Renewable energy dispatch optimization method based on AEM system

Abstract

The present application relates to the technical field of renewable energy dispatching, and specifically to a method for optimizing renewable energy dispatching based on an AEM system, the method comprising: determining the line loss weights of each subgrid under various power generation modes; determining the penalty function of the power system under various power generation modes; determining the local objective function of each subgrid in combination with the power demand of each subgrid, the power supply of various power generation modes, and the line loss weights; determining the global objective function of the power system in combination with the local objective functions of all subgrids, all the penalty functions, and the power generation unit prices of various power generation modes; constructing individual learning factors and group learning factors based on all the penalty functions, combining all the local objective functions and the global objective function to adjust the power supply ratio. The present application realizes the precise allocation of the proportion of power supply modes in the power system by constructing a global objective function and an adaptive learning factor.

Description

Technical Field

[0001] The present application relates to the technical field of renewable energy scheduling, and in particular to a renewable energy scheduling optimization method based on an AEM system. Background Art

[0002] Renewable energy generation has broad development prospects due to its pollution-free and sustainable characteristics. However, there are some inevitable problems with renewable energy generation. Its power intermittency and power generation stability are a challenge to the dispatch optimization of the power grid. Due to the instability of these renewable energy power supplies, strict and accurate renewable energy dispatch optimization is required during the operation of the power grid. When the power supply of the power grid exceeds the demand, the excess power needs to be stored to reduce resource waste. The hydrogenation energy storage of the anion exchange membrane (AEM) system is one of the commonly used energy storage methods; when the power supply of the power grid is insufficient, the release and charging of hydrogen electricity is used to supplement the power supply of the power grid to meet the power demand.

[0003] In the existing optimization of renewable energy scheduling, the optimization strategy is to maximize profits based on the cost and benefits of power generation, and the method of controlling profits is to sort the order of renewable energy power supply from low to high according to the cost of power generation. However, this scheduling method is too rough. When multiple renewable energy sources are needed for power supply, it will lead to scheduling waste. In addition, as an infrastructure construction, the power system must not only consider profits but also the safety of the system. For example, long-term line losses of transmission lines will reduce the service life of transmission lines, affect the safety of the power system, and lead to scheduling waste; secondly, the conversion of hydrogenation energy storage and hydrogen-to-electricity power supply of the AEM system will also increase costs. Therefore, there is an urgent need for a renewable energy scheduling optimization method that takes into account the impact of equipment loss and more detailed scheduling with less waste. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a renewable energy scheduling optimization method based on the AEM system to solve the existing problems.

[0005] The renewable energy scheduling optimization method based on the AEM system of this application adopts the following technical solutions:

[0006] An embodiment of the present application provides a renewable energy scheduling optimization method based on an AEM system, the method comprising the following steps:

[0007] Obtain the resistance of each transmission line in each subgrid under various power generation modes and all current data within a preset period to obtain the average power loss of each transmission line in each subgrid, and combine the length of each transmission line to determine the line loss weight of each subgrid under various power generation modes;

[0008] Obtain the power supply and power generation of various power generation modes in the power system, and combine the maximum value function to determine the penalty function of the power system under various power generation modes;

[0009] Obtaining the power purchase price and power generation price of various power generation methods, and determining the local objective function of each subgrid in combination with the power demand of each subgrid, the power supply of various power generation methods and the line loss weight;

[0010] The input power and hydrogen output of the electrolyzer in the power system during the power-to-hydrogen conversion process, as well as the amount of hydrogen consumed and the hydrogen-to-electricity power generated during the hydrogen-to-electricity conversion process are obtained, and the global objective function of the power system is determined by combining the local objective functions of all subgrids, all the penalty functions, and the power generation unit prices of various power generation modes;

[0011] An individual learning factor and a group learning factor are constructed based on all the penalty functions, and all the local objective functions and the global objective function are combined to adjust the power supply ratio.

[0012] Preferably, the method for determining the line loss weight of each sub-grid under each type of power generation mode is:

[0013] Analyze the transmission distance of each transmission line in each subgrid under various power generation modes;

[0014] Determine the power generation line loss of each subgrid under each power generation mode based on the average power loss and the power transmission distance;

[0015] The line loss weight of each subgrid under each power generation mode is the ratio of the power generation line loss of each subgrid under each power generation mode to the maximum value of the power generation line loss of all subgrids.

[0016] Preferably, the power generation line loss of each subgrid under each power generation mode is the result of integrating the average power loss of all transmission lines of each subgrid under each power generation mode and the transmission distance.

[0017] Preferably, the method for determining the penalty function of the power system under the various power generation modes is:

[0018] When the power generation mode is wind power or photovoltaic power generation, the wind power or photovoltaic penalty function of the power system is the maximum value of the difference between the power supply and power generation of the power system and 0;

[0019] When the power generation method is hydrogen power generation, the maximum value is selected from the difference between the power supply and power generation of the power system and 0, and compared with the wind power penalty function and the photovoltaic penalty function. The minimum value of the three is used as the hydrogen power penalty function of the power system.

[0020] Preferably, the process of determining the local objective function of each subgrid is as follows:

[0021] Analyze the product of the power demand of each sub-grid and the unit price of electricity purchased, and record it as the power demand income of each sub-grid;

[0022] The power supply of each type of power generation to each sub-grid is the product of the proportion of the power generation of each type of power generation in the power generation of all power generation methods multiplied by the power demand of each sub-grid;

[0023] Analyze the product of the power supply of each sub-grid and the power generation unit price of each power generation mode, and record it as the power supply cost of each sub-grid under each power generation mode;

[0024] Analyze the cumulative sum of the power supply cost of each subgrid under all power generation modes and the line loss weight, and record it as the first cumulative sum of each subgrid;

[0025] The local objective function of each subgrid is the difference between the power demand income of each subgrid and the first cumulative sum.

[0026] Preferably, the process of determining the global objective function is:

[0027] Analyze the ratio of the input power of the electrolyzer to the hydrogen output during the power-to-hydrogen conversion process of the power system, which is recorded as the hydrogen conversion rate of the power system;

[0028] Analyze the ratio of the amount of hydrogen consumed in the hydrogen-to-electricity process of the power system to the hydrogen-to-electricity power generation, which is recorded as the hydrogen-to-electricity conversion rate of the power system;

[0029] Determining the difference in hydrogen energy conversion costs of the power system based on the hydrogen conversion rate, the hydrogen-to-electricity conversion rate and the power generation unit prices of various power generation methods;

[0030] Analyze the difference between the sum of the local objective functions of all subgrids in the power system and the difference in hydrogen energy conversion costs, and record it as the first global objective function of the power system;

[0031] The global objective function of the power system is the difference between the first global objective function of the power system and the sum of all penalty functions.

[0032] Preferably, the method for determining the difference in hydrogen energy conversion costs of the power system is:

[0033] In the power system, the product of the average of the unit price of wind power generation and the unit price of photovoltaic power generation in the power generation unit price of various power generation modes and the hydrogen conversion rate is recorded as the power-to-hydrogen cost of the power system;

[0034] In the power system, the product of the unit price of hydrogen power generation and the hydrogen-to-electricity conversion rate in the unit price of power generation of various power generation methods is recorded as the hydrogen-to-electricity cost of the power system;

[0035] The difference in hydrogen energy conversion costs of the power system is the result of multiplying the amount of hydrogen when the power system is in the process of hydrogen-to-electricity release and charging and the difference between the power system's electricity-to-hydrogen cost and the hydrogen-to-electricity cost.

[0036] Preferably, the method for determining the individual learning factor is:

[0037] Analyze the ratio of the sum of the first preset value and the hydrogen-electricity penalty function to the sum of the photovoltaic penalty function, the wind power penalty function and the first preset value, and record it as a first ratio;

[0038] The individual learning factor is the minimum value of the product of the second preset value and the first ratio and the second preset value;

[0039] The second preset value is a positive number smaller than the first preset value.

[0040] Preferably, the construction process of the group learning factor is:

[0041] Determine a sum of a first preset value and a hydrogen-electric penalty function, recorded as a first sum;

[0042] Determine a sum of the photovoltaic penalty function, the wind power penalty function and the first preset value, recorded as a second sum;

[0043] Determine the product of the first sum and the second sum, recorded as the first product;

[0044] The group learning factor is a ratio of the first product to the second preset value and a minimum value of the second preset value.

[0045] Preferably, combining all the local objective functions and the global objective function to adjust the power supply ratio includes:

[0046] The local objective function, global objective function, individual learning factor and group learning factor of all subgrids in the power system are used as inputs of the multi-objective particle swarm optimization algorithm to obtain the optimal solution set;

[0047] The penalty function and global objective function corresponding to each solution in the optimal solution set are analyzed, and the global objective function in which the penalty functions corresponding to all power generation modes are 0 is recorded as the optimal objective function; the maximum value of the solutions of all optimal objective functions is taken as the power supply ratio of various power supply modes of the power system.

[0048] This application has at least the following beneficial effects:

[0049] This application calculates the line loss weight of each subgrid based on the average power loss of each transmission line and the transmission distance of various renewable energy or hydrogen power generation output terminals to supply power to the subgrid, evaluates the line burden during transmission through the line loss weight, and then accurately determines the degree of impact on the safety of the power system line; constructs a penalty function according to the constraints; finally, constructs the objective function using the penalty function, and performs adaptive learning factor adjustment, and uses a multi-objective particle swarm optimization algorithm to obtain the Pareto optimal solution set. The adaptive learning factor adjustment can make each subgrid reach the optimality at the fastest speed while satisfying the global constraints. When optimizing the power dispatching of wind power generation, this application not only considers the benefits of power supply, but also considers the burden on the line during power supply and the costs of hydrogen energy storage and hydrogen power supply of the AEM system, so that the dispatching optimization of the power system is more reasonable and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 A flowchart of the steps of the renewable energy dispatch optimization method based on the AEM system;

[0052] Figure 2 The flowchart for obtaining the power supply ratio is shown below;

[0053] Figure 3 Schematic diagram of the power supply ratio scheduling optimization process. DETAILED DESCRIPTION

[0054] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of the renewable energy scheduling optimization method based on the AEM system proposed in the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0055] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0056] The specific scheme of the renewable energy scheduling optimization method based on the AEM system provided in this application is described in detail below with reference to the accompanying drawings.

[0057] An embodiment of the present application provides a renewable energy scheduling optimization method based on an AEM system. Specifically, the following renewable energy scheduling optimization method based on an AEM system is provided. Figure 1 , the method comprises the following steps:

[0058] Step S1: Collect the power generation data of various power generation methods, the current data of the transmission lines, and the hydrogen data of the AEM system hydrogenation energy storage.

[0059] In the power grid, current data in each transmission line during transmission is collected by installing ammeters at both ends of each transmission line. The current data of each transmission line at each moment is the average of the current data at the beginning and end at the same moment.

[0060] The power analyzer installed at the power input end of the electrolyzer is used to collect input power in real time. The gas flow meter installed at the gas outlet of the electrolyzer is used to collect hydrogen flow data generated by the hydrogenation energy storage of the AEM system in real time.

[0061] The power analyzer installed at the output end of the photovoltaic array collects the power generated by photovoltaic power generation in real time;

[0062] The power analyzer installed at the output end of the wind turbine collects the power generated by the wind turbine in real time.

[0063] The amount of hydrogen required for hydrogen-electricity release and charging is collected by a gas flow meter installed at the hydrogen input end of the hydrogen-electricity release and charging device;

[0064] The power generated by hydrogen electricity is collected through a power analyzer installed at the power output end of the hydrogen electricity release and charging device.

[0065] Data is collected once at a preset time interval, and data for a preset period of time is collected in total. The resistance of each transmission line is obtained according to the technical specifications of the transmission line.

[0066] It should be noted that the value of the preset time interval and the value of the preset period are both manually set. The value of the preset time interval is 5 minutes, and the value of the preset period is 24 hours.

[0067] Step S2: Obtain the average power loss of each transmission line in each subgrid under each power generation mode, and combine the length of each transmission line to determine the line loss weight of each subgrid under each power generation mode.

[0068] In the power system, the entire power system is divided into different sub-grids according to different geographical locations or regional functions (industrial areas, commercial areas, residential areas), and power is supplied to each sub-grid through power dispatching. When dispatching wind power in the power system, not only the benefits of power dispatching should be considered, but also the safety of the power system.

[0069] For example, in areas with abundant sunshine, the cost of photovoltaic power generation is relatively low. If only the benefits are considered, it is more advantageous to use photovoltaic power generation first, and then use other renewable energy sources to supply power after all the power generated by photovoltaic power generation has been used up. However, if this power supply method is adopted for the entire power system, it may cause some sub-grids to need long-distance power transmission for a long time. Such long-distance power transmission will increase the burden on the transmission lines and the waste of electricity in the transmission process, thereby shortening the service life of the transmission lines and making it more prone to transmission line hazards that affect the safety of the entire power system.

[0070] Therefore, it is necessary to combine the benefits of power dispatching and the security of the power system and design the optimal plan for power dispatching and allocation of each sub-grid, so that the overall consideration can not only have higher benefits but also relatively improve the security of the power system.

[0071] S201: Determine the line loss weight of each subgrid under each power generation mode based on the average power loss of each transmission line in each subgrid and the transmission distance.

[0072] The use of photovoltaic power generation for the entire power system may cause some sub-grids to carry out long-distance power transmission for a long time. Long-distance power transmission will not only increase the burden on the transmission lines, but also shorten the service life of the transmission lines. Therefore, by analyzing the line loss weights of each sub-grid, the power supply method can be reasonably allocated, reducing unnecessary line losses while improving the transmission efficiency of the power grid.

[0073] Based on the average power loss of each transmission line in each subgrid and the transmission distance, the line loss weight of each subgrid under various power generation modes is determined, specifically:

[0074] All current data of each transmission line in a preset period of time and the resistance of the transmission line are taken as input to obtain the average power loss of each transmission line in the preset period of time.

[0075] It should be noted that there are many methods for calculating average power loss. In this embodiment, the root mean square current method is used to calculate the average power loss of each transmission line in each preset time period. The implementer may also use other methods for calculating power such as the average power method and the effective value method according to the specific circumstances. This embodiment does not impose any special restrictions.

[0076] Among them, the root mean square current method is a well-known technology for calculating line loss in the power system field, and the specific process will not be repeated here.

[0077] (1) Wind power line losses in each subgrid.

[0078] According to the average power loss of each transmission line in each subgrid and the length of the transmission line, the wind power generation line loss of each subgrid is constructed, which is as follows:

[0079] Analyze the transmission distance of each transmission line in each subgrid under various power generation modes;

[0080] The power generation line loss of each subgrid under each power generation mode is determined based on the average power loss and transmission distance. The power generation line loss of each subgrid under each power generation mode is the result of the fusion of the average power loss and transmission distance of all transmission lines of each subgrid under each power generation mode.

[0081] It should be understood that fusion refers to combining two or more indicators by adding or multiplying them together in order to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. The implementer can choose according to the specific situation, and this embodiment does not impose any special restrictions.

[0082] Preferably, as an embodiment of the present application, the construction process of the power generation line loss of each sub-grid under various power generation modes is:

[0083] Analyze the product of the average power loss of each subgrid and the transmission distance under various power generation modes, which is recorded as the line loss product of each transmission line of each subgrid under various power generation modes;

[0084] The power generation line loss of each subgrid under various power generation modes is the cumulative sum of the products of the line losses of all transmission lines of each subgrid under various power generation modes.

[0085] Preferably, as another embodiment of the present application, the construction process of the power generation line loss of each sub-grid under various power generation modes is:

[0086] Analyze the product of the average power loss of each subgrid and the transmission distance under various power generation modes, which is recorded as the line loss product of each transmission line of each subgrid under various power generation modes;

[0087] The generation line loss of each subgrid under various power generation modes is an exponential function value with a natural constant as the base and the cumulative sum of the line losses of all transmission lines of each subgrid under various power generation modes as the independent variable.

[0088] (2) Wind power line loss weights of each subgrid.

[0089] Based on the power generation line loss of each sub-grid under various power generation modes, the line loss weight of each sub-grid under various power generation modes is determined, specifically:

[0090] The line loss weight of each subgrid under each power generation mode is the ratio of the power generation line loss of each subgrid under each power generation mode to the maximum value of the power generation line loss of all subgrids.

[0091] It can be understood that the greater the line loss of the transmission line, the greater the corresponding power loss, that is, the greater the average power loss of the line; the longer the transmission distance of the line, that is, the greater the transmission distance, the greater the burden on the line, the greater the security threat generated, and thus the corresponding wind power line loss weight.

[0092] Among them, various types of power generation methods include wind power generation, photovoltaic power generation and hydrogen power generation, and the corresponding line loss weights are wind line loss weight, photovoltaic line loss weight and hydrogen line loss weight respectively.

[0093] S202: Obtain the power supply and power generation of each power generation mode in the power system, and combine the maximum value function to determine the penalty function of the power system under each power generation mode.

[0094] After obtaining the line loss weights of each subgrid under various power generation modes, it is necessary to optimize the dispatch of renewable energy power generation in the power system. Therefore, the optimal renewable energy power supply ratio of each subgrid is determined through the iteration of the optimization algorithm to achieve a balance between power supply benefits and power system security.

[0095] Before using the optimization algorithm to optimize power dispatching, we must first determine the constraints based on the power generation of various renewable energy sources in the power system, and use the constraints to design a penalty function to participate in the iterative optimization process of the optimization algorithm.

[0096] In the entire power system, the power generation of each renewable energy source is inconsistent. When optimizing the allocation of electricity, constraints need to be imposed based on the power generation, that is, there are constraints.

[0097] Constraints of the penalty function: The power supply of all power supply modes in the power system is less than or equal to the corresponding power generation.

[0098] According to the historical power supply data of each sub-grid in the database, based on the power supply data of the same day in previous years and previous days in the same year, the power demand of each sub-grid in the preset time period is constructed based on the convolutional neural network, so as to construct the penalty function, which is specifically:

[0099] When the power generation mode is wind power or photovoltaic power generation, the wind power penalty function of the power system is the maximum value of the difference between the power supply and power generation of the wind power generation of the power system and 0; the photovoltaic penalty function of the power system is the maximum value of the difference between the power supply and power generation of the photovoltaic power generation of the power system and 0;

[0100] It should be noted that the method of predicting power demand based on historical power supply data is a well-known technology in the power industry, and the specific process will not be repeated here.

[0101] When the power generation method is hydrogen power generation, the maximum value is selected from the difference between the power supply and power generation of the hydrogen power generation in the power system and 0, and compared with the wind power penalty function and the photovoltaic penalty function. The minimum value of the three is the hydrogen power penalty function of the power system.

[0102] S203: Determine the local objective function of each subgrid according to the power demand of each subgrid, the power supply of each power generation method and the line loss weight; determine the global objective function of the power system based on the local objective functions of all subgrids, all the penalty functions and the power generation unit price of each power generation method.

[0103] In order to determine the optimal power dispatching and allocation plan, a multi-objective particle swarm optimization algorithm is used in this embodiment to optimize power dispatching.

[0104] In the subgrid, we only need to consider the cost and benefits of supplying power to the subgrid; in the power system, we also need to consider the problem of using the AEM system to store and convert electricity when there is excess electricity in the power system. Therefore, it is necessary to construct local objective functions and global objective functions. The specific process is as follows:

[0105] (1) Local objective function of each subgrid.

[0106] The power purchase price and power generation price of various power generation methods are obtained, and the local objective function of each sub-grid is determined by combining the power demand of each sub-grid, the power supply of various power generation methods and the line loss weight, which is specifically:

[0107] Analyze the product of the power demand of each sub-grid and the unit price of electricity purchased, and record it as the power demand income of each sub-grid;

[0108] The power supply of each type of power generation to each sub-grid is the product of the proportion of the power generation of each type of power generation in the power generation of all power generation methods multiplied by the power demand of each sub-grid;

[0109] Analyze the product of the power supply of each sub-grid and the power generation unit price of each power generation mode, and record it as the power supply cost of each sub-grid under each power generation mode;

[0110] Analyze the cumulative sum of the power supply cost of each subgrid under all power generation modes and the line loss weight, and record it as the first cumulative sum of each subgrid;

[0111] The local objective function of each subgrid is the difference between the power demand income of each subgrid and the first cumulative sum.

[0112] (2) Global objective function of the power system.

[0113] The global objective function of the power system is determined based on the local objective functions of all subgrids, all the penalty functions and the power generation unit prices of various power generation modes, specifically:

[0114] Analyze the ratio of the input power of the electrolyzer to the hydrogen output during the power-to-hydrogen conversion process of the power system, which is recorded as the hydrogen conversion rate of the power system;

[0115] Analyze the ratio of the amount of hydrogen consumed in the hydrogen-to-electricity process of the power system to the hydrogen-to-electricity power generation, which is recorded as the hydrogen-to-electricity conversion rate of the power system;

[0116] In the power system, the product of the average of the unit price of wind power generation and the unit price of photovoltaic power generation in the power generation unit price of various power generation modes and the hydrogen conversion rate is recorded as the power-to-hydrogen cost of the power system;

[0117] In the power system, the product of the unit price of hydrogen power generation and the hydrogen-to-electricity conversion rate in the unit price of power generation of various power generation methods is recorded as the hydrogen-to-electricity cost of the power system;

[0118] The difference in hydrogen energy conversion cost of the power system is the result of multiplying the amount of hydrogen when the power system is in the process of hydrogen-to-electricity release and charging and the difference between the power system's electricity-to-hydrogen cost and the hydrogen-to-electricity cost;

[0119] Analyze the difference between the sum of the local objective functions of all subgrids in the power system and the difference in hydrogen energy conversion costs, and record it as the first global objective function of the power system;

[0120] The global objective function of the power system is the difference between the first global objective function of the power system and the sum of all penalty functions.

[0121] S204: constructing individual learning factors and group learning factors based on all the penalty functions.

[0122] In the original particle swarm optimization algorithm, the learning factor is fixed. However, in the multi-objective particle swarm optimization algorithm, due to the inconsistency between the local objective function and the global objective function, the calculation of the local objective function is relatively simple, and the speed of reaching the local optimum is relatively fast; while the penalty function is added to the global objective function for constraint, and the power dispatch and allocation of each sub-grid needs to be balanced, so that the speed of reaching the optimum is relatively slow, and the local optimum may not meet the global constraints. Therefore, it is necessary to continuously adjust the learning factor so that it is as close to the local optimum as possible while satisfying the global constraints; and as close to the global optimum as possible while not satisfying the global constraints.

[0123] Therefore, individual learning factors and group learning factors are constructed based on all the penalty functions, specifically:

[0124] (1) Individual learning factor.

[0125] Analyze the ratio of the sum of the first preset value and the hydrogen-electricity penalty function to the sum of the photovoltaic penalty function, the wind power penalty function and the first preset value, and record it as a first ratio;

[0126] The individual learning factor is the minimum value of the product of the second preset value and the first ratio and the second preset value.

[0127] It is understandable that when the global constraint is not met, at least one of the penalty functions of wind power, photovoltaic power, and hydrogen power must be greater than 0; and hydrogen power, as a supplementary means of power supply, is powered by wind power and photovoltaic power, which means that the penalty functions of wind power and photovoltaic power are both greater than 0, and the penalty function value of hydrogen power is less than or equal to the sum of the penalty function values ​​of wind power and photovoltaic power, so that the individual learning factor is relatively small, and it will be more inclined to explore the global solution, which will help the algorithm to jump out of the local optimum and increase the ability of global search. On the contrary, when the global constraint is met, the individual learning factor reaches the second preset value, and the particles are more inclined to local search and will converge to the local optimal solution faster.

[0128] (2) Group learning factor.

[0129] Determine a sum of a first preset value and a hydrogen-electric penalty function, recorded as a first sum;

[0130] Determine a sum of the photovoltaic penalty function, the wind power penalty function and the first preset value, recorded as a second sum;

[0131] Determine the product of the first sum and the second sum, recorded as the first product;

[0132] The group learning factor is a ratio of the first product to the second preset value and a minimum value of the second preset value.

[0133] The first preset value is a positive number that is smaller than the second preset value.

[0134] It is understandable that when the global constraints are not met, at least one of the penalty functions of wind power, photovoltaic power, and hydrogen power should be greater than 0, so that the group learning factor is relatively large. At this time, the particles are more inclined to global search and more inclined to move to the global optimal position. On the contrary, when the global constraints are met, the penalty functions of wind power, photovoltaic power, and hydrogen power are all 0, and the group learning factor reaches the minimum value. At this time, the local search ability of the particles will be enhanced, which is helpful to find the local optimal solution.

[0135] S205: Based on the local objective function, global objective function, individual learning factor and group learning factor of all subgrids in the power system, a Pareto optimal solution set is determined in combination with a multi-objective particle swarm optimization algorithm.

[0136] Set the parameters of the multi-objective particle swarm optimization algorithm to, and the number of particles to (In this embodiment ), particle dimensions (In this embodiment , respectively, power demand, wind power supply ratio, photovoltaic power supply ratio, and hydrogen power supply ratio), the maximum number of iterations of the system (In this embodiment ), inertia weight , the initial individual learning factor and group learning factor are both 1.5.

[0137] The local objective function, the global objective function, the initial position of each particle (the initial dispatch information data of each subgrid), the initial speed of each particle (the initial speed is 0), the unit price of wind power generation, the unit price of photovoltaic power generation and the unit price of hydrogen power generation are used as the input of the multi-objective particle swarm optimization algorithm. The multi-objective particle swarm optimization algorithm is used to output the Pareto optimal solution set of power dispatch allocation. The solution set contains multiple Pareto solutions, and each Pareto solution corresponds to a power dispatch allocation result. The multi-objective particle swarm optimization algorithm is a well-known technology, and the specific process is not repeated here.

[0138] It should be noted that the number of particles is , the maximum number of system iterations , inertia weight The initial individual learning factor and the group learning factor are all set manually, and the implementer can set them according to the specific situation. In this embodiment, the values ​​of the above four are 50, 100, 0.5, and 1.5 respectively.

[0139] The power demand is the result predicted based on historical data and remains unchanged during the iteration of the optimization algorithm; the initial data values ​​of the wind power supply ratio (the ratio of the power supply of wind power generation to the power generation), the photovoltaic power supply ratio (the ratio of the power supply of photovoltaic power generation to the power generation), and the hydrogen power supply ratio (the ratio of the power supply of hydrogen power generation to the power generation) are artificially set. In this embodiment, the values ​​are 0.4, 0.4, and 0.2 respectively. The implementer can set them according to the specific situation. This embodiment does not impose any special restrictions. The power supply ratio of each power generation method changes continuously according to the iteration process of the optimization algorithm.

[0140] Step S3: constructing individual learning factors and group learning factors based on all the penalty functions, combining all the local objective functions and the global objective function to adjust the power supply ratio.

[0141] According to the multi-objective particle swarm optimization algorithm in step S2, the Pareto optimal solution set can be obtained. The penalty function and the global objective function corresponding to each solution in the Pareto optimal solution set are analyzed. The global objective function in which the penalty functions corresponding to all power generation modes are 0 is recorded as the optimal objective function; the maximum value of the solutions of all optimal objective functions is taken as the final optimal solution.

[0142] This final optimal solution is the dispatch information data of each subgrid under the optimal power dispatch condition, which includes the power supply ratio of various power supply modes. When the power system optimizes the dispatch of renewable energy, the corresponding power allocation is carried out in advance for each subgrid according to the predicted power demand of each subgrid and the power supply ratio of various power supply modes. Since hydrogen power generation uses hydrogen fuel cells to convert electrical energy through corresponding chemical reactions, this process takes a certain amount of time, and due to the limited efficiency of hydrogen fuel cells, it also takes a certain amount of time to generate enough electricity. Therefore, when the hydrogen power supply ratio of a subgrid is not 0, the corresponding amount of hydrogen power supply is carried out in advance according to the predicted power demand, so as to avoid temporary hydrogen power generation when the power supply of renewable energy is insufficient, which may cause the power system to be unable to supply power continuously and stably.

[0143] In this way, according to the optimal power dispatch, the power allocation of wind power generation is completed in advance and the hydrogen power supply is planned in advance, so as to maintain the continuous stability of the power supply of the power system and realize the dispatch optimization of wind power generation and AEM system power storage and hydrogen power supply.

[0144] Preferably, as an embodiment of the present application, the flow chart for obtaining the power supply ratio is as follows: Figure 2 shown.

[0145] Preferably, as an embodiment of the present application, the schematic diagram of the power supply ratio scheduling optimization process is as follows Figure 3 shown.

[0146] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0147] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0148] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A renewable energy scheduling optimization method based on the AEM system, characterized in that: The method comprises the following steps: Obtain the resistance of each transmission line in each subgrid under each power generation mode and all current data in a preset time period to obtain the average power loss of each transmission line in each subgrid under each power generation mode, and combine the length of each transmission line to determine the line loss weight of each subgrid under each power generation mode; Obtain the power supply and power generation of various power generation modes in the power system, and combine the maximum value function to determine the penalty function of the power system under various power generation modes; Obtaining the power purchase price and power generation price of various power generation methods, and determining the local objective function of each subgrid in combination with the power demand of each subgrid, the power supply of various power generation methods and the line loss weight; The input power and hydrogen output of the electrolyzer in the power system during the power-to-hydrogen conversion process, as well as the amount of hydrogen consumed and the hydrogen-to-electricity power generated during the hydrogen-to-electricity conversion process are obtained, and the global objective function of the power system is determined by combining the local objective functions of all subgrids, all the penalty functions, and the power generation unit prices of various power generation modes; An individual learning factor and a group learning factor are constructed based on all the penalty functions, and all the local objective functions and the global objective function are combined to adjust the power supply ratio.

2. The renewable energy scheduling optimization method based on the AEM system according to claim 1, characterized in that: The method for determining the line loss weight of each sub-grid under the various power generation modes is as follows: Analyze the transmission distance of each transmission line in each subgrid under various power generation modes; Determine the power generation line loss of each subgrid under each power generation mode based on the average power loss and the power transmission distance; The line loss weight of each subgrid under each power generation mode is the ratio of the power generation line loss of each subgrid under each power generation mode to the maximum value of the power generation line loss of all subgrids.

3. The renewable energy scheduling optimization method based on the AEM system according to claim 2, characterized in that: The power generation line loss of each subgrid under each power generation mode is the result of the integration of the average power loss of all transmission lines of each subgrid under each power generation mode and the transmission distance.

4. The renewable energy scheduling optimization method based on the AEM system according to claim 1, characterized in that: The method for determining the penalty function of the power system under the various power generation modes is as follows: When the power generation mode is wind power or photovoltaic power generation, the wind power or photovoltaic penalty function of the power system is the maximum value of the difference between the power supply and power generation of the power system and 0; When the power generation method is hydrogen power generation, the maximum value is selected from the difference between the power supply and power generation of the power system and 0, and compared with the wind power penalty function and the photovoltaic penalty function. The minimum value of the three is used as the hydrogen power penalty function of the power system.

5. The renewable energy scheduling optimization method based on the AEM system according to claim 1, characterized in that: The determination process of the local objective function of each sub-grid is as follows: Analyze the product of the power demand of each sub-grid and the unit price of electricity purchased, and record it as the power demand income of each sub-grid; The power supply of each type of power generation to each sub-grid is the product of the proportion of the power generation of each type of power generation in the power generation of all power generation methods multiplied by the power demand of each sub-grid; Analyze the product of the power supply of each sub-grid and the power generation unit price of each power generation mode, and record it as the power supply cost of each sub-grid under each power generation mode; Analyze the cumulative sum of the power supply cost of each subgrid under all power generation modes and the line loss weight, and record it as the first cumulative sum of each subgrid; The local objective function of each subgrid is the difference between the power demand income of each subgrid and the first cumulative sum.

6. The renewable energy scheduling optimization method based on the AEM system according to claim 1, characterized in that: The determination process of the global objective function is: Analyze the ratio of the input power of the electrolyzer to the hydrogen output during the power-to-hydrogen conversion process of the power system, which is recorded as the hydrogen conversion rate of the power system; Analyze the ratio of the amount of hydrogen consumed in the hydrogen-to-electricity process of the power system to the hydrogen-to-electricity power generation, which is recorded as the hydrogen-to-electricity conversion rate of the power system; Determining the difference in hydrogen energy conversion costs of the power system based on the hydrogen conversion rate, the hydrogen-to-electricity conversion rate and the power generation unit prices of various power generation methods; Analyze the difference between the sum of the local objective functions of all subgrids in the power system and the difference in hydrogen energy conversion costs, and record it as the first global objective function of the power system; The global objective function of the power system is the difference between the first global objective function of the power system and the sum of all penalty functions.

7. The renewable energy scheduling optimization method based on the AEM system according to claim 6, characterized in that: The method for determining the difference in hydrogen energy conversion costs of the power system is as follows: In the power system, the product of the average of the unit price of wind power generation and the unit price of photovoltaic power generation in the power generation unit price of various power generation modes and the hydrogen conversion rate is recorded as the power-to-hydrogen cost of the power system; In the power system, the product of the unit price of hydrogen power generation and the hydrogen-to-electricity conversion rate in the unit price of power generation of various power generation methods is recorded as the hydrogen-to-electricity cost of the power system; The difference in hydrogen energy conversion costs of the power system is the result of multiplying the amount of hydrogen when the power system is in the process of hydrogen-to-electricity release and charging and the difference between the power system's electricity-to-hydrogen cost and the hydrogen-to-electricity cost.

8. The renewable energy scheduling optimization method based on the AEM system according to claim 1, characterized in that: The method for determining the individual learning factor is: Analyze the ratio of the sum of the first preset value and the hydrogen-electricity penalty function to the sum of the photovoltaic penalty function, the wind power penalty function and the first preset value, and record it as a first ratio; The individual learning factor is the minimum value of the product of the second preset value and the first ratio and the second preset value; The second preset value is a positive number smaller than the first preset value.

9. The renewable energy scheduling optimization method based on the AEM system according to claim 8, characterized in that: The construction process of the group learning factor is: Determine a sum of a first preset value and a hydrogen-electric penalty function, recorded as a first sum; Determine a sum of the photovoltaic penalty function, the wind power penalty function and the first preset value, recorded as a second sum; Determine the product of the first sum and the second sum, recorded as the first product; The group learning factor is a ratio of the first product to the second preset value and a minimum value of the second preset value.

10. The renewable energy scheduling optimization method based on the AEM system according to claim 1, characterized in that: The combining all the local objective functions and the global objective function to adjust the power supply ratio includes: The local objective function, global objective function, individual learning factor and group learning factor of all subgrids in the power system are used as inputs of the multi-objective particle swarm optimization algorithm to obtain the optimal solution set; The penalty function and global objective function corresponding to each solution in the optimal solution set are analyzed, and the global objective function in which the penalty functions corresponding to all power generation modes are 0 is recorded as the optimal objective function; the maximum value of the solutions of all optimal objective functions is taken as the power supply ratio of various power supply modes of the power system.

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