Microgrid optimization control related equipment and methods based on source, grid, load and storage

Abstract

The present application relates to the technical field of power supply system, and specifically to equipment and methods related to optimization control of microgrids based on source-grid-load-storage, the method comprising: obtaining photovoltaic power generation, wind power generation and load power consumption at each moment in each collection cycle before each collection moment; constructing an objective function; determining the power supply change rate and power consumption change rate of each collection cycle; determining the power supply fluctuation index and power consumption fluctuation index of each collection cycle; determining the supply and consumption balance coefficient of each collection cycle; determining the mutation coefficient of each collection moment; determining the inertia weight of the particle swarm optimization algorithm at each collection moment; combining the particle swarm optimization algorithm, determining the charging and discharging power of the energy storage side in the collection cycle of each collection moment, and controlling the charging and discharging power of the energy storage side in the microgrid. The present application can accurately optimize the objective function, so as to accurately regulate the charging and discharging power of the energy storage side, and improve the accuracy of the optimization control of the microgrid.

Description

Technical Field

[0001] The present application relates to the technical field of power supply systems, and specifically to equipment and methods related to optimization control of microgrids based on source, grid, load and storage. Background Art

[0002] Microgrids are small power systems that combine distributed power sources with user loads. Compared with traditional power grids, they are more flexible, reliable, and less expensive. How to reduce the impact of uncertainty in renewable energy and load demand, increase the renewable energy consumption rate, and optimize energy storage system control are urgent issues to be addressed. Under the new situation, the proportion of renewable energy power generation continues to increase. In order to solve the problem of imbalance between power supply and power consumption, a coordinated control system of source, grid, load, and storage has emerged.

[0003] Existing microgrid control methods usually rely on power supply and power consumption to construct the objective function, and then optimize the objective function. Particle swarm optimization is often used for optimization due to its advantages of fast search speed and high efficiency. Since the power supply capacity of distributed energy sources such as solar energy and wind energy is random, and the load demand also fluctuates greatly, the stability of the objective function curve varies greatly. Existing methods often use a single inertia weight parameter in the particle swarm optimization process, which makes it difficult to obtain the best optimization effect, and thus has the defect of low accuracy of microgrid optimization control. Summary of the invention

[0004] In order to solve the above technical problems, a microgrid optimization control related device and method based on source-grid-load-storage are provided to solve the existing problems.

[0005] The solution to the technical problem of this application is to provide a microgrid optimization control related device and method based on source-grid-load-storage, including the following steps:

[0006] In a first aspect, an embodiment of the present application provides a microgrid optimization control method based on source-grid-load-storage, the method comprising the following steps:

[0007] Obtain the photovoltaic power generation, wind power generation and load power consumption at each moment in each collection cycle before each collection moment; record the sum of the photovoltaic power generation and wind power generation at each moment as the total power generation at each moment;

[0008] Based on the total power generation and load power consumption at each moment in each acquisition cycle, an objective function is constructed;

[0009] Determine the power supply change rate and power consumption change rate of each collection period according to the difference between the peaks of the total power generation and load power consumption at all times in each collection period and their adjacent troughs, as well as the interval between adjacent troughs; analyze the discreteness of the total power generation and load power consumption at all times in each collection period, and determine the power supply fluctuation index and power consumption fluctuation index of each collection period in combination with the power supply change rate and power consumption change rate;

[0010] Determine the supply-consumption balance coefficient of each collection period according to the difference between the power supply fluctuation index and the power consumption fluctuation index of each collection period, and the correlation between the total power generation power and the load power consumption in each collection period; analyze the mutation characteristics and autocorrelation characteristics of the supply-consumption balance coefficient of multiple collection periods before each collection moment, and determine the mutation coefficient at each collection moment;

[0011] Based on the mutation coefficient, the inertia weight of the particle swarm optimization algorithm at each collection moment is determined; combined with the particle swarm optimization algorithm, the charging and discharging power of the energy storage side in the collection cycle at each collection moment is determined, and the charging and discharging power of the energy storage side in the microgrid is controlled.

[0012] Preferably, the constructing the objective function includes: ,in, For the The objective function of the acquisition cycle is For the The vector composed of the total power generated at all times in a collection cycle, For the The vector composed of the load power consumption at all times in a collection cycle, For the The charging and discharging power of the energy storage side during a collection cycle.

[0013] Preferably, determining the power supply change rate and the power consumption change rate of each acquisition cycle includes:

[0014] Obtain the peak and trough of the total power generation at all times in each acquisition cycle respectively;

[0015] The average of the difference in total power generated between each peak and its adjacent troughs on the left and right is taken as the local fluctuation of each peak;

[0016] The interval between the moments corresponding to the left and right adjacent troughs of each peak is taken as the change duration of each peak;

[0017] Calculating the ratio between the local fluctuation amount and the change duration, and taking the sum of the ratios of all the peaks in each acquisition cycle as the power supply change rate of each acquisition cycle;

[0018] For the load power consumption at all times in each collection period, the power consumption change rate of each collection period is obtained by adopting the same method as the power supply change rate of each collection period.

[0019] Preferably, the power supply fluctuation index of each acquisition cycle is the product of the discrete degree of the total power generation at all times in each acquisition cycle and the power supply change rate.

[0020] Preferably, the power consumption fluctuation index is the product of the dispersion of the load power consumption at all times in each collection cycle and the power consumption change rate.

[0021] Preferably, the determining of the supply-use balance coefficient of each collection cycle includes:

[0022] Recording the difference between the power supply fluctuation index and the power consumption fluctuation index as a first difference;

[0023] The ratio of the similarity between the total generated power and the load power at all times in each collection cycle and the first difference is used as the supply and demand balance coefficient of each collection cycle.

[0024] Preferably, determining the mutation coefficient at each acquisition moment includes:

[0025] Adopt mutation point detection algorithm to detect mutation points of supply and demand balance coefficients of multiple collection cycles before each collection moment, and count the number of all mutation points;

[0026] Calculate the autocorrelation coefficient of the supply and demand balance coefficient of multiple collection cycles before each collection moment;

[0027] The normalized result of the ratio of the number of all mutation points to the autocorrelation coefficient is used as the mutation coefficient at each acquisition moment.

[0028] Preferably, the inertia weight of the particle swarm optimization algorithm at each acquisition moment is determined by: ,in, For the The inertia weight of the group optimization algorithm at each acquisition moment, To preset the first threshold, To preset the second threshold, For the The mutation coefficient at each acquisition moment.

[0029] Preferably, the determining of the charging and discharging power of the energy storage side in the collection cycle at each collection moment includes:

[0030] Based on the inertia weight, a particle swarm optimization algorithm is used to iteratively optimize the objective function of multiple collection cycles before each collection moment to obtain the optimal charge and discharge power of each collection cycle on the energy storage side;

[0031] The average of the optimal charging and discharging powers of all collection cycles before each collection moment is used as the charging and discharging power of the energy storage side in the collection cycle at each collection moment.

[0032] In a second aspect, an embodiment of the present application further provides a microgrid optimization control-related device based on source, grid, load and storage, and the related device is implemented by any of the above-mentioned microgrid optimization control methods based on source, grid, load and storage.

[0033] This application has at least the following beneficial effects:

[0034] The present application determines the power supply change rate and power consumption change rate of each collection period according to the difference between the peaks of the total power generation and load power consumption at all times in each collection period and their adjacent troughs, as well as the interval between adjacent troughs; respectively analyzes the discreteness of the total power generation and load power consumption at all times in each collection period to determine the power supply fluctuation index and power consumption fluctuation index of each collection period, which has the beneficial effect of considering the fluctuation changes of the power generation on the power supply side and the power consumption on the load side, respectively, to reflect the randomness of the power generation capacity on the power supply side and the stability of the power consumption on the load side; determines the supply and consumption balance coefficient of each collection period according to the difference between the power supply fluctuation index and the power consumption fluctuation index of each collection period, as well as the similarity of the changes in the total power generation and load power consumption in each collection period; analyzes the mutation characteristics and autocorrelation characteristics of the supply and consumption balance coefficient of multiple collection periods before each collection moment, and determines each collection moment. The mutation coefficient of each collection moment has the beneficial effect of analyzing the difference degree of fluctuation between the power generation power on the power supply side and the power consumption power on the load side and the similarity of the changes, so as to reflect the balance of the supply and demand relationship between the power supply side and the load side. Secondly, the difference degree of supply and demand between the power supply side and the load side in a period of time before each collection moment is considered to reflect the curve fluctuation of the objective function; based on the mutation coefficient, the inertia weight of the particle swarm optimization algorithm at each collection moment is determined; combined with the particle swarm optimization algorithm, the charging and discharging power of the energy storage side in the collection cycle of each collection moment is determined, and the charging and discharging power of the energy storage side in the microgrid is controlled. The beneficial effect is that the inertia weight of the particle swarm optimization algorithm can be obtained in real time, so as to optimize the objective function more accurately, and then obtain the optimized charging and discharging power of the energy storage side, and accurately regulate the charging and discharging power of the energy storage side when the supply and demand of power generation and power consumption are unbalanced, thereby improving the accuracy of microgrid optimization control. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The source-grid-load-storage based microgrid optimization control method of the present application is further described in detail below in conjunction with the accompanying drawings.

[0036] Figure 1A flow chart of the steps of the microgrid optimization control method based on source-grid-load-storage provided in an embodiment of the present application;

[0037] Figure 2 A basic framework diagram of the coordinated operation of source, grid, load and storage provided in the embodiment of the present application;

[0038] Figure 3 A flowchart of the steps of a method for obtaining a power supply fluctuation index in each acquisition cycle provided in an embodiment of the present application;

[0039] Figure 4 A flowchart of the steps of the method for obtaining the mutation coefficient at each acquisition moment provided in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of the change in the optimal value of the objective function of the PSO algorithm before and after the improvement provided in the embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of this application more clear, the following is a further detailed description of the microgrid optimization control related equipment and method based on source-grid-load-storage proposed in this application in combination with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0042] 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.

[0043] See also Figure 1 , which shows a flow chart of the steps of a microgrid optimization control method based on source-grid-load-storage provided by an embodiment of the present application, the method comprising the following steps:

[0044] Step 1: Obtain the photovoltaic power generation, wind power generation and load power consumption at each moment in each collection cycle before each collection moment.

[0045] Source, grid, load and storage refer to power generation, distribution network, power load and energy storage system respectively. The core of microgrid is to achieve coordinated optimization of power supply side, grid side, load side and energy storage side, and improve the operation efficiency of power system and the comprehensive benefits of power development. Microgrid integrates various resources of source, grid, load and storage. When there is an imbalance between power supply and power consumption, the relevant equipment of energy storage system is regulated to balance power supply and power consumption.

[0046] The basic framework diagram of the coordinated operation of the source, grid, load and storage provided in this embodiment is as follows: Figure 2 As shown, Figure 2The source is renewable energy, with photovoltaic and wind power as power sources. The network is the distribution network, which includes the distribution system and transmission lines for power transmission. The load is the power load, which is the power consumption part in the microgrid. The storage is the energy storage system, which includes related battery equipment, and is charged and discharged according to actual needs in the power system. The optimization control can regulate the source, network, load, and storage according to actual needs. For example, if the power supply on the power side is sufficient to meet the power consumption on the load side, the control system will allocate part of the power to the related equipment of the energy storage system for storage.

[0047] Microgrids mainly rely on photovoltaic and wind power generation to provide electricity, which is transmitted to the load part for power consumption. In order to analyze the balance between power supply and power consumption on the power supply side and the load side, power sensors are installed on the photovoltaic and wind power sources on the power supply side to collect photovoltaic power generation and wind power generation, and power sensors are installed on the load side to collect load power consumption; where the collection time interval is t, the duration of each collection is T, and the collection duration is regarded as a collection cycle, and the photovoltaic power generation, wind power generation and load power consumption at each time in each collection cycle before each collection moment are obtained.

[0048] Preferably, in this embodiment, the collection time interval is 1 second, and each collection time is 10 minutes. As other implementation modes, the implementer can set them according to the actual situation.

[0049] At this point, the photovoltaic power generation, wind power generation and load power consumption at each moment in each collection cycle before each collection moment are obtained.

[0050] Step 2, constructing an objective function; determining the power supply change rate and power consumption change rate of each collection period according to the difference between the peaks of the total power generation and load power consumption at all times in each collection period and their adjacent troughs, as well as the interval between adjacent troughs; analyzing the discreteness of the total power generation and load power consumption at all times in each collection period, and combining the power supply change rate and power consumption change rate to determine the power supply fluctuation index and power consumption fluctuation index of each collection period.

[0051] The power supply side of the microgrid is generated by photovoltaic and wind power generation, and the generated power is transmitted to the load side for power supply. The power generation capacity of photovoltaic and wind power in actual applications is random, and the load demand also has many uncertain factors. Therefore, when the power generation on the power supply side is not enough to provide the power consumption on the load side, the energy storage system discharges to make up for the power required. When the power generation on the power supply side is greater than the power consumption on the load side, the energy storage system is charged for subsequent use during peak power demand.

[0052] Based on the above analysis, the power generation on the power supply side and the power consumption on the load side can be reflected by the corresponding power generation power and power consumption. Therefore, the objective function is constructed through the power generation power on the power supply side and the power consumption on the load side to control the charging and discharging power on the energy storage side, specifically:

[0053] The sum of the photovoltaic power generation and the wind power generation at each moment in each collection cycle is taken as the total power generation at each moment in each collection cycle;

[0054] ,in, For the The objective function of the acquisition cycle is For the The total power generated at all times during a collection cycle, For the The load power consumption at all times in a collection cycle, For the The charging and discharging power of the energy storage side during a collection cycle.

[0055] It should be noted that when the energy storage side is in the discharging state, the energy storage side and the power supply side simultaneously supply power to the load side. When the energy storage side is in the charging state, the power generation on the power supply side is greater than the power consumption on the load side, and the energy storage system is charged. The objective function aims to control the charging and discharging power of the energy storage side to try to meet the balance between power supply and power consumption; for example, in multiple consecutive acquisition cycles, the power supply of the power supply side and the energy storage side just meets the power consumption of the load side, or the power supply of the power supply side meets the charging demand of the energy storage side and the power consumption of the load side. Minimum, objective function It is also easy to obtain the optimal value and control the microgrid by optimizing the charging and discharging power on the energy storage side.

[0056] Furthermore, the power on the power supply side and the load side is random, and the degree of change is inconsistent, which leads to different degrees of change in the objective function curve. Therefore, the changing characteristics of power generation and power consumption are analyzed to adjust the parameters of the particle swarm optimization algorithm and improve the control accuracy of the microgrid.

[0057] First, the fluctuation of the power generation on the power supply side is analyzed to reflect the randomness of the power fluctuation on the power supply side. Specifically:

[0058] Obtain the peak and trough of the total power generation at all times in each acquisition cycle respectively;

[0059] Preferably, in this embodiment, an automatic multiscale-based peak detection (AMPD) algorithm is used to obtain the peak, wherein the AMPD algorithm is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the prior art, such as the differential method, etc. This embodiment does not impose any special restrictions on this.

[0060] Based on the above analysis, if the difference in power generation status between photovoltaic and wind power is greater or more unstable, the number of peaks will be greater and steeper. Therefore, the rate of peak fluctuation change is analyzed to reflect the degree of fluctuation of total power generation and determine the power supply fluctuation index, which is:

[0061] The average of the difference in total power generated between each peak and its adjacent troughs on the left and right is taken as the local fluctuation of each peak;

[0062] Preferably, in this embodiment, the average of the absolute values ​​of the differences in the total power generated between each peak and its left and right adjacent troughs is taken as the local fluctuation amount of each peak.

[0063] The interval between the moments corresponding to the left and right adjacent troughs of each peak is taken as the change duration of each peak;

[0064] Calculating the ratio between the local fluctuation amount and the change duration, and taking the sum of the ratios of all the peaks in each acquisition cycle as the power supply change rate of each acquisition cycle;

[0065] Calculate the degree of dispersion of the total power generated at all times in each acquisition cycle; multiply the degree of dispersion by the power supply change rate as the power supply fluctuation index of each acquisition cycle;

[0066] Preferably, in this embodiment, the calculation formula of the power supply fluctuation index of each acquisition cycle is: ,in, For the The power supply fluctuation index of a collection cycle, For the The standard deviation of the acquisition cycle, For the The first acquisition cycle The local fluctuation of the peak, For the The first acquisition cycle The duration of the change of the peak For the The number of all peaks in a collection cycle, and secondly, is the power supply change rate.

[0067] It can be understood that in this embodiment, the degree of dispersion is measured by standard deviation. As other implementation methods, implementers can adopt other methods of the prior art, such as variance, coefficient of variation, information entropy, etc. This embodiment does not impose any special restrictions on this.

[0068] It should be noted that the local fluctuation reflects the amplitude of the peak change, and the change duration reflects the speed of change of each peak. The larger the local fluctuation, the larger the amplitude of the power generation change at the peak position in the local time, and the smaller the change duration, the faster the power generation change at the peak position in the local time. The larger the value is, the more significant the local change of the corresponding peak is; the larger the degree of dispersion is, the greater the volatility of the change of the total power generation during the corresponding collection period is; the larger the obtained power supply fluctuation index is, the more random the power generation capacity on the power supply side is during the corresponding collection period.

[0069] Further, the step flow chart of the method for obtaining the power supply fluctuation index of each collection cycle provided in the embodiment of the present application is as follows: Figure 3 shown.

[0070] For the load power consumption at all times in each collection cycle, the power consumption change rate of each collection cycle is obtained by using the same method as the power supply change rate of each collection cycle;

[0071] The product of the discrete degree of load power consumption at all times in each collection period and the power consumption change rate is used as the power consumption fluctuation index of each collection period;

[0072] It should be noted that the larger the power consumption fluctuation index is, the more unstable the power consumption on the load side is.

[0073] At this point, the power supply fluctuation index and power consumption fluctuation index of each collection period are obtained.

[0074] Step 3: Determine the supply-consumption balance coefficient of each collection period according to the difference between the power supply fluctuation index and the power consumption fluctuation index of each collection period, and the correlation between the total power generation power and the load power consumption in each collection period; analyze the mutation characteristics and autocorrelation characteristics of the supply-consumption balance coefficient of multiple collection periods before each collection moment, and determine the mutation coefficient at each collection moment.

[0075] Furthermore, in the optimization control process of the microgrid, the greater the difference between the power generation and the power consumption, the more significant the change in the objective function; secondly, the electric energy generated on the power supply side has different degrees of loss during transmission, and the similar change characteristics of power generation and power consumption are not enough to accurately reflect the degree of change of the objective function. Based on the difference between the power supply fluctuation index and the power consumption fluctuation index, the supply and demand relationship characteristics between the power supply side and the load side are analyzed to reflect the difference in the degree of random changes between power generation and power consumption.

[0076] Based on the above analysis, the similarity of the changing trends between power generation and power consumption, as well as the degree of fluctuation difference, are analyzed to determine the supply-consumption balance coefficient, which is as follows:

[0077] Calculate the similarity between the total power generated at all times in each acquisition cycle and the load power consumption;

[0078] Preferably, in this embodiment, the absolute value of the Spearman correlation coefficient between the total power generation and the load power consumption at all times in each acquisition cycle is calculated; wherein, the Spearman correlation coefficient is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the prior art, such as the Pearson correlation coefficient, cosine similarity, etc., and this embodiment does not impose any special restrictions on this.

[0079] Recording the difference between the power supply fluctuation index and the power consumption fluctuation index as a first difference;

[0080] Preferably, in this embodiment, the absolute value of the difference between the power supply fluctuation index and the power consumption fluctuation index is recorded as the first difference.

[0081] Using the ratio of the similarity to the first difference as the supply-use balance coefficient of each collection period;

[0082] Preferably, in this embodiment, the supply-use balance coefficient of each acquisition cycle is calculated as follows: ,in, For the The supply and demand balance coefficient of a collection cycle, For the The absolute value of the Spearman correlation coefficient between the total power generated at all times in a collection cycle and the load power consumption, For the The power supply fluctuation index of a collection cycle, For the The power consumption fluctuation index of the collection cycle, To preset a value greater than 0 to avoid the denominator being 0, in this embodiment, The value is 1. As other implementation methods, the implementer can set it according to the actual situation. The first difference.

[0083] It should be noted that the first difference The smaller it is, the closer the random change characteristics of the generated power and the consumed power are. The greater the similarity, the larger the obtained supply-consumption balance coefficient, indicating that the generated power on the power supply side and the consumed power on the load side of the microgrid are more balanced within the corresponding collection period.

[0084] Furthermore, when there is a serious imbalance between power generation and power consumption in the microgrid, the supply-use balance coefficient in the local period may show significant mutation or unstable characteristics, and the curve fluctuation of the obtained objective function is more complex. When the power generation and power consumption of the microgrid are balanced, the supply-use balance coefficient in the local period is relatively stable and has a certain autocorrelation, and the curve fluctuation of the obtained objective function is smaller.

[0085] Based on the above analysis, the mutation degree of the supply-use balance coefficient in multiple collection cycles before each collection moment is analyzed to reflect the mutation of the supply-use balance coefficient and determine the mutation coefficient, which is specifically:

[0086] Adopt mutation point detection algorithm to detect mutation points of supply and demand balance coefficients of multiple collection cycles before each collection moment, and count the number of all mutation points;

[0087] Preferably, in this embodiment, the Pettitt algorithm is used for mutation point detection, wherein the Pettitt algorithm is a well-known technology and will not be described in detail here, wherein the significance level is set to 0.05 in the Pettitt algorithm, and the supply and demand balance coefficient corresponding to the collection period with a significance level less than 0.05 is obtained, which is recorded as the mutation point. As other implementation methods, the implementer can adopt other methods of the prior art, such as Pelt mutation point detection, Bayesian mutation point detection, etc., and this embodiment does not impose special restrictions on this; secondly, mutation point detection is performed on the supply and demand balance coefficient of all collection periods within a day collected before each collection moment. As other implementation methods, the implementer can set it according to the actual situation.

[0088] Calculate the autocorrelation coefficient of the supply and demand balance coefficient of multiple collection cycles before each collection moment;

[0089] The normalized result of the ratio of the number of all mutation points to the autocorrelation coefficient is used as the mutation coefficient at each acquisition moment;

[0090] Preferably, in this embodiment, the tanh activation function is used for normalization processing, wherein the use of the tanh activation function is a well-known technology and will not be repeated here. As other implementation methods, the implementer can adopt other methods of the prior art, such as the sigmoid activation function, etc. This embodiment does not impose any special restrictions on this.

[0091] Preferably, in this embodiment, the mutation coefficient at each acquisition moment is calculated as follows: ,in, For the The mutation coefficient at each acquisition moment, For the The number of all mutation points at the collection time, For the The autocorrelation coefficient of the supply and demand balance coefficient of the previous multiple collection cycles at the collection time, To preset a value greater than 0 to avoid the numerator and denominator being 0, in this embodiment, The value is 1. As other implementation methods, the implementer can set it according to the actual situation.

[0092] It should be noted that the greater the number of all mutation points, the greater the degree of mutation, the smaller the autocorrelation coefficient, the more unstable the change of the supply and demand balance coefficient of multiple collection cycles before each collection moment, and the larger the obtained mutation coefficient, the greater the difference between the power generation and power consumption of the microgrid in the past period of time, and the greater the curve fluctuation of the objective function.

[0093] Further, the step flow chart of the method for obtaining the mutation coefficient at each acquisition moment provided in the embodiment of the present application is as follows: Figure 4 Show.

[0094] At this point, the mutation coefficient at each acquisition moment is obtained.

[0095] Step 4: Based on the mutation coefficient, determine the inertia weight of the particle swarm optimization algorithm at each collection moment; combine the particle swarm optimization algorithm to determine the charging and discharging power of the energy storage side in the collection cycle at each collection moment, and control the charging and discharging power of the energy storage side in the microgrid.

[0096] Furthermore, in the traditional particle swarm optimization (PSO), the inertia weight reflects the ability of particles to inherit the speed of the previous iteration. A larger inertia weight is conducive to global search, and a smaller inertia weight is conducive to local search. Therefore, the larger the mutation coefficient, the greater the difference between the power generation and power consumption of the microgrid. A larger inertia weight needs to be set during the particle swarm optimization process to avoid falling into the local optimal solution. The smaller the mutation coefficient, the smaller the fluctuation of the objective function. A smaller inertia weight can be set during the particle swarm optimization process to improve the optimization accuracy of the algorithm. Therefore, based on the mutation coefficient, the inertia weight of the particle swarm optimization algorithm is determined to optimize the microgrid, specifically:

[0097] The calculation method of the inertia weight of the particle swarm optimization algorithm at each acquisition moment is: ,in, For the The inertia weight of the group optimization algorithm at each acquisition moment, To preset the first threshold, To preset the second threshold, For the The mutation coefficient at each acquisition moment;

[0098] Preferably, in this embodiment, the first threshold is preset The value is 0.4, and the second threshold is preset The value is 0.5. As other implementation methods, the implementer can set it according to the actual situation.

[0099] Further, based on the inertia weight, the PSO algorithm is used to find the optimal charging and discharging power , which can minimize the objective function value, specifically:

[0100] Based on the inertia weight, the PSO algorithm is used to iteratively optimize the objective function of multiple collection cycles before each collection moment to obtain the optimal charge and discharge power of each collection cycle on the energy storage side;

[0101] Preferably, in this embodiment, the objective function of all acquisition cycles within 1 hour before each acquisition moment is selected for optimization. As other implementation modes, the implementer can set them according to the actual situation.

[0102] The average of the optimal charge and discharge powers of all collection cycles before each collection moment is used as the charge and discharge power of the energy storage side in the collection cycle of each collection moment;

[0103] Based on the charging and discharging power of the energy storage side in the collection cycle at each collection moment, the energy storage side in the microgrid is controlled to achieve a balance between power generation and power consumption.

[0104] Furthermore, the schematic diagram of the change of the optimal value of the objective function of the PSO algorithm before and after the improvement provided in this embodiment is as follows: Figure 5 As shown, the horizontal axis is the number of iterations and the vertical axis is the minimum value of the objective function. Figure 5The solid line in the middle shows the change of the optimal value of the objective function of the improved PSO algorithm, and the dotted line shows the change of the optimal value of the objective function of the traditional PSO algorithm iteration. During the search process, both methods are prone to premature convergence and are in a stagnation state. When the improved PSO algorithm falls into the local optimal solution, it can quickly jump out of the local optimal solution after a short iterative operation to avoid the particle falling into premature convergence. Secondly, comparing the two methods, the improved PSO algorithm basically tends to be stable after 50 iterations, while the traditional PSO algorithm tends to be stable after 117 iterations. The convergence speed of the improved PSO algorithm is better than that of the traditional PSO algorithm, and the search effect of the improved PSO algorithm for the optimal solution of the objective function is better than that of the traditional PSO algorithm.

[0105] Based on the same inventive concept as the above method, an embodiment of the present application also provides related equipment for optimizing control of a microgrid based on source, grid, load and storage, and the related equipment is implemented by any one of the above-mentioned methods for optimizing control of a microgrid based on source, grid, load and storage.

[0106] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0107] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A microgrid optimization control method based on source, grid, load and storage, characterized in that: The method comprises the following steps: Obtain the photovoltaic power generation, wind power generation and load power consumption at each moment in each collection cycle before each collection moment; record the sum of the photovoltaic power generation and wind power generation at each moment as the total power generation at each moment; Based on the total power generation and load power consumption at each moment in each acquisition cycle, an objective function is constructed; Determine the power supply change rate and power consumption change rate of each collection period according to the difference between the peaks of the total power generation and load power consumption at all times in each collection period and their adjacent troughs, as well as the interval between adjacent troughs; analyze the discreteness of the total power generation and load power consumption at all times in each collection period, and determine the power supply fluctuation index and power consumption fluctuation index of each collection period in combination with the power supply change rate and power consumption change rate; Determine the supply-consumption balance coefficient of each collection period according to the difference between the power supply fluctuation index and the power consumption fluctuation index of each collection period, and the similarity of the changes in the total power generation and the load power consumption in each collection period; analyze the mutation characteristics and autocorrelation characteristics of the supply-consumption balance coefficient of multiple collection periods before each collection moment, and determine the mutation coefficient at each collection moment; Based on the mutation coefficient, the inertia weight of the particle swarm optimization algorithm at each collection moment is determined; combined with the particle swarm optimization algorithm, the charging and discharging power of the energy storage side in the collection cycle at each collection moment is determined, and the charging and discharging power of the energy storage side in the microgrid is controlled; The constructing of the objective function comprises: ,in, For the The objective function of the acquisition cycle is For the The vector composed of the total power generated at all times in a collection cycle, For the The vector composed of the load power consumption at all times in a collection cycle, For the The charging and discharging power of the energy storage side within a collection cycle; Determining the power supply change rate and the power consumption change rate of each acquisition cycle includes: Obtain the peak and trough of the total power generation at all times in each acquisition cycle respectively; The average of the difference in total power generated between each peak and its adjacent troughs on the left and right is taken as the local fluctuation of each peak; The interval between the moments corresponding to the left and right adjacent troughs of each peak is taken as the change duration of each peak; Calculating the ratio between the local fluctuation amount and the change duration, and taking the sum of the ratios of all the peaks in each acquisition cycle as the power supply change rate of each acquisition cycle; For the load power consumption at all times in each collection cycle, the power consumption change rate of each collection cycle is obtained by using the same method as the power supply change rate of each collection cycle; The power supply fluctuation index of each acquisition cycle is the product of the discrete degree of the total power generation at all times in each acquisition cycle and the power supply change rate; The power consumption fluctuation index is the product of the dispersion degree of load power consumption at all times in each collection cycle and the power consumption change rate; Determining the supply-use balance coefficient of each collection cycle includes: Recording the difference between the power supply fluctuation index and the power consumption fluctuation index as a first difference; The ratio of the similarity between the total generated power and the load power at all times in each collection cycle and the first difference is used as the supply and demand balance coefficient of each collection cycle.

2. The microgrid optimization control method based on source-grid-load-storage according to claim 1, characterized in that: Determining the mutation coefficient at each acquisition moment includes: Adopt mutation point detection algorithm to detect mutation points of supply and demand balance coefficients of multiple collection cycles before each collection moment, and count the number of all mutation points; Calculate the autocorrelation coefficient of the supply and demand balance coefficient of multiple collection cycles before each collection moment; The normalized result of the ratio of the number of all mutation points to the autocorrelation coefficient is used as the mutation coefficient at each acquisition moment.

3. The microgrid optimization control method based on source-grid-load-storage according to claim 1, characterized in that: The inertia weight of the particle swarm optimization algorithm at each acquisition moment is determined by: ,in, For the The inertia weight of the group optimization algorithm at each collection moment, is to preset a first threshold, To preset the second threshold, For the The mutation coefficient at each acquisition moment.

4. The microgrid optimization control method based on source-grid-load-storage according to claim 1, characterized in that: The determining of the charging and discharging power of the energy storage side in the collection cycle at each collection moment includes: Based on the inertia weight, a particle swarm optimization algorithm is used to iteratively optimize the objective function of multiple collection cycles before each collection moment to obtain the optimal charge and discharge power of each collection cycle on the energy storage side; The average of the optimal charging and discharging powers of all collection cycles before each collection moment is used as the charging and discharging power of the energy storage side in the collection cycle at each collection moment.

5. Microgrid optimization control related equipment based on source, grid, load and storage, characterized in that: The related equipment is implemented by the microgrid optimization control method based on source, grid, load and storage as described in any one of claims 1-4.

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