Multi-objective based micro energy grid group optimization method, device and medium

Abstract

The application discloses a multi-target-based micro energy network group optimization method, equipment and medium, relates to the field of smart grids, and solves the problems that current regional electric energy distribution management lacks effective combination of electric energy distribution and dispatching systems and fails to comprehensively consider transmission cost of power transmission lines, the method being: calculating total power generation of a current region; in combination with regional power consumption of all months in the past three years of the current region, judging whether the total power generation of the region can meet power consumption demand of the current region; judging whether an adjacent region can transmit electric energy to the current region, calculating electric energy transmission cost required for transmitting electric energy from the adjacent region to the current region, and selecting a power generation supplement mode according to a corresponding comparison result of the electric energy transmission cost and fuel combustion cost; and judging energy loss rate of a power transmission line in the current region, and reducing the energy loss rate of the corresponding power transmission line, so that the application realizes balance between supply and demand of regional electric energy and reduces energy loss of the power transmission line.

Description

Technical Field

[0001] This invention belongs to the field of smart grid technology, specifically a method, equipment, and medium for optimizing micro energy grid clusters based on multiple objectives. Background Technology

[0002] A microgrid cluster is a comprehensive energy system formed by interconnecting multiple relatively independent microgrids through various energy networks. While ensuring that each microgrid can operate independently, it achieves energy complementarity and coordinated scheduling within the cluster, thereby improving the renewable energy absorption capacity, energy supply reliability, and overall energy utilization efficiency.

[0003] In the existing technology, the power distribution management of any region usually relies on a fixed dispatching method. The power generation forecast and power demand forecast within the region are mostly calculated independently, lacking effective integration with the power distribution and dispatching system. This makes it difficult to transform the forecast results into actual dispatching schemes in a timely manner, and fails to comprehensively consider the transmission loss of transmission lines, transmission distance and power generation costs, resulting in uneconomical power dispatching. To this end, the present invention proposes a multi-objective micro-energy grid group optimization method, equipment, and medium. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method, device and medium for optimizing micro energy grid groups based on multiple objectives.

[0005] The technical problem to be solved by this invention is: How to achieve regional power supply and demand balance and reduce energy loss in transmission lines.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect is a multi-objective microgrid group optimization method, which includes: Step S100: Calculate the total power generation in the current region based on historical power generation data and regional environmental data; Step S200: Based on the regional electricity consumption of all months in the past three years, determine whether the total regional power generation can meet the current regional electricity demand. Step S300: Based on the electricity consumption data, determine whether the adjacent area can transmit electricity to the current area. Then, calculate the electricity transmission cost required to transmit electricity from the adjacent area to the current area through the transmission line data. Select the power generation supplementation method for the current area based on the comparison result of the electricity transmission cost and the fuel combustion cost. Step S400: Determine the energy loss rate of the transmission lines in the current area based on the line operation data, and reduce the energy loss rate of the corresponding transmission lines in conjunction with the smart grid.

[0007] Furthermore, the historical power generation data includes the historical power generation of hydropower equipment, wind power equipment, and photovoltaic power equipment for all months in the current region over the past three years. The regional environmental data includes the historical water flow, historical wind speed, and historical percentage of sunny days for all months in the current region over the past three years.

[0008] Further, step S100 includes the following sub-steps: Step S101: Obtain the historical power generation of different power generation equipment in the current area for all months in the past three years; Step S102: Sum the historical power generation of any power generation equipment in the same month over the past three years, take the average value, and calculate the average power generation of the corresponding power generation equipment in the same month. Step S103: Obtain the historical water flow of all months in the current area over the past three years, sum the historical water flow of the same month, and take the average value to obtain the historical monthly average water flow. Then, calculate the corresponding monthly hydropower generation of the hydropower equipment in the predicted month. Step S104: Obtain the historical wind speed of all months in the current area over the past three years, sum the historical wind speeds of the same month, take the average value to obtain the historical monthly average wind speed, and then calculate the wind power generation corresponding to the wind power generation equipment for the predicted month. Step S105: Obtain the historical sunny day ratio of all months in the current region over the past three years, sum the historical sunny day ratios of the same month, and take the average to obtain the historical monthly average sunny day ratio. Then, calculate the photovoltaic monthly power generation corresponding to the photovoltaic power generation equipment for the predicted month. Step S106: Add up the monthly hydropower generation, monthly wind power generation, and monthly photovoltaic power generation for the corresponding month to calculate the total regional power generation for the corresponding month.

[0009] Further, step S200 includes the following sub-steps: Step S201: Obtain the regional electricity consumption for all months in the past three years, compare the regional electricity consumption for the same month in the second year with that in the first year, and compare the regional electricity consumption for the same month in the third year with that in the second year. Step S202: If there are any two sets of equal regional electricity consumption in the same month within the past three years, the regional electricity consumption in the same month within the past three years is added together and the average is taken to obtain the predicted monthly electricity consumption for the current region. Step S203: If the regional electricity consumption in any month of the second year is greater than the regional electricity consumption in the same month of the first year, and the regional electricity consumption in any month of the third year is greater than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the corresponding regional electricity consumption in the second year and the same month of the first year, and the electricity fluctuation value of the corresponding regional electricity consumption in the third year and the same month of the second year. Then, add the two sets of electricity fluctuation values ​​together and take the average value to obtain the average electricity fluctuation value. Add the average electricity fluctuation value to the regional electricity consumption in the corresponding month of the third year to obtain the predicted monthly electricity consumption of the current region. Step S204: If the regional electricity consumption in any month of the second year is less than the regional electricity consumption in the same month of the first year, and the regional electricity consumption in any month of the third year is less than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the corresponding regional electricity consumption in the second year and the same month of the first year, and the electricity fluctuation value of the corresponding regional electricity consumption in the third year and the same month of the second year. Then, add the two sets of electricity fluctuation values ​​together and take the average value to obtain the average electricity fluctuation value. Add the average electricity fluctuation value to the regional electricity consumption in the corresponding month of the third year to obtain the predicted monthly electricity consumption of the current region.

[0010] Furthermore, step S200 also includes the following sub-steps: Step S204: If the regional electricity consumption in any month of the second year is greater than the regional electricity consumption in the same month of the first year, but the regional electricity consumption in any month of the third year is less than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the regional electricity consumption in the same month of the second year and the first year, the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year, and the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year. Then, sum the three sets of electricity fluctuation values ​​to obtain the actual electricity fluctuation value. Finally, sum the regional electricity consumption in the same month of the past three years, take the average value, and add the actual electricity fluctuation value to obtain the predicted monthly electricity consumption of the current region. Step S205: If the regional electricity consumption in any month of the second year is less than the regional electricity consumption in the same month of the first year, but the regional electricity consumption in any month of the third year is greater than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the regional electricity consumption in the same month of the second year and the first year, the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year, and the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year. Then, sum the three sets of electricity fluctuation values ​​to obtain the actual electricity fluctuation value. Finally, sum the regional electricity consumption in the same month of the past three years, take the average value, and add the actual electricity fluctuation value to obtain the predicted monthly electricity consumption of the current region. Step S206: Compare the total regional power generation with the predicted power consumption; If the total power generation in the region is greater than the predicted power consumption, proceed to step S207; If the total power generation in the region is less than or equal to the predicted power consumption, it is determined that the total power generation in the region cannot meet the current power demand of the region, and the process proceeds to step S300. Step S207: Subtract the predicted electricity consumption from the total regional power generation to obtain the remaining power generation of the current region, and store the remaining power generation through energy storage devices. When the total regional power generation in any month is less than or equal to the predicted electricity consumption, the power generation of the current region is supplemented through energy storage devices.

[0011] Furthermore, the electricity consumption data includes the generation cost of adjacent areas, the total generation of adjacent areas, and the predicted electricity consumption of adjacent areas; The transmission line data includes the line length, conductor cross-sectional area, and resistivity of the transmission lines connecting the adjacent area and the current area, as well as the transmission voltage and transmission time when power is transmitted between adjacent areas.

[0012] Further, step S300 includes the following sub-steps: Step S301: Taking the current area as the center, obtain the total power generation of all adjacent areas and the corresponding predicted power consumption of adjacent areas; if the total power generation of adjacent areas is greater than the predicted power consumption of adjacent areas, proceed to step S302; if the total power generation of adjacent areas is less than or equal to the predicted power consumption of adjacent areas, no operation is performed. Step S302: Subtract the total power generation of the adjacent areas from the predicted power consumption of the adjacent areas to calculate the remaining power generation value of the corresponding adjacent areas, and sum the remaining power generation values ​​of all adjacent areas to calculate the total remaining power generation value of the adjacent areas. Step S303: Subtract the predicted electricity consumption from the total power generation of the region to calculate the current electricity demand of the region, and compare the electricity demand with the remaining value of the total power generation; if the remaining value of the total power generation is greater than the electricity demand, proceed to step S304; if the remaining value of the total power generation is less than or equal to the electricity demand, generate electricity through fuel power generation equipment. Step S304: Calculate the power transmission cost required to transport electricity from adjacent areas to the current area. The calculation process is as follows: Step S3041: Obtain the line length of the transmission lines connecting the adjacent area and the current area, as well as the conductor cross-sectional area and resistivity of the transmission lines, and calculate the line resistance of the transmission lines. Step S3042: Obtain the transmission voltage and transmission time when power is transmitted between adjacent areas, and calculate the transmission current when power is transmitted between adjacent areas. Step S3043: Calculate the energy loss rate of the transmission line based on Joule's law formula; Step S3044: Multiply the power generation cost of the adjacent area by the energy loss rate and then multiply by the demand for electricity to calculate the loss cost caused by the energy loss of the transmission line. Step S3045: Add the loss cost to the power generation cost to calculate the power transmission cost required to transmit power from the adjacent area to the current area; Step S305: Calculate the fuel combustion cost when generating electricity using combustion power generation equipment in the current area. The calculation process is as follows: Step S3051: Convert the required electricity consumption into the total energy of fuel combustion during fuel power generation using a formula; Step S3052: Obtain the standard energy weight generated during fuel combustion, divide the total fuel energy by the standard energy weight, and calculate the total fuel weight. Step S3053: Multiply the total weight of fuel by the fuel price to calculate the fuel combustion cost; Step S306: Compare the fuel combustion cost with the power transmission cost; if the fuel combustion cost is less than the power transmission cost, select fuel power generation equipment to supplement the current area's electricity demand; if the fuel combustion cost is greater than or equal to the power transmission cost, select to transmit power from an adjacent area to meet the current area's electricity demand.

[0013] Furthermore, the line operation data includes the line length, transmission voltage, and transmission duration of all transmission lines in the current area; Step S400 includes the following sub-steps: Step S401: Obtain the line length, transmission voltage and transmission time of all transmission lines in the current area through the smart grid, and repeat steps S3041 to S3043 to calculate the energy loss rate of all transmission lines in the current area. If the energy loss rate of any transmission line is greater than or equal to the minimum loss rate, the energy loss rate of the transmission line is determined to be abnormal and enters standard S402. If the energy loss rate of all transmission lines is less than the minimum loss rate, the energy loss rate of the transmission lines is considered normal, and no operation is performed. Step S402: Detect the length of the transmission lines in the current area; If the line length is less than the length threshold, the transmission voltage is increased until the energy loss rate of the transmission line is less than the minimum loss rate. If the line length is greater than or equal to the length threshold, a booster station shall be set at the midpoint of the corresponding transmission line.

[0014] In a second aspect, a computer device, said computer device comprising: A memory that stores a computer program; The processor is communicatively connected to the memory. When the computer program is executed by the processor, it implements the multi-objective micro-energy grid group optimization method.

[0015] Thirdly, a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the aforementioned multi-objective microgrid group optimization method.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention calculates the total power generation in the current region based on historical power generation data and regional environmental data. Then, it combines the regional electricity consumption of the current region in all months over the past three years to determine whether the total power generation can meet the current region's electricity demand. Based on the electricity demand judgment, it determines whether power optimization is needed. 2. This invention determines whether adjacent areas can transmit electricity to the current area based on electricity consumption data, and then calculates the electricity transmission cost required to transmit electricity from adjacent areas to the current area through transmission line data. Based on the comparison between electricity transmission cost and fuel combustion cost, it selects the power generation supplementation method for the current area, thereby saving electricity costs. 3. This invention determines the energy loss rate of transmission lines in the current area by using line operation data, and combines it with smart grids to reduce the energy loss rate of corresponding transmission lines, thereby optimizing power transmission. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is an example diagram of the micro-energy grid in this invention; Figure 3 This is an example diagram illustrating the relationship between the current region and its adjacent regions in this invention; Figure 4 This is a schematic diagram of the electronic device in this invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figures 1-3As shown, the technical solution provided by this invention is: a multi-objective micro-energy grid group optimization method. This method is used to select multiple power generation acquisition modes through a smart grid and reduce the energy loss rate of transmission lines when the power generation in the current area cannot meet the electricity demand. In this method, the multi-objective specifically refers to multiple micro-energy grids, and the method is as follows: Step S100: Calculate the total power generation in the current region based on historical power generation data and regional environmental data; like Figure 2 and Figure 3 As shown, the microgrid group is divided into multiple microgrids. Each microgrid includes various power generation devices, energy storage devices, electrical devices, and transmission lines. Adjacent microgrids are connected through transmission lines. The microgrid being optimized is denoted as the current region, and the microgrids adjacent to the optimized microgrid are denoted as adjacent regions. Figure 3 The circle in the image can represent power generation equipment, energy storage equipment, or electrical equipment. Specifically, the historical power generation data includes the historical power generation of hydropower equipment, wind power equipment, and photovoltaic power equipment in the current region for all months over the past three years. The historical power generation of all equipment is detected by power generation measurement devices and stored in the current region's power generation detection database. The regional environmental data includes the historical water flow, historical wind speed, and historical sunny day ratio for all months over the past three years. The historical water flow for all months is obtained by summing the daily water flow for that month. The historical wind speed for all months is obtained by summing the daily wind speeds for that month and taking the average. The historical sunny day ratio for all months is obtained by dividing the number of sunny days in that month by the total number of days in that month. In this embodiment, step S100 includes the following sub-steps: Step S101: Obtain the historical power generation LFi(a) of different power generation equipment in the current area for all months in the past three years, where i is the type number of the power generation equipment, i=1, 2, 3, and a is the time number, a=1, 2, ..., 12; Specifically, when i=1, it represents the historical power generation of hydropower equipment in the current region for any month in the past three years; when i=2, it represents the historical power generation of wind power equipment in the current region for any month in the past three years; when i=3, it represents the historical power generation of photovoltaic power equipment in the current region for any month in the past three years. Step S102: Sum the historical power generation of any power generation equipment in the same month over the past three years and take the average value to calculate the average power generation of the corresponding power generation equipment in the same month, DJi(a). Step S103: Obtain the historical water flow LL(a) for all months in the current area over the past three years. Sum the historical water flows of the same month, take the average value, and calculate the historical monthly average water flow JS(a). Calculate the predicted monthly hydropower generation SF1(a) of the hydropower equipment using the following formula: The forecast month specifically refers to the month in the current year when the total power generation of the region needs to be predicted. Step S104: Obtain the historical wind speed LF(a) for all months in the current area over the past three years. Sum the historical wind speeds of the same month, take the average, and calculate the historical monthly average wind speed JF(a). Calculate the corresponding monthly wind power generation FF2(a) for the predicted month using the following formula: ; Step S105: Obtain the historical sunny day ratio LQ(a) for all months in the current region over the past three years. Sum the historical sunny day ratios for the same month and take the average to calculate the historical monthly average sunny day ratio JQ(a). Calculate the photovoltaic monthly power generation GF3(a) corresponding to the photovoltaic power generation equipment in the predicted month using the following formula: ; Step S106: Add up the monthly hydropower generation, monthly wind power generation, and monthly photovoltaic power generation for the corresponding month to calculate the total regional power generation for the corresponding month.

[0021] Step S200: Based on the regional electricity consumption of all months in the past three years, determine whether the total regional power generation can meet the current regional electricity demand. In this embodiment, step S200 includes the following sub-steps: Step S201: Obtain the regional electricity consumption for all months in the past three years, compare the regional electricity consumption for the same month in the second year with that in the first year, and compare the regional electricity consumption for the same month in the third year with that in the second year. Step S202: If there are any two sets of equal regional electricity consumption in the same month within the past three years, the regional electricity consumption in the same month within the past three years is added together and the average is taken to obtain the predicted monthly electricity consumption for the current region. Step S203: If the regional electricity consumption in any month of the second year is greater than the regional electricity consumption in the same month of the first year, and the regional electricity consumption in any month of the third year is greater than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the corresponding regional electricity consumption in the second year and the same month of the first year, and the electricity fluctuation value of the corresponding regional electricity consumption in the third year and the same month of the second year. Then, add the two sets of electricity fluctuation values ​​together and take the average value to obtain the average electricity fluctuation value. Add the average electricity fluctuation value to the regional electricity consumption in the corresponding month of the third year to obtain the predicted monthly electricity consumption of the current region. Step S204: If the regional electricity consumption in any month of the second year is less than the regional electricity consumption in the same month of the first year, and the regional electricity consumption in any month of the third year is less than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the corresponding regional electricity consumption in the second year and the same month of the first year, and the electricity fluctuation value of the corresponding regional electricity consumption in the third year and the same month of the second year. Then, add the two sets of electricity fluctuation values ​​together and take the average value to obtain the average electricity fluctuation value. Add the average electricity fluctuation value to the regional electricity consumption in the corresponding month of the third year to obtain the predicted monthly electricity consumption of the current region. The power fluctuation value is obtained by subtracting the regional power consumption of the month with the larger year from the regional power consumption of the month with the larger year. Therefore, the power fluctuation value is positive in step S203 and negative in step S204. Step S204: If the regional electricity consumption in any month of the second year is greater than the regional electricity consumption in the same month of the first year, but the regional electricity consumption in any month of the third year is less than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the regional electricity consumption in the same month of the second year and the first year, the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year, and the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year. Then, sum the three sets of electricity fluctuation values ​​to obtain the actual electricity fluctuation value. Finally, sum the regional electricity consumption in the same month of the past three years, take the average value, and add the actual electricity fluctuation value to obtain the predicted monthly electricity consumption of the current region. For example, if the regional electricity consumption in December 2022 is 100 kWh, the regional electricity consumption in December 2023 is 110 kWh, and the regional electricity consumption in December 2024 is 105 kWh, this meets the conditions in step S204. Three sets of electricity fluctuation values ​​can be obtained, namely 10 (regional electricity consumption in August 2023 - regional electricity consumption in August 2022), -5 (regional electricity consumption in August 2024 - regional electricity consumption in August 2023), and 5 (regional electricity consumption in August 2024 - regional electricity consumption in August 2022). The predicted electricity consumption = (10 - 5 + 5) + (100 + 110 + 105) / 3; Step S205: If the regional electricity consumption in any month of the second year is less than the regional electricity consumption in the same month of the first year, but the regional electricity consumption in any month of the third year is greater than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the regional electricity consumption in the same month of the second year and the first year, the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year, and the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year. Then, sum the three sets of electricity fluctuation values ​​to obtain the actual electricity fluctuation value. Finally, sum the regional electricity consumption in the same month of the past three years, take the average value, and add the actual electricity fluctuation value to obtain the predicted monthly electricity consumption of the current region. Step S206: Compare the total regional power generation with the predicted power consumption; If the total power generation in the region is greater than the predicted power consumption, proceed to step S207; If the total power generation in the region is less than or equal to the predicted power consumption, it is determined that the total power generation in the region cannot meet the current power demand of the region, and the process proceeds to step S300. Step S207: Subtract the predicted electricity consumption from the total regional power generation to obtain the remaining power generation of the current region, and store the remaining power generation through energy storage devices. When the total regional power generation in any month is less than or equal to the predicted electricity consumption, the power generation of the current region is supplemented through energy storage devices.

[0022] Step S300: Based on the electricity consumption data, determine whether the adjacent area can transmit electricity to the current area. Then, calculate the electricity transmission cost required to transmit electricity from the adjacent area to the current area through the transmission line data. Select the power generation supplementation method for the current area based on the comparison result of the electricity transmission cost and the fuel combustion cost. Specifically, the electricity consumption data includes the power generation cost of adjacent areas, the total power generation of adjacent areas, and the predicted power consumption of adjacent areas; the transmission line data includes the line length, conductor cross-sectional area, and resistivity of the transmission lines connecting adjacent areas to the current area, as well as the transmission voltage and transmission time when power is transmitted between adjacent areas; and the power generation replenishment methods include power transmission from adjacent areas and fuel power generation in the current area through fuel power generation equipment. In this embodiment, step S300 includes the following sub-steps: Step S301: Taking the current area as the center, obtain the total power generation of all adjacent areas and the corresponding predicted power consumption of adjacent areas. If the total power generation of adjacent regions is greater than the predicted power consumption of adjacent regions, proceed to step S302. If the total power generation of adjacent regions is less than or equal to the predicted power consumption of adjacent regions, no operation will be performed. Step S302: Subtract the total power generation of the adjacent areas from the predicted power consumption of the adjacent areas to calculate the remaining power generation value of the corresponding adjacent areas, and sum the remaining power generation values ​​of all adjacent areas to calculate the total remaining power generation value of the adjacent areas. Specifically, the remaining value of total power generation is the power value after deducting the power loss caused by the transmission lines when transmitting power from the adjacent area to the current area; Step S303: Subtract the predicted electricity consumption from the total power generation of the region to calculate the current electricity demand XQ of the region, and compare the electricity demand with the remaining value of the total power generation. If the remaining total power generation is greater than the required power consumption, proceed to step S304; If the remaining total power generation is less than or equal to the demand for electricity, then power will be generated through fuel-powered generators. Step S304: Calculate the power transmission cost required to transport electricity from adjacent areas to the current area. The calculation process is as follows: Step S3041: Obtain the line length Lj of the transmission lines connecting the adjacent area and the current area, where j is the number of the adjacent area, j=1, 2, ..., n, and n is a positive integer, as well as the conductor cross-sectional area A and resistivity ρ of the transmission line. Calculate the line resistance Rj of the transmission line using the resistance-length formula, as follows: Rj = ρ × Lj / A; Step S3042: Obtain the transmission voltage value Uj and transmission duration SCj when power is transmitted between adjacent areas. Calculate the transmission current value Ij when power is transmitted between adjacent areas using the following formula: Ij = XQ / (Uj × SCj); Step S3043: Calculate the energy loss rate SLj of the transmission line based on Joule's law formula. The specific formula is as follows: SLj=(Ij²×Rj×SCj) / XQ, specifically, the energy loss rate is a unitless value; the unit of Ij²×Rj×SCj is watt-hour, the unit of the value calculated by Ij²×Rj is watt, multiplying it by the transmission duration gives watt × duration, the unit of the required electricity consumption is kilowatt-hour, therefore the energy loss rate obtained by dividing is a unitless value. Step S3044: Multiply the power generation cost FCj of the adjacent area by the energy loss rate and then by the demand for electricity to calculate the loss cost SHj caused by the energy loss of the transmission line. Step S3045: Add the loss cost to the power generation cost to calculate the power transmission cost required to transmit power from the adjacent area to the current area; Step S305: Calculate the fuel combustion cost when generating electricity using combustion power generation equipment in the current area. The calculation process is as follows: Step S3051: Convert the required electricity consumption into the total fuel energy RL generated by fuel combustion during fuel power generation using a formula, as follows: RL = 3600 × XQ, where one watt-hour equals 3600 joules, and the unit of total fuel energy is joule; Step S3052: Obtain the standard energy weight generated during fuel combustion, divide the total fuel energy by the standard energy weight, and calculate the total fuel weight. The standard energy weight is measured in joules per kilogram, which is the energy produced when a fixed weight of fuel is burned. Step S3053: Multiply the total weight of fuel by the fuel price to calculate the fuel combustion cost; Step S306: Compare the fuel combustion cost with the electricity transmission cost; If the cost of fuel combustion is less than the cost of electricity transmission, then fuel-powered generators will be selected to supplement the current region's electricity demand. If the cost of fuel combustion is greater than or equal to the cost of electricity transmission, then electricity will be transmitted from an adjacent area to meet the current area's electricity demand. It should be specifically noted that the smart grid is used to supplement the power generation of the current area by fuel power generation equipment and merge it into the transmission lines of the current area; the smart grid is used to transmit the power generation of adjacent areas and merge it into the transmission lines of the current area; the above operations are existing technologies and will not be elaborated here.

[0023] Step S400: Determine the energy loss rate of the transmission lines in the current area based on the line operation data, and reduce the energy loss rate of the corresponding transmission lines in conjunction with the smart grid. Specifically, the line operation data includes the line length, transmission voltage, and transmission duration of all transmission lines in the current area; this data is collected and monitored in real time by the smart grid. In this embodiment, step S400 includes the following sub-steps: Step S401: Obtain the line length, transmission voltage and transmission time of all transmission lines in the current area through the smart grid, and repeat steps S3041 to S3043 to calculate the energy loss rate of all transmission lines in the current area. If the energy loss rate of any transmission line is greater than or equal to the minimum loss rate, the energy loss rate of the transmission line is determined to be abnormal and enters standard S402. If the energy loss rate of all transmission lines is less than the minimum loss rate, the energy loss rate of the transmission lines is considered normal, and no operation is performed. Step S402: Detect the length of the transmission lines in the current area; If the line length is less than the length threshold, the transmission voltage is increased until the energy loss rate of the transmission line is less than the minimum loss rate. If the line length is greater than or equal to the length threshold, a booster station shall be set at the midpoint of the corresponding transmission line. It should be specifically noted that, as described in the resistance-length formula in step S3041, the line resistance is directly proportional to the line length and inversely proportional to the conductor cross-sectional area. Therefore, when the conductor cross-sectional area and resistivity are kept constant, reducing the line length can reduce the line resistance of the transmission line. As described in the formula in step S3042, when the transmission duration of the transmission line is kept constant, increasing the transmission voltage of the transmission line can reduce the transmission current, thereby reducing the energy loss rate.

[0024] Example 2: This embodiment of the invention also provides a computer device for running the aforementioned multi-objective microgrid group optimization method; see [link to previous example]. Figure 4 The schematic diagram shown in this embodiment of the invention provides a computer device, which includes a memory and a processor. The memory is used to store one or more computer instructions, which are executed by the processor to implement the above-mentioned multi-objective micro-energy grid group optimization method. Furthermore, Figure 4 The computer device shown also includes a system bus and a communication interface, with the processor, communication interface, and memory connected via the communication bus; The memory may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The system bus can be an ISA bus, PCI bus, or EISA bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by only one bidirectional arrow, but this does not mean that there is only one communication bus or one type of system bus. The processor may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0025] Example 3: This embodiment of the invention also provides a computer storage medium that stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the above-mentioned multi-objective micro-energy network group optimization method. For specific implementation, please refer to the method embodiment, which will not be repeated here. The computer program product of the micro-energy grid group optimization method based on multi-objectives provided in the embodiments of the present invention includes a computer storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0026] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and / or device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0027] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0028] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A microgrid group optimization method based on multi-objectives, characterized in that, The methods include: Step S100: Calculate the total power generation in the current region based on historical power generation data and regional environmental data; Step S200: Based on the regional electricity consumption of all months in the past three years, determine whether the total regional power generation can meet the current regional electricity demand. Step S300: Based on the electricity consumption data, determine whether the adjacent area can transmit electricity to the current area. Then, calculate the electricity transmission cost required to transmit electricity from the adjacent area to the current area through the transmission line data. Select the power generation supplementation method for the current area based on the comparison result of the electricity transmission cost and the fuel combustion cost. Step S400: Determine the energy loss rate of the transmission lines in the current area based on the line operation data, and reduce the energy loss rate of the corresponding transmission lines in conjunction with the smart grid.

2. The microgrid group optimization method based on multi-objectives according to claim 1, characterized in that, Historical power generation data includes the historical power generation of hydropower equipment, wind power equipment, and photovoltaic power equipment for all months in the current region over the past three years. The regional environmental data includes the historical water flow, historical wind speed, and historical percentage of sunny days for all months in the current region over the past three years.

3. The microgrid group optimization method based on multi-objectives according to claim 2, characterized in that, Step S100 includes the following sub-steps: Step S101: Obtain the historical power generation of different power generation equipment in the current area for all months in the past three years; Step S102: Sum the historical power generation of any power generation equipment in the same month over the past three years, take the average value, and calculate the average power generation of the corresponding power generation equipment in the same month. Step S103: Obtain the historical water flow of all months in the current area over the past three years, sum the historical water flow of the same month, and take the average value to obtain the historical monthly average water flow. Then, calculate the corresponding monthly hydropower generation of the hydropower equipment in the predicted month. Step S104: Obtain the historical wind speed of all months in the current area over the past three years, sum the historical wind speeds of the same month, take the average value to obtain the historical monthly average wind speed, and then calculate the wind power generation corresponding to the wind power generation equipment for the predicted month. Step S105: Obtain the historical sunny day ratio of all months in the current region over the past three years, sum the historical sunny day ratios of the same month, and take the average to obtain the historical monthly average sunny day ratio. Then, calculate the photovoltaic monthly power generation corresponding to the photovoltaic power generation equipment for the predicted month. Step S106: Add up the monthly hydropower generation, monthly wind power generation, and monthly photovoltaic power generation for the corresponding month to calculate the total regional power generation for the corresponding month.

4. The microgrid group optimization method based on multi-objectives according to claim 1, characterized in that, Step S200 includes the following sub-steps: Step S201: Obtain the regional electricity consumption for all months in the past three years, compare the regional electricity consumption for the same month in the second year with that in the first year, and compare the regional electricity consumption for the same month in the third year with that in the second year. Step S202: If there are any two sets of equal regional electricity consumption in the same month within the past three years, the regional electricity consumption in the same month within the past three years is added together and the average is taken to obtain the predicted monthly electricity consumption for the current region. Step S203: If the regional electricity consumption in any month of the second year is greater than the regional electricity consumption in the same month of the first year, and the regional electricity consumption in any month of the third year is greater than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the corresponding regional electricity consumption in the second year and the same month of the first year, and the electricity fluctuation value of the corresponding regional electricity consumption in the third year and the same month of the second year. Then, add the two sets of electricity fluctuation values ​​together and take the average value to obtain the average electricity fluctuation value. Add the average electricity fluctuation value to the regional electricity consumption in the corresponding month of the third year to obtain the predicted monthly electricity consumption of the current region. Step S204: If the regional electricity consumption in any month of the second year is less than the regional electricity consumption in the same month of the first year, and the regional electricity consumption in any month of the third year is less than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the corresponding regional electricity consumption in the second year and the same month of the first year, and the electricity fluctuation value of the corresponding regional electricity consumption in the third year and the same month of the second year. Then, add the two sets of electricity fluctuation values ​​together and take the average value to obtain the average electricity fluctuation value. Add the average electricity fluctuation value to the regional electricity consumption in the corresponding month of the third year to obtain the predicted monthly electricity consumption of the current region.

5. The microgrid group optimization method based on multi-objectives according to claim 4, characterized in that, Step S200 further includes the following sub-steps: Step S204: If the regional electricity consumption in any month of the second year is greater than the regional electricity consumption in the same month of the first year, but the regional electricity consumption in any month of the third year is less than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the regional electricity consumption in the same month of the second year and the first year, the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year, and the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year. Then, sum the three sets of electricity fluctuation values ​​to obtain the actual electricity fluctuation value. Finally, sum the regional electricity consumption in the same month of the past three years, take the average value, and add the actual electricity fluctuation value to obtain the predicted monthly electricity consumption of the current region. Step S205: If the regional electricity consumption in any month of the second year is less than the regional electricity consumption in the same month of the first year, but the regional electricity consumption in any month of the third year is greater than the regional electricity consumption in the same month of the second year, then calculate the electricity fluctuation value of the regional electricity consumption in the same month of the second year and the first year, the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year, and the electricity fluctuation value of the regional electricity consumption in the same month of the third year and the first year. Then, sum the three sets of electricity fluctuation values ​​to obtain the actual electricity fluctuation value. Finally, sum the regional electricity consumption in the same month of the past three years, take the average value, and add the actual electricity fluctuation value to obtain the predicted monthly electricity consumption of the current region. Step S206: Compare the total regional power generation with the predicted power consumption; If the total power generation in the region is greater than the predicted power consumption, proceed to step S207; If the total power generation in the region is less than or equal to the predicted power consumption, it is determined that the total power generation in the region cannot meet the current power demand of the region, and the process proceeds to step S300. Step S207: Subtract the predicted electricity consumption from the total regional power generation to obtain the remaining power generation of the current region, and store the remaining power generation through energy storage devices. When the total regional power generation in any month is less than or equal to the predicted electricity consumption, the power generation of the current region is supplemented through energy storage devices.

6. The microgrid group optimization method based on multi-objectives according to claim 1, characterized in that, Electricity consumption data includes the generation cost of adjacent areas, the total generation of adjacent areas, and the predicted electricity consumption of adjacent areas; The transmission line data includes the line length, conductor cross-sectional area, and resistivity of the transmission lines connecting the adjacent area and the current area, as well as the transmission voltage and transmission time when power is transmitted between adjacent areas.

7. The microgrid group optimization method based on multi-objectives according to claim 6, characterized in that, Step S300 includes the following sub-steps: Step S301: Taking the current area as the center, obtain the total power generation of all adjacent areas and the corresponding predicted power consumption of adjacent areas; if the total power generation of adjacent areas is greater than the predicted power consumption of adjacent areas, proceed to step S302; if the total power generation of adjacent areas is less than or equal to the predicted power consumption of adjacent areas, no operation is performed. Step S302: Subtract the total power generation of the adjacent areas from the predicted power consumption of the adjacent areas to calculate the remaining power generation value of the corresponding adjacent areas, and sum the remaining power generation values ​​of all adjacent areas to calculate the total remaining power generation value of the adjacent areas. Step S303: Subtract the predicted electricity consumption from the total power generation of the region to calculate the current electricity demand of the region, and compare the electricity demand with the remaining value of the total power generation. If the remaining total power generation is greater than the required power consumption, proceed to step S304; if the remaining total power generation is less than or equal to the required power consumption, generate electricity through fuel power generation equipment. Step S304: Calculate the power transmission cost required to transport electricity from adjacent areas to the current area. The calculation process is as follows: Step S3041: Obtain the line length of the transmission lines connecting the adjacent area and the current area, as well as the conductor cross-sectional area and resistivity of the transmission lines, and calculate the line resistance of the transmission lines. Step S3042: Obtain the transmission voltage and transmission time when power is transmitted between adjacent areas, and calculate the transmission current when power is transmitted between adjacent areas. Step S3043: Calculate the energy loss rate of the transmission line based on Joule's law formula; Step S3044: Multiply the power generation cost of the adjacent area by the energy loss rate and then multiply by the demand for electricity to calculate the loss cost caused by the energy loss of the transmission line. Step S3045: Add the loss cost to the power generation cost to calculate the power transmission cost required to transmit power from the adjacent area to the current area; Step S305: Calculate the fuel combustion cost when generating electricity using combustion power generation equipment in the current area. The calculation process is as follows: Step S3051: Convert the required electricity consumption into the total energy of fuel combustion during fuel power generation using a formula; Step S3052: Obtain the standard energy weight generated during fuel combustion, divide the total fuel energy by the standard energy weight, and calculate the total fuel weight. Step S3053: Multiply the total weight of fuel by the fuel price to calculate the fuel combustion cost; Step S306: Compare the fuel combustion cost with the power transmission cost; if the fuel combustion cost is less than the power transmission cost, select fuel power generation equipment to supplement the current area's electricity demand; if the fuel combustion cost is greater than or equal to the power transmission cost, select to transmit power from an adjacent area to meet the current area's electricity demand.

8. The microgrid group optimization method based on multi-objectives according to claim 7, characterized in that, The line operation data includes the line length, transmission voltage, and transmission duration of all transmission lines in the current area; Step S400 includes the following sub-steps: Step S401: Obtain the line length, transmission voltage and transmission time of all transmission lines in the current area through the smart grid, and repeat steps S3041 to S3043 to calculate the energy loss rate of all transmission lines in the current area. If the energy loss rate of any transmission line is greater than or equal to the minimum loss rate, the energy loss rate of the transmission line is determined to be abnormal and enters standard S402. If the energy loss rate of all transmission lines is less than the minimum loss rate, the energy loss rate of the transmission lines is considered normal, and no operation is performed. Step S402: Detect the length of the transmission lines in the current area; If the line length is less than the length threshold, the transmission voltage is increased until the energy loss rate of the transmission line is less than the minimum loss rate. If the line length is greater than or equal to the length threshold, a booster station shall be set at the midpoint of the corresponding transmission line.

9. A computer device, characterized in that, The computer device includes: A memory that stores a computer program; The processor is communicatively connected to the memory, and when the computer program is executed by the processor, it implements the multi-objective micro-energy grid group optimization method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the multi-objective microgrid group optimization method as described in any one of claims 1 to 8.

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