Microgrid configuration method and device

Abstract

The present invention provides a microgrid configuration method and device, the method comprising: obtaining the expected use time and total power demand of the microgrid, and initializing the configuration parameters of the microgrid; determining the total power generated by each microsource within the expected use time according to the meteorological parameters of the regions corresponding to different coordinates; adjusting the number of microsources located at different coordinates based on the constraint conditions that the self-balancing rate is within a preset range, the sum of the total power generated by each microsource meets the total power demand, and the total configuration cost meets the preset cost threshold. The present application can fully consider the various characteristics of the microsources, energy storage systems and loads of the microgrid, take into account the changes of various key parameters throughout the life cycle of the microgrid, ensure the sustainable power supply of the load through the reasonable number configuration between the microsources and the energy storage device, meet various constraints under the condition of saving costs as much as possible, and obtain the optimal microgrid optimization configuration plan.

Description

Technical Field

[0001] The present invention relates to the field of smart grid micro-source planning, and in particular to a micro-grid configuration method and device. Background Art

[0002] In recent years, distributed generation technology (DG) has been adopted for its advantages of high energy utilization, flexible installation, and convenient power supply. In particular, microgrids composed of DG are more widely used in power systems. Due to the existence of microgrids and distributed power sources, the redundancy of the system is improved, which can greatly meet the reliable and continuous power supply of loads under various circumstances. Relying on geographical resource conditions, wind and light are used to solve the problem of lack of power supply. However, due to the random and unstable existence of new energy, non-green energy needs to be configured in microgrids to solve the problems of insufficient self-regulation ability and poor power supply capacity of microgrids. However, the existing configuration methods often cannot well allocate the number of various micro sources in microgrids, which ultimately leads to poor comprehensive benefits and poor feasibility of microgrids.

[0003] At present, the configuration of micro-sources and energy storage devices in microgrids generally judges the quantitative capacity and configuration access location of micro-sources through the voltage output characteristics of system nodes and the total electric power capacity cost, and finally verifies the correctness and rationality of the microgrid configuration method. However, the current configuration algorithm cannot take into account all external influencing factors, including: the self-balancing rate of microgrids under different conditions, the load demand of microgrids, the power generation cost of micro-sources, various parameters of each device in the whole life cycle of microgrids, and the price of microgrid electricity sales / purchase. The existing configuration method only considers and calculates certain factors, and it is difficult to establish a more accurate microgrid configuration method to make the microgrid achieve economic optimization. Summary of the invention

[0004] The present invention provides a microgrid configuration method and device, which are used to solve the problem that the existing microgrid configuration method only considers some factors, the optimized configuration result is inaccurate, and it is difficult to establish a more accurate microgrid configuration method to achieve economic optimization of the microgrid.

[0005] A first aspect of the present invention provides a microgrid configuration method, the method comprising:

[0006] Obtain the estimated usage time and total power demand of the microgrid, and initialize the configuration parameters of the microgrid, wherein the configuration parameters include the coordinates of each microsource and the number of microsources at different coordinates;

[0007] Determine the total power generated by each micro-source within the expected usage time according to the meteorological parameters of the regions corresponding to the different coordinates;

[0008] The number of micro sources located at different coordinates is adjusted based on the constraint conditions that the self-balancing rate is within a preset range, the sum of the total power generated by the micro sources meets the required total power, and the total configuration cost meets the preset cost threshold, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro source, and the self-balancing rate is the ratio of the power provided by the microgrid in a preset period of time to the total power required for the operation of each micro source.

[0009] Optionally, initializing at least one set of configuration parameters with the same coordinate positions of each micro-source further includes performing at least one of the following steps to update the configuration parameters:

[0010] Delete the configuration parameters whose self-balancing rates are not within the preset range among multiple sets of configuration parameters;

[0011] Delete the configuration parameters whose total configuration cost does not meet the preset cost threshold among the multiple sets of configuration parameters;

[0012] Among the multiple sets of configuration parameters, the configuration parameters whose sum of the total power generated by each micro source does not meet the required total power are deleted.

[0013] Optionally, initializing at least one set of configuration parameters with the same coordinate positions of each micro-source, and adjusting the number of micro-sources located at different coordinates comprises:

[0014] Mapping the number of micro-sources of different coordinates in each set of configuration parameters into a micro-source number matrix, and using the micro-source number matrix as chromosome X in the genetic algorithm to obtain an initial population;

[0015] The chromosome X in the initial population is cross-compiled and mutated multiple times until the iteration stop condition is met. During the cross-compilation and mutation generation process, the self-balancing rate corresponding to each X in the population, the sum of the total power generation of each micro-source, and the total configuration cost meet the above constraints;

[0016] From the population obtained after the iteration stops, the number of micro-sources located at different coordinates is determined according to the chromosome X corresponding to the total cost of the optimal configuration.

[0017] Optionally, multiple cross-compilations and mutations are performed on chromosome X in the initial population, including:

[0018] Perform multiple cross-compilations and mutations on the chromosomes X in the initial population, wherein during the cross-compilation and mutation process, the chromosomes X whose first adaptive function modulus does not meet the preset cost threshold and the chromosomes X whose second adaptive function modulus does not meet the required total power are deleted from the population respectively;

[0019] The first adaptive function is X*Y, the second adaptive function is X*Z, Y is the configuration cost of each micro source, and Z is the total power generated by each micro source.

[0020] Optionally, determining whether an iteration stop condition is satisfied includes:

[0021] When it is determined that the number of cross-compilation and variation generation reaches a preset number K, it is determined that an iteration stop condition is satisfied; or

[0022] When it is determined that in each cross-compilation and mutation generated population, the total configuration cost corresponding to at least one chromosome X is lower than the target configuration cost, it is determined that the iteration stop condition is met.

[0023] Optionally, the configuration parameters of the initialized microgrid are determined by inputting the total power generation and total power demand of each micro source within the expected usage time into a data planning and solving model, and the data planning and solving model is used to determine multiple sets of configuration parameters that are not less than the total power demand based on the total power generation of each micro source as the configuration parameters of the initialized microgrid.

[0024] Optionally, determining the total power generated by each micro source within the expected usage time according to the meteorological parameters of the regions corresponding to the different coordinates includes:

[0025] Inputting the meteorological parameters within the period into the meteorological simulation software to obtain micro-source power generation parameters, wherein the micro-source power generation parameters include: wind speed, air density, and irradiance;

[0026] The total power generation of each micro source is determined according to the micro source power generation parameters.

[0027] Optionally, determining the total power generation of each micro-source according to the micro-source power generation parameter comprises at least one of the following steps:

[0028] The total power generated by the wind turbine is determined using the following formula: The P 风 is the power generated by the wind turbine, ρ is the air density, and C p is the wind energy utilization coefficient, v is the wind speed, and R is the radius of the wind turbine blade;

[0029] The total power generated by the photovoltaic panel is determined using the following formula: The P 光 is the power generated by the photovoltaic panel, and f pv is the power derating factor, the P rate is the rated power of the photovoltaic panel, A is the irradiance, and A S is the irradiance at rated power.

[0030] A second aspect of the present invention provides a microgrid configuration device, the device comprising:

[0031] A memory for storing instructions;

[0032] The processor is configured to read the instructions in the memory and execute the following processes:

[0033] Obtain the estimated usage time and total power demand of the microgrid, and initialize the configuration parameters of the microgrid, wherein the configuration parameters include the coordinates of each microsource and the number of microsources at different coordinates;

[0034] Determine the total power generated by each micro-source within the expected usage time according to the meteorological parameters of the regions corresponding to the different coordinates;

[0035] The number of micro sources located at different coordinates is adjusted based on the constraint conditions that the self-balancing rate is within a preset range, the sum of the total power generated by the micro sources meets the required total power, and the total configuration cost meets the preset cost threshold, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro source, and the self-balancing rate is the ratio of the power provided by the microgrid in a preset period of time to the total power required for the operation of each micro source.

[0036] Optionally, the processor is used to initialize at least one set of configuration parameters with the same coordinate position of each micro-source, and further includes executing at least one of the following steps to update the configuration parameters:

[0037] Delete the configuration parameters whose self-balancing rates are not within the preset range among multiple sets of configuration parameters;

[0038] Delete the configuration parameters whose total configuration cost does not meet the preset cost threshold among the multiple sets of configuration parameters;

[0039] Among the multiple sets of configuration parameters, the configuration parameters whose sum of the total power generated by each micro source does not meet the required total power are deleted.

[0040] Optionally, the processor is used to initialize at least one set of configuration parameters for the same coordinate positions of the micro-sources, and adjusting the number of micro-sources located at different coordinates includes:

[0041] Mapping the number of micro-sources of different coordinates in each set of configuration parameters into a micro-source number matrix, and using the micro-source number matrix as chromosome X in the genetic algorithm to obtain an initial population;

[0042] The chromosome X in the initial population is cross-compiled and mutated multiple times until the iteration stop condition is met. During the cross-compilation and mutation generation process, the self-balancing rate corresponding to each X in the population, the sum of the total power generation of each micro-source, and the total configuration cost meet the above constraints;

[0043] From the population obtained after the iteration stops, the number of micro-sources located at different coordinates is determined according to the chromosome X corresponding to the total cost of the optimal configuration.

[0044] Optionally, the processor is used to perform multiple cross-compilations and mutation generation on chromosome X in the initial population, including:

[0045] Perform multiple cross-compilations and mutations on the chromosomes X in the initial population, wherein during the cross-compilation and mutation process, the chromosomes X whose first adaptive function modulus does not meet the preset cost threshold and the chromosomes X whose second adaptive function modulus does not meet the required total power are deleted from the population respectively;

[0046] The first adaptive function is X*Y, the second adaptive function is X*Z, Y is the configuration cost of each micro source, and Z is the total power generated by each micro source.

[0047] Optionally, the processor is configured to determine whether an iteration stop condition is satisfied, including:

[0048] When it is determined that the number of cross-compilation and variation generation reaches a preset number K, it is determined that an iteration stop condition is satisfied; or

[0049] When it is determined that in each cross-compilation and mutation generated population, the total configuration cost corresponding to at least one chromosome X is lower than the target configuration cost, it is determined that the iteration stop condition is met.

[0050] Optionally, the processor is used to determine the configuration parameters for initializing the microgrid by inputting the total power generation and the total power demand of each micro source within the expected usage time into a data planning and solving model, and the data planning and solving model is used to determine multiple sets of configuration parameters that are not less than the total power demand based on the total power generation of each micro source as the configuration parameters for initializing the microgrid.

[0051] Optionally, the processor is used to determine the total power generated by each micro source within the expected usage time according to the meteorological parameters of the regions corresponding to the different coordinates, including:

[0052] Inputting the meteorological parameters within the period into the meteorological simulation software to obtain micro-source power generation parameters, wherein the micro-source power generation parameters include: wind speed, air density, and irradiance;

[0053] The total power generation of each micro source is determined according to the micro source power generation parameters.

[0054] Optionally, the processor is used to determine the total power generation of each micro source according to the micro source power generation parameter, including at least one of the following steps:

[0055] The total power generated by the wind turbine is determined using the following formula: The P 风 is the power generated by the wind turbine, ρ is the air density, and C p is the wind energy utilization coefficient, v is the wind speed, and R is the radius of the wind turbine blade;

[0056] The total power generated by the photovoltaic panel is determined using the following formula: The P 光 is the power generated by the photovoltaic panel, and f pv is the power derating factor, the P rate is the rated power of the photovoltaic panel, A is the irradiance, and A S is the irradiance at rated power.

[0057] A third aspect of the present invention provides a microgrid configuration device, the device comprising the following modules:

[0058] A configuration parameter determination module is used to obtain the expected usage time and total power demand of the microgrid, and initialize the configuration parameters of the microgrid, wherein the configuration parameters include the coordinates of each microsource and the number of microsources located at different coordinates;

[0059] A total power generation determination module, used to determine the total power generation of each micro source within the expected use time according to the meteorological parameters of the areas corresponding to the different coordinates;

[0060] The micro-source number adjustment module is used to adjust the number of micro-sources located at different coordinates based on the constraint conditions that the self-balancing rate is within a preset range, the sum of the total power generated by the micro-sources meets the required total power, and the total configuration cost meets the preset cost threshold, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro-source, and the self-balancing rate is the ratio of the power provided by the microgrid in a preset period of time to the total power required for the operation of each micro-source.

[0061] The configuration parameter determination module is also used to initialize at least one set of configuration parameters with the same coordinate position of each micro-source, and also includes performing at least one of the following steps to update the configuration parameters:

[0062] Delete the configuration parameters whose self-balancing rates are not within the preset range among multiple sets of configuration parameters;

[0063] Delete the configuration parameters whose total configuration cost does not meet the preset cost threshold among the multiple sets of configuration parameters;

[0064] Among the multiple sets of configuration parameters, the configuration parameters whose sum of the total power generated by each micro source does not meet the required total power are deleted.

[0065] The micro-source number adjustment module is also used to initialize at least one set of configuration parameters of the same micro-source coordinate position. Adjusting the number of micro-sources located at different coordinates includes:

[0066] Mapping the number of micro-sources of different coordinates in each set of configuration parameters into a micro-source number matrix, and using the micro-source number matrix as chromosome X in the genetic algorithm to obtain an initial population;

[0067] The chromosome X in the initial population is cross-compiled and mutated multiple times until the iteration stop condition is met. During the cross-compilation and mutation generation process, the self-balancing rate corresponding to each X in the population, the sum of the total power generation of each micro-source, and the total configuration cost meet the above constraints;

[0068] From the population obtained after the iteration stops, the number of micro-sources located at different coordinates is determined according to the chromosome X corresponding to the total cost of the optimal configuration.

[0069] The micro-source number adjustment module is also used to perform multiple cross-compilations and mutations on chromosome X in the initial population, including:

[0070] Perform multiple cross-compilations and mutations on the chromosomes X in the initial population, wherein during the cross-compilation and mutation process, the chromosomes X whose first adaptive function modulus does not meet the preset cost threshold and the chromosomes X whose second adaptive function modulus does not meet the required total power are deleted from the population respectively;

[0071] The first adaptive function is X*Y, the second adaptive function is X*Z, Y is the configuration cost of each micro source, and Z is the total power generated by each micro source.

[0072] The micro-source number adjustment module is also used to determine whether the iteration stop conditions are met, including:

[0073] When it is determined that the number of cross-compilation and variation generation reaches a preset number K, it is determined that an iteration stop condition is satisfied; or

[0074] When it is determined that in each cross-compilation and mutation generated population, the total configuration cost corresponding to at least one chromosome X is lower than the target configuration cost, it is determined that the iteration stop condition is met.

[0075] The configuration parameter determination module is also used to initialize the configuration parameters of the microgrid by inputting the total power generation and the total power demand of each micro source within the expected usage time into the data planning solution model. The data planning solution model is used to determine multiple sets of configuration parameters that are not less than the total power demand according to the total power generation of each micro source as the configuration parameters for initializing the microgrid.

[0076] The total power generation determination module is also used to determine the total power generation of each micro source within the expected use time according to the meteorological parameters of the areas corresponding to the different coordinates, including:

[0077] Inputting the meteorological parameters within the period into the meteorological simulation software to obtain micro-source power generation parameters, wherein the micro-source power generation parameters include: wind speed, air density, and irradiance;

[0078] The total power generation of each micro source is determined according to the micro source power generation parameters.

[0079] The total power generation determination module is also used to determine the total power generation of each micro source according to the micro source power generation parameters, including at least one of the following steps:

[0080] The total power generated by the wind turbine is determined using the following formula: The P 风 is the power generated by the wind turbine, ρ is the air density, and C p is the wind energy utilization coefficient, v is the wind speed, and R is the radius of the wind turbine blade;

[0081] The total power generated by the photovoltaic panel is determined using the following formula: The P 光 is the power generated by the photovoltaic panel, and f pv is the power derating factor, the P rate is the rated power of the photovoltaic panel, A is the irradiance, and A S is the irradiance at rated power.

[0082] A fourth aspect of the present invention provides a computer medium having a computer program stored thereon, wherein the computer program, when executed by a processor, is used to implement the microgrid configuration method provided by the first aspect of the present invention.

[0083] By utilizing a microgrid configuration method and device provided by the present invention, various characteristics of the micro-source, energy storage system and load of the microgrid can be fully considered, and changes in various key parameters throughout the life cycle of the microgrid can be taken into account. Even when the system is operating under different working conditions, the sustainable power supply of the load can be guaranteed through a reasonable number configuration between the micro-source and the energy storage device, and the constraints affecting the system optimization are fully considered. Under the condition of saving costs as much as possible, the constraints of various aspects are met, and the optimal microgrid optimization configuration plan is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 A flowchart of steps of a microgrid configuration method;

[0085] Figure 2 A complete step flow chart of a microgrid configuration method;

[0086] Figure 3 A schematic diagram of a microgrid configuration;

[0087] Figure 4 A schematic diagram of the modules of a microgrid configuration. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0089] The following is a further detailed description of the application embodiments in conjunction with the accompanying drawings of the specification. It should be understood that the embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0090] For ease of understanding, the terms involved in the embodiments of the present invention are explained below:

[0091] 1) Self-balancing coefficient: The grid-connected microgrid is connected to the grid and can be provided with a certain amount of power by the grid. The proportion of load that the grid-connected microgrid can supply by itself reflects its power supply capacity and its dependence on the grid to a certain extent. The proportion of load that the grid-connected microgrid can supply by itself within a certain period is defined as the self-balancing coefficient.

[0092] 2) Genetic Algorithm is a computational model of biological evolution that simulates the natural selection and genetic mechanism of Darwin's theory of biological evolution. It is a method of searching for the optimal solution by simulating the natural evolution process. The genetic algorithm starts with a population that represents the potential solution set of the problem, and a population consists of a certain number of individuals encoded by chromosomes. Each individual is actually an entity with characteristics of the chromosome. As the main carrier of genetic material, the chromosome is a collection of multiple genes. Its internal expression is a certain gene combination, which determines the external expression of the shape of the individual. At the beginning, it is necessary to realize the mapping from phenotype to genotype, that is, encoding. Because the work of imitating genetic encoding is very complicated, we often simplify it, such as binary encoding. After the first generation of population is generated, according to the principle of survival of the fittest and survival of the fittest, it evolves from generation to generation to produce better and better approximate solutions. In each generation, individuals are selected according to the fitness of individuals in the problem domain, and cross-compiled and mutated with the help of genetic operators of natural genetics to generate a population representing a new solution set. This process will result in the population being better adapted to the environment than the previous generation, just like natural evolution. The optimal individuals in the last generation of the population can be decoded and used as an approximate optimal solution to the problem.

[0093] An embodiment of the present invention provides a microgrid configuration method, wherein the specific structure of the microgrid includes the following devices:

[0094] Distributed micro-sources, load devices and power control devices, the distributed micro-sources and load devices are connected, and when the micro-grid is connected to the grid, the required load is provided by the main grid and the distributed micro-sources in the micro-grid;

[0095] Among them, the distributed micro-sources in this embodiment include: wind power generation system, photovoltaic power generation system, diesel power generation system and energy storage device. The microgrid includes a power generation data acquisition module for collecting power generation data of each power generation system, and sends the power generation information of each power generation system to the acquisition server. The acquisition module is used to detect various power generation information, such as: current instant power generation, daily power generation, total power generation and other parameters.

[0096] (1) Power generation model of wind power generation system:

[0097] The electrical energy of wind turbines is mainly converted from wind energy. The blades of wind turbines capture energy from the wind, convert it into rotational kinetic energy, and then convert the kinetic energy into electrical energy.

[0098] (2) Power generation model of photovoltaic power generation system:

[0099] Solar energy is one of the clean and abundant renewable energy sources. Making full use of solar energy can effectively alleviate the shortage of fossil energy and produce huge environmental and economic benefits. The power generated by a single photovoltaic cell is small, and the voltage and current are low. Photovoltaic cells are connected in series and parallel to form a photovoltaic array. The photovoltaic array can obtain higher voltage and current and improve the quality of electric energy.

[0100] (3) Power generation model of diesel generator system:

[0101] As a main control DG (Distributed Generation), diesel generators are becoming increasingly important and can make up for the lack of system power. A certain number of diesel generators will greatly enhance the stability of independent operation of the system. Although its power generation cost is high and causes serious environmental pollution, its power supply reliability is high and power generation is not restricted by the environment. It is generally used as a controllable power source.

[0102] Energy storage devices are controllable mobile micro-sources. There are several types of energy storage devices: 1) Physical energy storage: using the principle of physical work to convert electrical energy into other forms of energy, but the initial investment cost is high, and there are also requirements for site selection. It is often used for peak load regulation and frequency regulation of power systems; 2) Electromagnetic energy storage: using the principle of magnetic field conversion, converting electrical energy into electric field energy and magnetic field energy. This method has a relatively fast power response and a long life, but has defects such as low energy storage and low density. It is often used for power regulation and load smoothing; 3) Electrochemical energy storage: using the electrodes in the battery and the solutes in the capacitor liquid to undergo a reversible chemical reaction to convert electrical energy into chemical energy. This method has a good energy conversion rate and maturity, and is widely used in the market. Electrochemical energy storage has the advantages of fast discharge, long working time, and high energy density, so it is used in the system's "peak load shaving and valley filling" and power stability regulation. Some technical parameters of the energy storage device are shown in Table 1.

[0103] Table 1 Technical parameters of energy storage device

[0104]

[0105]

[0106] Considering factors such as the maturity of energy storage device technology, cost adaptability and service life, and combining experience and actual conditions to select a model, this embodiment uses lead-acid batteries as energy storage devices.

[0107] Lead-acid batteries generally have the following important parameters: 1) State of Charge (SOC); 2) Rated capacity; 3) Depth of Discharge (DOD); 4) Number of cycles. Because the internal temperature of the battery is constantly changing during use and affects the discharge effect of the battery.

[0108] The load device is connected to the load equipment to collect the load power, power factor, voltage value, current value, frequency, harmonics, cumulative required electric energy, peak and valley power consumption data, etc.

[0109] Specifically, in the microgrid system, the load is divided into static load and dynamic load. Static load: The system needs to maintain power supply for a long time. When the system power is insufficient, consider partially cutting out; Dynamic load: Use the system real-time electricity price to adjust the transfer in or out of dynamic load to improve the stability of system power supply. When the system power supply is insufficient, cut off the interruptible load to ensure the stable operation of important loads; when the active power in the power system cannot be absorbed, use dynamic load to absorb and improve power utilization.

[0110] The collected data is transmitted to the power control device, which organizes and compiles the data after receiving it to optimize the capacity configuration between source, storage and load.

[0111] The embodiment of the present invention provides a microgrid configuration method for configuring the number of each microsource in the distributed microsources to optimize the configuration cost of the microgrid. Figure 1 As shown, the following steps are included:

[0112] Step S101, obtaining the estimated usage time and total power demand of the microgrid, and initializing the configuration parameters of the microgrid, wherein the configuration parameters include the coordinates of each microsource and the number of microsources located at different coordinates;

[0113] First, the distribution coordinates of each micro-source in the micro-grid and the estimated use time of the micro-grid are determined, and the total power demand corresponding to the power receiving end connected to the micro-grid is determined.

[0114] Specifically, the configuration parameters of the initialized microgrid are determined by inputting the total power generation and total power demand of each micro source within the expected usage time into the data planning and solving model. The data planning and solving model is used to determine multiple sets of configuration parameters that are not less than the total power demand according to the total power generation of each micro source as the configuration parameters of the initialized microgrid.

[0115] The total power generated by each micro-source includes: wind turbine power, photovoltaic power and diesel generator power.

[0116] Determine the total power generated by the micro-sources using the distribution coordinates of each micro-source and the estimated usage time of the micro-grid;

[0117] Specifically, the data planning and solving model is an inner mixed integer programming solving software (CPLEX, constraint programming). By using the inner mixed integer programming solving software, multiple sets of configuration parameters not less than the required total power are obtained as the configuration parameters for initializing the microgrid within a certain simulation step.

[0118] The configuration parameters of the initialized microgrid are determined by inputting the total power generation and total power demand of each micro source within the expected usage time into the data planning and solving model. The data planning and solving model is used to determine multiple sets of configuration parameters that are not less than the total power demand according to the total power generation of each micro source as the configuration parameters of the initialized microgrid.

[0119] As an optional implementation, initializing at least one set of configuration parameters with the same coordinate position of each micro-source further includes performing at least one of the following steps to update the configuration parameters:

[0120] Delete the configuration parameters whose self-balancing rates are not within the preset range among multiple sets of configuration parameters;

[0121] Delete the configuration parameters whose total configuration cost does not meet the preset cost threshold among the multiple sets of configuration parameters;

[0122] Among the multiple sets of configuration parameters, the configuration parameters whose sum of the total power generated by each micro source does not meet the required total power are deleted, and the multiple sets of configuration parameters are the configuration parameters of the initialized microgrid.

[0123] Step S102, determining the total power generated by each micro-source within the expected usage time according to the meteorological parameters of the regions corresponding to the different coordinates;

[0124] Input the meteorological parameters of the regions corresponding to different coordinates within a period of time into the meteorological simulation software to obtain the micro-source power generation parameters, wherein the micro-source power generation parameters include: wind speed, air density, and irradiance;

[0125] After determining the coordinates of each micro-source, the coordinate values ​​are input into the NASA Meteorological Bureau website to query the meteorological parameters for a period of time. The meteorological parameters for a period of time include original meteorological parameters such as wind speed, light intensity, and temperature. The meteorological parameters for a period of time are input into the Homer meteorological simulation software for simulation to obtain the micro-source power generation parameters for calculating the total power generation of the micro-source. The micro-source power generation parameters include wind speed, air density, and irradiance. The micro-source power generation parameters are not limited to the above data. Those skilled in the art can obtain other parameters related to micro-source power generation, which are not limited here.

[0126] The total power generation of each micro source is determined according to the micro source power generation parameters. In this embodiment, the micro source includes: a wind turbine, a photovoltaic panel and a diesel generator.

[0127] Determining the total power generation of each micro-source according to the micro-source power generation parameters includes at least one of the following steps:

[0128] The total power generated by the wind turbine is determined using the following formula: The P 风 is the power generated by the wind turbine, ρ is the air density, and C p is the wind energy utilization coefficient, v is the wind speed, and R is the radius of the wind turbine blade;

[0129] As an optional implementation, the power generation of the wind turbine can actually be calculated using a more complex calculation method, and the result is more accurate, as shown in the following formula:

[0130]

[0131] In the above formula, P t (v) is the output power at time t; P r is the rated power of the wind turbine; vci is the cut-in wind speed; v r is the rated wind speed; v co To cut out wind speed.

[0132] Because wind energy is random and intermittent, and is easily affected by geography and space, the air density at different spatial locations will affect the output power of wind turbines. The factors that affect the output power of wind turbines mainly include:

[0133] 1) Influence of actual height wind speed:

[0134] The wind speed is calculated based on the height of the wind turbine outline, but the actual wind speed is different from the wind speed at the wind turbine outline height. If the test point height is different from the wind turbine test point height, errors will occur. It is necessary to accurately convert the wind speed through logarithmic and exponential laws to obtain the actual accurate wind speed.

[0135] The expression for wind speed conversion using the logarithmic law is:

[0136]

[0137] The expression for wind speed conversion using the exponential law is:

[0138]

[0139] In the formula, v 机 Measure wind speed for wind turbine profile height; v 测 is the wind speed of the wind turbine at the actual speed measurement point; Z 机 is the height of the wind turbine profile; Z 测 is the actual measuring point height of the wind turbine; Z0 is the surface roughness; α is the surface roughness factor, which is 0.14-0.25. It is generally 1 / 7 in open and stable areas. Table 2 below is the common surface roughness data;

[0140] Table 2 Common surface roughness

[0141] Terrain description <![CDATA[Ground roughness Z0]]> Very smooth surface, ice 0.00001 Calm sea 0.0002 Windblown sea 0.0005 Ice and snow surface 0.003 grassland 0.008 Rough Grass 0.010 Fallow land 0.03 Planting Field 0.05 A small number of trees 0.10 Lots of trees and some buildings 0.25 Forests and woodlands 0.5 Suburbs 1.5 City center, high-rise buildings 3.0

[0142] 2) Influence of air density:

[0143] The parameters of the wind turbine output power model are obtained by fitting the output power characteristic curve provided by the wind turbine manufacturer. 3 ) is tested under normal conditions. According to the actual local air density ρ, if it is above ±0.05, it needs to be recalculated. Generally, the average air density ratio method is used for correction to avoid large errors. The specific correction method will not be repeated here.

[0144] The total power generated by the photovoltaic panel is determined using the following formula: The P 光 is the power generated by the photovoltaic panel, and f pv is the power derating factor, the P rate is the rated power of the photovoltaic panel, A is the irradiance, and A S is the irradiance at rated power.

[0145] The above is a method for calculating the total power generation of photovoltaic panels without considering temperature. When considering temperature, in order to reduce the error, it is necessary to consider the actual light intensity, ambient temperature, and wind speed. The actual temperature expression of the photovoltaic cell surface is:

[0146] T=T α +θ G [1+θ T T α ](1-θ W v)A

[0147] A=S×θ

[0148]

[0149] Where, T α is the actual temperature of the photovoltaic cell; α p is the power temperature coefficient, generally -0.47% / ℃; T is the actual temperature of the photovoltaic cell surface; T STC is the temperature of the photovoltaic system surface under standard conditions; θ G ,θ T ,θ W The values ​​are 0.0138, 0.031, and 0.042; S is the radiation intensity on the surface of the photovoltaic cell under natural conditions; θ is the tilt angle of the photovoltaic array; and v is the actual wind speed.

[0150] Determine the total power generated by the diesel generator using the following formula:

[0151] F=F0P derate +F1P de

[0152] In the above formula, F is the fuel consumption; F1 is the fuel curve coefficient; P derate , P de They are the rated power and output power of the diesel generator respectively. Specifically, the power constraint expression of the diesel generator set is as follows:

[0153] P de (t)≤N de P derate

[0154] In the above formula, Pde (t) is the output power at time t; N de is the number of diesel generators.

[0155] Step S103, with the self-balancing rate being within a preset range, the sum of the total power generated by the micro sources meeting the required total power, and the total configuration cost meeting the preset cost threshold as constraint conditions, adjusting the number of micro sources located at different coordinates, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro source, and the self-balancing rate is the ratio of the power provided by the microgrid during a preset period of time to the total power required for the operation of each micro source.

[0156] The self-balancing rate is a ratio of the power provided by the microgrid during a preset period of time to the total power required for the operation of each micro-source.

[0157] The grid-connected microgrid is connected to the grid and can be supported by the grid to a certain extent. The proportion of load that the grid-connected microgrid can supply by itself reflects its power supply capacity and its dependence on the grid to a certain extent. The proportion of load that the grid-connected microgrid can supply by itself within a certain period can be defined as the self-balancing rate, which can be expressed as:

[0158]

[0159] In the formula, R self is the self-balancing rate; E self E is the load supplied by the grid-connected microgrid itself; grid-to-load is the total amount of electricity flowing from the grid to the load; E total is the total load demand. The greater the self-balancing capacity of the grid-connected microgrid, that is, the greater the self-balancing rate value, the smaller the proportion of load supplied by the grid. Different expected levels of self-balancing rate will affect the final capacity configuration plan of the grid-connected microgrid.

[0160] Among them, in the microgrid system, the load is divided into static load and dynamic load. Static load: The system needs to maintain power supply for a long time. When the system power is insufficient, consider partially cutting out; dynamic load: Use the system real-time electricity price to adjust the transfer in or out of dynamic load to improve the stability of system power supply. When the system power supply is insufficient, cut off the interruptible load to ensure the stable operation of important loads; when the active power in the power system cannot be absorbed, use dynamic load to absorb and improve power utilization.

[0161] Some common load models are as follows:

[0162] 1) Important load model

[0163] Important loads are sensitive loads and have the highest priority in power supply at any time unless a fault occurs and the power is cut off. The model is as follows:

[0164]

[0165] In the above formula: P in (t) is the actual power of important load at time t; P im (t) is the rated power consumption of the important load at time t;

[0166] 2) Interruptible load model

[0167] Interruptible loads are non-sensitive loads. When the system power is insufficient or a system failure occurs, the load can be selectively cut off to ensure stable operation of the system. When working at rated power, after the system receives an interruption signal, the power supply of the interruptible load is 0. Its model is as follows:

[0168]

[0169] In the above formula, P IL,i (t) is the actual power consumption at time t; is the rated power consumption at time t;

[0170] (3) Load transfer model

[0171] The transfer-in and transfer-out power of the transferred load is:

[0172]

[0173] In the above formula, D il,t is the input power of the system load; D ol,t is the output power of the system load; is the maximum power transferred into the system load; The minimum power transferred into the system load.

[0174] The constraint formula for the transfer load operating power in the system is:

[0175] f i,t D 1,min ≤D 1,t +D il,t -D ol,t ≤f i,t D 1,max

[0176] In the above formula, 0 means there is no transfer load at that moment; 1 means there is transfer load at that moment;

[0177] The total amount of transferable load in the system is as follows:

[0178]

[0179] In the above formula, D total The total amount of load that can be transferred; Δt is the time step.

[0180] The total configuration cost includes: annual value cost, annual replacement cost, annual operation and maintenance cost, diesel generator environmental cost, diesel generator fuel cost, subsidy cost, power distribution cost and micro-source self-generation cost;

[0181] Among them, the total cost objective function of the grid-connected microgrid is as follows:

[0182]

[0183] In the above formula, i is the type of cost; F(x) is the total configuration cost; f i (x) is the cost of each configuration.

[0184] Among them, the specific calculation method of each configuration cost is:

[0185] (1) Equivalent annual cost: the product of the total initial investment cost of the micro-energy source and the capital recovery coefficient. The specific calculation formula is as follows.

[0186] f1=sff(i,y r )(N WT C WT1 +N PV C PV1 +N bat C bat1 +N de C de1 )

[0187]

[0188] In the above formula, N WT is the number of wind turbines; N bat N is the number of batteries; PV is the number of photovoltaic panels; N de is the number of diesel generators; C WT1 is the unit price of wind turbine; C bat1 is the battery unit price; C PV1 is the unit price of photovoltaic panels; C de1 is the unit price of diesel generator; sff(i,y r ) is the capital recovery coefficient; i is the actual loan interest rate; f is the inflation rate (%); i′ is the nominal loan interest rate; f1 is the equivalent annual cost; y r is the expected usage time of the microgrid.

[0189] (2) Annual replacement cost: The cost obtained by multiplying the replacement unit price of each micro-energy source by its corresponding annual replacement rate.

[0190] f2=N WT C WT2 +N PV CPV2 +N bat C bat2 +N de C de2

[0191] In the above formula, C WT2 , C bat2 , C PV2 , C de2 They are the replacement costs of wind turbines, batteries, photovoltaic panels, and diesel generators respectively. The following is the formula for calculating the replacement cost of wind turbines;

[0192]

[0193]

[0194]

[0195] The annual average replacement cost of other types of micro-sources is calculated using the same method, where β is the replacement coefficient; C rw is the replacement cost of each micro-source; C w is the residual equipment value of each micro source; y is the design life of each micro source; y rw is the residual lifetime of the micro source.

[0196] (3) Annual operation and maintenance cost: the cost of maintaining the micro-source each year.

[0197] f3=N WT C WT3 +N PV C PV3 +N bat C bat3 +N de C de3

[0198] In the above formula, C WT3 is the maintenance cost of wind turbine; C bat3 is the battery maintenance cost; C PV3 is the maintenance cost of photovoltaic panels; C de3 Maintenance costs for diesel generators.

[0199] (4) Environmental costs of diesel generators: It consists of three parts: environmental cost of power generation, pollution penalty cost and pollution control cost.

[0200]

[0201] In the above formula, α is the type of pollutant; k α is the pollutant emission coefficient; is the environmental value standard coefficient; β α is the emission penalty standard coefficient; θα is the pollution control standard coefficient. The environmental cost data of diesel generators are shown in Table 3:

[0202] Table 3 Environmental cost data of diesel generators

[0203]

[0204] (5) Annual fuel cost: the cost of fuel consumed by the micro-source for electricity generation each year.

[0205]

[0206] In the above formula, μ is the unit price of fuel; f5 is the total cost of fuel; ξ is the fuel consumption coefficient. Table 4 below shows the cost data of various micro-sources:

[0207] Table 4 Cost data of various micro-sources

[0208]

[0209] (6) Subsidy costs. Since the randomness and volatility of wind and solar micro-source output are not only large, but also the initial investment cost is relatively high, in order to alleviate the pressure of energy shortage, there is support and subsidies for new energy.

[0210]

[0211] In the above formula, λ is the subsidy coefficient of 0.015 yuan / kWh; f6 is the total subsidy amount.

[0212] (7) Distribution cost: The system can both transmit power and receive power support from the external network. When the system cannot meet the load power demand, it relies on the external network to provide power support to ensure the reliable operation of the system. It not only generates electricity sales income, but also generates electricity purchase expenses. That is, the distribution cost can be expressed as:

[0213] f7=C1E in -C2E out

[0214] In the above formula, C1 is the purchase price of electricity; C2 is the sales price of electricity; E in is the total power purchased by the system; E out is the total electricity sold by the system.

[0215] (8) The cost of power generation from micro-sources: the cost required to generate one kilowatt-hour of electricity from various micro-sources.

[0216] f8=N PV C PV E PV +N WT C WT E WT +N bat C bat Ebat +N de C de E de

[0217] In the above formula, C PV is the cost of photovoltaic panel power generation; C WT is the wind turbine power generation cost, C bat Cost of charging the battery; E PV is the total power generation of each photovoltaic panel; E WT is the total power generation of each wind turbine; E bat The total charge capacity of each battery, the power generation cost of various micro sources is shown in Table 5 below:

[0218] Table 5 Data on power generation costs of micro-sources

[0219] Various micro sources Fan Photovoltaic array Battery charging and discharging Cost (yuan / kWh) 0.52 0.75 0.2

[0220] The number of micro sources located at different coordinates is adjusted based on the constraint conditions that the self-balancing rate is within a preset range, the sum of the total power generated by the micro sources meets the required total power, and the total configuration cost meets the preset cost threshold, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro source.

[0221] According to the number of micro sources in the configuration parameters of the microgrid, under the constraints that the preset range of the self-balancing rate is met, the sum of the total power generated by each micro source meets the required total power, and the total configuration cost meets the preset cost threshold, a solution is determined in which the number of micro sources meets the optimal total configuration cost, and the number of micro sources located at different coordinates is adjusted.

[0222] As an optional implementation, initializing at least one set of configuration parameters with the same coordinate position of each micro-source, and adjusting the number of micro-sources located at different coordinates includes:

[0223] Mapping the number of micro-sources of different coordinates in each set of configuration parameters into a micro-source number matrix, and using the micro-source number matrix as chromosome X in the genetic algorithm to obtain an initial population;

[0224] Among them, different coordinate positions correspond to different matrix bits of the micro-source number matrix, and non-zero values ​​in the matrix correspond to the number of micro-sources at different coordinate positions in the microgrid. Multiple sets of configuration parameters are mapped into multiple micro-source number matrices. Each micro-source number matrix is ​​a chromosome X, and the initial population is composed of multiple sets of configuration parameters.

[0225] The chromosome X in the initial population is cross-compiled and mutated multiple times until the iteration stop condition is met. During the cross-compilation and mutation generation process, the self-balancing rate corresponding to each X in the population, the sum of the total power generation of each micro-source, and the total configuration cost meet the above constraints;

[0226] Since the number of chromosome X samples in the initial population is small, the initial population needs to be supplemented until the number of chromosome samples in the population reaches the target number.

[0227] Cross-compilation and mutation generation are methods for supplementing populations, wherein cross-compilation is to cross-replace certain bits of the matrix between two chromosomes X by random combination between each chromosome X, and compile the two chromosomes after cross-replacement to generate a new cross-compiled chromosome X. The cross-compilation method is not limited to the method described above, and any cross-compilation method used in genetic algorithms can be used as the cross-compilation method provided in this embodiment, which is not limited here;

[0228] The mutation is generated by modifying at least one codeword in different matrix positions of chromosome X. The modification method may be to invert at least one codeword of the original one, or to reset at least one codeword, which is not limited here, and the obtained new chromosome X is added to the population until the number of the population is restored to the original population number.

[0229] From the population obtained after the iteration stops, the number of micro-sources located at different coordinates is determined according to the chromosome X corresponding to the total cost of the optimal configuration.

[0230] The condition for stopping the iteration may be manually set by a person skilled in the art, or may be determined by a genetic algorithm according to the amount of data in the task.

[0231] Specifically, multiple cross-compilations and mutations are performed on chromosome X in the initial population, including:

[0232] Perform multiple cross-compilations and mutations on the chromosomes X in the initial population, wherein during the cross-compilation and mutation process, the chromosomes X whose first adaptive function modulus does not meet the preset cost threshold and the chromosomes X whose second adaptive function modulus does not meet the required total power are deleted from the population respectively;

[0233] The first adaptive function is X*Y, the second adaptive function is X*Z, Y is the configuration cost of each micro source, and Z is the total power generated by each micro source.

[0234] For example, the chromosome in the first adaptive function is [5, 10, 2, 4], where each matrix codeword represents the number of each micro-source. According to the configuration cost of each type of micro-source calculated in the above embodiment, the total configuration cost is obtained by summing up the costs in the matrix: F 风 (x)*5+F 光 (x)*10+F 柴 (x)*2+F 蓄(x)*4, determine whether the total configuration cost meets the preset cost threshold, where the preset cost threshold is related to the total configuration cost corresponding to each chromosome in the population, and the preset cost threshold is a certain proportion of the total configuration cost.

[0235] The chromosome in the second adaptive function is [5, 10, 2, 4], where each matrix codeword represents the number of each micro-source. According to the total power generation of each type of micro-source calculated in the above embodiment, the total configuration cost is obtained by accumulating the costs of each item in the matrix: P 风 *5+P 光 *10+P 柴 *2+P 蓄 *4. Determine whether the total output power of various micro-sources meets the required total power.

[0236] Wherein, determining whether the iteration stopping condition is satisfied includes:

[0237] When it is determined that the number of cross-compilation and mutation generation reaches a preset number of times K, it is determined that the iteration stop condition is satisfied. Specifically, when the preset number of times K is satisfied, the population is no longer cross-compiled and the mutation generation is no longer performed, and the configuration parameters corresponding to the optimal configuration total cost are determined in the population; or

[0238] When it is determined that in each population generated by cross-compilation and mutation, the total configuration cost corresponding to at least one chromosome X is lower than the target configuration cost, it is determined that the iteration stop condition is satisfied. Specifically, when there is at least one chromosome in the population with a cost lower than the target configuration cost, the population is no longer cross-compiled and mutated, and the configuration parameters corresponding to the optimal configuration total cost are determined in the population.

[0239] The embodiment of the present invention provides a complete microgrid configuration method, such as Figure 2 As shown, the following steps are included:

[0240] Step S201, determining the coordinate values ​​of the configured microgrid, determining the meteorological parameters for a period of time according to the coordinate values, and obtaining the micro-source power generation parameters through simulation calculation according to the meteorological parameters for a period of time;

[0241] Step S202, determining the total power generation of various types of micro-sources according to the micro-source power generation parameters;

[0242] Step S203, inputting the total power demand and the total power generated by various micro-sources into the data planning solution model, and obtaining multiple sets of configuration parameters not less than the total power demand as configuration parameters for initializing the microgrid;

[0243] Step S204, using a genetic algorithm to adjust the number of micro sources at different coordinates in the multiple sets of configuration parameters, with the self-balancing rate being within a preset range, the sum of the total power generated by the micro sources meeting the required total power, and the total configuration cost meeting a preset cost threshold as constraint conditions;

[0244] Step S205, determining the configuration parameters of the optimal configuration total cost among multiple sets of configuration parameters, and determining the number of micro sources located at different coordinates according to the configuration parameters to determine the final configuration scheme of each micro source in the microgrid.

[0245] By using the method provided in the embodiment of the present application, the various characteristics of the micro-source, energy storage system and load of the microgrid can be fully considered, and the changes in various key parameters throughout the life cycle of the microgrid can be taken into account. Even when the system is operating under different working conditions, the sustainable power supply of the load can be guaranteed through the reasonable quantity configuration between the micro-source and the energy storage device, and the constraints affecting the system optimization are fully considered. Under the condition of saving costs as much as possible, the constraints of various aspects are met to obtain the optimal microgrid optimization configuration plan.

[0246] An embodiment of the present invention provides a microgrid configuration device, the device is as follows Figure 3 As shown, the device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (full name in English: central processing units, abbreviated in English: CPU) 301 (for example, one or more processors) and memory 302, one or more storage media 303 storing application programs 304 or data 306 (for example, one or more mass storage devices). Among them, the memory 302 and the storage medium 303 can be short-term storage or permanent storage. The program stored in the storage medium 303 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the information processing device. Furthermore, the processor 301 can be configured to communicate with the storage medium 303 and execute a series of instruction operations in the storage medium 303 on the device 300.

[0247] The device 300 may also include one or more power supplies 309, one or more wired or wireless network interfaces 307, one or more input and output interfaces 308, and / or one or more operating systems 305, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.

[0248] The processor is used to read the instructions in the memory and execute the following process:

[0249] Obtain the estimated usage time and total power demand of the microgrid, and initialize the configuration parameters of the microgrid, wherein the configuration parameters include the coordinates of each microsource and the number of microsources at different coordinates;

[0250] Determine the total power generated by each micro-source within the expected usage time according to the meteorological parameters of the regions corresponding to the different coordinates;

[0251] The number of micro sources located at different coordinates is adjusted based on the constraint conditions that the self-balancing rate is within a preset range, the sum of the total power generated by the micro sources meets the required total power, and the total configuration cost meets the preset cost threshold, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro source, and the self-balancing rate is the ratio of the power provided by the microgrid in a preset period of time to the total power required for the operation of each micro source.

[0252] Optionally, the processor is used to initialize at least one set of configuration parameters with the same coordinate position of each micro-source, and further includes executing at least one of the following steps to update the configuration parameters:

[0253] Delete the configuration parameters whose self-balancing rates are not within the preset range among multiple sets of configuration parameters;

[0254] Delete the configuration parameters whose total configuration cost does not meet the preset cost threshold among the multiple sets of configuration parameters;

[0255] Among the multiple sets of configuration parameters, the configuration parameters whose sum of the total power generated by each micro source does not meet the required total power are deleted.

[0256] Optionally, the processor is used to initialize at least one set of configuration parameters for the same coordinate positions of the micro-sources, and adjusting the number of micro-sources located at different coordinates includes:

[0257] Mapping the number of micro-sources of different coordinates in each set of configuration parameters into a micro-source number matrix, and using the micro-source number matrix as chromosome X in the genetic algorithm to obtain an initial population;

[0258] The chromosome X in the initial population is cross-compiled and mutated multiple times until the iteration stop condition is met. During the cross-compilation and mutation generation process, the self-balancing rate corresponding to each X in the population, the sum of the total power generation of each micro-source, and the total configuration cost meet the above constraints;

[0259] From the population obtained after the iteration stops, the number of micro-sources located at different coordinates is determined according to the chromosome X corresponding to the total cost of the optimal configuration.

[0260] Optionally, the processor is used to perform multiple cross-compilations and mutation generation on chromosome X in the initial population, including:

[0261] Perform multiple cross-compilations and mutations on the chromosomes X in the initial population, wherein during the cross-compilation and mutation process, the chromosomes X whose first adaptive function modulus does not meet the preset cost threshold and the chromosomes X whose second adaptive function modulus does not meet the required total power are deleted from the population respectively;

[0262] The first adaptive function is X*Y, the second adaptive function is X*Z, Y is the configuration cost of each micro source, and Z is the total power generated by each micro source.

[0263] Optionally, the processor is configured to determine whether an iteration stop condition is satisfied, including:

[0264] When it is determined that the number of cross-compilation and variation generation reaches a preset number K, it is determined that an iteration stop condition is satisfied; or

[0265] When it is determined that in each cross-compilation and mutation generated population, the total configuration cost corresponding to at least one chromosome X is lower than the target configuration cost, it is determined that the iteration stop condition is met.

[0266] Optionally, the processor is used to determine the configuration parameters for initializing the microgrid by inputting the total power generation and the total power demand of each micro source within the expected usage time into a data planning and solving model, and the data planning and solving model is used to determine multiple sets of configuration parameters that are not less than the total power demand based on the total power generation of each micro source as the configuration parameters for initializing the microgrid.

[0267] Optionally, the processor is used to determine the total power generated by each micro source within the expected usage time according to the meteorological parameters of the regions corresponding to the different coordinates, including:

[0268] Inputting the meteorological parameters within the period into the meteorological simulation software to obtain micro-source power generation parameters, wherein the micro-source power generation parameters include: wind speed, air density, and irradiance;

[0269] The total power generation of each micro source is determined according to the micro source power generation parameters.

[0270] Optionally, the processor is used to determine the total power generation of each micro source according to the micro source power generation parameter, including at least one of the following steps:

[0271] The total power generated by the wind turbine is determined using the following formula: The P 风 is the power generated by the wind turbine, ρ is the air density, and C p is the wind energy utilization coefficient, v is the wind speed, and R is the radius of the wind turbine blade;

[0272] The total power generated by the photovoltaic panel is determined using the following formula: The P光 is the power generated by the photovoltaic panel, and f pv is the power derating factor, the P rate is the rated power of the photovoltaic panel, A is the irradiance, and A S is the irradiance at rated power.

[0273] An embodiment of the present invention provides a microgrid configuration device, such as Figure 4 As shown, the device includes the following modules:

[0274] The configuration parameter determination module 401 is used to obtain the estimated usage time and required total power of the microgrid, and initialize the configuration parameters of the microgrid, wherein the configuration parameters include the coordinates of each microsource and the number of microsources located at different coordinates;

[0275] A total power generation determination module 402 is used to determine the total power generation of each micro source within the expected use time according to the meteorological parameters of the regions corresponding to the different coordinates;

[0276] The micro-source number adjustment module 403 is used to adjust the number of micro-sources located at different coordinates based on the constraint conditions that the self-balancing rate is within a preset range, the sum of the total power generated by the micro-sources meets the required total power, and the total configuration cost meets the preset cost threshold, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro-source, and the self-balancing rate is the ratio of the power provided by the microgrid in a preset period of time to the total power required for the operation of each micro-source.

[0277] The configuration parameter determination module 401 is also used to initialize at least one set of configuration parameters with the same coordinate position of each micro-source, and also includes performing at least one of the following steps to update the configuration parameters:

[0278] Delete the configuration parameters whose self-balancing rates are not within the preset range among multiple sets of configuration parameters;

[0279] Delete the configuration parameters whose total configuration cost does not meet the preset cost threshold among the multiple sets of configuration parameters;

[0280] Among the multiple sets of configuration parameters, the configuration parameters whose sum of the total power generated by each micro source does not meet the required total power are deleted.

[0281] The micro-source number adjustment module 403 is further used to initialize at least one set of configuration parameters of the same micro-source coordinate positions. Adjusting the number of micro-sources located at different coordinates includes:

[0282] Mapping the number of micro-sources of different coordinates in each set of configuration parameters into a micro-source number matrix, and using the micro-source number matrix as chromosome X in the genetic algorithm to obtain an initial population;

[0283] The chromosome X in the initial population is cross-compiled and mutated multiple times until the iteration stop condition is met. During the cross-compilation and mutation generation process, the self-balancing rate corresponding to each X in the population, the sum of the total power generation of each micro-source, and the total configuration cost meet the above constraints;

[0284] From the population obtained after the iteration stops, the number of micro-sources located at different coordinates is determined according to the chromosome X corresponding to the total cost of the optimal configuration.

[0285] The micro-source number adjustment module 403 is also used to perform multiple cross-compilations and mutation generation on chromosome X in the initial population, including:

[0286] Perform multiple cross-compilations and mutations on the chromosomes X in the initial population, wherein during the cross-compilation and mutation process, the chromosomes X whose first adaptive function modulus does not meet the preset cost threshold and the chromosomes X whose second adaptive function modulus does not meet the required total power are deleted from the population respectively;

[0287] The first adaptive function is X*Y, the second adaptive function is X*Z, Y is the configuration cost of each micro source, and Z is the total power generated by each micro source.

[0288] The micro-source number adjustment module 403 is also used to determine whether the iteration stop condition is met, including:

[0289] When it is determined that the number of cross-compilation and variation generation reaches a preset number K, it is determined that an iteration stop condition is satisfied; or

[0290] When it is determined that in each cross-compilation and mutation generated population, the total configuration cost corresponding to at least one chromosome X is lower than the target configuration cost, it is determined that the iteration stop condition is met.

[0291] The configuration parameter determination module 401 is also used to initialize the configuration parameters of the microgrid by inputting the total power generation and the total power demand of each micro source within the expected usage time into the data planning solution model. The data planning solution model is used to determine multiple sets of configuration parameters that are not less than the total power demand according to the total power generation of each micro source as the configuration parameters for initializing the microgrid.

[0292] The total power generation determination module 402 is further used to determine the total power generation of each micro source within the expected use time according to the meteorological parameters of the regions corresponding to the different coordinates, including:

[0293] Inputting the meteorological parameters within the period into the meteorological simulation software to obtain micro-source power generation parameters, wherein the micro-source power generation parameters include: wind speed, air density, and irradiance;

[0294] The total power generation of each micro source is determined according to the micro source power generation parameters.

[0295] The total power generation determination module 402 is further used to determine the total power generation of each micro source according to the micro source power generation parameters, including at least one of the following steps:

[0296] The total power generated by the wind turbine is determined using the following formula: The P 风 is the power generated by the wind turbine, ρ is the air density, and C p is the wind energy utilization coefficient, v is the wind speed, and R is the radius of the wind turbine blade;

[0297] The total power generated by the photovoltaic panel is determined using the following formula: The P 光 is the power generated by the photovoltaic panel, and f pv is the power derating factor, the P rate is the rated power of the photovoltaic panel, A is the irradiance, and A S is the irradiance at rated power.

[0298] A fourth aspect of the present invention provides a computer medium having a computer program stored thereon, wherein the computer program, when executed by a processor, is used to implement the microgrid configuration method provided in the above embodiment.

[0299] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0300] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0301] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0302] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0303] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A microgrid configuration method, characterized in that: The method comprises: Obtain the estimated usage time and total power demand of the microgrid, and initialize the configuration parameters of the microgrid, wherein the configuration parameters include the coordinates of each microsource and the number of microsources at different coordinates; Determine the total power generated by each micro-source within the expected usage time according to the meteorological parameters of the regions corresponding to the different coordinates; The number of micro sources located at different coordinates is adjusted based on the constraint conditions that the self-balancing rate is within a preset range, the sum of the total power generated by each micro source meets the required total power, and the total configuration cost meets the preset cost threshold, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro source, and the self-balancing rate is the ratio of the power provided by the micro grid during a preset period of time to the total power required for the operation of each micro source; Wherein, initializing at least one set of configuration parameters of the same coordinate position of each micro-source, and adjusting the number of micro-sources located at different coordinates comprises: Mapping the number of micro-sources of different coordinates in each set of configuration parameters into a micro-source number matrix, and using the micro-source number matrix as chromosome X in the genetic algorithm to obtain an initial population; The chromosome X in the initial population is cross-compiled and mutated multiple times until the iteration stop condition is met. During the cross-compilation and mutation generation process, the self-balancing rate corresponding to each X in the population, the sum of the total power generation of each micro-source, and the total configuration cost meet the above constraints; From the population obtained after the iteration stops, the number of micro-sources located at different coordinates is determined according to the chromosome X corresponding to the total cost of the optimal configuration.

2. The method according to claim 1, characterized in that Initializing at least one set of configuration parameters with the same coordinate position of each micro-source, and also includes performing at least one of the following steps to update the configuration parameters: Delete the configuration parameters whose self-balancing rates are not within the preset range among multiple sets of configuration parameters; Delete the configuration parameters whose total configuration cost does not meet the preset cost threshold among the multiple sets of configuration parameters; Among the multiple sets of configuration parameters, the configuration parameters whose sum of the total power generated by each micro source does not meet the required total power are deleted.

3. The method according to claim 1, characterized in that Perform multiple cross-compilations and mutations on chromosome X in the initial population, including: Perform multiple cross-compilations and mutations on the chromosomes X in the initial population, wherein during the cross-compilation and mutation process, the chromosomes X whose first adaptive function modulus does not meet the preset cost threshold and the chromosomes X whose second adaptive function modulus does not meet the required total power are deleted from the population respectively; The first adaptive function is X*Y, the second adaptive function is X*Z, Y is the configuration cost of each micro source, and Z is the total power generated by each micro source.

4. The method according to claim 1, characterized in that: Determine whether the iteration stopping conditions are met, including: When it is determined that the number of cross-compilation and variation generation reaches a preset number K, it is determined that an iteration stop condition is satisfied; or When it is determined that in each cross-compilation and mutation generated population, the total configuration cost corresponding to at least one chromosome X is lower than the target configuration cost, it is determined that the iteration stop condition is met.

5. The method according to claim 1, characterized in that The configuration parameters of the initialized microgrid are determined by inputting the total power generation and total power demand of each micro source within the expected usage time into the data planning and solving model. The data planning and solving model is used to determine multiple sets of configuration parameters that are not less than the total power demand according to the total power generation of each micro source as the configuration parameters of the initialized microgrid.

6. The method according to claim 1, characterized in that The determining, according to the meteorological parameters of the regions corresponding to the different coordinates, the total power generated by each micro-source within the expected usage time includes: Input meteorological parameters within a period of time into meteorological simulation software to obtain micro-source power generation parameters, wherein the micro-source power generation parameters include: wind speed, air density, and irradiance; The total power generation of each micro source is determined according to the micro source power generation parameters.

7. The method according to claim 6, characterized in that Determining the total power generation of each micro-source according to the micro-source power generation parameters comprises at least one of the following steps: The total power generated by the wind turbine is determined using the following formula: The P 风 is the power generated by the wind turbine, ρ is the air density, and C p is the wind energy utilization coefficient, v is the wind speed, and R is the radius of the wind turbine blade; The total power generated by the photovoltaic panel is determined using the following formula: The P 光 is the power generated by the photovoltaic panel, and f pv is the power derating factor, the P rate is the rated power of the photovoltaic panel, A is the irradiance, and A S is the irradiance at rated power.

8. A microgrid configuration device, characterized in that: The device comprises: A memory for storing instructions; The processor is configured to read the instructions in the memory and execute the following processes: Obtain the estimated usage time and total power demand of the microgrid, and initialize the configuration parameters of the microgrid, wherein the configuration parameters include the coordinates of each microsource and the number of microsources at different coordinates; Determine the total power generated by each micro-source within the expected usage time according to the meteorological parameters of the regions corresponding to the different coordinates; The number of micro sources located at different coordinates is adjusted based on the constraint conditions that the self-balancing rate is within a preset range, the sum of the total power generated by each micro source meets the required total power, and the total configuration cost meets the preset cost threshold, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro source, and the self-balancing rate is the ratio of the power provided by the micro grid during a preset period of time to the total power required for the operation of each micro source; Wherein, initializing at least one set of configuration parameters with the same coordinate position of each micro-source, and adjusting the number of micro-sources located at different coordinates comprises: Mapping the number of micro-sources of different coordinates in each set of configuration parameters into a micro-source number matrix, and using the micro-source number matrix as chromosome X in the genetic algorithm to obtain an initial population; The chromosome X in the initial population is cross-compiled and mutated multiple times until the iteration stop condition is met. During the cross-compilation and mutation generation process, the self-balancing rate corresponding to each X in the population, the sum of the total power generation of each micro-source, and the total configuration cost meet the above constraints; From the population obtained after the iteration stops, the number of micro-sources located at different coordinates is determined according to the chromosome X corresponding to the total cost of the optimal configuration.

9. A microgrid configuration device, characterized in that: The device comprises the following modules: A configuration parameter determination module is used to obtain the expected usage time and total power demand of the microgrid, and initialize the configuration parameters of the microgrid, wherein the configuration parameters include the coordinates of each microsource and the number of microsources located at different coordinates; A total power generation determination module, used to determine the total power generation of each micro source within the expected use time according to the meteorological parameters of the areas corresponding to the different coordinates; A micro-source number adjustment module, used to adjust the number of micro-sources located at different coordinates with the self-balancing rate being within a preset range, the sum of the total power generated by the micro-sources meeting the required total power, and the total configuration cost meeting a preset cost threshold as a constraint condition, wherein the total configuration cost is the sum of the configuration costs determined according to the total power generated by each micro-source, and the self-balancing rate is the ratio of the power provided by the microgrid in a preset period of time to the total power required for the operation of each micro-source; The micro-source number adjustment module is further used to initialize at least one set of configuration parameters of the same micro-source coordinate position, and adjusting the number of micro-sources located at different coordinates includes: Mapping the number of micro-sources of different coordinates in each set of configuration parameters into a micro-source number matrix, and using the micro-source number matrix as chromosome X in the genetic algorithm to obtain an initial population; The chromosome X in the initial population is cross-compiled and mutated multiple times until the iteration stop condition is met. During the cross-compilation and mutation generation process, the self-balancing rate corresponding to each X in the population, the sum of the total power generation of each micro-source, and the total configuration cost meet the above constraints; From the population obtained after the iteration stops, the number of micro-sources located at different coordinates is determined according to the chromosome X corresponding to the total cost of the optimal configuration.

10. A computer medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the microgrid configuration method according to any one of claims 1 to 7 is implemented.

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