Micro-grid cluster power scheduling method, device, equipment and medium

Abstract

The invention belongs to the technical field of micro-grid cluster power scheduling, and particularly relates to a micro-grid cluster power scheduling method, device, equipment and medium, and the method comprises the steps: determining a photovoltaic power generation model, an energy storage unit model and a load model; determining each cost of the micro-grid cluster; on the basis of each cost, by taking minimization of the economic cost of the micro-grid as a target, constructing a target function, and determining constraint conditions of the target function; based on the photovoltaic power generation model, the energy storage unit model, the load model and the constraint conditions, an improved snake algorithm is adopted to solve the target function, and an optimal scheduling scheme is obtained. The target function is optimized through the snake algorithm based on the elite reverse learning strategy and sine and cosine algorithm optimization, the local development capability is improved, the accuracy is improved, the calculation speed is increased, and a more economical scheduling scheme is found.

Description

Technical Field

[0001] This invention belongs to the field of microgrid cluster power dispatching technology, specifically relating to microgrid cluster power dispatching methods, devices, equipment, and media. Background Technology

[0002] With the increasing severity of global climate change and other issues, a high proportion of renewable distributed photovoltaic (PV) power, flexible loads, and energy storage units are being integrated into the distribution network. However, the output of distributed PV is affected by natural factors such as weather and seasons, exhibiting intermittency and uncertainty. On the other hand, the charging and discharging strategies of energy storage units and the fluctuations in electric heating loads further increase the complexity of the scheduling problem. Traditional optimization algorithms (such as particle swarm optimization and genetic algorithms) suffer from drawbacks when dealing with high-dimensional, nonlinear microgrid power allocation problems, including a tendency to get trapped in local optima, slow convergence speed, and an inability to effectively balance global search and local exploitation capabilities.

[0003] Chinese invention patent CN116760102A discloses a multi-objective optimization method and system for microgrids based on an improved snake optimization algorithm, belonging to the field of power system dispatching. The method includes the following steps: establishing a microgrid operation mode with a fuzzy management system at the upper level and a microgrid energy optimization management method with an improved snake optimization algorithm at the lower level; establishing a multi-objective optimization model with microgrid operation economy, network carbon emissions, and active power loss as objective functions, combining the output of each thermal power generating unit, renewable energy generation, energy storage battery state of charge, and the amount of power exchange between the microgrid and the distribution network as constraints; solving the model by improving the snake optimization algorithm and introducing a lévy flight optimization strategy, and obtaining the optimal solution through a membership function. However, this method has increased debugging and maintenance complexity, high hierarchical coordination costs, and only optimizes based on predicted data, leading to decreased stability under extreme weather or load change scenarios. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, equipment and medium for power scheduling of microgrid clusters, and to solve the problems of complex debugging and low stability in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a microgrid cluster power scheduling method, comprising: Determine the photovoltaic power generation model, energy storage unit model, and load model; Determine the various costs of the microgrid cluster; based on these costs, construct an objective function with the goal of minimizing the economic cost of the microgrid, and determine the constraints of the objective function; Based on the photovoltaic power generation model, energy storage unit model, load model, and constraints, an improved snake algorithm is used to solve the objective function to obtain the optimal scheduling scheme. Specifically, the improved snake algorithm introduces an elite back-learning strategy to generate the final population during initialization; and in battle mode, a sine and cosine algorithm is introduced to update the individual snake positions.

[0006] Preferably, in the steps of determining the photovoltaic power generation model, energy storage unit model, and load model, the photovoltaic power generation model includes the output power of photovoltaic power generation under the target environment, the irradiance of photovoltaic power generation under the target environment, the output power of photovoltaic power generation under the standard test environment, the irradiance of photovoltaic power generation under the standard test environment, and the temperature and power temperature coefficient of photovoltaic power generation under the standard test environment. The energy storage unit model includes the energy storage unit's SOC value, energy storage unit's charging and discharging power, and energy storage unit's rated capacity; The load model includes total load power, fixed load power, movable load power, and disconnectable load power.

[0007] Preferably, the objective function is: ; In the formula: For the economic cost of microgrids; Cost of photovoltaic power generation; For the operating cost of energy storage units; Cost of exchanging electrical energy between the microgrid and the main grid; Compensation for power outages for users; Compensation for user electricity load scheduling.

[0008] Preferably, the constraints of the objective function are: power balance constraint, grid interaction power constraint, dispatch load power constraint, energy storage unit charging and discharging power constraint, SOC value constraint, and user satisfaction constraint.

[0009] Preferably, the decision variables of the objective function include: daily output power, daily charging and discharging power, daily microgrid and main grid interaction power, daily disconnectable load power, and daily transferable load power.

[0010] Preferably, the step of introducing an elite reverse learning strategy to generate the final population during population initialization includes: N snake individuals are randomly generated in the search space as the initial population. An elite reverse learning strategy is used to obtain the reverse population of the initial population. The initial population and the reverse population are merged to generate 2N snake individuals. The snake individuals are sorted from largest to smallest fitness value, and the top N snake individuals are selected as the final population. In the final population, m snake individuals were selected as males, and the remaining Nm snake individuals were selected as females.

[0011] Preferably, when updating the position in battle mode, the method of introducing a sine and cosine algorithm to update the individual snake position includes: When the snake is in combat mode, the sine and cosine algorithms are as follows: ; The locations of male snakes have been updated as follows: ; The locations of female snakes have been updated as follows: ; In the formula: As a learning factor, This is the lower bound of the learning factor. This is the upper limit of the learning factor. T This represents the maximum number of iterations.

[0012] The position of the male snake at time t+1. Let t be the position of the male snake. Let t be the position of the i-th snake individual in the male group. This is the optimal position for the male population; The position of the female snake at time t+1. Let t be the position of the female snake. Let t be the position of the i-th snake in the female group. This is the optimal position for the female population; for Random numbers within, yes Random numbers within.

[0013] A second aspect of the present invention provides a microgrid cluster power scheduling device, comprising: The acquisition module is used to determine the photovoltaic power generation model, energy storage unit model, and load model; A construction module is used to determine the various costs of a microgrid cluster; based on each of the costs, an objective function is constructed with the goal of minimizing the economic cost of the microgrid, and the constraints of the objective function are determined. The solution module is used to solve the objective function based on the photovoltaic power generation model, energy storage unit model, load model and the constraints, and obtain the optimal scheduling scheme by using an improved snake algorithm. The improved snake algorithm introduces an elite back-learning strategy to generate the final population when initializing the population; and introduces a sine and cosine algorithm to update the position of individual snakes in the battle mode.

[0014] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the microgrid cluster power scheduling method described above.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the microgrid cluster power scheduling method.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: The objective function is optimized by a snake algorithm based on elite back learning and sine and cosine algorithms. The elite back learning strategy is introduced to enhance population diversity and avoid getting trapped in local optima. The sine and cosine ideas are combined and a learning factor is introduced. The learning factor has a large value in the early stage, which helps global exploration. In the later stage of the search, it has a small value, which helps to improve local exploration ability, accuracy, and calculation speed, and find a more economical scheduling scheme. By shifting the power of movable loads across time periods, peak shaving and valley filling of loads can be achieved, providing corresponding economic and optimized dispatching and reducing the operating costs of microgrids; By taking comfort and economy as hard constraints, we ensure that load scheduling does not exceed the user's acceptable range, and avoid user resistance caused by simply pursuing economy. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the microgrid cluster power scheduling method in Embodiment 1 of the present invention; Figure 2 This is a flowchart of the snake algorithm based on elite back learning and sine / cosine algorithm optimization in Embodiment 1 of the present invention. Figure 3 This is a structural block diagram of the microgrid cluster power dispatching device according to Embodiment 2 of the present invention; Figure 4 This is a structural block diagram of an electronic device according to Embodiment 3 of the present invention. Detailed Implementation

[0018] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0019] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0020] Example 1 like Figure 1-2 As shown, the microgrid cluster power scheduling method includes: S1. Determine the photovoltaic power generation model, energy storage unit model, and load model; A photovoltaic (PV) power generation model is constructed. PV power generation converts light energy into electrical energy based on semiconductor materials. The key factors affecting power generation are light intensity and the temperature surrounding the semiconductor material. The output power is expressed as: (1) In the formula: Let L be the output power of photovoltaic power generation under the target environment, and L be the irradiance of photovoltaic power generation under the target environment. The temperature at which photovoltaic power generation occurs under the target environment. The output power of photovoltaic power generation under standard test conditions, Photovoltaic power generation irradiance under standard test conditions. These represent the temperatures of photovoltaic power generation under standard testing conditions. This is the power temperature coefficient.

[0021] A model of energy storage units is constructed. The energy of the energy storage units in the microgrid changes at different times. The state of charge (SOC) value is used to represent the power stored in the energy storage units at each time. The expression for calculating the SOC value at time t: (2) In the formula: for The SOC value of the energy storage unit at any given time; For energy storage units in the first The charging and discharging power at any given moment is taken as a negative value during charging and a positive value during discharging. This refers to the rated capacity of the energy storage unit. For time periods.

[0022] This invention constructs a load model, classifying user loads into three categories from the perspective of load operation and scheduling: fixed load power, movable load power, and disconnectable load power. Fixed load power refers to loads with relatively fixed usage time and power that cannot be scheduled; movable load power refers to load power whose usage time and power can be shifted to other times as needed; disconnectable load power refers to load power that can be partially or completely interrupted as needed. In terms of microgrid optimal scheduling, microgrids achieve operational cost optimization by scheduling movable load power.

[0023] microgrids The total load power at any given time is the sum of the fixed load power, the movable load power, and the disconnectable load power, and its expression is: (3) In the formula: For microgrids Total load power at any given time; for Fixed load power at any given time for The power of the portable load at any given time. for Disconnectable load power at any given time.

[0024] For movable load power, when the load power is moved, both the amount of load transferred in at the current moment and the amount of load transferred out at the same moment must be considered. The expression for movable load power at a certain moment is Equation (4): (4) In the formula: From Time shift The power of the portable load at any given time; for The power of the load that can be removed at any given time.

[0025] S2. Determine the various costs of the microgrid cluster; based on each of the costs, construct an objective function with the goal of minimizing the economic cost of the microgrid, and determine the constraints of the objective function; (1) The economic cost of a microgrid includes the cost of photovoltaic power generation, the operating cost of energy storage units, the cost of exchanging electricity between the microgrid and the main grid, and the load response compensation. The objective function is: (5) In the formula: For the economic cost of microgrids; Cost of photovoltaic power generation; For the operating cost of energy storage units; Cost of exchanging electrical energy between the microgrid and the main grid; Compensation for power outages for users; Compensation for user electricity load scheduling.

[0026] Specifically: (6) In the formula: The cost per unit of photovoltaic power generation, The output power of photovoltaic power generation is calculated using equation (1); for The state of the energy storage unit at any time is a 0-1 variable, where 1 indicates that the energy storage unit is in a discharging state and 0 indicates that the energy storage unit is in a state of neither charging nor discharging. The unit discharge cost of the energy storage unit, The energy storage unit calculated for the energy storage unit model represented by equation (2) is in t The charging and discharging power at any given moment; The unit electricity purchase cost of the microgrid is 0 when electricity is sold. This represents the revenue from electricity sales per unit of the microgrid; the revenue is zero when purchasing electricity. This represents the power exchange between the microgrid and the main grid; it is positive when the microgrid purchases electricity and negative when the microgrid sells electricity. This is the power compensation coefficient for disconnectable loads. The result calculated in equation (3) t Disconnectable load power at any time; For load from Time shift to The compensation coefficient at time, The result calculated in equation (4) Time shift The power of the movable load at any given time; Δt is the time period.

[0027] (7) In the formula: , , , These are the compensation coefficients for different time periods of power movement of the movable load.

[0028] (2) Constraints The constraints on the objective function include power balance constraints, grid interaction power constraints, dispatch load power constraints, energy storage unit charging and discharging power constraints, SOC value constraints, and user satisfaction constraints.

[0029] 1) Power balance constraints (8) The charging and discharging power of the energy storage unit; This represents the total load power of the microgrid. 2) Power Constraints Between Grids Under grid-connected operation, the interaction power between the microgrid and the main grid should meet the main grid's limitation on interaction power, i.e., equation (9): (9) In the formula: This represents the upper limit of the power that a microgrid can sell to the main grid, expressed as a positive number. This represents the upper limit of the power that the microgrid can purchase from the main grid, expressed as a positive number.

[0030] 3) Dispatch load power constraints To meet users' basic electricity needs, the dispatch power of the load is limited, as expressed by: (10) In the formula: To remove the upper limit of load power, This is the upper limit of the power of the disconnectable load. Power that can be removed from the load; This refers to the power of the disconnectable load.

[0031] 4) Energy storage unit charging and discharging power constraints The power constraints for charging and discharging of the energy storage unit are shown in equation (11): (11) In the formula: This represents the maximum discharge power. This is the maximum charging power; This refers to the charging and discharging power of the energy storage unit.

[0032] The charge / discharge cycle condition for the energy storage unit is shown in the following formula: (12) In the formula: for The state of the energy storage unit at any given time is a 0-1 variable, where 1 indicates that the energy storage unit is in a charging state and 0 indicates that the energy storage unit is neither charging nor discharging.

[0033] The energy storage unit cannot discharge and charge at the same time, nor can it charge and discharge at the same time, therefore it satisfies equation (13): (13) 5) Energy storage unit state constraints (14) In the formula: This represents the upper limit of the SOC value of the energy storage unit. This represents the lower limit of the SOC value of the energy storage unit.

[0034] 6) User satisfaction constraints When the load responds, the dispatch of portable load power directly affects users' electricity consumption habits, and the impact of load dispatch on users can be measured by user satisfaction.

[0035] User satisfaction can be divided into two parts: user comfort and user economy. This invention calculates user satisfaction based on user comfort and economy in electricity use, using the following formula: (15) In the formula: For user satisfaction; Optimized power comfort for user load; The economic efficiency of the user's electricity load.

[0036] The user's comfort level is calculated based on the difference between the user's actual electricity consumption curve and the maximum comfort curve. The calculation formula is as follows: (16) The user's economic efficiency is calculated based on the difference in electricity costs before and after load optimization, using the following formula: (17) In the formula: This refers to the user's electricity cost before load dispatching; This represents the electricity cost for users after load dispatching.

[0037] like If the load is optimized, the user's electricity cost will decrease, and its economic efficiency will be better than before the load response; conversely, if Therefore, its economic performance is not as good as before the response. The higher the value, the better the economic benefits for the user, and the greater the user satisfaction value.

[0038] S3. Based on the photovoltaic power generation model, energy storage unit model, load model and the constraints, the objective function is solved using an improved snake algorithm to obtain the optimal scheduling scheme; S31. Define temperature, food quantity, and behavior patterns; the behavior patterns include food search mode, combat mode, and mating mode. Temperature (Temp) is defined as follows: (18) In the formula: This indicates the current iteration number. This indicates the maximum number of iterations.

[0039] Food quantity The definition is as follows: (19) In the formula: It is a constant and equal to 0.5.

[0040] S32, in D-dimensional space, the first j Randomly generated in dimension Individual snakes were used as the initial population. , No. i The individual snake was in the first j The position in 3D space is , i =1, 2, 3… N ; j =1, 2, 3…D; in, Then its reverse point ; In the formula: It is the first j The minimum value of a snake individual on the dimension. It is the first j The maximum value of a snake individual in the dimension; Get the j The reverse individuals of N snake individuals on the dimension This constitutes a reverse population B; The initial population A and the reverse population B are merged to generate 2N snake individuals. The snake individuals are sorted from largest to smallest fitness value, and the top N snake individuals are selected as the final population C. In the final population C, male and female snakes were allocated in a 1:1 ratio, and the positions of the male snakes were determined. and the location of the female snake ; In this invention, D is set to 24, which corresponds to the optimization of 24 hours in a day in the actual problem. Subsequently, algorithm optimization is performed in each dimension. Since the calculation process is the same in each dimension, the dimension is no longer emphasized in the calculation. S33. Update the global optimal position and the global optimal fitness value; When Q < 0.25, the snake population is in the global search phase. The formula for updating the male snake's position is as follows: (20) In the formula: Represents the time t+1. A male position, This represents the position of a random male at time t. Indicates the upper bound of the problem to be solved. Indicates the lower bound of the problem to be solved. Constant and equal to , It is a random number between 0 and 1. It refers to the male's ability to find food, calculated using the following formula: ;(twenty one) In the formula: yes Suitability yes Suitability; The formula for female snakes to update their positions is as follows: ;(twenty two) In the formula: Represents the time t+1. A female position, This represents the position of a random female at time t. It refers to the female's ability to find food, calculated using the following formula: ;(twenty three) In the formula: yes Suitability yes Suitability; Under the condition Q≥0.25, when Temp≥0.6, the snake swarm begins to approach the food and update its position: ;(twenty four) In the formula, This represents the position of the i-th individual snake, either male or female. The position of the optimal individual in the entire group; Under the conditions of Q≥0.25 and Temp<0.6, when rand≥0.6, the snake swarm enters combat mode and updates its position: The locations of male snakes have been updated as follows: (25) The locations of female snakes have been updated as follows: (26) The sine and cosine algorithms are as follows: (27) In the formula: As a learning factor, This is the lower bound of the learning factor. This is the upper limit of the learning factor; The position of the male snake at time t+1. Let t be the position of the male snake. Let t be the position of the i-th snake individual in the male group. This is the optimal position for the male population; The position of the female snake at time t+1. Let t be the position of the female snake. Let t be the position of the i-th snake in the female group. This is the optimal position for the female population; for Random numbers within, yes Random numbers within.

[0041] Given Q≥0.25 and Temp<0.6, when rand<0.6, the snake swarm enters mating mode and updates its position: (28) (29) In the formula, Indicates a male's mating ability. c3 represents the female's mating ability and is a constant.

[0042] S34. Determine if the termination condition is met. If it is met, terminate and output the optimal value and the optimal solution. Otherwise, go to S32. The termination condition is reaching the required number of iterations or the required iteration precision.

[0043] After the above solution process, the optimal scheduling scheme is obtained; power scheduling of the microgrid cluster is then carried out based on the optimal scheduling scheme.

[0044] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a microgrid cluster power dispatching device, comprising: The acquisition module is used to determine the photovoltaic power generation model, energy storage unit model, and load model; A construction module is used to determine the various costs of a microgrid cluster; based on each of the costs, an objective function is constructed with the goal of minimizing the economic cost of the microgrid, and the constraints of the objective function are determined. The solution module is used to solve the objective function based on the photovoltaic power generation model, energy storage unit model, load model and the constraints, and obtain the optimal scheduling scheme by using an improved snake algorithm.

[0045] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for implementing a microgrid cluster power scheduling method; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0046] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the microgrid cluster power scheduling method of Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0047] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0048] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0049] The memory 101 in the electronic device 100 stores multiple instructions to implement a microgrid cluster power scheduling method, and the processor 102 can execute multiple instructions to achieve the following: Determine the photovoltaic power generation model, energy storage unit model, and load model; Determine the various costs of the microgrid cluster; based on these costs, construct an objective function with the goal of minimizing the economic cost of the microgrid, and determine the constraints of the objective function; Based on the photovoltaic power generation model, energy storage unit model, load model, and constraints, an improved snake algorithm is used to solve the objective function to obtain the optimal scheduling scheme.

[0050] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0051] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0055] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A microgrid cluster power dispatching method, characterized in that, include: Determine the photovoltaic power generation model, energy storage unit model, and load model; Determine the various costs of the microgrid cluster; Based on the aforementioned costs, with the goal of minimizing the economic cost of the microgrid, an objective function is constructed, and the constraints of the objective function are determined. Based on the photovoltaic power generation model, energy storage unit model, load model, and constraints, an improved snake algorithm is used to solve the objective function to obtain the optimal scheduling scheme. Specifically, the improved snake algorithm introduces an elite back-learning strategy to generate the final population during initialization; and in battle mode, a sine and cosine algorithm is introduced to update the individual snake positions.

2. The microgrid cluster power scheduling method as described in claim 1, characterized in that, In the steps of determining the photovoltaic power generation model, energy storage unit model, and load model, the photovoltaic power generation model includes: the output power of photovoltaic power generation under the target environment, the irradiance of photovoltaic power generation under the target environment, the output power of photovoltaic power generation under the standard test environment, the irradiance of photovoltaic power generation under the standard test environment, and the temperature and power temperature coefficient of photovoltaic power generation under the standard test environment. The energy storage unit model includes the energy storage unit's SOC value, energy storage unit's charging and discharging power, and energy storage unit's rated capacity; The load model includes total load power, fixed load power, movable load power, and disconnectable load power.

3. The microgrid cluster power scheduling method as described in claim 1, characterized in that, The objective function is: ； In the formula: For the economic cost of microgrids; Cost of photovoltaic power generation; For the operating cost of energy storage units; Cost of exchanging electrical energy between the microgrid and the main grid; Compensation for power outages for users; Compensation for user electricity load scheduling.

4. The microgrid cluster power scheduling method as described in claim 1, characterized in that, The constraints of the objective function are: power balance constraint, grid interaction power constraint, dispatch load power constraint, energy storage unit charging and discharging power constraint, SOC value constraint, and user satisfaction constraint.

5. The microgrid cluster power scheduling method as described in claim 1, characterized in that, The decision variables of the objective function include: daily output power, daily charging and discharging power, daily microgrid and main grid interaction power, daily disconnectable load power, and daily transferable load power.

6. The microgrid cluster power scheduling method as described in claim 1, characterized in that, The step of introducing an elite reverse learning strategy to generate the final population during population initialization includes: N snake individuals are randomly generated in the search space as the initial population. An elite reverse learning strategy is used to obtain the reverse population of the initial population. The initial population and the reverse population are merged to generate 2N snake individuals. The snake individuals are sorted from largest to smallest fitness value, and the top N snake individuals are selected as the final population. In the final population, m snake individuals were selected as males, and the remaining Nm snake individuals were selected as females.

7. The microgrid cluster power scheduling method as described in claim 1, characterized in that, The method of updating the position of individual snakes by introducing a sine and cosine algorithm when updating the position in battle mode includes: When the snake is in combat mode, the sine and cosine algorithms are as follows: ； The locations of male snakes have been updated as follows: ； The locations of female snakes have been updated as follows: ； In the formula: As a learning factor, This is the lower bound of the learning factor. This is the upper limit of the learning factor. T This represents the maximum number of iterations. The position of the male snake at time t+1. Let t be the position of the male snake. Let t be the position of the i-th snake individual in the male group. This is the optimal position for the male population; The position of the female snake at time t+1. Let t be the position of the female snake. Let t be the position of the i-th snake in the female group. This is the optimal position for the female population; for Random numbers within, yes Random numbers within.

8. A microgrid cluster power dispatching device, characterized in that, include: The acquisition module is used to determine the photovoltaic power generation model, energy storage unit model, and load model; Build modules to determine the various costs of a microgrid cluster; Based on the aforementioned costs, with the goal of minimizing the economic cost of the microgrid, an objective function is constructed, and the constraints of the objective function are determined. The solution module is used to solve the objective function based on the photovoltaic power generation model, energy storage unit model, load model and the constraints, and obtain the optimal scheduling scheme by using an improved snake algorithm; wherein, the improved snake algorithm introduces an elite back-learning strategy to generate the final population when initializing the population; and in the battle mode, a sine and cosine algorithm is introduced to update the position of individual snakes.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the microgrid cluster power scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the microgrid cluster power scheduling method as described in any one of claims 1 to 7.

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