Micro-grid optimization scheduling method and device for distributed energy

Abstract

The invention discloses a distributed energy micro-grid optimization scheduling method and device, and the method comprises the steps: constructing a distributed energy micro-grid system; an objective function, a penalty function and a plurality of constraint conditions of the distributed energy microgrid system are constructed, and the penalty function is determined according to the new energy consumption rate and the power grid interaction peak-valley difference; the objective function is optimized based on an enhanced dung beetle optimization algorithm, optimal equipment operation parameters of the distributed energy microgrid system are obtained, and the optimal equipment operation parameters meet multiple constraint conditions; and scheduling the distributed energy micro-grid system based on the optimal equipment operation parameters of the distributed energy micro-grid system. The distributed energy microgrid system in the target scene is efficiently utilized on the basis of ensuring new energy consumption, the risk that the stability of a power grid is threatened due to sharp increase of power purchase of the power grid after new energy consumption is completed is reduced, and therefore the stability of the microgrid is improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method and device for optimizing the scheduling of a distributed energy microgrid. Background Art

[0002] Microgrid refers to a small power system composed of a variety of distributed energy resources and loads, usually including solar photovoltaic, wind power, energy storage equipment, etc. The multi-objective optimization scheduling of microgrid refers to the coordinated scheduling of multiple optimization objectives, such as reducing costs, improving reliability, and reducing emissions, under the premise of meeting the safety and economy of microgrid operation. After the proposal of the "dual carbon" goal, the capacity of distributed power sources dominated by new energy has gradually increased. The local consumption of new energy and the high proportion of new energy have led to the challenge of the "canyon curve" in the power grid (after the consumption of new energy, the users' purchase of electricity from the power grid has increased sharply, resulting in a significant increase in the peak-to-valley difference of the power grid interaction). Due to the instability of new energy output, it is difficult for energy storage to be localized, and the local consumption of new energy is crucial. The current optimization scheduling algorithm only considers the consumption rate of new energy and user costs, but the balance between the consumption of new energy and the peak-to-valley difference of the power grid needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a distributed energy microgrid optimization scheduling method and device, which can improve the new energy consumption rate of the microgrid and reduce the peak-to-valley difference.

[0004] In a first aspect, the present application provides a distributed energy microgrid optimization scheduling method, including:

[0005] Build a distributed energy microgrid system;

[0006] Constructing an objective function, a penalty function and multiple constraints of a distributed energy microgrid system, wherein the penalty function is determined according to the new energy consumption rate and the peak-to-valley difference of the grid interaction;

[0007] Optimizing the objective function based on the enhanced dung beetle optimization algorithm to obtain the optimal equipment operating parameters of the distributed energy microgrid system, wherein the optimal equipment operating parameters satisfy the plurality of constraints;

[0008] The distributed energy microgrid system is scheduled based on optimal equipment operating parameters of the distributed energy microgrid system.

[0009] Optionally, the penalty function satisfies the following formula:

[0010] F=Y mp +Y lo

[0011]

[0012] Among them, F is the penalty function, Y mp is the new energy consumption rate; Y lo is the peak-to-valley difference of the grid interaction; are the predicted output values ​​of photovoltaic equipment and wind energy equipment in the distributed energy microgrid system respectively; P i pv , P i wt They are the actual output values ​​of photovoltaic equipment and wind power equipment respectively.

[0013] Optionally, the objective function is optimized based on the enhanced dung beetle optimization algorithm to obtain the optimal equipment operating parameters of the distributed energy microgrid system, including:

[0014] Input the objective function and initialize the population;

[0015] Update the location information of each population;

[0016] Calculate the states of each parameter after the position update, update the population fitness, and sort the non-dominated solutions and update the non-dominated solution set;

[0017] When the number of iterations reaches the maximum number of iterations, the current optimal non-dominated solution set is output to obtain the optimal equipment operating parameters of the distributed energy microgrid system.

[0018] Optionally, the inputting the objective function and performing population initialization includes:

[0019] First, the positions of four populations were randomly generated. The four populations were adult dung beetles, brood balls, young dung beetles, and thief dung beetles.

[0020] Calculate the fitness at the current position, save the current solution and use non-dominated sorting, where the solution that is not dominated by any solution is assigned to order one, the solution with only one dominant solution is assigned to order two, and the solution with only two dominant solutions is assigned to order three.

[0021] Optionally, updating the location information of each population includes:

[0022] Adult dung beetles update their location information according to the following formula:

[0023] x i (t+1)=x i (t)+α·k·x i (t-1)+b·Δx

[0024] Δx=|x i (t)-X W |

[0025] Among them, x i(t) represents the position information of the ith adult dung beetle at the tth iteration; α represents whether it deviates from the direction, which is assigned to -1 and 1 according to probability; k represents the deflection coefficient; b represents a constant; X w represents the global worst position; Δx simulates the light intensity;

[0026] The brooding ball updates the position information according to the following formula:

[0027] L * =max(X * (1-R), L)

[0028] U * =min(X * (1+R),U)

[0029] B i (t+1)=X * +b 1 ·(B i (t)-L * )+b 2 ·(B i (t)-U * )

[0030] Among them, X* represents the current local optimum; L* and U* represent the lower and upper limits of the region; R = 1-t / Tmax, Tmax is the maximum number of iterations; B represents the position information of the brooding ball, and b1 and b2 represent two independent random vectors;

[0031] The small dung beetle updates its position information according to the following formula:

[0032] x i (t+1)=x i (t)+C 1 ·(x i (t)-L b )+C 2 ·(x i (t)-U b )

[0033] Among them, L b and U b represents the lower and upper limits of the optimal foraging area of ​​the dung beetle; x i (t) represents the position information of the dung beetle, C1 is a random number following the normal distribution; C2 is a random vector.

[0034] Optionally, updating the location information of each population includes:

[0035] When encountering an insurmountable obstacle, the adult dung beetle adjusts its direction using the following formula:

[0036] x i (t+1)=x i (t)+tan(θ)·|x i (t)-x i (t-1)|

[0037] Among them, x i (t) represents the position information of the i-th adult dung beetle at the t-th iteration; θ represents the deflection angle.

[0038] In a second aspect, the microgrid optimization dispatching device for distributed energy provided by the present application includes:

[0039] The first building module is used to build a distributed energy microgrid system;

[0040] The second construction module is used to construct an objective function, a penalty function and a plurality of constraints of a distributed energy microgrid system, wherein the penalty function is determined according to the new energy consumption rate and the peak-to-valley difference of the grid interaction;

[0041] An optimization module, configured to optimize the objective function based on an enhanced dung beetle optimization algorithm to obtain optimal equipment operating parameters of the distributed energy microgrid system, wherein the optimal equipment operating parameters satisfy a plurality of the constraint conditions;

[0042] A scheduling module is used to schedule the distributed energy microgrid system based on the optimal equipment operating parameters of the distributed energy microgrid system.

[0043] On the third aspect, the electronic device provided in the present application includes a memory and a processor, the memory stores a computer program, and the processor is used to run the computer program in the memory to implement the steps in the distributed energy microgrid optimization scheduling method provided in the present application.

[0044] In a fourth aspect, the computer-readable storage medium provided in the present application stores a plurality of instructions, which are suitable for loading by a processor to implement the steps in the distributed energy microgrid optimization scheduling method provided in the present application.

[0045] In a fifth aspect, the computer program product provided in the present application includes a computer program or instructions, which, when executed by a processor, implement the steps in the distributed energy microgrid optimization scheduling method provided in the present application.

[0046] In this application, compared with the related art, the distributed energy microgrid optimization scheduling method includes: constructing a distributed energy microgrid system; constructing the objective function, penalty function and multiple constraints of the distributed energy microgrid system, wherein the penalty function is determined according to the new energy consumption rate and the peak-to-valley difference of the grid interaction; optimizing the objective function based on the enhanced dung beetle optimization algorithm to obtain the optimal equipment operating parameters of the distributed energy microgrid system, and the optimal equipment operating parameters meet multiple constraints; scheduling the distributed energy microgrid system based on the optimal equipment operating parameters of the distributed energy microgrid system. This application realizes the efficient use of the distributed energy microgrid system in the target scenario on the basis of ensuring the consumption of new energy, reducing the risk of threatening the stability of the grid due to the sharp increase in power purchases by the grid after the consumption of new energy, thereby improving the stability of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 This is a schematic diagram of a scenario of a microgrid optimization dispatching system for distributed energy provided in an embodiment of the present application;

[0049] Figure 2 This is a flow chart of an embodiment of a method for optimizing the scheduling of distributed energy microgrids provided in an embodiment of the present application;

[0050] Figure 3 It is a flowchart of another embodiment of the distributed energy microgrid optimization scheduling method provided in the embodiment of the present application;

[0051] Figure 4 It is a structural diagram of an embodiment of a microgrid optimization scheduling device for distributed energy provided in an embodiment of the present application;

[0052] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] It should be noted that the principles of the present application are illustrated by implementing them in an appropriate computing environment. The following description is based on the illustrated specific embodiments of the present application and should not be considered as limiting other specific embodiments of the present application that are not described in detail herein.

[0054] In the following description of the present application, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0055] In the following description of the present application, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0057] In order to improve the effect of distributed energy microgrid optimization scheduling, the embodiments of the present application provide a distributed energy microgrid optimization scheduling method, a distributed energy microgrid optimization scheduling device, an electronic device, a computer-readable storage medium, and a computer program product. The distributed energy microgrid optimization scheduling method can be executed by the distributed energy microgrid optimization scheduling device, or by an electronic device integrated with the distributed energy microgrid optimization scheduling device.

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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 those skilled in the art without creative work are within the scope of protection of this application.

[0059] Please refer to Figure 1 , the present application also provides a distributed energy microgrid optimization dispatching system, such as Figure 1 As shown, the electronic device 100 of the distributed energy microgrid optimization scheduling system, the electronic device 100 integrates the distributed energy microgrid optimization scheduling device provided by the present application.

[0060] Among them, the electronic device 100 can be any device equipped with a processor and has processing capabilities, such as mobile electronic devices with processors such as smart phones, tablet computers, PDAs, laptops, smart speakers, or fixed electronic devices with processors such as desktop computers, televisions, servers, industrial equipment, etc.

[0061] In addition, if Figure 1As shown, the distributed energy microgrid optimization dispatching system may further include a memory 200 for storing original data, intermediate data, and result data.

[0062] In the embodiment of the present application, the memory 200 can be a cloud storage. Cloud storage is a new concept extended and developed from the concept of cloud computing. A distributed cloud storage system (hereinafter referred to as the storage system) refers to a storage system that uses cluster applications, grid technology, and distributed storage file systems to bring together a large number of different types of storage devices (storage devices are also called storage nodes) in the network through application software or application interfaces to work together and provide external data storage and business access functions.

[0063] At present, the storage method of the storage system is: create a logical volume, and when creating a logical volume, allocate physical storage space for each logical volume. The physical storage space may be composed of disks of a storage device or several storage devices. The client stores data on a logical volume, that is, stores the data on the file system. The file system divides the data into many parts, each of which is an object. The object contains not only data but also additional information such as data identification (ID, ID entity). The file system writes each object into the physical storage space of the logical volume, and the file system records the storage location information of each object, so that when the client requests to access the data, the file system can allow the client to access the data according to the storage location information of each object.

[0064] The process of the storage system allocating physical storage space to a logical volume is as follows: based on the estimated capacity of the objects stored in the logical volume (this estimate often has a large margin relative to the actual capacity of the objects to be stored) and the grouping of independent redundant disk arrays (RAID, Redundant Array of Independent Disk), the physical storage space is divided into stripes in advance. A logical volume can be understood as a stripe, thereby allocating physical storage space to the logical volume.

[0065] It should be noted that Figure 1 The scenario diagram of the distributed energy microgrid optimization and scheduling system shown is only an example. The distributed energy microgrid optimization and scheduling system and the scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the distributed energy microgrid optimization and scheduling system and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0066] It should be noted that the serial numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0067] Please refer to Figure 2 , Figure 2 FIG. 1 is a flow chart of an embodiment of a method for optimizing the scheduling of distributed energy microgrids provided in an embodiment of the present application. Figure 2 As shown, the process of the distributed energy microgrid optimization scheduling method provided in this application is as follows:

[0068] 201. Build a distributed energy microgrid system.

[0069] In the implementation of this application, a high-proportion distributed energy scenario is established, the operating characteristics, costs and coordinated interaction methods of photovoltaic, wind power, gas turbines and energy storage are analyzed and modeled, and a distributed energy microgrid system is constructed. The distributed energy microgrid system includes multiple photovoltaic devices, multiple wind power devices, multiple gas turbines and multiple energy storage devices.

[0070] 202. The number of objective functions, penalty functions, and multiple constraints for constructing a distributed energy microgrid system.

[0071] Among them, the penalty function is determined according to the new energy consumption rate and the peak-to-valley difference of grid interaction.

[0072] In an embodiment of the present application, the multiple constraints include energy storage constraints, distributed power supply constraints, and power balance constraints of a distributed energy microgrid system.

[0073] Among them, the energy storage constraint condition satisfies the relationship shown in formula (1):

[0074]

[0075] Where: P i E,H (0) are the initial energy storage and upper and lower limits of energy storage of the energy storage device; P i E,H (t) are the current charging / discharging power and maximum power of the energy storage device respectively; T ch , T disch , They are the charge / discharge time and life span of the energy storage device.

[0076] Among them, the distributed power constraints satisfy the relationship shown in formula (2) and formula (3),

[0077]

[0078] in, are the maximum output power of photovoltaic equipment, wind power equipment, and gas turbine, respectively, i pv ,P i wt ,P i gb They are the actual output values ​​of photovoltaic equipment, wind equipment and gas turbines respectively.

[0079] Among them, the electrical balance constraint condition satisfies the relationship shown in formula (4),

[0080] P e +P b +P i pv +P i wt +P i gb +P i edischarge -P i echarge -P s =0 (4)

[0081] Among them, P e is the initial electrical load; P i pv ,P i wt ,P i gb ,P i edischarge ,P i echarge ,P b ,P s , which are the actual output values ​​of photovoltaic equipment, wind power equipment, gas turbine equipment, energy storage equipment discharge, energy storage equipment charging, power grid purchase of electricity, and power grid sales of electricity.

[0082] Among them, the penalty function satisfies the relationship shown in formulas (5), (6), and (7),

[0083] F=Y mp +Y lo (5)

[0084]

[0085] Among them, Y mp is the new energy consumption rate; Y lo is the peak-to-valley difference of the grid interaction; are the predicted output values ​​of photovoltaic equipment and wind power equipment respectively; P i pv , P i wtThey are the actual output values ​​of photovoltaic equipment and wind power equipment respectively.

[0086] In the embodiment of the present application, the objective function is shown in formulas (8)-(13),

[0087]

[0088] C bs_grid =c buy P b +c sell P s (9)

[0089]

[0090] Where: C bs_grid The cost of electricity sales and purchase; C pw_om is the operation and maintenance cost of wind and solar equipment; C fu_gas is the fuel cost; C sto_om is the energy storage operation cost; C co2 is the carbon trading cost, c sell 、c buy , P b , P s are the electricity selling and purchasing prices of the power grid and their corresponding electricity quantities; P i pv , are the power consumption and operating cost of the i-th and j-th equipment of wind power generation and photovoltaic power generation respectively; c gb is the gas turbine fuel cost, P i gb are the gas cost coefficient and electricity consumption of the i-th gas turbine respectively; η is the gas turbine electricity-gas conversion coefficient; c elec is the operating cost of electric energy storage, P i echarge , P i edischarge 、w sto are the charging and discharging power of the i-th energy storage device and the operation and maintenance cost coefficient of the energy storage device respectively; They are MG carbon emissions and carbon emission quotas, P i are the carbon emission trading price, the electricity consumption of the ith device and its carbon emission coefficient respectively.

[0091] 203. Based on the enhanced dung beetle optimization algorithm, the objective function is optimized to obtain the optimal equipment operating parameters of the distributed energy microgrid system. The optimal equipment operating parameters meet multiple constraints.

[0092] In the embodiment of the present application, the objective function is optimized based on the enhanced dung beetle optimization algorithm to obtain the optimal equipment operating parameters of the distributed energy microgrid system, including:

[0093] (1) Input the objective function and initialize the population.

[0094] In the embodiment of the present application, the objective function is input and the population is initialized, including: firstly, the positions of four populations are randomly generated, and the four populations are adult dung beetles, brood balls, small dung beetles and thief dung beetles; the fitness under the current position is calculated, the current solution is saved and non-dominated sorting is used, wherein the solution that is not dominated by any solution is assigned as the first order, the solution with only one dominant solution is assigned as the second order, and the solution with only two dominant solutions is assigned as the third order. And so on. Then, the solution is selected according to their order to improve the quality of the population.

[0095] (2) Update the location information of each population.

[0096] In the embodiment of the present application, the adult dung beetles use natural factors such as the intensity of the sun's light source and wind to change their travel routes, thereby ensuring that their dung balls roll in a straight path. Updating the position information of each population includes: the adult dung beetles update the position information according to the following formula (14) and formula (15):

[0097] x i (t+1)=x i (t)+α·k·x i (t-1)+b·Δx (14)

[0098] Δx=|x i (t)-X W | (15)

[0099] Among them, x i (t) represents the position information of the ith adult dung beetle at the tth iteration; α represents whether it deviates from the direction, which is assigned to -1 and 1 according to probability; k represents the deflection coefficient; b represents a constant; X w represents the global worst position; Δx simulates the light intensity.

[0100] When encountering an insurmountable obstacle, the adult dung beetle adjusts its direction using the following formula (16):

[0101] x i (t+1)=x i (t)+tan(θ)·|x i (t)-x i (t-1)| (16)

[0102] Among them, x i(t) represents the position information of the i-th adult dung beetle at the t-th iteration; θ represents the deflection angle.

[0103] The brooding ball is generated in a fixed spawning area, and the brooding ball updates its position information according to the following formulas (17)-(19):

[0104] L * =max(X * ·(1-R),L) (17)

[0105] U * =min(X * ·(1+R),U) (18)

[0106] B i (t+1)=X * +b 1 ·(B i (t)-L * )+b 2 ·(B i (t)-U * ) (19)

[0107] Among them, X* represents the current local optimum; L* and U* represent the lower and upper limits of the area; R = 1-t / Tmax, Tmax is the maximum number of iterations; B represents the position information of the brooding ball, and b1 and b2 represent two independent random vectors.

[0108] The dung beetle searches for food in the optimal foraging area and updates its location information according to the following formula:

[0109] x i (t+1)=x i (t)+C 1 ·(x i (t)-L b )+C 2 ·(x i (t)-U b ) (20)

[0110] Among them, L b and U b represents the lower and upper limits of the optimal foraging area of ​​the dung beetle; xi(t) represents the location information of the dung beetle, C1 is a random number following the normal distribution; C2 is a random vector.

[0111] The thief dung beetle updates its position information according to the following formula:

[0112] x i (t+1)=X b +S·g·(|x i (t)-X * |+|xi (t)-X b |) (21)

[0113] Where: x i (t) represents the position information of the thief dung beetle, g is a random vector following the normal distribution, and S is a constant.

[0114] (3) Calculate the states of each parameter after the position update, update the population fitness, and sort the non-dominated solutions and update the non-dominated solution set;

[0115] (4) When the number of iterations reaches the maximum number of iterations, the current optimal non-dominated solution set is output to obtain the optimal equipment operating parameters of the distributed energy microgrid system.

[0116] When the number of iterations does not reach the maximum number of iterations, the iteration is updated, the states of each parameter after the position update are calculated, the population fitness is updated, and the non-dominated sorting is performed and the non-dominated solution set is updated.

[0117] 204. Dispatching distributed energy microgrid system based on optimal equipment operating parameters of distributed energy microgrid system.

[0118] The optimal equipment operating parameter scheduling of the distributed energy microgrid system includes the actual output value of photovoltaic equipment, the actual output value of wind power equipment, the charging and discharging power of energy storage equipment, and the operation and maintenance cost coefficient of energy storage equipment, etc.

[0119] like Figure 3 As shown, specifically, a high-proportion distributed energy scenario is established, and a distributed energy system is modeled based on photovoltaic, wind, gas turbines and energy storage designs, and the characteristics and interaction capabilities of each module of the system are analyzed. An optimization scheduling model is constructed. Specifically, an objective function is established based on user costs, environmental friendliness, and equipment safety, and constraints and penalty functions are constructed based on equipment characteristics, new energy absorption, and peak-to-valley differences in the power grid. The algorithm is initialized, fitness is updated, non-dominated sorting is performed and non-dominated sets are saved, congestion is calculated, the positions of rolling ball dung beetles, brooding balls, small dung beetles, and thief dung beetles are updated, population fitness is updated, non-dominated sorting is performed and the solution set is updated to reach the maximum number of iterations, and the optimal scheduling plan is obtained.

[0120] The present application belongs to the technical field of coordinated optimization and dispatching of distributed energy, and specifically relates to a research on a microgrid optimization dispatching method considering a high proportion of distributed energy, which is used to ensure the balance of energy supply and demand and the stable operation of the system while improving the consumption of new energy. First, a high-proportion distributed energy scenario is established, and a distributed energy microgrid system is designed according to the target scenario to aggregate photovoltaic, wind, gas turbine and energy storage resources in the area. Secondly, the resources under each module in the energy system are modeled, and the equipment cost, output, energy storage characteristic factors and the coordination and interaction capabilities between devices are analyzed. Finally, the social benefits such as new energy consumption, user cost and economic benefits, and equipment safety are taken as objective functions, and the best optimization dispatching solution is found through the non-dominated sorting dung beetle optimizer (NSDBO).

[0121] This application efficiently utilizes the distributed energy microgrid system in the target scenario, reduces the risk of threatening the stability of the power grid due to a sharp increase in power purchases after the new energy is consumed, and applies this method to enterprises and even regions with good applicability, economy and sociality.

[0122] This application can alleviate the risk of large grid interaction peak-to-valley differences caused by the increase in new energy capacity. This application first considers the user's electricity purchase cost under time-of-use electricity prices, the safety of distributed power equipment, the equipment operating cost and environmental friendliness as the objective function. Secondly, constraints are set for each device in the target area, including the carbon emissions, operating time and characteristics of the equipment. The new energy consumption rate and the grid interaction peak-to-valley difference are used as penalty functions. The load control parameters are optimized through the NSDBO algorithm to obtain the operating results of each distributed power source under the optimal objective function, and realize the optimized scheduling and operation plan of microgrids with high consumption rate and low peak-to-valley difference.

[0123] In order to facilitate better implementation of the distributed energy microgrid optimization scheduling method provided in the embodiment of the present application, the embodiment of the present application also provides a distributed energy microgrid optimization scheduling device based on the above distributed energy microgrid optimization scheduling method. The meanings of the terms are the same as those in the above distributed energy microgrid optimization scheduling method. For specific implementation details, please refer to the description in the above method embodiment.

[0124] Please refer to Figure 4 , Figure 4 1 is a schematic diagram of a structure of an embodiment of a microgrid optimization scheduling device for distributed energy provided in an embodiment of the present application. The microgrid optimization scheduling device for distributed energy may include:

[0125] The first construction module 701 is used to construct a distributed energy microgrid system;

[0126] The second construction module 702 is used to construct an objective function, a penalty function and a plurality of constraints of a distributed energy microgrid system, wherein the penalty function is determined according to the new energy consumption rate and the peak-to-valley difference of the grid interaction;

[0127] An optimization module 703 is used to optimize the objective function based on the enhanced dung beetle optimization algorithm to obtain the optimal equipment operating parameters of the distributed energy microgrid system, and the optimal equipment operating parameters meet multiple constraints;

[0128] The scheduling module 704 is used to schedule the distributed energy microgrid system based on the optimal equipment operating parameters of the distributed energy microgrid system.

[0129] The specific implementation of each of the above modules can be found in the previous embodiments and will not be described in detail here.

[0130] In this application, compared with the related art, the distributed energy microgrid optimization scheduling method includes: constructing a distributed energy microgrid system; constructing the objective function, penalty function and multiple constraints of the distributed energy microgrid system, wherein the penalty function is determined according to the new energy consumption rate and the peak-to-valley difference of the grid interaction; optimizing the objective function based on the enhanced dung beetle optimization algorithm to obtain the optimal equipment operating parameters of the distributed energy microgrid system, and the optimal equipment operating parameters meet multiple constraints; scheduling the distributed energy microgrid system based on the optimal equipment operating parameters of the distributed energy microgrid system. This application realizes the efficient use of the distributed energy microgrid system in the target scenario on the basis of ensuring the consumption of new energy, reducing the risk of threatening the stability of the grid due to the sharp increase in power purchases by the grid after the consumption of new energy, thereby improving the stability of the microgrid.

[0131] Please refer to Figure 5 , Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0132] The electronic device may include components such as a processor 101 with one or more processing cores, a memory 102 with one or more computer-readable storage media, a power supply 103, and an input unit 104. Those skilled in the art will appreciate that the electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them:

[0133] The processor 101 is the control center of the electronic device, which uses various interfaces and lines to connect various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 102, and calling data stored in the memory 102. Optionally, the processor 101 may include one or more processing cores; optionally, the processor 101 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 101.

[0134] The memory 102 can be used to store software programs and modules. The processor 101 executes various functional applications and distributed energy microgrid optimization scheduling by running the software programs and modules stored in the memory 102. The memory 102 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 102 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 102 may also include a memory controller to provide the processor 101 with access to the memory 102.

[0135] The electronic device also includes a power supply 103 for supplying power to each component. Optionally, the power supply 103 can be logically connected to the processor 101 through a power management system, so as to manage charging, discharging, power consumption and other functions through the power management system. The power supply 103 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0136] The electronic device may further include an input unit 104, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0137] Although not shown, the electronic device may also include a display unit, an image acquisition element, etc., which will not be described in detail here. Specifically in this embodiment, the processor 101 in the electronic device will load the executable code corresponding to one or more computer programs into the memory 102 according to the following instructions, and the processor 101 will execute the steps in the distributed energy microgrid optimization scheduling method provided in the present application, such as:

[0138] The distributed energy microgrid optimization scheduling method includes: constructing a distributed energy microgrid system; constructing an objective function, a penalty function and multiple constraints of the distributed energy microgrid system, wherein the penalty function is determined according to the new energy consumption rate and the peak-to-valley difference of the grid interaction; optimizing the objective function based on the enhanced dung beetle optimization algorithm to obtain the optimal equipment operating parameters of the distributed energy microgrid system, and the optimal equipment operating parameters meet multiple constraints; scheduling the distributed energy microgrid system based on the optimal equipment operating parameters of the distributed energy microgrid system.

[0139] It should be noted that the electronic device provided in the embodiment of the present application belongs to the same concept as the microgrid optimization scheduling method for distributed energy in the above embodiment. The specific implementation process is detailed in the above related embodiments and will not be repeated here.

[0140] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program stored therein is executed on a processor of an electronic device provided in an embodiment of the present application, the processor of the electronic device executes the steps in the microgrid optimization scheduling method for distributed energy provided in the present application. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0141] The present application also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes various optional implementations of the above-mentioned distributed energy microgrid optimization scheduling method.

[0142] The above is a detailed introduction to a distributed energy microgrid optimization scheduling method and device provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0143] It should be noted that when the above embodiments of the present application are applied to specific products or technologies, the relevant data of the user is involved, and the user's permission or consent is required, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant region.

Claims

1. A distributed energy microgrid optimization scheduling method, characterized in that: The distributed energy microgrid optimization dispatching method comprises: Build a distributed energy microgrid system; Constructing an objective function, a penalty function and multiple constraints of a distributed energy microgrid system, wherein the penalty function is determined according to the new energy consumption rate and the peak-to-valley difference of the grid interaction; Optimizing the objective function based on the enhanced dung beetle optimization algorithm to obtain the optimal equipment operating parameters of the distributed energy microgrid system, wherein the optimal equipment operating parameters satisfy the plurality of constraints; The distributed energy microgrid system is scheduled based on optimal equipment operating parameters of the distributed energy microgrid system.

2. The distributed energy microgrid optimization scheduling method according to claim 1 is characterized in that: The penalty function satisfies the following formula: F=Y mp +Y lo Among them, F is the penalty function, Y mp is the new energy consumption rate; Y lo is the peak-to-valley difference of the grid interaction; are the predicted output values ​​of photovoltaic equipment and wind energy equipment in the distributed energy microgrid system respectively; P i pv , P i wt They are the actual output values ​​of photovoltaic equipment and wind power equipment respectively.

3. The distributed energy microgrid optimization scheduling method according to claim 1 is characterized in that: The objective function is optimized based on the enhanced dung beetle optimization algorithm to obtain the optimal equipment operating parameters of the distributed energy microgrid system, including: Input the objective function and initialize the population; Update the location information of each population; Calculate the states of each parameter after the position update, update the population fitness, and sort the non-dominated solutions and update the non-dominated solution set; When the number of iterations reaches the maximum number of iterations, the current optimal non-dominated solution set is output to obtain the optimal equipment operating parameters of the distributed energy microgrid system.

4. The distributed energy microgrid optimization scheduling method according to claim 3 is characterized in that: The inputting the objective function and initializing the population includes: First, the positions of four populations were randomly generated. The four populations were adult dung beetles, brood balls, young dung beetles, and thief dung beetles. Calculate the fitness at the current position, save the current solution and use non-dominated sorting, where the solution that is not dominated by any solution is assigned to order one, the solution with only one dominant solution is assigned to order two, and the solution with only two dominant solutions is assigned to order three.

5. The distributed energy microgrid optimization scheduling method according to claim 4 is characterized in that: The updating of the location information of each population includes: Adult dung beetles update their location information according to the following formula: x i (t+1)=x i (t)+α·k·x i (t-1)+b·Δx Δx=|x i (t)-X W | Among them, x i (t) represents the position information of the ith adult dung beetle at the tth iteration; α represents whether it deviates from the direction, which is assigned to -1 and 1 according to probability; k represents the deflection coefficient; b represents a constant; X w represents the global worst position; Δx simulates the light intensity; The brooding ball updates the position information according to the following formula: L * =max(X * ·(1-R),L) U * =min(X * ·(1+R),U) B i (t+1)=X * +b1·(B i (t)-L * )+b2·(B i (t)-U * ) Among them, X* represents the current local optimum; L* and U* represent the lower and upper limits of the region; R = 1-t / Tmax, Tmax is the maximum number of iterations; B represents the position information of the brooding ball, and b1 and b2 represent two independent random vectors; The small dung beetle updates its position information according to the following formula: x i (t+1)=x i (t)+C1·(x i (t)-L b )+C2·(x i (t)-U b ) Among them, L b and U b represents the lower and upper limits of the optimal foraging area of ​​the dung beetle; x i (t) represents the position information of the dung beetle, C1 is a random number following the normal distribution; C2 is a random vector.

6. The distributed energy microgrid optimization scheduling method according to claim 5 is characterized in that: The updating of the location information of each population includes: When encountering an insurmountable obstacle, the adult dung beetle adjusts its direction using the following formula: x i (t+1)=x i (t)+tan(θ)·|x i (t)-x i (t-1)| Among them, x i (t) represents the position information of the i-th adult dung beetle at the t-th iteration; θ represents the deflection angle.

7. A distributed energy microgrid optimization dispatching device, characterized in that: The distributed energy microgrid optimization dispatching device comprises: The first building module is used to build a distributed energy microgrid system; The second construction module is used to construct an objective function, a penalty function and a plurality of constraints of a distributed energy microgrid system, wherein the penalty function is determined according to the new energy consumption rate and the peak-to-valley difference of the grid interaction; An optimization module, configured to optimize the objective function based on an enhanced dung beetle optimization algorithm to obtain optimal equipment operating parameters of the distributed energy microgrid system, wherein the optimal equipment operating parameters satisfy a plurality of the constraint conditions; A scheduling module is used to schedule the distributed energy microgrid system based on the optimal equipment operating parameters of the distributed energy microgrid system.

8. An electronic device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the steps in the distributed energy microgrid optimization scheduling method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, which are suitable for loading by a processor to execute the steps in the distributed energy microgrid optimization scheduling method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps in the distributed energy microgrid optimization scheduling method described in any one of claims 1 to 6 are implemented.

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