingly optimizing the modal function and center frequency , so that the function minimize.

[0068] For each modal function , calculate the corresponding Hilbert transform as and center frequency , update the modal function and center frequency , so that the objective function is minimized.

[0069] (3) Convergence condition: When the change of the mode function and the center frequency is less than the set threshold, or when the maximum number of iterations is reached, the optimization is stopped;

[0070] (4) Output modal function The fluctuation amplitude and frequency of The fluctuation amplitude and center frequency are used to determine the frequency components of electric energy load, gas energy load and thermal energy load.

[0071] It should be noted that the low-frequency, medium-frequency and high-frequency components of the electric energy load represent different meanings, specifically: (11) Low-frequency component: base load component, with small fluctuation amplitude and low frequency; (12) Medium-frequency component: peak-valley load component, with medium fluctuation amplitude and moderate frequency; (13) High-frequency component: random fluctuation component, with large fluctuation amplitude and high frequency.

[0072] The low-frequency, medium-frequency and high-frequency components of the heat load represent different meanings, specifically: (21) Low-frequency component: basic heat load, with small fluctuation amplitude and low frequency; (22) Medium-frequency component: daily cycle heat load, with medium fluctuation amplitude and moderate frequency; (23) High-frequency component: instantaneous heat load, with large fluctuation amplitude and high frequency;

[0073] The low-frequency, medium-frequency and high-frequency components of the gas energy load represent different meanings, specifically: (31) Low-frequency component: basic gas load, with small fluctuation amplitude and low frequency; (32) Medium-frequency component: daily cycle gas load, with medium fluctuation amplitude and moderate frequency; (33) High-frequency component: instantaneous gas load, with large fluctuation amplitude and high frequency.

[0074] Step S3: constructing a configuration scheme for energy storage devices of the integrated energy system based on the obtained frequency components of the multi-energy load; wherein the configuration scheme includes a combination of the capacity and type of the energy storage devices;

[0075] The specific process is to determine the corresponding energy storage configuration requirements (including the capacity size and type combination of energy storage equipment, etc.) according to the characteristics of different frequency components. This is achieved by using common technical means in this field and will not be described in detail here.

[0076] Step S4: determine the constraints to construct an energy storage optimization objective function, and combine the preprocessed energy storage device status data and external environment data, use an improved multi-objective particle swarm optimization algorithm to find the optimal solution for the energy storage optimization objective function, and further optimize the configuration scheme of the energy storage equipment of the integrated energy system based on the optimal solution.

[0077] The specific process is as follows: first, determine the constraints and construct an energy storage optimization objective function with economic cost, carbon emissions and energy supply reliability as the minimum goals;

[0078] The constraints include charge and discharge power constraints, capacity constraints, life constraints, and energy supply reliability constraints, as follows:

[0079] The expression of charge and discharge power constraint is: ;in, is the maximum charging power of the energy storage device, is the maximum discharge power of the energy storage device, is the charging power of the energy storage device at time t;

[0080] The expression of capacity constraint is ; Where S is the total capacity of the energy storage device, is the maximum permissible capacity of the energy storage device;

[0081] The expression of the life constraint is ; Where T is the operating cycle of the system, C is the rated capacity of the energy storage device, and L is the cycle life of the energy storage device;

[0082] The expression of energy supply reliability constraint is: ;in, These are the minimum reliability requirements for the system's power supply, heat supply, and gas supply respectively.

[0083] Among them, the expression of energy storage optimization objective function is: ;in, Economic cost, including the investment cost of energy storage equipment , operation and maintenance costs And the charging and discharging costs based on electricity and gas prices ; E is carbon emissions, including carbon dioxide emissions during the operation of energy storage equipment and methane emissions ; R is the energy supply reliability index, including the inverse of the power supply reliability rate , the inverse of the heating reliability rate and the inverse of the gas supply reliability rate .

[0084] Then, based on the preprocessed energy storage device status data and external environment data, an improved multi-objective particle swarm optimization algorithm is used to find the optimal solution to the energy storage optimization objective function; wherein, the improved multi-objective particle swarm optimization algorithm is constructed by introducing adaptive weight adjustment, elite retention strategy and simulated annealing ideas into the multi-objective particle swarm optimization algorithm.

[0085] In an example, the specific process of using the improved multi-objective particle swarm optimization algorithm to find the optimal solution to the energy storage optimization objective function is as follows:

[0086] Initialization parameters: particle swarm size N = 50, maximum number of iterations , inertia weight range Learning factor c1 = c2 = 2.0, penalty factor 0, the elite retention ratio is 10%, the initial temperature T0 = 100, and the cooling rate is 0.9;

[0087] Position xi represents the charge and discharge power of the energy storage device in different time periods, ranging from , the speed vi range is ;

[0088] The fitness function is:

[0089]

[0090] Inertia weight adjustment:

[0091]

[0092] Retain particles with high fitness values and high non-dominated rankings as elite particles;

[0093] The idea of simulated annealing is that the probability of accepting an inferior solution is:

[0094] where k = 1.0

[0095] Position and velocity updates:

[0096]

[0097]

[0098] When the number of iterations reaches Or the fitness value change is less than When , the non-dominated solution is output to obtain the optimal balance among economic cost, carbon emissions and energy supply reliability.

[0099] At this time, adaptive weight adjustment: inertia weight in particle swarm optimization algorithm It has a significant impact on the algorithm's global search capability and convergence speed. Adopting an adaptive weight adjustment strategy, the inertia weight is dynamically adjusted according to the number of iterations and the current fitness value of the particle, so that it has a greater global search capability in the early stage of the search and a faster local convergence speed in the later stage of the search. The specific adjustment formula is:

[0100]

[0101] in, and are the maximum and minimum values of the inertia weight respectively, T is the current number of iterations, is the maximum number of iterations, f is the fitness value of the current particle, and are the maximum and minimum fitness values of all particles in the current population respectively.

[0102] In each iteration, a certain number of excellent particles are retained as elite particles and directly enter the next generation of population to prevent the loss of excellent solutions and improve the convergence accuracy of the algorithm. The selection of elite particles is based on their fitness value and non-dominated ranking results. Particles with high fitness values and high non-dominated rankings are preferentially selected as elite particles.

[0103] Combined with the idea of simulated annealing: In the particle update process, the acceptance criterion of the simulated annealing algorithm is introduced to accept inferior solutions with a certain probability to prevent the algorithm from falling into the local optimal solution. The specific acceptance probability formula is:

[0104]

[0105] in, is the fitness difference between the current solution and the new solution, k is the Boltzmann constant, T is the current temperature, the initial temperature T0 is set according to the scale and complexity of the problem, and the temperature is gradually reduced according to a certain cooling strategy.

[0106] It should be noted that the particle position xi represents the charge and discharge power of the energy storage device at different time periods, and the particle velocity vi represents the changing trend of the charge and discharge power. By continuously updating the particle position and velocity, the optimal charge and discharge strategy is searched to minimize the objective function. During the algorithm iteration process, the particle search direction and velocity are adjusted in real time based on multi-energy load fluctuations and external factors (such as electricity and gas prices) to ensure that the generated charge and discharge strategy effectively smooths multi-energy load fluctuations while achieving an optimal balance between economic costs, carbon emissions, and energy supply reliability.

[0107] Finally, based on the optimal solution sought, the configuration scheme of the energy storage equipment of the integrated energy system is optimized, that is, the capacity, type, etc. of the energy storage equipment are optimized to obtain the optimal energy storage configuration scheme to meet the needs of the integrated energy system in multi-energy load fluctuation smoothing and comprehensive benefit optimization.

[0108] like Figure 2 As shown in the figure, an integrated energy system energy storage optimization configuration device is provided in an embodiment of the present invention, comprising:

[0109] The data acquisition and preprocessing module 110 is used to obtain and preprocess the multi-energy load data, energy storage device status data, and external environment data of the integrated energy system; wherein the multi-energy load data includes electrical energy load data, gas energy load data, and thermal energy load data;

[0110] The multi-energy load fluctuation analysis module 120 is configured to decompose the pre-processed multi-energy load data using an improved variational mode decomposition method to obtain frequency components of the multi-energy load; wherein the frequency components include low-frequency components, medium-frequency components, and high-frequency components;

[0111] The energy storage configuration generation module 130 is used to construct a configuration scheme of energy storage devices of the integrated energy system according to the obtained frequency components of the multi-energy load; wherein the configuration scheme includes the capacity size and type combination of the energy storage devices;

[0112] The energy storage configuration optimization module 140 is used to determine the constraints to construct the energy storage optimization objective function, and to use the improved multi-objective particle swarm optimization algorithm in combination with the preprocessed energy storage device status data and external environment data to find the optimal solution for the energy storage optimization objective function, and further optimize the configuration scheme of the energy storage equipment of the integrated energy system based on the optimal solution.

[0113] The multi-energy load fluctuation analysis module 120 includes:

[0114] Decomposition modal function submodule is used to decompose the preprocessed multi-energy load data into K A linear combination of modal functions is formed, and each modal function is subjected to Hilbert transform to obtain an analytical signal of each modal function, and further the center frequency of each modal function is determined according to the analytical signal of each modal function; K is a positive integer;

[0115] The objective function construction submodule is used to construct the fluctuation characteristic objective analysis function based on the analytical signal and center frequency of each modal function obtained by the preprocessed multi-energy load data and its corresponding decomposition, and introduces an adaptive penalty factor adjustment mechanism;

[0116] An objective function optimization submodule is used to alternately optimize each modal function and its center frequency using a variational mode decomposition method to minimize the wave characteristic target analysis function until the change in the modal function and the center frequency is less than a set threshold or the number of iterative calculations reaches a maximum number of iterations, at which point the optimization is stopped;

[0117] The frequency component extraction submodule is used to determine the frequency components of the electric energy load, gas energy load and thermal energy load according to the fluctuation amplitude and center frequency of each modal function when the fluctuation characteristic target analysis function is minimized.

[0118] The implementation of the embodiments of the present invention has the following beneficial effects:

[0119] 1. The present invention introduces an improved multi-objective particle swarm optimization algorithm, which can effectively cope with the complex coupling fluctuations of multiple energy loads in the integrated energy system, significantly improve the energy supply reliability of the system, and optimize the energy storage configuration effect;

[0120] 2. This invention comprehensively considers multiple objectives, including economic costs, carbon emissions, and energy supply reliability, and achieves a multi-objective optimization balance through an improved multi-objective particle swarm optimization algorithm. While ensuring stable energy supply for the system, it reduces operating costs and carbon emissions, meeting the sustainable development needs of the current energy system.

[0121] 3. The present invention dynamically adjusts the charging and discharging strategies of energy storage equipment in combination with external factors such as electricity prices and gas prices, maximizes the energy storage configuration scheme, and improves the application value and economic feasibility of energy storage equipment in the integrated energy system.

[0122] It is worth noting that in the above system embodiment, the various system modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the various functional modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0123] Those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0124] The above disclosure is merely a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for optimizing energy storage configuration in an integrated energy system, characterized in that: The method comprises the following steps: Acquire multi-energy load data, energy storage device status data, and external environment data of the integrated energy system and perform preprocessing; wherein the multi-energy load data includes electric energy load data, gas energy load data, and thermal energy load data; The improved variational mode decomposition method is used to decompose the preprocessed multi-energy load data to obtain the frequency components of the multi-energy load; wherein the frequency components include low-frequency components, medium-frequency components and high-frequency components; Constructing a configuration scheme for energy storage devices of the integrated energy system based on the frequency components of the obtained multi-energy load; wherein the configuration scheme includes a combination of the capacity and type of the energy storage devices; Determine constraints to construct an energy storage optimization objective function, and use an improved multi-objective particle swarm optimization algorithm in combination with preprocessed energy storage device status data and external environment data to find an optimal solution for the energy storage optimization objective function, and further optimize the configuration scheme of the energy storage equipment of the integrated energy system based on the found optimal solution; The specific steps of using the improved variational modal decomposition method to decompose the preprocessed multi-energy load data to obtain the frequency components of the multi-energy load include: Decompose the preprocessed multi-energy load data into K A linear combination of modal functions is formed, and each modal function is subjected to Hilbert transform to obtain an analytical signal of each modal function, and further the center frequency of each modal function is determined according to the analytical signal of each modal function; K is a positive integer; Based on the pre-processed multi-energy load data and its corresponding decomposition of the analytical signal and center frequency of each modal function, and introducing an adaptive penalty factor adjustment mechanism, a fluctuation characteristic target analysis function is constructed; Using the variational mode decomposition method, each mode function and its center frequency are alternately optimized to minimize the wave characteristic target analysis function until the change of the mode function and the center frequency is less than a set threshold or the number of iterative calculations reaches a maximum number of iterations, then the optimization is stopped; Determine the frequency components of the electric energy load, gas energy load, and thermal energy load based on the fluctuation amplitude and center frequency of each modal function when the fluctuation characteristic target analysis function is minimized; The expression of the wave characteristic target analysis function is: ;in, It is the pre-processed multi-energy load data, including electric energy load data, gas energy load data and thermal energy load data, and ; For the modal functions; is the center frequency, and ; For the modal function The analytical signal of ; For the modal function The function after Hilbert transform is the analytical signal The imaginary part of is the penalty factor, which is used to control the bandwidth of the modal function; is the quadratic norm calculation; .

2. The method for optimizing energy storage configuration of an integrated energy system according to claim 1, wherein: The specific steps of determining the constraint conditions, constructing the energy storage optimization objective function, and using the improved multi-objective particle swarm optimization algorithm to find the optimal solution for the energy storage optimization objective function in combination with the preprocessed energy storage device state data and external environment data, and further optimizing the configuration scheme of the energy storage device of the integrated energy system based on the found optimal solution include: Determine the constraints and construct an energy storage optimization objective function that minimizes economic costs, carbon emissions, and energy supply reliability; Based on the pre-processed energy storage device status data and external environment data, an improved multi-objective particle swarm optimization algorithm is used to find the optimal solution for the energy storage optimization objective function; wherein the improved multi-objective particle swarm optimization algorithm is constructed by introducing adaptive weight adjustment, elite retention strategy and simulated annealing into the multi-objective particle swarm optimization algorithm; According to the desired optimal solution, the configuration scheme of the energy storage equipment of the integrated energy system is optimized.

3. The method for optimizing energy storage configuration of an integrated energy system according to claim 2, wherein: The expression of the energy storage optimization objective function is: ;in, Economic cost, including the investment cost of energy storage equipment , operation and maintenance costs And the charging and discharging costs based on electricity and gas prices ; E is carbon emissions, including carbon dioxide emissions during the operation of energy storage equipment and methane emissions ; R is the energy supply reliability index, including the inverse of the power supply reliability rate , the inverse of the heating reliability rate and the inverse of the gas supply reliability rate The constraints include charge and discharge power constraints, capacity constraints, life constraints and energy supply reliability constraints; wherein, The expression of the charge and discharge power constraint is: ;in, is the maximum charging power of the energy storage device, is the maximum discharge power of the energy storage device, is the charging power of the energy storage device at time t; The expression of the capacity constraint is ; Where S is the total capacity of the energy storage device, is the maximum permissible capacity of the energy storage device; The expression of the life constraint is ; Where T is the operating cycle of the system, C is the rated capacity of the energy storage device, and L is the cycle life of the energy storage device; The expression of the energy supply reliability constraint is: ;in, These are the minimum reliability requirements for the system's power supply, heat supply, and gas supply respectively.

4. A device for optimizing energy storage configuration of an integrated energy system, characterized in that: include: A data acquisition and preprocessing module is used to obtain and preprocess the multi-energy load data, energy storage device status data, and external environment data of the integrated energy system; wherein the multi-energy load data includes electrical energy load data, gas energy load data, and thermal energy load data; A multi-energy load fluctuation analysis module is used to decompose the pre-processed multi-energy load data using an improved variational modal decomposition method to obtain frequency components of the multi-energy load; wherein the frequency components include low-frequency components, medium-frequency components, and high-frequency components; An energy storage configuration generation module is used to construct a configuration scheme for energy storage devices of the integrated energy system based on the frequency components of the obtained multi-energy load; wherein the configuration scheme includes the capacity size and type combination of the energy storage devices; The energy storage configuration optimization module is used to determine the constraints to construct the energy storage optimization objective function, and to use the improved multi-objective particle swarm optimization algorithm to find the optimal solution to the energy storage optimization objective function in combination with the pre-processed energy storage device status data and external environment data. The configuration scheme of the energy storage equipment of the integrated energy system is further optimized based on the optimal solution. The specific steps of using the improved variational modal decomposition method to decompose the preprocessed multi-energy load data to obtain the frequency components of the multi-energy load include: Decompose the preprocessed multi-energy load data into K A linear combination of modal functions is formed, and each modal function is subjected to Hilbert transform to obtain an analytical signal of each modal function, and further the center frequency of each modal function is determined according to the analytical signal of each modal function; K is a positive integer; Based on the pre-processed multi-energy load data and its corresponding decomposition of the analytical signal and center frequency of each modal function, and introducing an adaptive penalty factor adjustment mechanism, a fluctuation characteristic target analysis function is constructed; Using the variational mode decomposition method, each mode function and its center frequency are alternately optimized to minimize the wave characteristic target analysis function until the change of the mode function and the center frequency is less than a set threshold or the number of iterative calculations reaches a maximum number of iterations, then the optimization is stopped; Determine the frequency components of the electric energy load, gas energy load, and thermal energy load based on the fluctuation amplitude and center frequency of each modal function when the fluctuation characteristic target analysis function is minimized; The expression of the wave characteristic target analysis function is: ;in, It is the pre-processed multi-energy load data, including electric energy load data, gas energy load data and thermal energy load data, and ; For the modal functions; is the center frequency, and ; For the modal function The analytical signal of ; For the modal function The function after Hilbert transform is the analytical signal The imaginary part of is the penalty factor, which is used to control the bandwidth of the modal function; is the quadratic norm calculation; .

5. The integrated energy system energy storage optimization configuration device according to claim 4, characterized in that: The multi-energy load fluctuation analysis module includes: Decomposition modal function submodule is used to decompose the preprocessed multi-energy load data into K A linear combination of modal functions is formed, and each modal function is subjected to Hilbert transform to obtain an analytical signal of each modal function, and further the center frequency of each modal function is determined according to the analytical signal of each modal function; K is a positive integer; The objective function construction submodule is used to construct the fluctuation characteristic objective analysis function based on the analytical signal and center frequency of each modal function obtained by the preprocessed multi-energy load data and its corresponding decomposition, and introduces an adaptive penalty factor adjustment mechanism; An objective function optimization submodule is used to alternately optimize each modal function and its center frequency using a variational mode decomposition method to minimize the wave characteristic target analysis function until the change in the modal function and the center frequency is less than a set threshold or the number of iterative calculations reaches a maximum number of iterations, at which point the optimization is stopped; The frequency component extraction submodule is used to determine the frequency components of the electric energy load, gas energy load and thermal energy load according to the fluctuation amplitude and center frequency of each modal function when the fluctuation characteristic target analysis function is minimized.

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