Power Allocation Method, Device and Medium of Electro-Hydrogen Hybrid Energy Storage System in Microgrid

Abstract

The present invention discloses a power distribution method, device and medium for an electric-hydrogen hybrid energy storage system in a microgrid. The electric-hydrogen hybrid energy storage system includes an electrochemical energy storage system and a hydrogen energy storage system. The method includes: decomposing the current operating power of the microgrid that meets the established requirements into the power to be smoothed and the grid-connected power required to control the grid connection of the microgrid; aiming at minimizing the operating cost, combining the current SOC value of the electrochemical energy storage system and the current SOH value of the hydrogen energy storage system, and distributing the power to be smoothed into the first optimal charge-discharge power required to control the operation of the electrochemical energy storage system and the second optimal charge-discharge power required to control the operation of the hydrogen energy storage system; while controlling the overall operating state of the microgrid, adjusting the SOC state of the electrochemical energy storage system according to the protection strategy determined by the power to be smoothed and the preset SOC operating range and the current SOC value of the electrochemical energy storage system. The present invention can meet the energy requirements of the microgrid in different scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and specifically to a method, device, and medium for power distribution of an electric-hydrogen hybrid energy storage system in a microgrid. Background Art

[0002] In a new power system dominated by renewable energy, the adoption of various energy storage technologies (such as electrochemical energy storage technology, hydrogen energy storage technology, etc.) can enhance the flexibility and stability of the power grid, solve the problems of intermittency and instability of renewable energy such as wind power, improve energy utilization efficiency, and reduce energy waste.

[0003] During the operation of the power system, it is necessary to flexibly distribute the power of different types of energy storage devices according to the real-time energy demand situation. However, the existing power distribution methods of energy storage systems are usually based on simple rules or static strategies, such as fixed ratio distribution or priority-based distribution, without fully considering the real-time operating state of the energy storage devices, such as the SOC value of the electrochemical energy storage device and the SOH value of the hydrogen energy storage device, resulting in inflexible and inefficient power distribution, and it is also difficult to optimize the overall performance of the power system. Summary of the Invention

[0004] The present invention provides a method, device, and medium for power distribution of an electric-hydrogen hybrid energy storage system in a microgrid to solve one or more technical problems existing in the prior art, and at least provide a beneficial alternative or create conditions.

[0005] In a first aspect, a method for power distribution of an electric-hydrogen hybrid energy storage system in a microgrid is provided. The electric-hydrogen hybrid energy storage system includes an electrochemical energy storage system and a hydrogen energy storage system. The method includes:

[0006] Obtain the current operating power of the microgrid, the current SOC value of the electrochemical energy storage system, and the current SOH value of the hydrogen energy storage system;

[0007] When the absolute value of the current operating power is greater than a preset power threshold, decompose the current operating power according to the grid connection requirement to obtain the grid-connected power and the power to be smoothed;

[0008] With the goal of minimizing the operating cost, combine the current SOC value and the current SOH value to distribute the power to be smoothed, and obtain the first optimal charge-discharge power of the electrochemical energy storage system and the second optimal charge-discharge power of the hydrogen energy storage system;

[0009] Determine a protection strategy according to the power to be smoothed, the preset SOC operating range of the electrochemical energy storage system, and the current SOC value;

[0010] Control the microgrid to connect to the grid according to the grid-connected power, control the electro-chemical energy storage system to operate according to the first optimal charge-discharge power, control the hydrogen energy storage system to operate according to the second optimal charge-discharge power, and adjust the SOC state of the electro-chemical energy storage system according to the protection strategy.

[0011] Further, the current operating power of the microgrid is obtained in the following manner:

[0012] Obtain the power generation power of the photovoltaic system in the microgrid and the power consumption power of the DC load in the microgrid, and take the difference between the power generation power and the power consumption power as the current operating power of the microgrid.

[0013] Further, the decomposition of the current operating power according to the grid connection demand to obtain the grid-connected power and the power to be smoothed includes:

[0014] Process the current operating power using the variational mode decomposition algorithm, and optimize the number of mode decompositions and the penalty factor required by the variational mode decomposition algorithm using the grey wolf algorithm during the processing to obtain several optimal mode components;

[0015] Divide the several optimal mode components according to the grid connection demand to obtain multiple low-frequency optimal mode components and multiple high-frequency optimal mode components;

[0016] Synthesize the multiple low-frequency optimal mode components to obtain the grid-connected power;

[0017] Synthesize the multiple high-frequency optimal mode components to obtain the power to be smoothed.

[0018] Further, taking minimizing the operating cost as the goal, combining the current SOC value and the current SOH value, the distribution of the power to be smoothed to obtain the first optimal charge-discharge power of the electro-chemical energy storage system and the second optimal charge-discharge power of the hydrogen energy storage system includes:

[0019] Taking minimizing the operating cost as the goal, determine the fitness function;

[0020] Take the first charge-discharge power of the electro-chemical energy storage system and the second charge-discharge power of the hydrogen energy storage system as the target variables to be optimized;

[0021] According to the target variables and the power to be smoothed, determine the power balance constraint conditions;

[0022] According to the target variables and the current SOC value, determine the SOC constraint conditions;

[0023] According to the target variables and the current SOH value, determine the SOH constraint conditions;

[0024] According to the fitness function, the power balance constraint condition, the SOC constraint condition, and the SOH constraint condition, the improved genetic algorithm is used to iteratively optimize the target variables, and an adaptive probability function is introduced for crossover and mutation operations in each iteration process to obtain the first optimal charge-discharge power of the electrochemical energy storage system and the second optimal charge-discharge power of the hydrogen energy storage system.

[0025] Further, the power balance constraint condition includes: defining the sum of the first charge-discharge power of the electrochemical energy storage system and the second charge-discharge power of the hydrogen energy storage system as the power to be smoothed.

[0026] Further, the operating cost is solved through the following expression:

[0027]

[0028] where C use is the operating cost, is the unit power cost of the electrochemical energy storage system, is the unit capacity cost of the electrochemical energy storage system, is the unit power cost of the hydrogen energy storage system, is the unit capacity cost of the hydrogen energy storage system, T s is the sampling period, P ba_set (t) is the first charge-discharge power of the electrochemical energy storage system, P he_set (t) is the second charge-discharge power of the hydrogen energy storage system.

[0029] Further, the preset SOC operating range of the electrochemical energy storage system consists of a high SOC range, a normal SOC range, and a low SOC range. Based on the power to be smoothed, the preset SOC operating range of the electrochemical energy storage system, and the current SOC value, the determination of the protection strategy includes:

[0030] When the power to be smoothed is greater than zero and the current SOC value falls within the high SOC range, the determined protection strategy is: controlling the electrochemical energy storage system to discharge until the SOC value drops to the normal SOC range, and the electric energy generated by the electrochemical energy storage system is supplied to the hydrogen energy storage system;

[0031] When the power to be smoothed is less than zero and the current SOC value falls within the low SOC range, the determined protection strategy is: controlling the electrochemical energy storage system to charge until the SOC value rises to the normal SOC range, and the electric energy required by the electrochemical energy storage system is provided by the hydrogen energy storage system.

[0032] Further, the method further includes: when the absolute value of the current operating power is less than or equal to a preset power threshold, keeping the operating state of the microgrid unchanged.

[0033] In a second aspect, a computer device is provided, including a memory and a processor. A computer program is stored on the memory, and the processor executes the computer program to implement the power distribution method of the electrical-hydrogen hybrid energy storage system in the microgrid as described in the first aspect.

[0034] In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the power distribution method of the electrical-hydrogen hybrid energy storage system in the microgrid as described in the first aspect is implemented.

[0035] The present invention has at least the following beneficial effects: By using the variational mode decomposition algorithm optimized by the grey wolf algorithm to reasonably decompose the current operating power of the microgrid, the situation of missing important frequency feature information can be avoided. By fully considering the SOC value of the electrochemical energy storage system and the SOH value of the hydrogen energy storage system, the power to be smoothed of the electrical-hydrogen hybrid energy storage system is flexibly and efficiently allocated. And in this process, an improved genetic algorithm is introduced and the optimization goal is to minimize the operating cost to improve the power distribution accuracy, so as to meet the energy requirements of the microgrid in different scenarios to the greatest extent and make the overall performance of the microgrid reach the best. By enabling a protection strategy while adjusting the overall operating state of the microgrid, the real-time SOC value of the electrochemical energy storage system is always within the normal SOC range to maintain the life and working stability of the lithium battery as much as possible, and at the same time, the frequent startup phenomenon of the PEM electrolyzer and the hydrogen fuel cell in the hydrogen energy storage system can be reduced. Description of the Drawings

[0036] The drawings are used to provide a further understanding of the technical solutions of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation to the technical solutions of the present invention.

[0037] Figure 1 is a schematic diagram of the composition of the microgrid in the embodiment of the present invention;

[0038] Figure 2 is a schematic flowchart of a power distribution method for an electrical-hydrogen hybrid energy storage system in a microgrid in an embodiment of the present invention;

[0039] Figure 3 is a schematic diagram of the hardware structure of the computer device in the embodiment of the present invention. Detailed Embodiments

[0040] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that in the flowchart. Terms such as "first", "second", etc. in the description, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in orders other than those illustrated or described here.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0043] In addition, the features, structures or characteristics described can be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to give a full understanding of the embodiments of this application. However, those skilled in the art will realize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0044] The flowchart shown in the accompanying drawings is only an exemplary illustration, and does not necessarily include all contents and operations / steps, nor does it necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may change according to the actual situation.

[0045] First, some terms involved in the present invention are explained as follows:

[0046] The variational mode decomposition algorithm (VMD, Variational Mode Decomposition) is an adaptive signal decomposition method that can decompose a time series data into multiple intrinsic mode functions (IMFs, Intrinsic Mode Functions) with specific central frequencies and specific bandwidths. Each IMF can be regarded as an oscillation mode of the original signal, and they together constitute a complete representation of the original signal.

[0047] Grey Wolf Optimizer (GWO) is an optimization algorithm inspired by the behavior of grey wolves in nature. It mainly simulates the social hierarchy and hunting strategies of grey wolf groups. The grey wolf group is divided into four roles: Alpha (wolf leader), Beta (wolf deputy), Delta (wolf advisor), and Omega (worker wolf). This hierarchical structure helps the grey wolf group organize hunting and resource allocation in an efficient way. In the algorithm, the Alpha represents the optimal solution, the Beta represents the sub-optimal solution, the Delta represents the third-best solution, and the Omega follows the guidance of these leaders to explore the search space. The running process of the whole algorithm mainly refers to the collaborative hunting method of grey wolf groups, and gradually approaches the optimal solution by tracking, surrounding, and attacking the prey.

[0048] Genetic Algorithm (GA) is a type of optimization algorithm that finds the optimal solution by simulating biological evolution. It mainly relies on the mechanisms of inheritance, mutation, and natural selection to perform an efficient iterative search for problem-solving. The basic idea of the whole algorithm is to represent the solutions of the problem as multiple different individuals, and then evaluate the adaptability of each individual according to the definition of the fitness function and determine its probability in reproduction. After operations such as crossover and mutation, the new individuals replace the original individuals and continue to compete with other generated individuals. This process is iterated until the optimal solution is found or the upper limit of the iteration times is reached.

[0049] Please refer to Figure 1 , Figure 1 is the schematic diagram of the composition of the microgrid in the embodiment of the present invention. The microgrid specifically includes an energy router, a DC bus, and a photovoltaic system, an electro-hydrogen hybrid energy storage system, and a DC load connected to the DC bus via the energy router. The electro-hydrogen hybrid energy storage system includes a hydrogen energy storage system and an electrochemical energy storage system. The hydrogen energy storage system includes a PEM (Proton Exchange Membrane) electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell. Lithium batteries are usually used in the electrochemical energy storage system.

[0050] Basically, the energy router includes a first DC / DC converter, a second DC / DC converter, a third DC / DC converter, a fourth DC / DC converter, a bidirectional DC / DC converter, and a bidirectional DC / AC converter. The photovoltaic system is connected to the DC bus through the first DC / DC converter, the PEM electrolyzer is connected to the DC bus through the second DC / DC converter, the PEM electrolyzer is connected to the hydrogen storage tank, the hydrogen storage tank is connected to the hydrogen fuel cell, the hydrogen fuel cell is connected to the DC bus through the third DC / DC converter, the DC load is connected to the DC bus through the fourth DC / DC converter, the electrochemical energy storage system is connected to the DC bus through the bidirectional DC / DC converter, and the DC bus is connected to the external AC bus through the bidirectional DC / AC converter; the energy router can achieve electrical isolation, voltage conversion, and bidirectional power flow, provide a "plug and play" standardized interface for different levels and forms of source loads, and can adjust the voltage and current of each converter in real time.

[0051] In practical applications, the photovoltaic system converts the solar energy captured by itself into electrical energy using the photovoltaic effect. After the electrical energy is converted and processed by the first DC / DC converter, it is directly incorporated into the DC bus, and then can supply power to the PEM electrolyzer, can also supply power to the DC load, and can also be stored through the electrochemical energy storage system; the electrical energy provided by the DC bus is converted to the voltage level by the second DC / DC converter and then supplies power to the PEM electrolyzer to achieve consumption, and the hydrogen produced by the PEM electrolyzer during operation will be stored through the hydrogen storage tank; the hydrogen fuel cell can convert the hydrogen stored in the hydrogen storage tank into electrical energy, and after the electrical energy is converted to the voltage level by the third DC / DC converter, it is incorporated into the DC bus to achieve compensation; the electrical energy provided by the DC bus is converted to the voltage level by the bidirectional DC / DC converter and then input to the electrochemical energy storage system for storage to achieve consumption; the electrochemical energy storage system incorporates the electrical energy stored in it into the DC bus through the bidirectional DC / DC converter for voltage level conversion to achieve compensation; the electrical energy provided by the DC bus is converted to the voltage level by the fourth DC / DC converter and then supplies power to the DC load to achieve consumption; the bidirectional DC / AC converter can be understood as a grid connection port, which is used to achieve AC-DC conversion and bidirectional power flow between the DC bus and the external AC bus. It should be noted that the microgrid can be configured and used in industrial parks, residential communities, island-type power consumption sites, standby sites, or other places.

[0052] On this basis, Figure 2 is a schematic flowchart of a power distribution method for an electric-hydrogen hybrid energy storage system in a microgrid provided by an embodiment of the present invention. The method includes the following:

[0053] Step S110, obtain the current operating power of the microgrid, the current SOC value of the electrochemical energy storage system, and the current SOH value of the hydrogen energy storage system;

[0054] Step S120: Determine whether the absolute value of the current operating power of the microgrid is greater than the preset power threshold; if so, execute Step S130; if not, execute Step S170;

[0055] Step S130: Decompose the current operating power of the microgrid according to the grid connection demand to obtain the grid connection power and the power to be smoothed;

[0056] Step S140: With the goal of minimizing the operating cost, combine the current SOC value of the electrochemical energy storage system and the current SOH value of the hydrogen energy storage system, and allocate the power to be smoothed to obtain the first optimal charge-discharge power of the electrochemical energy storage system and the second optimal charge-discharge power of the hydrogen energy storage system;

[0057] Step S150: Determine the protection strategy according to the power to be smoothed, the preset SOC operating range of the electrochemical energy storage system, and the current SOC value;

[0058] Step S160: Control the microgrid to be connected to the grid according to the grid connection power, control the electrochemical energy storage system to operate according to the first optimal charge-discharge power, control the hydrogen energy storage system to operate according to the second optimal charge-discharge power, and adjust the SOC state of the electrochemical energy storage system according to the protection strategy;

[0059] Step S170: Keep the operating state of the microgrid unchanged.

[0060] In some embodiments, the method for obtaining the current operating power of the microgrid mentioned in Step S110 above is as follows: Obtain the power generation power of the photovoltaic system and the power consumption power of the DC load, and then subtract the power generation power of the photovoltaic system from the power consumption power of the DC load to obtain the current operating power of the microgrid.

[0061] By comparing the current operating power of the microgrid with the preset power threshold after determining it, it is possible to judge whether grid connection operation is required for the microgrid and whether the current operating state of the electric-hydrogen hybrid energy storage system needs to be adjusted, further ensuring the overall operating stability of the microgrid.

[0062] In some embodiments, the implementation process of Step S130 above includes but is not limited to the following:

[0063] Step S131: Process the current operating power of the microgrid through the variational mode decomposition algorithm, and optimize the penalty factor and the number of mode decompositions used in the variational mode decomposition algorithm through the grey wolf algorithm during the processing, and finally generate several optimal mode components, where each optimal mode component represents different frequency component information in the current operating power of the microgrid;

[0064] Step S132: Divide a number of optimal modal components according to grid connection requirements to obtain multiple high-frequency optimal modal components and multiple low-frequency optimal modal components; where the grid connection requirements include the regulations on the frequency range of signals that the external main power grid is allowed to receive considering grid connection stability.

[0065] Step S133: Synthesize multiple low-frequency optimal modal components to obtain grid-connected power.

[0066] Step S134: Synthesize multiple high-frequency optimal modal components to obtain power to be smoothed.

[0067] In the above step S131, by introducing the Grey Wolf Algorithm for parameter optimization to obtain the optimal penalty factor and the optimal number of modal decompositions, it is possible to better control the bandwidth and center frequency of each decomposed modal component, thereby more accurately mining the inherent characteristics of the current operating power of the microgrid.

[0068] More specifically, regarding the processing of the current operating power of the microgrid through the variational mode decomposition algorithm mentioned in the above step S131, the corresponding implementation process includes but is not limited to the following:

[0069] Step A1: Set the basic parameters used in the variational mode decomposition algorithm, including the sampling window width, sampling window type, sampling frequency, penalty factor, and number of modal decompositions.

[0070] Step A2: Perform Hilbert transform on the current operating power of the microgrid to obtain the single-sided spectrum of each modal function as:

[0071]

[0072] where p k (t) is the modal function, A is the single-sided spectrum, δ(t) is the Dirac function, j is the imaginary symbol, t is time, and * is the convolution operation symbol;

[0073] Step A3: Adjust the estimated center frequency of the analytic signal corresponding to each modal function, and modulate the spectrum of each modal function to the corresponding baseband:

[0074]

[0075] where B is the baseband, ω k is the center frequency, and e is the exponential function;

[0076] Step A4: To ensure that the sum of the estimated bandwidths of all modal functions is minimized, it should be constrained that the sum of all modal functions is equal to the current operating power of the microgrid, that is, determine the constrained variational problem as:

[0077]

[0078] where: {p k} = {p1, p2,..., p K}, {ω k} = {ω1, ω2,..., ω K};

[0079] In the formula, K is the modal decomposition number, that is, the number of all modal functions, is the gradient of the demodulation signal, f is the current operating power of the microgrid, is the norm symbol;

[0080] Step A5: Introduce the Lagrange multiplier operator and combine the penalty factor to transform the above constrained variational problem into an unconstrained variational problem, which can be represented by an augmented Lagrangian expression:

[0081]

[0082] In the formula, L is the augmented Lagrangian function, λ is the Lagrange multiplier operator, γ is the penalty factor, <·> is the vector inner product;

[0083] Step A6: Use the ADMM algorithm (Alternate Direction Method of Multipliers) to solve the above unconstrained variational problem, and determine the extreme point in the above augmented Lagrangian expression by alternately updating and λ n+1 so as to realize the decomposition of the current operating power of the microgrid into several optimal modal components.

[0084] It should be noted that in the above Step A6, the Parseval / Plancherel Fourier isometric transform method is used to transform the above unconstrained variational problem into the ω frequency domain, and then combined with the Hermitian symmetry principle to determine the updated values of and in the simplified ω frequency domain, and its formula is:

[0085]

[0086] In the formula, is the Wiener filter of the current remaining component , is 's center frequency.

[0087] More specifically, regarding the optimization of the penalty factor and the number of mode decompositions used in the variational mode decomposition algorithm through the grey wolf algorithm mentioned in the above step S131, the corresponding implementation process includes but is not limited to the following:

[0088] Step B1: Set the basic parameters required for the grey wolf algorithm, including the population size and the maximum number of iterations. Take the penalty factor and the number of mode decompositions as the target variables to be optimized by the grey wolf algorithm, and determine the fitness function used in the iterative optimization process. This fitness function is preferably set as the average correlation coefficient between all mode components and the current operating power of the microgrid;

[0089] Step B2: Randomly initialize the grey wolf population so that the initial positions of different grey wolf individuals represent different initial target variable values;

[0090] Step B3: Perform variational mode decomposition on the current operating power of the microgrid according to the initial position of each grey wolf, then calculate the initial fitness value corresponding to each grey wolf, obtain and save the initial positions of the three grey wolves with the best initial fitness values, and define these three grey wolves as the optimal wolf leader, the optimal wolf deputy, and the optimal wolf advisor respectively;

[0091] Step B4: In the t-th iteration process, use the following mathematical expression to update the positions of the current grey wolf population:

[0092]

[0093] In the formula, is the position of the grey wolf individual after the (t + 1)-th iteration, is the position of the grey wolf individual after the t-th iteration, is the position of the prey after the t-th iteration, and are coefficient vectors, is a reference parameter, and are random numbers in the interval [0, 1], and t max is the maximum number of iterations;

[0094] Step B5: Perform variational mode decomposition on the current operating power of the microgrid according to the current position of each grey wolf, then calculate the current fitness value corresponding to each grey wolf, to re-determine the optimal wolf leader, the optimal wolf deputy, and the optimal wolf advisor and save their current positions and the corresponding current fitness values;

[0095] Step B6: Determine whether the maximum number of iterations is reached; if so, output the current position of the latest saved optimal wolf leader as the optimal target variable value, which includes the optimal penalty factor and the optimal mode decomposition number; if not, assign t + 1 to t, and then return to the above Step B4;

[0096] It should be noted that the above Step B4 starts to execute from t = 1.

[0097] In some embodiments, the implementation process of the above Step S140 includes but is not limited to the following:

[0098] Step S141: Take minimizing the operating cost as the optimization goal, and define the fitness function as:

[0099]

[0100] where C use is the operating cost, is the unit power cost of the electrochemical energy storage system, is the unit power cost of the hydrogen energy storage system, is the unit capacity cost of the electrochemical energy storage system, is the unit capacity cost of the hydrogen energy storage system, T s is the sampling period, P ba_set (t) is the first charge and discharge power of the electrochemical energy storage system, P he_set (t) is the second charge and discharge power of the hydrogen energy storage system, and f(t) is the fitness function;

[0101] Step S142: Take the first charge and discharge power of the electrochemical energy storage system and the second charge and discharge power of the hydrogen energy storage system as the target variables to be optimized by the improved genetic algorithm;

[0102] Step S143: Based on the target variables and the power to be smoothed, define the power balance constraint condition as:

[0103]

[0104]

[0105] where P hess (t) is the power to be smoothed, P ba_set (t) is the first charge and discharge power of the electrochemical energy storage system, P he_set (t) is the second charge and discharge power of the hydrogen energy storage system, is the charging power of the lithium battery, is the discharging power of the lithium battery, is the power generation power of the hydrogen fuel cell, is the power consumption power of the PEM electrolyzer, is the minimum charging power of the lithium battery, is the maximum charging power of the lithium battery, is the minimum discharging power of the lithium battery, is the maximum discharging power of the lithium battery, is the minimum power generation of the hydrogen fuel cell, is the maximum power generation of the hydrogen fuel cell, is the minimum power consumption of the PEM electrolyzer, is the maximum power consumption of the PEM electrolyzer;

[0106] Step S144, based on the target variable and the current SOC value of the electrochemical energy storage system, define the SOC constraint condition as:

[0107]

[0108] In the formula, SOC(t) is the SOC value of the electrochemical energy storage system after operating at the first charge-discharge power, [SOC min , SOC max is the preset SOC operating range of the electrochemical energy storage system, SOC min is the minimum SOC value of the electrochemical energy storage system, SOC max is the maximum SOC value of the electrochemical energy storage system, SOC(t - 1) is the current SOC value of the electrochemical energy storage system, E ba_r is the rated capacity of the lithium battery;

[0109] Step S145, based on the target variable and the current SOH value of the hydrogen energy storage system, define the SOH constraint condition as:

[0110]

[0111] In the formula, SOH(t) is the SOH value of the hydrogen energy storage system after operating at the second charge-discharge power, [SOH min , SOH max is the preset SOH operating range of the hydrogen energy storage system, SOH min is the minimum SOH value of the hydrogen energy storage system, SOH max is the maximum SOH value of the hydrogen energy storage system, SOH(t - 1) is the current SOH value of the hydrogen energy storage system, is the intake volume of the hydrogen storage tank, is the outlet volume of the hydrogen storage tank, m H2_rated is the maximum hydrogen storage capacity of the hydrogen storage tank, V H2 is the hydrogen gas volume under the ideal gas equation, I elis the working current of the PEM electrolyzer, F is the Faraday constant, R is the ideal gas constant, T is the working temperature of the PEM electrolyzer, p is the working pressure of the PEM electrolyzer, U ideal_el is the ideal working voltage of the PEM electrolyzer, U fc is the working voltage of the hydrogen fuel cell;

[0112] Step S146: Based on the power balance constraint condition, SOC constraint condition, SOH constraint condition and fitness function, use the improved genetic algorithm to iteratively optimize the target variables, and introduce an adaptive probability function for crossover and mutation during each iterative optimization process, so as to obtain the first optimal charge-discharge power of the electrochemical energy storage system and the second optimal charge-discharge power of the hydrogen energy storage system.

[0113] In the above step S146, by introducing an adaptive probability function for crossover and mutation, the algorithm can avoid the local optimal situation and at the same time allow fine-grained search to improve the quality of the solution.

[0114] More specifically, regarding the iterative optimization of the target variables by the improved genetic algorithm mentioned in the above step S146, the corresponding implementation process includes but is not limited to the following:

[0115] Step S146.1: Set the basic parameters required for the improved genetic algorithm, including the population size and the maximum number of iterations, and use the first charge-discharge power of the electrochemical energy storage system and the second charge-discharge power of the hydrogen energy storage system as the target variables to be optimized by the improved genetic algorithm;

[0116] Step S146.2: Randomly initialize the population so that the initial positions of different individuals represent different initial target variable values and at the same time satisfy the power balance constraint condition, SOC constraint condition and SOH constraint condition;

[0117] Step S146.3: In the t-th iteration process, calculate the fitness value corresponding to each individual according to the position of each individual and sort them, select the top 10% of the individuals with the largest fitness value as elite individuals and specify that they do not participate in crossover and mutation, and use the remaining 90% of the individuals as non-elite individuals;

[0118] Step S146.4: Introduce the following adaptive probability function to perform crossover and mutation on non-elite individuals to obtain new individuals:

[0119]

[0120] In the formula, P c is the crossover probability, is the upper limit value of the crossover probability, is the lower limit value of the crossover probability, P mis the mutation probability, is the upper limit value of the mutation probability, is the lower limit value of the mutation probability, e is the natural constant, approximately equal to 2.718, and f(t) is the fitness value corresponding to the individual after the t-th iteration, is the average fitness value corresponding to the population after the t-th iteration, f max (t) is the maximum fitness value corresponding to the population after the t-th iteration;

[0121] Step S146.5: Form a new generation of population with the elite individuals and the new individuals, and then screen out all the individuals that simultaneously satisfy the power balance constraint condition, the SOC constraint condition, and the SOH constraint condition to form a feasible optimal new generation of population;

[0122] Step S146.6: Determine whether the maximum number of iterations is reached; if so, select the individual with the maximum fitness value from the current optimal new generation of population, and output the current position of this individual as the optimal target variable value, where the optimal target variable value includes the first optimal charge and discharge power of the electrochemical energy storage system and the second optimal charge and discharge power of the hydrogen energy storage system; if not, assign t + 1 to t, and then return to the above Step S146.3;

[0123] It should be noted that the above Step S146.3 starts to execute from t = 1.

[0124] In some embodiments, the present invention proposes to further divide the preset SOC operating range [SOC min , SOC max of the electrochemical energy storage system into three non-overlapping ranges, namely the low SOC range [SOC min , SOC he-fc ), the normal SOC range [SOC he-fc , SOC he-el , and the high SOC range (SOC he-el , SOC max , SOC he-el is the maximum critical SOC value, SOC he-fc is the minimum critical SOC value. Then, in combination with the power to be smoothed and the current SOC value of the electrochemical energy storage system, a protection strategy required to adjust the overall operating state of the microgrid is formulated. That is, the implementation content of the above Step S150 is specifically as follows:

[0125] (1) When it is recognized that the power to be smoothed is greater than zero and the current SOC value of the electrochemical energy storage system is in the high SOC range (SOC he-el , SOC maxWhen it is determined that the electrochemical energy storage system is currently in the charging state and the PEM electrolyzer is currently in the operating state, the corresponding protection strategy is formulated as follows: control the electrochemical energy storage system to perform a discharging operation until its SOC value drops to the normal SOC range [SOC he-fc ,SOC he-el . It can be understood that when the SOC value of the electrochemical energy storage system drops to SOC he-el , the discharging can be stopped, and the electric energy generated by the operation of the electrochemical energy storage system will be supplied to the hydrogen energy storage system in real time to maintain the operation of the PEM electrolyzer;

[0126] (2) When it is recognized that the power to be smoothed is less than zero and the current SOC value of the electrochemical energy storage system is within the low SOC range [SOC min ,SOC he-fc )], it is determined that the electrochemical energy storage system is currently in the discharging state and the hydrogen fuel cell is currently in the operating state. At this time, the corresponding protection strategy is formulated as follows: control the electrochemical energy storage system to perform a charging operation until its SOC value rises to the normal SOC range [SOC he-fc ,SOC he-el . It can be understood that when the SOC value of the electrochemical energy storage system rises to SOC he-fc , the charging can be stopped, and under the condition of maintaining the operation of the hydrogen fuel cell, the electric energy required for the operation of the electrochemical energy storage system will be supplied by the hydrogen energy storage system in real time.

[0127] In the embodiment of the present invention, by reasonably decomposing the current operating power of the microgrid by using the variational mode decomposition algorithm optimized by the gray wolf algorithm, the situation of missing important frequency feature information can be avoided. By fully considering the SOC value of the electrochemical energy storage system and the SOH value of the hydrogen energy storage system, the power to be smoothed of the electric-hydrogen hybrid energy storage system is flexibly and efficiently allocated. And in this process, an improved genetic algorithm is introduced and the optimization goal is to minimize the operating cost to improve the power allocation accuracy, so as to meet the energy requirements of the microgrid in different scenarios to the greatest extent, and make the overall performance of the microgrid reach the best. By enabling the protection strategy while adjusting the overall operating state of the microgrid, the real-time SOC value of the electrochemical energy storage system is always within the normal SOC range to maintain the life and working stability of the lithium battery as much as possible, and at the same time, the frequent startup phenomenon of the PEM electrolyzer and the hydrogen fuel cell in the hydrogen energy storage system can be reduced.

[0128] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a power distribution method for an electric-hydrogen hybrid energy storage system in a microgrid in the above embodiment. Among them, the computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. That is to say, the storage device includes any medium that stores or transmits information in a readable form by a device (such as a computer, mobile phone, etc.), and can be a read-only memory, a disk, or an optical disk, etc.

[0129] In addition, Figure 3 is a schematic diagram of the hardware structure of a computer device provided by an embodiment of the present invention. The computer device includes devices such as a processor 220, a memory 230, an input unit 240, and a display unit 250. Those skilled in the art can understand that Figure 3 the device structure devices shown do not constitute a limitation on all devices, and may include more or fewer components than shown, or combine certain components. The memory 230 can be used to store the computer program 210 and each functional module. The processor 220 runs the computer program 210 stored in the memory 230, thereby executing various functional applications and data processing of the device. The memory can be an internal memory or an external memory, or include both an internal memory and an external memory. The internal memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), flash memory, or a random access memory. The external memory can include a hard disk, a floppy disk, a USB flash drive, a magnetic tape, etc. The memory 230 disclosed in the embodiments of the present invention includes, but is not limited to, these types of memories. The memory 230 disclosed in the embodiments of the present invention is only an example and not a limitation.

[0130] The input unit 240 is used to receive the input of signals and the keywords input by the user. The input unit 240 may include a touch panel and other input devices. The touch panel can collect the touch operations of the user on or near it (such as the operations of the user using any suitable object or accessory such as a finger or a stylus on or near the touch panel), and drive the corresponding connection device according to a preset program; the other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as play control keys, switch keys, etc.), a trackball, a mouse, a joystick, etc. The display unit 250 can be used to display the information input by the user or the information provided to the user and various menus of the terminal device. The display unit 250 can be in the form of a liquid crystal display, an organic light emitting diode, etc. The processor 220 is the control center of the terminal device, connects various parts of the entire device through various interfaces and lines, and executes various functions and processes data by running or executing the software programs and / or modules stored in the memory 230, and calling the data stored in the memory 230.

[0131] As an embodiment, the computer device includes a processor 220, a memory 230, and a computer program 210, wherein the computer program 210 is stored in the memory 230 and is configured to be executed by the processor 220, and the computer program 210 is configured to execute a power distribution method of an electric-hydrogen hybrid energy storage system in a microgrid in the above embodiment.

[0132] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations.

[0133] The terms "including" and "having" in the specification of this application and the above drawings, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0134] In this application, it should be understood that "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that there can be three relationships. For example, "A and / or B" can represent three situations: only A exists, only B exists, and both A and B exist at the same time. Here, A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the associated objects before and after. "At least one (item) of the following" or a similar expression means any combination of these items, including any combination of a single item or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0135] Although this application has been described in considerable detail and particularly with respect to several of the above embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but rather should be regarded as effectively covering the intended scope of this application by providing a broad interpretation of these claims in light of the prior art. Additionally, the application has been described above in terms of embodiments foreseeable by the inventors for the purpose of providing a useful description, and non-substantive changes to this application that are not currently foreseeable may still represent equivalent changes to this application.

Claims

1. A power distribution method for an electric-hydrogen hybrid energy storage system in a microgrid, the electric-hydrogen hybrid energy storage system comprising an electrochemical energy storage system and a hydrogen energy storage system, characterized in that, The method includes: Obtaining the current operating power of the microgrid, the current SOC value of the electrochemical energy storage system, and the current SOH value of the hydrogen energy storage system; When the absolute value of the current operating power is greater than a preset power threshold, decomposing the current operating power according to grid connection requirements to obtain grid-connected power and power to be smoothed; Aiming at minimizing the operating cost, combining the current SOC value and the current SOH value, distributing the power to be smoothed to obtain the first optimal charge-discharge power of the electrochemical energy storage system and the second optimal charge-discharge power of the hydrogen energy storage system; wherein, the operating cost is solved through the following expression: Wherein, is the operating cost, is the unit power cost of the electrochemical energy storage system, is the unit capacity cost of the electrochemical energy storage system, is the unit power cost of the hydrogen energy storage system, is the unit capacity cost of the hydrogen energy storage system, is the sampling period, is the first charge-discharge power of the electrochemical energy storage system, is the second charge-discharge power of the hydrogen energy storage system; Determining a protection strategy according to the power to be smoothed, the preset SOC operating range of the electrochemical energy storage system, and the current SOC value; Controlling the microgrid to be grid-connected according to the grid-connected power, controlling the electrochemical energy storage system to operate according to the first optimal charge-discharge power, controlling the hydrogen energy storage system to operate according to the second optimal charge-discharge power, and adjusting the SOC state of the electrochemical energy storage system according to the protection strategy; Wherein, the preset SOC operating range of the electrochemical energy storage system consists of a high SOC range, a normal SOC range, and a low SOC range, and determining the protection strategy according to the power to be smoothed, the preset SOC operating range of the electrochemical energy storage system, and the current SOC value includes: When the power to be smoothed is greater than zero and the current SOC value falls within the high SOC range, determining the protection strategy as: controlling the electrochemical energy storage system to discharge until the SOC value drops to the normal SOC range, and the electric energy generated by the electrochemical energy storage system is supplied to the hydrogen energy storage system; When the power to be smoothed is less than zero and the current SOC value falls within the low SOC range, determining the protection strategy as: controlling the electrochemical energy storage system to charge until the SOC value rises to the normal SOC range, and the electric energy required by the electrochemical energy storage system is provided by the hydrogen energy storage system.

2. The power distribution method of the electric-hydrogen hybrid energy storage system in the microgrid according to claim 1, characterized in that The current operating power of the microgrid is obtained by the following method: Obtaining the power generation power of the photovoltaic system in the microgrid and the power consumption power of the DC load in the microgrid, and taking the difference between the power generation power and the power consumption power as the current operating power of the microgrid.

3. The power distribution method of the electric-hydrogen hybrid energy storage system in the microgrid according to claim 1, characterized in that, The decomposing the current operating power according to grid connection requirements to obtain grid-connected power and power to be smoothed includes: Processing the current operating power by using the variational mode decomposition algorithm, and optimizing the number of mode decompositions and the penalty factor required by the variational mode decomposition algorithm by using the grey wolf algorithm during the processing to obtain several optimal mode components; Dividing the several optimal mode components according to grid connection requirements to obtain a plurality of low-frequency optimal mode components and a plurality of high-frequency optimal mode components; Synthesizing the plurality of low-frequency optimal mode components to obtain grid-connected power; Synthesizing the plurality of high-frequency optimal mode components to obtain power to be smoothed.

4. The power distribution method of the electric-hydrogen hybrid energy storage system in the microgrid according to claim 1, characterized in that Taking the minimization of the operating cost as the goal, combining the current SOC value and the current SOH value, and allocating the power to be smoothed to obtain the first optimal charge-discharge power of the electrochemical energy storage system and the second optimal charge-discharge power of the hydrogen energy storage system includes: Taking the minimization of the operating cost as the goal, determining the fitness function; Taking the first charge-discharge power of the electrochemical energy storage system and the second charge-discharge power of the hydrogen energy storage system as the target variables to be optimized; Determining the power balance constraint conditions according to the target variables and the power to be smoothed; Determining the SOC constraint conditions according to the target variables and the current SOC value; Determining the SOH constraint conditions according to the target variables and the current SOH value; According to the fitness function, the power balance constraint conditions, the SOC constraint conditions and the SOH constraint conditions, using the improved genetic algorithm to iteratively optimize the target variables, and introducing an adaptive probability function for crossover and mutation operations in each iteration process to obtain the first optimal charge-discharge power of the electrochemical energy storage system and the second optimal charge-discharge power of the hydrogen energy storage system.

5. The power distribution method of the electric-hydrogen hybrid energy storage system in the microgrid according to claim 4, characterized in that The power balance constraint conditions include: limiting the sum of the first charge-discharge power of the electrochemical energy storage system and the second charge-discharge power of the hydrogen energy storage system to be the power to be smoothed.

6. The power distribution method of the electric-hydrogen hybrid energy storage system in the microgrid according to claim 1, wherein The method further includes: when the absolute value of the current operating power is less than or equal to the preset power threshold, keeping the operating state of the microgrid unchanged.

7. A computer device, comprising a memory and a processor, wherein a computer program is stored on the memory, characterized in that, The processor executes the computer program to implement the power allocation method of the electric-hydrogen hybrid energy storage system in the microgrid according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the power allocation method of the electric-hydrogen hybrid energy storage system in the microgrid according to any one of claims 1 to 6.

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