New energy scheduling method and system with hierarchical regulation and control, and storage medium

Abstract

The invention relates to the technical field of electric power, and provides a hierarchical and hierarchical regulation new energy scheduling method and system, and a storage medium. According to the method, a total network cost optimization scheduling model based on the minimum total cost, a sub-network optimization scheduling model based on high-proportion new energy sub-network operation new energy full consumption, and a total network income optimization scheduling model based on the maximum total network income expected value are constructed; then, a multi-level and multi-target new energy optimization scheduling model is formed; and finally, based on a preset constraint condition, solving the new energy optimization scheduling model to obtain a final new energy scheduling method. A multi-level and multi-target optimization scheduling model of a total network-multiple sub-networks is established, the total network layer is responsible for cost-income optimal scheduling of the whole network range, and the sub-network layer realizes regional autonomy of energy on the basis of responding to the optimization result of the total network layer.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a new energy scheduling method, system and storage medium for hierarchical and graded regulation. Background Art

[0002] With the continuous advancement of new energy systems, the power supply structure and grid structure are also constantly adjusted and deepened. Compared with traditional power systems, new energy systems are very different in power supply structure and grid structure, and the operating characteristics of the system have also changed greatly.

[0003] Renewable energy generation such as solar energy and light energy is highly random and intermittent, and wind power also has the characteristic of anti-peak regulation. In addition, in some places, new energy sources such as wind power are basically in a "free" operation state, that is, they do not participate in the grid dispatch plan. This has brought increasing pressure on the grid connection and operation dispatch control of wind power-rich areas.

[0004] Due to the strong volatility of wind power and photovoltaic power, the planning of high-proportion renewable energy grid connection is a common problem in realizing a clean and safe power system. Some mainstream research institutions believe that the current power structure and grid planning framework are only suitable for medium and low proportions. In the scenario of high proportion of renewable energy, wind power and photovoltaic power will become the main source of power supply. Conventional thermal power units need to start and stop quickly during the day, and the original power generation characteristics of the power system will change. The existing planning system will face severe challenges. When independent microgrids use renewable energy as the main power supply and there is no controllable power supply support such as large-capacity gas units or diesel generators, flexible and diversified demand-side management mechanisms will become necessary and important means to ensure the energy balance of independent microgrids.

[0005] Through coordinated operation, the consumption of renewable energy can be greatly improved, providing controllable, adjustable and stable power supply to the power grid. The commonly used power dispatch optimization algorithm is generally the ABC algorithm. As a new modern intelligent optimization algorithm, the ABC algorithm is widely used in solving multivariable optimization problems and can greatly improve the probability of finding the optimal solution. However, the algorithm also has disadvantages such as premature and local convergence during the iterative convergence process. Summary of the invention

[0006] The purpose of the present invention is to provide a new energy scheduling method, system and storage medium with hierarchical and graded regulation to solve the above-mentioned technical problems.

[0007] The present invention provides a new energy scheduling method with hierarchical and graded regulation, comprising the steps of: S1, construct a total network cost optimization scheduling model based on the minimum total cost, where the objective function of the total network cost optimization scheduling model is expressed as: , where t is the time period number, T is the scheduling period; N is the number of energy subnets; is the total power generation of subgrid n, is the power generation cost of subgrid n; is the exchange power between subnet n and the distribution network. A positive value indicates that region n sells electricity to the distribution network, and a negative value indicates that it purchases electricity from the distribution network. The exchange power cost between subnet n and the distribution network; S2, build a subgrid optimization dispatching model based on high proportion of new energy subgrid operation and full consumption of new energy, where the objective function of the subgrid optimization dispatching model is expressed as the average distance of traditional power generation load: , where is the traditional power generation load of the traditional power generation energy in the t period, in KW; S3, construct a total network revenue optimization scheduling model based on the highest expected value of total network revenue, where the objective function of the total network revenue optimization scheduling model is expressed as: ], where represents the total network operation revenue in period t; S4, a multi-level and multi-objective new energy optimization scheduling model composed of the total network cost optimization scheduling model, the subnetwork optimization scheduling models of multiple high-proportion new energy subnetworks, and the total network revenue optimization scheduling model; S5, based on the preset constraints, solving the new energy optimization scheduling model to obtain the final new energy scheduling method.

[0008] Furthermore, , ; In the formula, each subgrid is equivalent to a power generation unit, and its power generation cost is a quadratic function of the total power generation of the grid. , , is the subgrid power generation cost coefficient; is the transmission cost coefficient, For electricity price.

[0009] Furthermore, It is equal to the total load output income in the tth period minus the generation cost of the traditional power generation load output in the total load and the discharge cost of the power generation and / or storage device output of the new energy in the total load.

[0010] Furthermore, the constraints of the total network cost optimization scheduling model include upper and lower processing limits of the energy subnetwork, power balance, interconnection line constraints, and climbing capability constraints.

[0011] Furthermore, the constraints of the subgrid optimization scheduling model include power balance constraints, dispatchable power constraints of renewable energy power generation, dispatchable power constraints of traditional power generation, output constraints of renewable energy power generation, output constraints of traditional power generation, and climbing ability constraints.

[0012] Furthermore, the constraints of the total network revenue optimization scheduling model include upper and lower processing limits of the energy subnetwork, power balance, interconnection line constraints, and climbing capability constraints.

[0013] Furthermore, the step S5 comprises: S51, obtaining a set of initial solutions by random generation or chaotic mapping and performing population initialization; S52, evaluating each solution in the initial solution set, calculating its value on each objective function, and selecting solutions that meet preset conditions as parents; S53, according to the parent generation, a new solution is generated as a child generation through a population update strategy, and then the solutions of the parent generation and the child generation are used as a new solution set and population, and the process returns to step S2 for repeated iteration; S4, after reaching a preset number of iterations or reaching a preset convergence condition, an optimal solution set is obtained.

[0014] Further, the step S52 includes: Perform non-dominated sorting and crowding calculation on all individuals in the population: first, determine the dominance relationship between individuals according to the size of the objective function value of each individual in the population; then divide the non-dominated levels according to the dominance relationship between individuals and perform non-dominated sorting; then calculate the crowding degree of each individual in the same level; According to the calculation results of non-dominated sorting and crowding degree, all individuals in the population are divided into elite individuals and ordinary individuals, and the elite individuals are used as parents; The step S53 comprises: Update the parameters of the individuals in the parent population and perform differential evolution operations to obtain the offspring population; Combine the individuals in the parent population and the offspring population to generate a new population, and the size of the new population is 2*N; N represents the number of individuals in the parent population; Perform non-dominated sorting and crowding degree calculation on the new population; According to the non-dominated sorting and crowding degree calculation results of the new population, use the elitist retention mechanism to screen the new population, and the size of the screened new population is N; For the current new population, first remove N-S individuals with low non-dominated sorting levels and small crowding degrees until the population size reaches S, where S < N; Then, store the removed individuals in the external set; Next, randomly initialize N-S individuals within the search range and perform non-dominated sorting again, calculating the crowding degree; Finally, according to the non-dominated sorting and crowding degree calculation results, use the elitist retention mechanism to screen the new population; The maximum size of the external set is N, and if it is greater than N, randomly delete individuals to make it equal to N; Finally, obtain the new solution set and population; Return to step S2 for repeated iteration.

[0015] The present invention also provides a new energy scheduling system with hierarchical control, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the new energy scheduling method with hierarchical control as described in any one of the above are implemented.

[0016] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the new energy scheduling method with hierarchical control as described in any one of the above are implemented.

[0017] A new energy scheduling method with hierarchical control provided by the present invention constructs a multi-level and multi-objective new energy optimization scheduling model composed of a total network cost optimization scheduling model based on the minimum total cost, a sub-network optimization scheduling model for full consumption of new energy in the sub-network with a high proportion of new energy, and a total network revenue optimization scheduling model based on the highest expected total network revenue, and then; Finally, based on the preset constraint conditions, solve the new energy optimization scheduling model to obtain the final new energy scheduling method. A multi-level and multi-objective optimization scheduling model of "total network - multiple sub-networks" is established. The total network level is responsible for the cost-revenue optimal scheduling of the entire network, and the sub-network layer realizes the regional autonomy of energy on the basis of responding to the optimization results of the total network layer. The following conclusions can be obtained through simulation analysis: (1) Within the sub-network, distributed renewable new energy sources, such as wind turbines and photovoltaic devices, should work in the maximum power tracking point mode and should be preferentially consumed as much as possible; (2) The shortfall after the consumption of new energy is made up by traditional controllable power generation equipment. The algorithm based on population example optimization uses the idea of ​​evolutionary optimization to find the optimal coordination strategy that minimizes the traditional power generation of the subgrid; (3) The multi-level multi-meter coordinated optimization model provides an effective solution for the processing of large-scale decision variables and realizes the management and control of distributed devices in the energy Internet through the architectural design of the physical system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The figure is a flow chart of a new energy scheduling method with hierarchical and graded regulation in one embodiment of the present invention.

[0019] Figure 2 It is a schematic diagram of the hardware structure of a system running a new energy scheduling method with hierarchical and graded regulation in one embodiment of the present invention.

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0022] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0023] The present invention will now be described in further detail with reference to the accompanying drawings.

[0024] Please combine Figure 1 and Figure 2 , Figure 1 1 is a flowchart of a new energy scheduling method with hierarchical and graded control in one embodiment of the present invention. Figure 2 Schematic diagram of the framework structure of the new energy scheduling method system with hierarchical and graded regulation.

[0025] In this embodiment, the new energy dispatching method with hierarchical and graded regulation includes the following steps: S1, construct a total network cost optimization scheduling model based on the minimum total cost, where the objective function of the total network cost optimization scheduling model is expressed as: , where t is the time period number, T is the scheduling period; N is the number of energy subnets; is the total power generation of subgrid n, is the power generation cost of subgrid n; is the exchange power between subnet n and the distribution network. A positive value indicates that region n sells electricity to the distribution network, and a negative value indicates that it purchases electricity from the distribution network. The power exchange cost between subnet n and the distribution network.

[0026] in, , ; In the formula, each subgrid is equivalent to a power generation unit, and its power generation cost is a quadratic function of the total power generation of the grid. , , is the subgrid power generation cost coefficient; is the transmission cost coefficient, For electricity price.

[0027] S2, build a subgrid optimization dispatching model based on high proportion of new energy subgrid operation and full consumption of new energy, where the objective function of the subgrid optimization dispatching model is expressed as the average distance of traditional power generation load: , where is the traditional power generation load of traditional power generation energy in the tth period, in KW. Specifically, by taking the full consumption of new energy as the boundary condition and taking the minimum average distance of traditional power generation load as the objective function, it can be constructed that distributed renewable energy, such as wind turbines and photovoltaic equipment, works in the maximum power tracking point mode within the subnet and should be consumed as much as possible.

[0028] S3, construct a total network revenue optimization scheduling model based on the highest expected value of total network revenue, where the objective function of the total network revenue optimization scheduling model is expressed as: ], where Represents the total network operating income in period t. It is equal to the total load output income in the tth period minus the generation cost of the traditional power generation load output in the total load and the discharge cost of the power generation and / or storage device output of the new energy in the total load.

[0029] S4, a multi-level and multi-objective new energy optimization scheduling model is formed based on the total network cost optimization scheduling model, the subnetwork optimization scheduling models of multiple high-proportion new energy subnetworks, and the total network revenue optimization scheduling model.

[0030] S5, based on the preset constraints, solving the new energy optimization scheduling model to obtain the final new energy scheduling method.

[0031] Furthermore, the constraints of the total network cost optimization scheduling model include upper and lower processing limits of the energy subnetwork, power balance, interconnection line constraints, and climbing capability constraints.

[0032] Furthermore, the constraints of the subgrid optimization scheduling model include power balance constraints, dispatchable power constraints of renewable energy power generation, dispatchable power constraints of traditional power generation, output constraints of renewable energy power generation, output constraints of traditional power generation, and climbing ability constraints.

[0033] Furthermore, the constraints of the total network revenue optimization scheduling model include upper and lower processing limits of the energy subnetwork, power balance, interconnection line constraints, and climbing capability constraints.

[0034] Those skilled in the art should know that in the prior art, in order to solve multiple objective functions, multiple constraints can be set. The specific setting of the constraints can be selected by technical personnel in the relevant field according to actual needs and will not be elaborated here.

[0035] Furthermore, the step S5 comprises: S51, obtaining a set of initial solutions by random generation or chaotic mapping and performing population initialization; S52, evaluating each solution in the initial solution set, calculating its value on each objective function, and selecting solutions that meet preset conditions as parents; S53, according to the parent generation, a new solution is generated as a child generation through a population update strategy, and then the solutions of the parent generation and the child generation are used as a new solution set and population, and the process returns to step S2 for repeated iteration; S4, after reaching a preset number of iterations or reaching a preset convergence condition, an optimal solution set is obtained.

[0036] Further, the step S52 includes: Perform non-dominated sorting and crowding calculation on all individuals in the population: first, determine the dominance relationship between individuals according to the size of the objective function value of each individual in the population; then divide the non-dominated levels according to the dominance relationship between individuals and perform non-dominated sorting; then calculate the crowding degree of each individual in the same level; According to the calculation results of non-dominated sorting and crowding degree, all individuals in the population are divided into elite individuals and ordinary individuals, and the elite individuals are used as parents; The step S53 comprises: Update the parameters of the individuals in the parent population and perform differential evolution operations to obtain the offspring population; Combine the individuals in the parent population and the offspring population to generate a new population, and the size of the new population is 2*N; N represents the number of individuals in the parent population; Perform non-dominated sorting and crowding degree calculation on the new population; According to the non-dominated sorting and crowding degree calculation results of the new population, use the elitist retention mechanism to screen the new population, and the size of the screened new population is N; For the current new population, first remove the N-S individuals with low non-dominated sorting levels and small crowding degrees until the population size reaches S, where S < N; Then, store the removed individuals in the external set; Next, randomly initialize N-S individuals within the search range and perform non-dominated sorting again, calculating the crowding degree; Finally, according to the non-dominated sorting and crowding degree calculation results, use the elitist retention mechanism to screen the new population; The maximum size of the external set is N, and if it is greater than N, randomly delete individuals to make it equal to N; Finally, obtain the new solution set and population; Return to step S2 for repeated iteration.

[0037] A hierarchical and graded regulation new energy scheduling method provided by the present invention, by constructing a multi-level and multi-objective new energy optimization scheduling model composed of a total network cost optimization scheduling model based on the minimum total cost, a sub-network optimization scheduling model for full consumption of new energy in the operation of a high-proportion new energy sub-network, and a total network revenue optimization scheduling model based on the highest expected value of the total network revenue; Finally, based on the preset constraint conditions, solve the new energy optimization scheduling model to obtain the final new energy scheduling method. A multi-level and multi-objective optimization scheduling model of "total network - multiple sub-networks" is established. The total network level is responsible for the optimal scheduling of cost - revenue across the entire network, and the sub-network layer realizes the regional autonomy of energy on the basis of responding to the optimization results of the total network layer. The following conclusions can be obtained through simulation analysis: (1) Within the sub-network, distributed renewable new energy sources, such as wind turbines and photovoltaic devices, operate in the maximum power tracking point mode and should be preferentially consumed as much as possible; (2) The shortage after new energy consumption is made up by traditional power generation controllable devices, and the algorithm optimized based on the population example uses the idea of evolutionary optimization to find the optimal coordination strategy that minimizes the traditional power generation of the sub-network; (3) The multi-level and multi-table coordination optimization model provides an effective solution for the processing of large-scale decision variables, and realizes the control and management of distributed devices in the energy Internet through the architecture design of the physical system.

[0038] Please also combine with Figure 2The present invention also provides a new energy dispatching system with hierarchical and graded regulation. The new energy dispatching system with hierarchical and graded regulation is established and operated on the basis of a computer system, and specifically includes a memory 61, a processor 62, and a computer program 63 stored in the memory 61 and executable on the processor 62. When the processor 62 executes the computer program 63, the steps of the online monitoring method of the power system as described in any one of the above items are implemented.

[0039] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the new energy scheduling method with hierarchical and graded regulation as described in any of the above items.

[0040] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0041] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0042] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0043] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0044] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0045] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0046] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0047] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0048] In the description of this specification, the description with reference to the terms "an embodiment", "another embodiment", "other embodiments", or "first embodiment to Xth embodiment" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, method steps or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0049] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0050] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A new energy dispatching method with hierarchical and graded regulation, characterized in that: Including the steps: S1, construct a total network cost optimization scheduling model based on the minimum total cost, where the objective function of the total network cost optimization scheduling model is expressed as: , where t is the time period number, T is the scheduling period; N is the number of energy subnets; is the total power generation of subgrid n, is the power generation cost of subgrid n; is the exchange power between subnet n and the distribution network. A positive value indicates that region n sells electricity to the distribution network, and a negative value indicates that it purchases electricity from the distribution network. The exchange power cost between subnet n and the distribution network; S2, build a subgrid optimization dispatching model based on high proportion of new energy subgrid operation and full consumption of new energy, where the objective function of the subgrid optimization dispatching model is expressed as the average distance of traditional power generation load: , where is the traditional power generation load of the traditional power generation energy in the t period, in KW; S3, construct a total network revenue optimization scheduling model based on the highest expected value of total network revenue, where the objective function of the total network revenue optimization scheduling model is expressed as: ], where represents the total network operation revenue in period t; S4. An optimization scheduling model for new energy with multiple levels and multiple objectives composed of the overall network cost optimization scheduling model, the subnet optimization scheduling models of multiple high-proportion new energy subnets, and the overall network revenue optimization scheduling model; S5. Based on the preset constraint conditions, solve the new energy optimization scheduling model to obtain the final new energy scheduling method.

2. The new energy dispatching method with hierarchical and graded control according to claim 1 is characterized in that: Among them, ， ； In the formula, each subgrid is equivalent to a power generation unit, and its power generation cost is a quadratic function of the total power generation of the grid. , , is the subgrid power generation cost coefficient; is the transmission cost coefficient, For electricity price.

3. The new energy dispatching method with hierarchical and graded control according to claim 1 is characterized in that: It is equal to the total load output income in the tth period minus the generation cost of the traditional power generation load output in the total load and the discharge cost of the power generation and / or storage device output of the new energy in the total load.

4. The new energy dispatching method with hierarchical and graded control according to claim 1 is characterized in that: The constraint conditions of the overall network cost optimization scheduling model include the upper and lower limits of energy subnet processing, power balance, tie-line constraints, and ramp rate constraints.

5. The new energy dispatching method with hierarchical and graded control according to claim 1 is characterized in that: The constraint conditions of the subnet optimization scheduling model include power balance constraints, schedulable power constraints of new energy generation, schedulable power constraints of traditional generation, new energy generation output constraints, traditional generation output constraints, and ramp rate constraints.

6. The new energy dispatching method with hierarchical and graded control according to claim 1 is characterized in that: The constraint conditions of the overall network revenue optimization scheduling model include the upper and lower limits of energy subnet processing, power balance, tie-line constraints, and ramp rate constraints.

7. The new energy dispatching method with hierarchical and graded control according to claim 1 is characterized in that: The step S5 includes: S51. Obtain a set of initial solution sets through random generation or chaotic mapping and perform population initialization; S52. Evaluate each solution in the initial solution set, calculate its values on each objective function, and select the solutions that meet the preset conditions as the parent generation; S53. According to the parent generation, generate new solutions as the offspring through the population update strategy, and then use the solutions of the parent generation and the offspring as the new solution set and population, and return to step S2 for repeated iteration; S4. After reaching the preset number of iterations or the preset convergence condition, obtain the optimal solution set.

8. The new energy dispatching method with hierarchical and graded control according to claim 7 is characterized in that: The step S52 includes: Perform non-dominated sorting and crowding degree calculation on all individuals in the population: first, judge the dominance relationship between individuals according to the magnitudes of the objective function values of each individual in the population; then divide the non-dominated levels according to the dominance relationship between individuals for non-dominated sorting; and then calculate the crowding degree of each individual within the same level; According to the results of non-dominated sorting and crowding degree calculation, divide all individuals in the population into elite individuals and ordinary individuals, and the elite individuals are used as the parent generation; The step S53 includes: Update the parameters of the individuals in the parent population and perform differential evolution operations to obtain the offspring population; merge the individuals in the parent population and the offspring population to generate a new population, and the size of the new population is 2*N; N represents the number of individuals in the parent population; perform non-dominated sorting and crowding degree calculation on the new population; according to the results of non-dominated sorting and crowding degree calculation of the new population, use the elite retention mechanism to screen the new population, and the size of the screened new population is N; for the current new population, first remove N-S individuals with low non-dominated sorting levels and small crowding degrees until the population size reaches S, S < N; then, store the removed individuals in the external set; then, randomly initialize N-S individuals within the search range and re-perform non-dominated sorting and calculate the crowding degree; finally, according to the results of non-dominated sorting and crowding degree calculation, use the elite retention mechanism to screen the new population; the maximum size of the external set is N, if it is greater than N, randomly delete individuals to make it equal to N; finally, obtain the new solution set and population; Return to step S2 for repeated iteration.

9. A new energy dispatching system with hierarchical and graded regulation, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the new energy scheduling method with hierarchical and graded regulation are implemented as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the steps of the new energy scheduling method with hierarchical and graded regulation as described in any one of claims 1 to 8 are implemented.

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