Comprehensive energy system energy storage optimal configuration method and device

Abstract

The invention provides an energy storage optimization configuration method for an integrated energy system. The method comprises the steps of obtaining multi-energy load data, energy storage equipment state data and external environment data of the integrated energy system and performing preprocessing; an improved variational mode decomposition method is adopted to decompose the preprocessed multi-energy load data to obtain a frequency component of the multi-energy load; constructing a configuration scheme of the energy storage equipment of the integrated energy system according to the obtained frequency component of the multi-energy load; the method comprises the following steps: determining a constraint condition, constructing an energy storage optimization objective function, solving an optimal solution for the energy storage optimization objective function by adopting an improved multi-objective particle swarm optimization algorithm in combination with preprocessed energy storage equipment state data and external environment data, and further optimizing a configuration scheme of the energy storage equipment of the integrated energy system according to the optimal solution. By implementing the method, the complex coupling fluctuation of the multi-energy load in the comprehensive energy system can be effectively dealt with, the energy supply reliability of the system is remarkably improved, and the energy storage configuration effect is optimized.

Description

Technical Field

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

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

[0003] However, most of the existing energy storage optimization configuration methods focus on suppressing the fluctuations of a single energy source and are difficult to adapt to the complex coupling relationships and fluctuation characteristics among multi-energy loads. For example, when dealing with the fluctuations of electrical energy loads, traditional methods ignore the impact of changes in thermal energy and gas energy loads on the overall energy supply reliability of the system, resulting in the inability of energy storage configuration to fully play its role and effectively cope with the complex fluctuations of multi-energy loads. At the same time, there are also deficiencies in the comprehensive optimization of 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 an integrated energy system, significantly improve the energy supply reliability of the system, 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 multi-energy loads in an integrated energy system, significantly improve the energy supply reliability of the system, and optimize the energy storage configuration effect.

[0006] To solve the above technical problem, an embodiment of the present invention provides a method for optimizing the configuration of energy storage in an integrated energy system, the method comprising the following steps: Obtain 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 electrical energy load data, gas energy load data, and thermal energy load data; Adopt an improved variational mode decomposition method 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; According to the obtained frequency components of the multi-energy load, construct a configuration plan for the energy storage device in the integrated energy system; wherein, the configuration plan includes the capacity size and type combination of the energy storage device; Determine the constraint conditions to construct the energy storage optimization objective function, and combined with 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 of the energy storage optimization objective function, and further optimize the configuration scheme of the energy storage device in the integrated energy system according to the obtained optimal solution.

[0007] Among them, 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: Decompose the preprocessed multi-energy load data into K a linear combination of modal functions, and perform Hilbert transform on each modal function to obtain the analytic signal of each modal function, and further determine the central frequency of each modal function according to the analytic signal of each modal function; K is a positive integer; Based on the preprocessed multi-energy load data and the analytic signals and central frequencies of each modal function obtained by the corresponding decomposition, and introducing an adaptive penalty factor adjustment mechanism, construct a fluctuation characteristic target analysis function; Use the variational mode decomposition method to alternately optimize each modal function and its central frequency to minimize the fluctuation characteristic target analysis function until the changes in the modal function and the central frequency are less than the set threshold or the number of iterative calculations reaches the maximum number of iterations, and then stop the optimization; According to the fluctuation amplitude and central frequency of each modal function when the fluctuation characteristic target analysis function is minimized, determine the frequency components of the electric energy load, gas energy load and heat energy load.

[0008] Among them, the expression of the fluctuation characteristic target analysis function is ; where, is the preprocessed multi-energy load data, including electric energy load data, gas energy load data and heat energy load data, and ; is the th modal function; is the central frequency, and ; is the th modal function of the analytic signal, and ; is the th modal function after Hilbert transform, which is the imaginary part of the analytic signal ; is the penalty factor, which is used to control the bandwidth of the modal function; is the quadratic norm calculation; 。

[0009] Among them, the specific steps of determining the constraint conditions, constructing the energy storage optimization objective function, and combining the preprocessed energy storage device status data and external environment data, and using the improved multi-objective particle swarm optimization algorithm to find the optimal solution of the energy storage optimization objective function, and further optimizing the configuration scheme of the energy storage device of the integrated energy system according to the obtained optimal solution include: Determine the constraint conditions and construct an energy storage optimization objective function with the minimum objectives of economic cost, carbon emissions, and energy supply reliability; According to the preprocessed energy storage device status data and external environment data, and using the improved multi-objective particle swarm optimization algorithm, find the optimal solution of the energy storage optimization objective function; among them, the improved multi-objective particle swarm optimization algorithm is constructed by introducing adaptive weight adjustment, elite retention strategy, and combining the simulated annealing idea in the multi-objective particle swarm optimization algorithm; Optimize the configuration scheme of the energy storage device of the integrated energy system according to the obtained optimal solution.

[0010] Among them, the expression of the energy storage optimization objective function is ; among them, is the economic cost, including the investment cost of the energy storage device , operation and maintenance cost and charge and discharge cost based on electricity price and gas price ; E is the carbon emissions, including carbon dioxide emissions during the operation of the energy storage device and methane emissions ; R is the energy supply reliability index, including the reciprocal of the power supply reliability rate , the reciprocal of the heat supply reliability rate and the reciprocal of the gas supply reliability rate The constraint conditions include charge and discharge power constraint, capacity constraint, life constraint, and energy supply reliability constraint; among them, The expression of the charge and discharge power constraint is ; among them, 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 allowable capacity of the energy storage device; The expression of the life constraint is ; where T is the operating period 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 ; where are the minimum reliability rate requirements for power supply, heat supply, and gas supply of the system respectively.

[0011] The embodiment of the present invention also provides an energy storage optimization configuration device for an integrated energy system, including: A data acquisition and preprocessing module, which is used to acquire multi-energy load data, energy storage device status data, and external environment data of the integrated energy system, and perform preprocessing; among them, the multi-energy load data includes electric energy load data, gas energy load data, and thermal energy load data; A multi-energy load fluctuation analysis module, which is used to decompose the preprocessed multi-energy load data by using an improved variational mode decomposition method to obtain the frequency components of the multi-energy load; among them, the frequency components include low-frequency components, intermediate-frequency components, and high-frequency components; An energy storage configuration generation module, which is used to construct a configuration plan for the energy storage device of the integrated energy system according to the obtained frequency components of the multi-energy load; among them, the configuration plan includes the capacity size and type combination of the energy storage device; An energy storage configuration optimization module, which is used to determine the constraint conditions to construct an energy storage optimization objective function, and combine the preprocessed energy storage device status data and external environment data, and use an improved multi-objective particle swarm optimization algorithm to find the optimal solution of the energy storage optimization objective function, and further optimize the configuration plan of the energy storage device of the integrated energy system according to the obtained optimal solution.

[0012] Among them, the multi-energy load fluctuation analysis module includes: A decomposition mode function sub-module, which is used to decompose the preprocessed multi-energy load data into a linear combination of K mode functions, and perform Hilbert transform on each mode function to obtain the analytic signal of each mode function, and further determine the central frequency of each mode function according to the analytic signal of each mode function; K is a positive integer; A target function construction sub-module, which is used to construct a fluctuation characteristic target analysis function based on the preprocessed multi-energy load data, the analytic signal and central frequency of each mode function obtained by its corresponding decomposition, and introduce an adaptive penalty factor adjustment mechanism; A target function optimization sub-module, which is used to alternately optimize each mode function and its central frequency by using the variational mode decomposition method to minimize the fluctuation characteristic target analysis function until the changes of the mode function and the central frequency are less than the set threshold or the number of iterative calculations reaches the maximum number of iterative times, and then stop the optimization; A frequency component extraction sub-module 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 mode function when the target analysis function of the fluctuation characteristics is minimized.

[0013] Implementing the embodiments of the present invention has the following beneficial effects: 1. The present invention introduces an improved multi-objective particle swarm optimization algorithm, which 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; 2. The present invention comprehensively considers multiple objectives such as economic cost, carbon emissions, and energy supply reliability, and realizes the optimization balance of multiple objectives through an improved multi-objective particle swarm optimization algorithm. While ensuring the stable energy supply of the system, it reduces the operating cost and carbon emissions, meeting the sustainable development needs of the current energy system; 3. The present invention dynamically adjusts the charge and discharge strategies of energy storage devices 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 devices in the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, obtaining other drawings based on these drawings still belongs to the scope of the present invention.

[0015] Figure 1 It is a flowchart of a method for optimizing the energy storage configuration of an integrated energy system provided by an embodiment of the present invention; Figure 2 It is a schematic structural diagram of a device for optimizing the energy storage configuration of an integrated energy system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings.

[0017] As Figure 1 shown, in an embodiment of the present invention, a method for optimizing the energy storage configuration of an integrated energy system is provided. The method includes the following steps: Step S1: Obtain 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 specific process is as follows. First, high-precision sensors are installed at each key node of the integrated energy system to collect data on multi-energy loads such as electric energy, thermal energy, and gas energy in real time, as well as status data of energy storage devices such as battery power, charge and discharge power, and temperature. At the same time, external environmental data such as electricity prices and gas prices at different times are obtained.

[0018] Secondly, the collected data is cleaned to remove obvious errors and abnormal data points; the median filtering method is used to filter the data to eliminate the interference of random noise; finally, normalization processing is performed to unify the data into the range of [0,1] for subsequent analysis and processing.

[0019] For example, for the electric energy load data , its normalization formula is: where and are the minimum and maximum values of the electric energy load data respectively, and is the normalized electric energy load data.

[0020] Step S2: Use the improved variational mode decomposition method to decompose the preprocessed multi-energy load data to obtain the frequency components of the multi-energy load; among them, the frequency components include low-frequency components, medium-frequency components, and high-frequency components; The specific process is as follows. Based on the traditional variational mode decomposition method (VMD), an adaptive penalty factor adjustment mechanism is introduced to automatically adjust the penalty factor according to the characteristics of the load data to improve the accuracy and stability of the decomposition. At this time, the specific decomposition process is as follows First, the preprocessed multi-energy load data is decomposed into a linear combination of K modal functions, and Hilbert transform is performed on each modal function to obtain the analytic signal of each modal function, and further, according to the analytic signal of each modal function, the central frequency of each modal function is determined; K is a positive integer; Secondly, based on the preprocessed multi-energy load data, the analytic signals and central frequencies of each modal function obtained by the corresponding decomposition, and an adaptive penalty factor adjustment mechanism is introduced to construct a target analysis function for the fluctuation characteristics; Then, using the variational mode decomposition method, each modal function and its central frequency are alternately optimized to minimize the target analysis function for the fluctuation characteristics until the changes in the modal function and the central frequency are less than the set threshold or the number of iterative calculations reaches the maximum number of iterations, and the optimization is stopped; Finally, according to the fluctuation amplitude and central frequency of each mode function when the fluctuation characteristic target analysis function is minimized, the frequency components of the electric energy load, gas energy load, and thermal energy load are determined.

[0021] Among them, the expression of the fluctuation characteristic target analysis function is ; where is the preprocessed multi-energy load data, including electric energy load data, gas energy load data, and thermal energy load data, and ; is the th mode function; is the central frequency, and ; is the th mode function of the analytic signal, and ; is the th mode function after performing the Hilbert transform, which is the imaginary part of the analytic signal ; is the penalty factor, used to control the bandwidth of the mode function; is the quadratic norm calculation; .

[0022] In an example, taking the electric energy load data as an example for illustration, as follows: The multi-energy load signal can be decomposed into K linear combinations of mode functions , that is ; For each mode function , its corresponding Hilbert transform is , then its analytic signal is The central frequency of the mode function is calculated by the formula ; Under the VMD framework, by solving the optimization problem to obtain the mode function and the central frequency , specifically (1) Initialization: Set the initial values of the mode function and the central frequency ; (2) Alternating optimization: By alternately optimizing the mode function and the central frequency , making the function Minimize.

[0023] For each modal function , calculate the corresponding Hilbert transform as and the center frequency , update the modal function and the center frequency to minimize the objective function.

[0024] (3) Convergence condition: When the changes in the modal function and the center frequency are less than the set threshold, or when the maximum number of iterations is reached, stop the optimization; (4) Output the fluctuation amplitude and fluctuation frequency of the modal function , and determine the frequency components of the electric energy load, gas energy load, and heat energy load according to the fluctuation amplitude and center frequency of the modal function .

[0025] 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.

[0026] The low-frequency, medium-frequency, and high-frequency components of the heat energy 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; 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.

[0027] Step S3: Construct a configuration plan for the energy storage equipment of the integrated energy system according to the obtained frequency components of the multi-energy load; wherein, the configuration plan includes the capacity size and type combination of the energy storage equipment; The specific process is to determine the corresponding energy storage configuration requirements (including the capacity size and type combination of the energy storage equipment, etc.) according to the characteristics of different frequency components, which is achieved by using common technical means in the art and will not be elaborated here one by one.

[0028] Step S4: Determine the constraint conditions to construct the energy storage optimization objective function, and combine the preprocessed energy storage device status data and external environment data, and use the improved multi-objective particle swarm optimization algorithm to find the optimal solution of the energy storage optimization objective function, and further optimize the configuration scheme of the energy storage device of the integrated energy system according to the obtained optimal solution.

[0029] Specifically, first, determine the constraint conditions and construct an energy storage optimization objective function with the minimum objectives of economic cost, carbon emissions, and energy supply reliability. Among them, the constraint conditions include charge and discharge power constraints, capacity constraints, life constraints, and energy supply reliability constraints, which are specifically as follows: The expression of the charge and discharge power constraint is ; where 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 allowable capacity of the energy storage device; The expression of the life constraint is ; where T is the operation 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 ; where are the minimum reliability requirements for power supply, heat supply, and gas supply of the system respectively.

[0030] Among them, the expression of the energy storage optimization objective function is ; where is the economic cost, including the investment cost of the energy storage device , operation and maintenance cost and charge and discharge cost based on electricity price and gas price ; E is the carbon emissions, including carbon dioxide emissions and methane emissions during the operation of the energy storage device; R is the energy supply reliability index, including the reciprocal of the power supply reliability rate , the reciprocal of the heat supply reliability rate and the reciprocal of the gas supply reliability rate .

[0031] Next, based on the preprocessed energy storage device state data and external environment data, and using an improved multi-objective particle swarm optimization algorithm, the optimal solution of the energy storage optimization objective function is sought; wherein, the improved multi-objective particle swarm optimization algorithm is constructed by introducing adaptive weight adjustment, elite retention strategy and combining simulated annealing ideas in the multi-objective particle swarm optimization algorithm.

[0032] 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: Initialization parameters: particle swarm size N = 50, maximum number of iterations , inertia weight range Learning factor c1 = c2 = 2.0, penalty factor 0, elite retention ratio is 10%, initial temperature T0 = 100, cooling rate is 0.9; Position xi represents the charging and discharging power of the energy storage device in different time periods, ranging from , the speed vi range is ; The fitness function is: Inertia Weight Adjustment: The particles with high fitness values and high non-dominated rankings are retained as elite particles; According to the idea of simulated annealing, the probability of accepting an inferior solution is: where k = 1.0 Position and velocity updates: When the number of iterations reaches Or the fitness value changes less than When , the non-dominated solution is output to obtain the optimal balance among economic cost, carbon emissions and energy supply reliability.

[0033] At this time, adaptive weight adjustment: Inertia weight in particle swarm optimization algorithm It has an important impact on the global search capability and convergence speed of the algorithm. 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: 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.

[0034] In each iteration, a certain number of excellent particles are retained as elite particles and directly enter the next generation 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 sorting results. Particles with high fitness values and high non-dominated sorting are preferred as elite particles.

[0035] 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 avoid the algorithm falling into the local optimal solution. The specific acceptance probability formula is: 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.

[0036] It should be noted that the particle position xi represents the charge and discharge power of the energy storage device in different time periods, and the particle velocity vi represents the change trend of the charge and discharge power. By continuously updating the position and velocity of the particles, the optimal charge and discharge strategy is searched to minimize the objective function value. During the algorithm iteration process, the search direction and speed of the particles are adjusted in real time according to the multi-energy load fluctuations and external factors (electricity prices, gas prices, etc.) to ensure that the generated charge and discharge strategy can effectively smooth the multi-energy load fluctuations and achieve an optimal balance between economic costs, carbon emissions, and energy supply reliability.

[0037] Finally, according to 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.

[0038] 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: The data acquisition and preprocessing module 110 is used to obtain the 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 multi - energy load fluctuation analysis module 120 is used to decompose the pre - processed multi - energy load data by using an improved variational mode decomposition method to obtain the frequency components of the multi - energy load; wherein, the frequency components include low - frequency components, intermediate - frequency components and high - frequency components; The energy storage configuration generation module 130 is used to construct a configuration plan for the energy storage devices of the integrated energy system according to the obtained frequency components of the multi - energy load; wherein, the configuration plan includes the capacity size and type combination of the energy storage devices; The energy storage configuration optimization module 140 is used to determine the constraint conditions to construct an energy storage optimization objective function, and in combination with the pre - processed state data of the energy storage devices and external environment data, use an improved multi - objective particle swarm optimization algorithm to find the optimal solution of the energy storage optimization objective function, and further optimize the configuration plan of the energy storage devices of the integrated energy system according to the obtained optimal solution.

[0039] Among them, the multi - energy load fluctuation analysis module 120 includes: The decomposition mode function sub - module is used to decompose the pre - processed multi - energy load data into a linear combination of K mode functions, and perform Hilbert transform on each mode function to obtain the analytic signal of each mode function, and further determine the central frequency of each mode function according to the analytic signal of each mode function; K is a positive integer; The objective function construction sub - module is used to construct a fluctuation characteristic target analysis function based on the pre - processed multi - energy load data, the analytic signal and central frequency of each mode function obtained by its corresponding decomposition, and introduce an adaptive penalty factor adjustment mechanism; The objective function optimization sub - module is used to alternately optimize each mode function and its central frequency by using the variational mode decomposition method to minimize the fluctuation characteristic target analysis function until the changes in the mode function and central frequency are less than the set threshold or the number of iterative calculations reaches the maximum number of iterations, and then stop the optimization; The frequency component extraction sub - module is used to determine the frequency components of the electric energy load, gas energy load and heat energy load according to the fluctuation amplitude and central frequency of each mode function when the fluctuation characteristic target analysis function is minimized.

[0040] Implementing the embodiments of the present invention has the following beneficial effects: 1. The present invention introduces an improved multi - objective particle swarm optimization algorithm, which 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; 2. The present invention comprehensively considers multiple objectives such as economic cost, carbon emissions, and energy supply reliability, and realizes the optimal balance of multiple objectives through an improved multi-objective particle swarm optimization algorithm. While ensuring the stable energy supply of the system, it reduces the operating cost and carbon emissions, meeting the sustainable development requirements of the current energy system. 3. The present invention dynamically adjusts the charging and discharging strategies of energy storage devices in combination with external factors such as electricity prices and gas prices, maximizes the energy storage configuration plan, and improves the application value and economic feasibility of energy storage devices in the integrated energy system.

[0041] It should be noted that in the above system embodiments, the included system modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for easy distinction from each other and do not limit the protection scope of the present invention.

[0042] Those of ordinary skill in the art can understand that all or part of the steps in implementing the above method embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, disk, optical disc, etc.

[0043] The above-disclosed are only the preferred embodiments of the present invention, and of course, the scope of the rights of the present invention cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A method for optimizing the energy storage configuration of an integrated energy system, characterized in that The method includes the following steps: Obtain the multi - energy load data, energy storage device status data, and external environment data of the integrated energy system, and perform pre - processing; wherein, the multi - energy load data includes electric energy load data, gas energy load data, and thermal energy load data; Use an improved variational mode decomposition method to decompose the pre - processed 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; According to the obtained frequency components of the multi - energy load, construct a configuration plan for the energy storage device of the integrated energy system; wherein, the configuration plan includes the capacity size and type combination of the energy storage device; Determine the constraint conditions to construct an energy storage optimization objective function, and combine the pre - processed energy storage device status data and external environment data, and use an improved multi - objective particle swarm optimization algorithm to find the optimal solution of the energy storage optimization objective function, and further optimize the configuration plan of the energy storage device of the integrated energy system according to the obtained optimal solution.

2. The energy storage optimal configuration method for the integrated energy system according to claim 1, wherein The specific steps of using the improved variational mode decomposition method to decompose the pre - processed 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, perform Hilbert transform on each modal function to obtain the analytic signal of each modal function, and further determine the central frequency of each modal function according to the analytic signal of each modal function; K is a positive integer; Based on the pre - processed multi - energy load data, the analytical signal and central frequency of each mode function obtained by corresponding decomposition, and introduce an adaptive penalty factor adjustment mechanism to construct a fluctuation characteristic target analysis function; Use the variational mode decomposition method to alternately optimize each mode function and its central frequency to minimize the fluctuation characteristic target analysis function until the change in the mode function and central frequency is less than the set threshold or the number of iterative calculations reaches the maximum number of iterations, and then stop the optimization; According to the fluctuation amplitude and central frequency of each mode function when the fluctuation characteristic target analysis function is minimized, determine the frequency components of the electric energy load, gas energy load, and thermal energy load.

3. The integrated energy system energy storage optimization configuration method according to claim 2, wherein The expression of the wave characteristic target analysis function is ; where is the preprocessed multi-energy load data, including electric energy load data, gas energy load data, and heat energy load data, and ; is the th modal function; is the central frequency, and ; is the th modal function of the analytic signal, and ; is the th modal function after performing the Hilbert transform, which is the imaginary part of the analytic signal ; is the penalty factor used to control the bandwidth of the modal function; is the quadratic norm calculation; .

4. The energy storage optimization configuration method for the integrated energy system according to claim 2, wherein The specific steps of determining the constraint conditions, constructing the energy storage optimization objective function, and combining the pre - processed energy storage device status data and external environment data, and using an improved multi - objective particle swarm optimization algorithm to find the optimal solution of the energy storage optimization objective function, and further optimizing the configuration plan of the energy storage device of the integrated energy system according to the obtained optimal solution include: Determine the constraint conditions and construct an energy storage optimization objective function with the minimum objectives of economic cost, carbon emissions, and energy supply reliability; According to the pre - processed energy storage device status data and external environment data, and use an improved multi - objective particle swarm optimization algorithm to find the optimal solution of the energy storage optimization objective function; wherein, the improved multi - objective particle swarm optimization algorithm is constructed by introducing an adaptive weight adjustment, an elite retention strategy, and combining the idea of simulated annealing in the multi - objective particle swarm optimization algorithm; Optimize the configuration plan of the energy storage device of the integrated energy system according to the obtained optimal solution.

5. The method for optimizing the configuration of energy storage in the integrated energy system according to claim 4, wherein The expression of the energy storage optimization objective function is ; where is the economic cost, including the investment cost of the energy storage device , the operation and maintenance cost and the charge-discharge cost based on electricity price and gas price ; E is the carbon emission, including the carbon dioxide emission during the operation of the energy storage device and the methane emission ; R is the energy supply reliability index, including the reciprocal of the power supply reliability rate , the reciprocal of the heat supply reliability rate and the reciprocal of the gas supply reliability rate The constraint conditions include charge - discharge power constraints, capacity constraints, life constraints, and energy supply reliability constraints; wherein, The expression of the charge-discharge power constraint is ; where is the maximum charging power of the energy storage device, is the maximum discharging 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 allowable capacity of the energy storage device; The expression of the lifespan 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 power supply reliability constraint is ; where are the minimum reliable rate requirements for system power supply, heat supply, and gas supply respectively.

6. An energy storage optimization configuration device for an integrated energy system, characterized in that, include: The data acquisition and preprocessing module is used to obtain the multi - energy load data, energy storage device status data and external environment data of the integrated energy system, and perform preprocessing; among them, the multi - energy load data includes electric energy load data, gas energy load data and thermal energy load data; The multi - energy load fluctuation analysis module is used to decompose the preprocessed multi - energy load data by using an improved variational mode decomposition method to obtain the frequency components of the multi - energy load; among them, the frequency components include low - frequency components, medium - frequency components and high - frequency components; The energy storage configuration generation module is used to construct a configuration scheme for the energy storage devices of the integrated energy system according to the obtained frequency components of the multi - energy load; among them, 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 constraint conditions to construct an energy storage optimization objective function, and combine the preprocessed energy storage device status data and external environment data, and use an improved multi - objective particle swarm optimization algorithm to find the optimal solution of the energy storage optimization objective function, and further optimize the configuration scheme of the energy storage devices of the integrated energy system according to the obtained optimal solution.

7. The energy storage optimal configuration device for the integrated energy system according to claim 6, wherein The multi - energy load fluctuation analysis module includes: The decomposed modal function sub-module is used to decompose the pre-processed multi-energy load data into K a linear combination of modal functions, perform Hilbert transform on each modal function to obtain the analytic signal of each modal function, and further determine the central frequency of each modal function according to the analytic signal of each modal function; K is a positive integer; The objective function construction sub - module is used to construct a fluctuation characteristic target analysis function based on the preprocessed multi - energy load data, the analytic signal and the central frequency of each mode function obtained by corresponding decomposition, and introduce an adaptive penalty factor adjustment mechanism; The objective function optimization sub - module is used to alternately optimize each mode function and its central frequency by using the variational mode decomposition method to minimize the fluctuation characteristic target analysis function until the changes of the mode function and the central frequency are less than the set threshold or the number of iterative calculations reaches the maximum number of iterations, and then stop the optimization; The frequency component extraction sub - module 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 central frequency of each mode function when the fluctuation characteristic target analysis function is minimized.

Images