Park micro-grid self-balancing-based load optimization method and storage medium

Abstract

The invention discloses a load optimization method based on park micro-grid self-balancing and a storage medium, and belongs to the technical field of micro-grid load optimization, and the method comprises the steps: S1, carrying out the preprocessing of the obtained historical operation data of a park micro-grid, and calculating a corresponding micro-grid source-to-load ratio based on the characteristics of the park; s2, drawing a source-load curve graph according to the preprocessed historical operation data and a source-load ratio, and constructing a micro-grid self-balancing model based on a park balance condition; s3, calculating the maximum allowable on-grid electric quantity ratio according to the source load curve graph and the micro-grid self-balancing model; s4, calculating an optimal storage-load ratio according to the maximum allowable on-grid electric quantity ratio and an energy storage configuration optimization algorithm taking the minimum energy storage consumption electric quantity as a target; s5, verifying an optimal storage-load ratio based on a micro-grid load optimization proportion analysis model and the park typical load curve; and S6, the micro-grid executes a load optimization action in response to the verified optimal storage-load ratio, and the regulation and control capability of the micro-grid is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid load optimization, and in particular to a load optimization method and storage medium based on self-balancing of a park microgrid. Background Art

[0002] With the development of modern smart distribution network technology, grid-connected campus microgrids are showing a rapid development trend. Microgrids are a type of transmission line network that mainly generates electricity from renewable energy, has a small scale and capacity, and directly transmits electricity to electricity users. The distributed renewable energy power generation devices inside them are greatly affected by meteorological conditions and have strong random volatility. In order to solve the power supply quality problems caused by this random volatility, the existing technology usually uses the distribution network to provide power and backup support for the microgrid. The microgrid itself does not have the ability to regulate and control energy balance and efficient utilization.

[0003] Chinese patent, publication number: CN115622096A, publication date: January 17, 2023, discloses a capacity configuration method and system for a grid-connected microgrid system based on load characteristics, including: collecting typical daily load data of different types of users as samples, and determining typical daily load characteristic curves of different types of users; determining the networking mode of the microgrid according to different access methods of distributed energy and energy storage in the microgrid system, and determining the objective function and constraints, establishing a microgrid system capacity configuration model based on load characteristic curve analysis, and determining the energy scheduling strategy of the microgrid system; solving the microgrid system capacity configuration model, and obtaining the energy storage configuration when the distributed power installed capacity accounts for different proportions of the maximum load for different types of loads according to the energy scheduling results; however, the invention does not take into account the different characteristics of microgrids in different parks, resulting in limited improvement in the regulation and control capabilities of the microgrid when the corresponding microgrid is adjusted according to the energy storage configuration obtained. Summary of the invention

[0004] The purpose of the present invention is to address the problem that the existing technology does not take into account the different characteristics of microgrids in different parks, resulting in the inapplicability of energy storage configuration to the corresponding microgrid; a load optimization method and storage medium based on self-balancing of the park microgrid are proposed, and a source-load curve diagram is drawn through the historical operation data of the park microgrid and the characteristics of the park, and a microgrid self-balancing model is constructed. Then, the optimal storage-load ratio is calculated based on the energy storage configuration optimization algorithm combined with the maximum allowed online power ratio calculated according to the source-load curve diagram and the microgrid self-balancing model, and the optimal storage-load ratio is verified based on the microgrid load optimization ratio analysis model. Finally, the microgrid executes load optimization actions in response to the verified optimal storage-load ratio, which significantly improves the regulation and control capabilities of the microgrid.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is a load optimization method based on self-balancing of a campus microgrid, comprising the following steps: S1. Preprocessing the acquired historical operation data of the park microgrid, and calculating the source-to-load ratio of the corresponding microgrid based on the characteristics of the park; S2. Draw a source-load curve diagram based on the pre-processed historical operation data and the source-load ratio, and build a microgrid self-balancing model based on the balance condition of the park; S3. Calculate the maximum allowable online power ratio according to the source-load curve diagram and the microgrid self-balancing model; S4, calculating the optimal storage-to-load ratio according to the maximum allowable online power ratio and the energy storage configuration optimization algorithm with the goal of minimizing the energy storage power consumption; S5. Verify the optimal storage-load ratio based on the microgrid load optimization ratio analysis model and the typical load curve of the park; S6. The microgrid performs load optimization actions in response to completing the verified optimal storage-to-load ratio.

[0006] In this scheme, the acquired historical operation data of the park microgrid is preprocessed, the abnormal values ​​in the historical operation data are eliminated, and the source-to-load ratio of the corresponding microgrid is calculated based on the characteristics of the park, so as to obtain the source-to-load ratio of the microgrid that matches the load and new energy power generation characteristics of the park. Then, a source-to-load curve is drawn according to the historical operation data and the source-to-load ratio to make the characteristics of the corresponding park microgrid visible, and the maximum allowable online power ratio is calculated according to the source-to-load curve and the microgrid self-balancing model constructed based on the park balance conditions. Subsequently, the optimal storage-to-load ratio suitable for the actual working conditions of the corresponding park microgrid is calculated according to the maximum allowable online power ratio and the energy storage configuration optimization algorithm with the minimum energy storage power consumption as the goal, and the optimal storage-to-load ratio is verified based on the microgrid load optimization ratio analysis model and the typical load curve of the park. Finally, the microgrid executes load optimization action in response to the verified optimal storage-to-load ratio, which significantly improves the regulation and control capabilities of the microgrid.

[0007] Preferably, in S1, the specific process of preprocessing the acquired historical operation data of the park microgrid is as follows: S111, cleaning the historical operation data of the park microgrid, and sorting the historical operation data after data cleaning according to the time scale change of the historical operation data; S112, scaling the sorted historical operation data by the same proportion to obtain historical operation data within a standard range.

[0008] In this solution, the sorted historical operation data are scaled in the same proportion, and all the historical operation data are converted into numerical values ​​of a standard range, so as to facilitate drawing corresponding curve graphs in subsequent steps.

[0009] Preferably, in S1, the specific process of calculating the source-to-load ratio of the corresponding microgrid based on the characteristics of the park is as follows: S121, extracting the capacity of the new energy power generation device, the maximum utilization hours of the new energy power generation device, the maximum load of the user, and the maximum load utilization hours of the user from the historical operation data of the park microgrid based on the characteristics of the park; S122. Calculate the annual power generation of the new energy power generation device based on the capacity of the new energy power generation device and the maximum utilization hours of the new energy power generation device, and calculate the annual load demand of the user based on the maximum load of the user and the maximum load utilization hours of the user; S123. Calculate the source-to-load ratio of the corresponding microgrid based on the annual power generation of the new energy power generation device and the annual load demand of the user.

[0010] In this solution, the source-load ratio can be used to accurately understand the power supply and demand situation of the park, and accurately evaluate the self-sufficiency of the park microgrid, which provides support for the subsequent drawing of the source-load curve.

[0011] Preferably, in S2, the specific process of drawing the source-load curve diagram according to the pre-processed historical operation data and the source-load ratio is: S211, arranging the output data of the new energy power generation device in the historical operation data to obtain an output data set, and arranging the load data in the historical operation data to obtain a load data set; S212, drawing an output curve diagram of the new energy device based on the output data set, and drawing a load curve diagram of the park based on the load data set; S213, obtaining a source-load curve diagram by fitting the output curve diagram of the new energy device and the load curve diagram of the park based on the source-load ratio.

[0012] In this solution, the output curve and the park load curve are drawn according to the historical operation data, which is convenient for analyzing the relationship between the two. It is easier to find the degree of matching and potential problems between the two. The source-load curve is obtained by fitting the output curve and the park load curve based on the source-load ratio, and the best matching point between the two can be found.

[0013] Preferably, in S2, the microgrid self-balancing model is specifically: gdl+fdl=ydl+swdl; 1-fdl+2swdl≤0.5; In the formula, gdl represents the proportion of power supply, fdl represents the proportion of power generation, swdl represents the proportion of grid-connected power, and ydl represents power consumption.

[0014] Preferably, in S3, the specific process of calculating the maximum allowable online power proportion according to the source-load curve diagram and the microgrid self-balancing model is: S311, based on the microgrid self-balancing model, filter the data in the source-load curve diagram to obtain the power generation of the new energy device that meets the corresponding park balance condition and the amount of electricity allowed to be connected to the grid; S312, selecting the power generation of the new energy device and the power generation allowed to be online according to the power generation constraint conditions of the new energy device, the power generation constraint conditions allowed to be online and the time change scale of the source-load curve diagram to obtain the maximum allowable power generation ratio.

[0015] In this solution, based on the park balance conditions, the constraints on the power generation of new energy devices and the constraints on the amount of power allowed to be online, the corresponding maximum allowable online power ratio is calculated when the power generation of new energy devices is determined, which facilitates the subsequent steps to find the load absorption power corresponding to the maximum allowable online power ratio.

[0016] Preferably, in S4, the specific process of calculating the optimal storage-to-load ratio according to the maximum allowable online power ratio and the energy storage configuration optimization algorithm with the goal of minimizing the energy storage power consumption is: S411, calculating the cumulative returned power, load absorbed power and returned power based on the source-load curve diagram and the time scale change of the source-load curve diagram; S412, calculating the storable amount of energy storage according to the accumulated returned amount and the load absorbed amount, and determining a better energy storage configuration based on the storable amount of energy storage and the returned amount; S413: Based on the maximum allowable online power ratio, select a better energy storage configuration to obtain an optimal storage-to-load ratio.

[0017] In this scheme, the calculation of the cumulative returned power, the load absorbed power and the returned power can clearly indicate the flow of electric energy in the corresponding microgrid, and the storable power at the corresponding moment can be clearly indicated according to the cumulative returned power, the load absorbed power and the returned power, so as to obtain the theoretically optimal energy storage configuration that can be used in the microgrid, and finally, the optimal storage-load ratio that meets the self-balancing conditions of the microgrid can be obtained by using the maximum allowable online power ratio, so that the optimal storage-load ratio matches the actual working environment of the corresponding microgrid.

[0018] Preferably, in S5, the specific process of verifying the optimal storage-load ratio based on the microgrid load optimization ratio analysis model and the typical load curve of the park is: S511, based on the optimal storage-load ratio and the typical load curve of the park, the peak load characteristic is fitted to obtain a verification fitting curve; S512, the verification fitting curve is input into the microgrid load optimization ratio analysis model to calculate the returned power, and the peak load ratio is verified based on the returned power calculation; S513. Compare the verified peak shaving load ratio with a preset peak shaving load ratio threshold. If the verified peak shaving load ratio is greater than or equal to the peak shaving load ratio threshold, the optimal storage-load ratio verification is successful. If the verified peak shaving load ratio is less than the peak shaving load ratio threshold, the optimal storage-load ratio verification fails.

[0019] In this scheme, the verified peak-shaving load ratio calculated based on the optimal storage-load ratio and the typical load curve of the park is used as an indicator to verify the optimal storage-load ratio, thereby ensuring the promotion effect of the optimal storage-load ratio on the corresponding park microgrid and ensuring the efficient use of the corresponding energy storage device in the park microgrid to avoid waste of resources.

[0020] Preferably, in S6, the specific process of the microgrid performing the load optimization action in response to the optimal storage-to-load ratio is as follows: S611, determining the load scheduling action and the energy storage scheduling action based on the optimal storage-to-load ratio, and arranging the load scheduling action and the energy storage scheduling action to obtain a load optimization strategy; S612: The microgrid executes corresponding scheduling actions according to the load optimization strategy.

[0021] On the other hand, a technical solution also provided in an embodiment of the present invention is a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0022] The present invention has at least the following substantial effects: The present application pre-processes the acquired historical operation data of the park microgrid, removes abnormal values ​​in the historical operation data, keeps the historical operation data within the standard range, and calculates the source-to-load ratio of the corresponding microgrid based on the characteristics of the park, and obtains the source-to-load ratio of the microgrid that matches the load and new energy power generation characteristics of the park. Then, a source-to-load curve is drawn according to the historical operation data and the source-to-load ratio to make the characteristics of the corresponding park microgrid visible, and the maximum allowable online power ratio is calculated according to the source-to-load curve and the microgrid self-balancing model constructed based on the park balance condition. Subsequently, the optimal storage-to-load ratio suitable for the actual working condition of the corresponding park microgrid is calculated according to the maximum allowable online power ratio and the energy storage configuration optimization algorithm with the minimum energy storage consumption as the goal, and the optimal storage-to-load ratio is verified based on the microgrid load optimization ratio analysis model and the typical load curve of the park to ensure the efficient utilization of the corresponding energy storage device in the park microgrid and avoid waste of resources. Finally, the microgrid executes load optimization action in response to the verified optimal storage-to-load ratio, which significantly improves the regulation and control capabilities of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0024] Figure 1 This is a flow chart of a load optimization method based on self-balancing of a park microgrid according to the present invention; Figure 2 This is the typical daily fitting curve when the source-to-load ratio of the park microgrid is 1.1. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0027] Embodiment 1: like Figure 1 As shown, this embodiment provides a load optimization method based on self-balancing of a park microgrid, comprising the following steps: S1, preprocessing the acquired historical operation data of the park microgrid, and calculating the source-to-load ratio of the corresponding microgrid based on the characteristics of the park.

[0028] S111, cleaning the historical operation data of the park microgrid, and sorting the historical operation data after data cleaning according to the time scale change of the historical operation data; S112, scaling the sorted historical operation data by the same proportion to obtain historical operation data within a standard range.

[0029] In this embodiment, when missing values ​​and outliers are detected in the historical operation data of the park microgrid, the missing values ​​and outliers are filled with the left values ​​of the missing values ​​and outliers on the time scale, and the sorted historical operation data is scaled to the range of 0 to 1 at the same ratio.

[0030] S121, extracting the capacity of the new energy power generation device, the maximum utilization hours of the new energy power generation device, the maximum load of the user, and the maximum load utilization hours of the user from the historical operation data of the park microgrid based on the characteristics of the park; S122. Calculate the annual power generation of the new energy power generation device based on the capacity of the new energy power generation device and the maximum utilization hours of the new energy power generation device, and calculate the annual load demand of the user based on the maximum load of the user and the maximum load utilization hours of the user; S123. Calculate the source-to-load ratio of the corresponding microgrid based on the annual power generation of the new energy power generation device and the annual load demand of the user.

[0031] In this embodiment, 8760 hours of park microgrid operation data throughout the year are used for analysis and evaluation, but different parks have different characteristics, such as: administrative office park, generally the maximum load utilization hours of a typical administrative office park are less than 2000h, from the daily load characteristics of the administrative office park, there are two peaks in the morning and afternoon, and the trough period is from 6 pm to 7 am the next day. From the annual load characteristics of the administrative office park, the peak load is significant in summer, the spring and autumn are the trough periods, and there is a certain heating load in winter. The overall impact is greater due to holidays and the load fluctuates greatly; commercial park, generally the maximum load utilization hours of a typical commercial park are 2000h to 3000h. From the daily load characteristics of the commercial park, it has the double peak characteristics of the noon peak and the evening peak, and the trough period is from 10 pm to 8 am the next day. From the annual load characteristics of the commercial park, the peak load in summer is significant, the load is low in spring and autumn, and there is a certain heating load in winter; for industrial parks, the maximum load utilization hours of typical industrial parks are generally greater than 3000h. From the daily load characteristics of the industrial park, the load level from 8 am to 12 pm is relatively high. From the annual load characteristics of the industrial park, the peak load in all four seasons is significant, and the overall fluctuation is not large; based on this, different parks have different source-load ratios, and it is necessary to extract the maximum utilization hours of distributed photovoltaic power generation in the historical operation data of the corresponding park, that is, the maximum utilization hours of new energy power generation devices, and the maximum load utilization hours of users to calculate the source-load ratio of the corresponding park microgrid. Under normal circumstances, the source-load ratio is as low as 1.1, that is, the ratio of distributed photovoltaic capacity to microgrid load is as low as 1.1.

[0032] S2. Draw a source-load curve diagram based on the pre-processed historical operation data and the source-load ratio, and build a microgrid self-balancing model based on the balance condition of the park.

[0033] S211, arranging the output data of the new energy power generation device in the historical operation data to obtain an output data set, and arranging the load data in the historical operation data to obtain a load data set; S212, drawing an output curve diagram of the new energy device based on the output data set, and drawing a load curve diagram of the park based on the load data set; S213, obtaining a source-load curve diagram by fitting the output curve diagram of the new energy device and the load curve diagram of the park based on the source-load ratio.

[0034] In this embodiment, since the collected load data are all discrete data, it is necessary to draw a fitting curve based on the discrete data points, and use a continuous curve to approximately characterize or compare the functional relationship represented by a set of discrete points. Taking a typical day in a park with a source-to-load ratio of 1.1 as an example, based on the distributed photovoltaic power output data in the historical operation data and the typical day load data, the following can be drawn: Figure 2 Typical daily fitting curve shown.

[0035] In one embodiment, the microgrid self-balancing model is specifically: gdl+fdl=ydl+swdl; 1-fdl+2swdl≤0.5; In the formula, gdl represents the proportion of power supply, fdl represents the proportion of power generation, swdl represents the proportion of grid-connected power, and ydl represents power consumption.

[0036] In this embodiment, the microgrid self-balancing model includes a power generation and consumption function and a self-balancing function. The parameters of the power generation and consumption function include online power, power supply, power consumption, power generation, and energy storage release. According to the requirement that the corresponding park exchange power does not exceed 50%, the power generation and consumption function is specifically as follows: gdl+fdl=ydl+swdl; In the formula, gdl represents power supply, fdl represents power generation, ydl represents power consumption, and swdl represents online power. Considering that there are many parameters, other variables are converted into proportions of power consumption. At the same time, according to the requirement that the exchange power of the corresponding park does not exceed 50%, it can be inferred that the sum of the power supply and the online power does not exceed 50% of the power consumption of the microgrid. Based on this, the following self-balancing function can be established: gdl+swdl≤0.5; 1-fdl+2swdl≤0.5; According to the self-balancing function, we can further obtain the upper and lower limits of the online power and power generation as shown below: swdl≤fdl / 2-0.25; fdl ≥ 0.5 + 2swdl; According to the self-balancing function, the proportion of power generation in the microgrid must be at least more than 50% to meet the minimum requirement of self-balancing. When the power generation accounts for exactly 50%, the online power does not exceed 0, that is, it is required that the power generation of all distributed power sources can be consumed by themselves. However, in actual situations, it is inevitable that the output of distributed power sources will be greater than the load output. Therefore, when the power generation accounts for 50%, energy storage is required to store all the electric energy that the distributed power sources cannot consume by themselves in order to meet the self-balancing requirements.

[0037] S3. Calculate the maximum allowable online power ratio according to the source-load curve diagram and the microgrid self-balancing model.

[0038] S311, based on the microgrid self-balancing model, filter the data in the source-load curve diagram to obtain the power generation of the new energy device that meets the corresponding park balance condition and the amount of electricity allowed to be connected to the grid; S312, selecting the power generation of the new energy device and the power generation allowed to be online according to the power generation constraint conditions of the new energy device, the power generation constraint conditions allowed to be online and the time change scale of the source-load curve diagram to obtain the maximum allowable power generation ratio.

[0039] In this embodiment, when the proportion of power generation is determined, the allowable on-grid power is limited, and the minimum power consumption of energy storage depends on the on-grid power without energy storage minus the maximum allowable on-grid power. The corresponding relationship between the proportion of distributed power generation and the maximum proportion of on-grid power allowed by the microgrid is shown in Table 1: Table 1. Correspondence between the maximum allowed on-grid power and power generation S4. Calculate the optimal storage-to-load ratio based on the maximum allowable online power ratio and an energy storage configuration optimization algorithm with the goal of minimizing the amount of energy storage consumed.

[0040] S411, calculating the cumulative returned power, load absorbed power and returned power based on the source-load curve diagram and the time scale change of the source-load curve diagram; S412, calculating the storable amount of energy storage according to the accumulated returned amount and the load absorbed amount, and determining a better energy storage configuration based on the storable amount of energy storage and the returned amount; S413: Based on the maximum allowable online power ratio, select a better energy storage configuration to obtain an optimal storage-to-load ratio.

[0041] In this embodiment, when the source-load ratio is low, the energy storage needs to absorb a high amount of new energy power. According to the data in Table 1, it can be inferred that the maximum allowed online power ratio is extremely low at this time, and the energy storage configuration requirements are extremely high. The corresponding calculated energy storage power is twice the load, which is costly. As the source-load ratio increases, a certain proportion of power can be allowed to be returned to the grid, and the energy storage does not need to absorb the full amount of the returned power. Based on the above situation, an energy storage configuration optimization algorithm is constructed with the goal of minimizing the amount of energy storage consumption to calculate the optimal storage-load ratio. The operation procedure of the energy storage configuration optimization algorithm is as follows: Function sxzsx(sxfwAs Range,fwgcsj As Range,chunengglAs Double)AsDouble; Din iAs Integer; Dim iweekAs Integer; Dim wkAs Range; i=1; Dim firstDateAs Date; Dim lastDateAs Date; firstDate=sxfw.Cells(1.1); lastDate=sxfw.Cells(24.1); iweek=DateDiff("d",lastDate,firstDate); Dim rileijiAs Double; Dim xx As Double; Dim smallest As Double; Dim zdgldlAs Double; Dim cndlAs Double; Dim cnce As Double; 'From day one'; iweek=0; cnce=0; Do While iweek<365; i=1; zuixiaozhi=0; 'Calculate the cumulative reverse power for one day'; 'Calculate the maximum power consumption corresponding to the load absorbed in one day'; i=1; rileiji=0; zdgldl=0; Do while i < 24; 'Get the feedback data of the i-th day'; xx=fwgcsj.Cells(24*iweek+i,1); 'The accumulated reverse power each day'; If xx<0Then; rileiji=rileiji+xx; End If; i=i+1; Loop; 'Compare the amount of reverse transmission and the amount of energy that can be stored'; cncc=cncc+WorksheetFunction.Min((0-rileiji),chunenggl*2); iweek=iweek+1; Loop; sxzsx=cncc; EndFunetion.

[0042] S5. Verify the optimal storage-to-load ratio based on the microgrid load optimization ratio analysis model and the typical load curve of the park.

[0043] S511, performing peak load characteristic fitting based on the optimal storage-to-load ratio and the typical load curve of the park to obtain a verification fitting curve; S512, inputting the verification fitting curve into the microgrid load optimization ratio analysis model to calculate the returned power, and verifying the peak load ratio according to the returned power calculation; S513. Compare the verified peak shaving load ratio with a preset peak shaving load ratio threshold. If the verified peak shaving load ratio is greater than or equal to the peak shaving load ratio threshold, the optimal storage-load ratio verification is successful. If the verified peak shaving load ratio is less than the peak shaving load ratio threshold, the optimal storage-load ratio verification fails.

[0044] In this embodiment, a corresponding microgrid load optimization ratio analysis model is established according to the optimal storage-to-load ratio, and the operation procedure of the microgrid load optimization ratio analysis model is as follows: Function zdfhbl(fwgcsj As Range,chunengglAs Double)As Double; Dim iAs lnteger; Dim iweekAs Integer; Dim wkAs Range; Dim cc As Integer; i=1; Dim rileijiAs Double; Dim xx As Double; Dim smallest As Double; Din zdgldlAs Double; Dim cndlAs Double; Dim cnce As Double; 'From day one'; iweek=0; cnce=0; Do While iweek<365; i=1; zuidazhi=0; 'Calculate the maximum value of a day'; Do while i < 24; 'Get the feedback data of the i-th day'; xx=fwgcsj.Cells(24*iweek+i, 1); 'Every day's black plan is the best'; If xx>zuidazhi Then; zuidazhi=xx; End If; i=i+1; Loop; i=1; rileiji=0; zdgldi=0; 'When the aggregate value is greater than the energy storage capacity, exit the loop'; cc=1; Do while rileiji <chunenggl*2And cc<100; rileifi=0; i=1; Do while i < 24; 'Get the feedback data of the i-th day'; xx=fugcsj.Cells(24*iveek+i, 1); 'The accumulated reverse power each day'; Ifxx>zuidazhi-cc*0.01Then; Loop; zuixieozhi=zuidazhi-ce*0.01; cc=cc+1; Loop; 'Traverse and obtain the maximum peak load for comparison'; cnce=WorksheetFunetion.Max(cnee,zuidazhi-cc*0.01); iweek=iweek+1; Loop; zdfhbl=cncc; EndFunction.

[0045] S6. The microgrid performs load optimization actions in response to completing the verified optimal storage-to-load ratio.

[0046] S611, determining a load dispatching action and an energy storage dispatching action based on the optimal storage-to-load ratio, and arranging the load dispatching action and the energy storage dispatching action to obtain a load optimization strategy; S612: The microgrid executes corresponding scheduling actions according to the load optimization strategy.

[0047] On the other hand, a technical solution also provided in an embodiment of the present invention is a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0048] This embodiment has at least the following substantial effects: This embodiment pre-processes the acquired historical operation data of the park microgrid, removes abnormal values ​​in the historical operation data, keeps the historical operation data within the standard range, and calculates the source-to-load ratio of the corresponding microgrid based on the characteristics of the park, and obtains the source-to-load ratio of the microgrid that matches the load and new energy power generation characteristics of the park. Then, a source-to-load curve is drawn according to the historical operation data and the source-to-load ratio to make the characteristics of the corresponding park microgrid visible, and the maximum allowable online power ratio is calculated according to the source-to-load curve and the microgrid self-balancing model constructed based on the park balance condition. Subsequently, the optimal storage-to-load ratio suitable for the actual working condition of the corresponding park microgrid is calculated according to the maximum allowable online power ratio and the energy storage configuration optimization algorithm with the minimum energy storage consumption as the goal, and the optimal storage-to-load ratio is verified based on the microgrid load optimization ratio analysis model and the typical load curve of the park to ensure the efficient utilization of the corresponding energy storage device in the park microgrid and avoid waste of resources. Finally, the microgrid executes load optimization action in response to the verified optimal storage-to-load ratio, which significantly improves the regulation and control capabilities of the microgrid.

[0049] The above specific embodiments are preferred embodiments of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A load optimization method based on self-balancing of a park microgrid, characterized in that: The method comprises the following steps: S1. Preprocessing the acquired historical operation data of the park microgrid, and calculating the source-to-load ratio of the corresponding microgrid based on the characteristics of the park; S2. Draw a source-load curve diagram based on the pre-processed historical operation data and the source-load ratio, and build a microgrid self-balancing model based on the balance condition of the park; S3. Calculate the maximum allowable online power ratio according to the source-load curve diagram and the microgrid self-balancing model; S4, calculating the optimal storage-to-load ratio according to the maximum allowable online power ratio and the energy storage configuration optimization algorithm with the goal of minimizing the energy storage power consumption; S5. Verify the optimal storage-load ratio based on the microgrid load optimization ratio analysis model and the typical load curve of the park; S6. The microgrid performs load optimization actions in response to the verified optimal storage-to-load ratio.

2. A load optimization method based on self-balancing of a park microgrid according to claim 1, characterized in that: In S1, the specific process of preprocessing the acquired historical operation data of the park microgrid is as follows: S111, cleaning the historical operation data of the park microgrid, and sorting the historical operation data after data cleaning according to the time scale change of the historical operation data; S112, scaling the sorted historical operation data by the same proportion to obtain historical operation data within a standard range.

3. The load optimization method based on self-balancing of a park microgrid according to claim 1 is characterized in that: In S1, the specific process of calculating the source-to-load ratio of the corresponding microgrid based on the characteristics of the park is: S121, extracting the capacity of the new energy power generation device, the maximum utilization hours of the new energy power generation device, the maximum load of the user, and the maximum load utilization hours of the user from the historical operation data of the park microgrid based on the characteristics of the park; S122. Calculate the annual power generation of the new energy power generation device based on the capacity of the new energy power generation device and the maximum utilization hours of the new energy power generation device, and calculate the annual load demand of the user based on the maximum load of the user and the maximum load utilization hours of the user; S123. Calculate the source-to-load ratio of the corresponding microgrid based on the annual power generation of the new energy power generation device and the annual load demand of the user.

4. The load optimization method based on self-balancing of a park microgrid according to claim 1 is characterized in that: In S2, the specific process of drawing the source-load curve diagram according to the pre-processed historical operation data and the source-load ratio is as follows: S211, arranging the output data of the new energy power generation device in the historical operation data to obtain an output data set, and arranging the load data in the historical operation data to obtain a load data set; S212, drawing an output curve diagram of the new energy device based on the output data set, and drawing a load curve diagram of the park based on the load data set; S213, obtaining a source-load curve diagram by fitting the output curve diagram of the new energy device and the load curve diagram of the park based on the source-load ratio.

5. The load optimization method based on self-balancing of a park microgrid according to claim 1 is characterized in that: In S2, the microgrid self-balancing model is specifically: gdl+fdl=ydl+swdl; 1-fdl+2swdl≤0.5; In the formula, gdl represents the proportion of power supply, fdl represents the proportion of power generation, swdl represents the proportion of grid-connected power, and ydl represents power consumption.

6. The load optimization method based on self-balancing of a park microgrid according to claim 1 is characterized in that: In S3, the specific process of calculating the maximum allowable online power proportion according to the source-load curve diagram and the microgrid self-balancing model is: S311, based on the microgrid self-balancing model, filter the data in the source-load curve diagram to obtain the power generation of the new energy device that meets the corresponding park balance condition and the amount of electricity allowed to be connected to the grid; S312, selecting the power generation of the new energy device and the power generation allowed to be online according to the power generation constraint conditions of the new energy device, the power generation constraint conditions allowed to be online and the time variation scale of the source-load curve diagram to obtain the maximum allowable power generation ratio.

7. The load optimization method based on self-balancing of a park microgrid according to claim 1 is characterized in that: In S4, the specific process of calculating the optimal storage-to-load ratio according to the maximum allowable online power ratio and the energy storage configuration optimization algorithm with the goal of minimizing the energy storage power consumption is: S411, calculating the cumulative returned power, load absorbed power and returned power based on the source-load curve diagram and the time scale change of the source-load curve diagram; S412, calculating the storable amount of energy storage according to the accumulated returned amount and the absorbed amount of load, and determining a better energy storage configuration based on the storable amount of energy storage and the returned amount; S413: Based on the maximum allowable online power ratio, select a better energy storage configuration to obtain an optimal storage-to-load ratio.

8. The load optimization method based on self-balancing of a park microgrid according to claim 1 is characterized in that: In S5, the specific process of verifying the optimal storage-load ratio based on the microgrid load optimization ratio analysis model and the typical load curve of the park is: S511, performing peak load characteristic fitting based on the optimal storage-to-load ratio and the typical load curve of the park to obtain a verification fitting curve; S512, inputting the verification fitting curve into the microgrid load optimization ratio analysis model to calculate the returned power, and verifying the peak load ratio according to the returned power calculation; S513. Compare the verified peak shaving load ratio with a preset peak shaving load ratio threshold. If the verified peak shaving load ratio is greater than or equal to the peak shaving load ratio threshold, the optimal storage-load ratio verification is successful. If the verified peak shaving load ratio is less than the peak shaving load ratio threshold, the optimal storage-load ratio verification fails.

9. The load optimization method based on self-balancing of a park microgrid according to claim 1 is characterized in that: In S6, the specific process of the microgrid performing load optimization action in response to the optimal storage-to-load ratio is: S611, determining a load dispatching action and an energy storage dispatching action based on the optimal storage-to-load ratio, and arranging the load dispatching action and the energy storage dispatching action to obtain a load optimization strategy; S612: The microgrid executes corresponding scheduling actions according to the load optimization strategy.

10. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 9.

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