Source-network load storage coordinated microgrid double-layer economic optimization scheduling method and system

Abstract

The invention relates to the technical field of micro-grid scheduling and optimization, in particular to a source-grid load storage coordinated micro-grid double-layer economic optimization scheduling method and system, and the method comprises the steps: firstly collecting and sorting the power generation data of distributed resources and the power consumption peak condition; secondly, establishing a source network load-storage side model; then, based on the source network load-storage side model, establishing a double-layer collaborative economic optimization scheduling model of the micro-grid, covering a lower micro-grid layer and an upper power distribution network layer; then, the income of the power distribution network is increased and the cost of the micro-grid is reduced through an iterative updating algorithm; and finally, by effectively scheduling controllable resources, the operation cost is reduced, the peak regulation function is enhanced, the economic benefit of power exchange is improved, the waste of renewable energy sources is reduced, and the utilization rate of the renewable energy sources is improved. Optimized cooperation between the micro-grid and the power distribution network is achieved, and important support is provided for stable and economical operation of a modern electric power system.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid scheduling and optimization, and particularly to a two-layer economic optimal scheduling method and system for a microgrid with coordinated source-network-load-storage. Background Art

[0002] With the continuous development of modern industry, there is a huge demand for various resources. The development of the economic structure has also promoted the change of the energy consumption structure. Adjustable resource power generation technologies based on new technologies such as automatic control systems, advanced material technologies, and flexible manufacturing processes play an important role in load regulation and optimization. After integrating numerous distributed power sources into the distribution network, their intermittency and randomness may lead to voltage and frequency fluctuations, increased short-circuit current, reduced power supply reliability, and degraded power quality. These problems pose significant challenges to the stable operation of the distribution network. Therefore, finding effective solutions to mitigate the impact of distributed power source integration has become an urgent task.

[0003] The two-layer economic optimal scheduling scheme for a microgrid distribution network with coordinated source-network-load-storage is an effective means to optimize resource allocation in the power system, improve system economy and stability. By reasonably scheduling the relationship between the source (renewable energy), network (power grid), load (user demand), and energy storage system, it is possible to reduce costs and improve system reliability while meeting the power supply demand. The two-layer optimization method can achieve the global optimal operation of the entire system by considering the upper-layer decision-making and lower-layer constraints. With the development of smart grid and microgrid technologies, this field will further develop, bringing more efficient power system scheduling schemes. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0005] Therefore, the technical problem to be solved by the present invention is that through effective scheduling of controllable resources in the network, the microgrid can effectively reduce operating costs and play an important role in "peak shaving". By exchanging power with the distribution network, not only can the economic benefits of the microgrid and the distribution network be improved, but also the occurrence of renewable energy waste can be reduced, and the utilization ability of renewable resources can be enhanced.

[0006] To solve the above technical problems, the present invention provides the following technical solution, a two-layer economic optimal scheduling method for a microgrid with coordinated source-network-load-storage, including: establishing a source-network-load-storage side model; further establishing a lower-layer microgrid layer of a two-layer coordinated economic optimal scheduling model for a microgrid distributed network according to the source-network-load-storage side model; further establishing an upper-layer distribution network layer of a two-layer coordinated economic optimal scheduling model for a microgrid distributed network according to the source-network-load-storage side model; updating and iterating the algorithm to increase the income of the distribution network.

[0007] As a preferred embodiment of the two - layer economic optimal dispatch method for a source - network - load - storage coordinated micro - grid according to the present invention, wherein: the source - network - load - storage side model includes a source - side model, a load - side model, and an energy - storage side model.

[0008] As a preferred embodiment of the two - layer economic optimal dispatch method for a source - network - load - storage coordinated micro - grid according to the present invention, wherein: the source - side model includes photovoltaic power generation, a diesel generator, and a micro - gas turbine;

[0009] Among them, the photovoltaic power generation is expressed as:

[0010] CPV &OM,i = K PV P PV,i,t T PV

[0011] Among them, C PV&OM,i represents the operation and maintenance cost of the photovoltaic panels of the i - th micro - grid, K PV represents the operation and maintenance coefficient of the photovoltaic panels, P PV,i,t represents the output of the i - th PV micro - grid at time t, T PV represents the time of photovoltaic power generation in the total dispatch time;

[0012] Among them, the diesel generator is expressed as:

[0013]

[0014] Among them, C DE&OM,i represents the operation and maintenance cost of DE in the i - th micro - grid, C DE&Fuel,i represents the fuel cost of DE in the i - th micro - grid, C DE&Waste,i represents the pollutant treatment cost of DE in the i - th micro - grid, P DE,i,t represents the output of DE of the micro - grid at time t, K DE represents the operation and maintenance coefficient of DE, α DE 、β DE 、γ DE represent the fuel coefficients of DE, C DE,k represents the cost coefficient of DE for treating k - type polluting gases, γ DE,k represents the emission coefficient of polluting gases of the diesel generator;

[0015] Among them, the micro - gas turbine is expressed as:

[0016]

[0017] Among them, η MT represents the operation efficiency of MT, P MT,i,t represents the output power of the i - th MT micro - grid at time t;

[0018]

[0019] Among them, C DE&OM,i represents the operation and maintenance cost of MT in the i-th microgrid, C DE&Fuel,i represents the fuel cost of MT in the i-th microgrid, C DE&Waste,i represents the pollutant treatment cost of MT in the i-th microgrid, K MT represents the MT operation and maintenance coefficient, C f represents the natural gas price, LHV represents the lower heating value of natural gas, C MT,k represents the cost coefficient of MT treatment of the k-th type of polluting gas, γ MT,k represents the emission coefficient of the k-th type of polluting gas of the micro gas turbine.

[0020] As a preferred scheme of the two-layer economic optimal scheduling method of the microgrid with source-network-load storage coordination described in the present invention, wherein: the load-side model is expressed as:

[0021] C DR,i = r i,t P DR,i,t

[0022] Among them, C DR,i represents the adjusted income of the microgrid users after DR, P DR,i,t represents the DR power in the microgrid, r i,t represents the DR compensation price in the microgrid;

[0023] The energy storage side model is expressed as:

[0024]

[0025] Among them, C ES,i represents the adjustment cost of the microgrid energy storage unit, η0 represents the depreciation coefficient, represents the charging power of the battery, represents the discharging power of the storage battery.

[0026] As a preferred scheme of the two-layer economic optimal scheduling method of the microgrid with source-network-load storage coordination described in the present invention, wherein: the lower layer microgrid layer of the distributed network two-layer collaborative economic optimal scheduling model is expressed as:

[0027]

[0028] Among them, T represents the total number of hours of grid dispatching, N represents the number of microgrids, C PV,i,t represents the cost of the photovoltaic panel, C DE,i,t represents the cost of the diesel generator, C MT,i,t represents the cost of the micro gas turbine, CDR,i,t Denote the cost of DR in the microgrid as C ES,i,t Denote the cost of ES as C PV&OM,i,t Denote the operation and maintenance cost of the photovoltaic panels of the i-th microgrid at time t as C DE&OM,i,t Denote the operation and maintenance cost of DE in the i-th microgrid at time t as C DE&Fuel,i,t Denote the fuel cost of DE in the i-th microgrid at time t as C DE&Waste,i,t Denote the pollution treatment cost of DE in the i-th microgrid at time t as C MT&OM,i,t Denote the operation and maintenance cost of MT in the i-th microgrid at time t as C MT&Fuel,i,t Denote the fuel cost of MT in the i-th microgrid at time t as C MT&Waste,i,t Denote the pollution treatment cost of MT in the i-th microgrid at time t as C Net,i,t Denote the power purchase cost and sales revenue of the microgrid from / to the distribution network, expressed as:

[0029]

[0030] Where, P Net,i,t Denote the purchase and sale of electricity from / to the distribution network in the microgrid. ρ1 represents the purchase price. When P Net,i,t is positive, the microgrid purchases electricity from the distribution network at this time. ρ2 represents the electricity price. When P Net,i,t is negative, it means the microgrid sells electricity to the distribution network.

[0031] As a preferred scheme of the two-layer economic optimal scheduling method for the source-network-load storage collaborative microgrid described in the present invention, where: the upper distribution network layer of the distributed network two-layer collaborative economic optimal scheduling model is expressed as:

[0032]

[0033] The constraint conditions are expressed as:

[0034]

[0035] Where, ρ set Denote the fixed electricity price, and ρ t Denote the time-of-use price.

[0036] As a preferred solution of the two - layer economic optimal scheduling method for a micro - grid with source - network - load - storage coordination according to the present invention, the following steps are included: updating and iterating the algorithm includes using an enhanced adaptive differential evolution (DE) algorithm to update the purchase and sale prices of the upper - layer distribution network, taking the objective of the upper - layer model as known quantities and inputting them into the lower - layer model; in the lower - layer model, using a CPLEX solver to minimize the total expenditure and power consumption of the micro - grid under the guidance of the upper - layer input; communicating the optimization results back to the upper - layer and obtaining the final result through iterative optimization.

[0037] Another object of the present invention is to provide a two - layer economic optimal scheduling system for a micro - grid with source - network - load - storage coordination. This system can effectively improve the economy of the distribution network and the operation efficiency of the micro - grid through the collaborative work of a source - network model module, a lower - layer micro - grid layer module, an upper - layer distribution network layer module, and an iterative optimization module.

[0038] To solve the above - mentioned technical problems, the present invention provides the following technical solution: A two - layer economic optimal scheduling system for a micro - grid with source - network - load - storage coordination, including: a source - network model module, a lower - layer micro - grid layer module, an upper - layer distribution network layer module, and an iterative optimization module;

[0039] The source - network model module establishes a source - network load - storage - side model;

[0040] The lower - layer micro - grid layer module further establishes a lower - layer micro - grid layer of a two - layer collaborative economic optimal scheduling model for the micro - grid distributed network according to the source - network load - storage - side model;

[0041] The upper - layer distribution network layer module further establishes an upper - layer distribution network layer of a two - layer collaborative economic optimal scheduling model for the micro - grid distributed network according to the source - network load - storage - side model;

[0042] The iterative optimization module updates and iterates the algorithm to increase the revenue of the distribution network.

[0043] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of the two - layer economic optimal scheduling method for a micro - grid with source - network - load - storage coordination as described above are implemented.

[0044] A computer - readable storage medium stores a computer program. It is characterized in that when the computer program is executed by a processor, the steps of the two - layer economic optimal scheduling method for a micro - grid with source - network - load - storage coordination as described above are implemented.

[0045] Advantages of the present invention: effectively reduce unnecessary energy waste, rationally utilize the production advantages of renewable energy, and reasonably dispatch in combination with an energy storage system to ensure the economy of the system; optimize energy utilization and load management; promote the application of renewable energy and the popularization of distributed generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 It is a flowchart of a two-layer economic optimal dispatch method for a microgrid with source-network-load-storage coordination provided by an embodiment of the present invention.

[0048] Figure 2 It is an optimization flowchart of a two-layer economic optimal dispatch method for a microgrid with source-network-load-storage coordination provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] Example 1, referring to Figure 1 - Figure 2 , which is an embodiment of the present invention. This embodiment provides a two-layer economic optimal dispatch method for a microgrid with source-network-load-storage coordination, including:

[0051] S1: Establish a source-network load-storage side model.

[0052] It should be noted that collect and sort out the power generation data of each distributed resource in each time period and statistically evaluate the peak power consumption situation of each power source. As shown in S1 in Figure 1 , respectively establish mathematical models for the power supply side, load side, and energy storage side. The source-network load-storage side model includes a source side model, a load side model, and an energy storage side model; among them, the source side model includes photovoltaic power generation, diesel generators, and micro gas turbines.

[0053] Furthermore, since photovoltaic is a clean energy source and there are no harmful exhaust gases to the environment during power generation, only its operation and maintenance costs can be considered. Photovoltaic power generation (PV) is expressed as:

[0054] C PV&OM,i = K PV P PV,i,t T PV

[0055] Among them, C PV&OM,i represents the operation and maintenance cost of the i-th microgrid photovoltaic panel, and K PV represents the operation and maintenance coefficient of the photovoltaic panel, P PV,i,t represents the output of the i-th PV microgrid at time t, and T PV represents the time of photovoltaic power generation in the total dispatching time;

[0056] During the power generation process of a diesel generator, the combustion of diesel will produce a large amount of harmful gases such as carbon dioxide and sulfur dioxide, polluting the environment; in order to reduce its harm to the environment, the costs related to fuel, operation, maintenance, and pollutant treatment need to be considered. The diesel engine generator (DE) is expressed as:

[0057]

[0058] Among them, C DE&OM,i represents the operation and maintenance cost of DE in the i-th microgrid, C DE&Fuel,i represents the fuel cost of DE in the i-th microgrid, C DE&Waste,i represents the pollutant treatment cost of DE in the i-th microgrid, P DE,i,t represents the output of DE of the microgrid at time t, K DE represents the DE operation and maintenance coefficient, ɑ DE , β DE , γ DE represents the DE fuel coefficient, C DE,k represents the cost coefficient of DE for treating k-type polluting gases, and γ DE,k represents the emission coefficient of polluting gases of the diesel generator;

[0059] MT provides a flexible and fast output adjustment function. The operating efficiency of the micro gas turbine (MT) is expressed as:

[0060]

[0061] Among them, η MT represents the operating efficiency of MT, and P MT,i,t represents the output power of the i-th MT microgrid at time t;

[0062]

[0063] Among them, C DE&OM,i represents the operation and maintenance cost of MT in the i-th microgrid, C DE&Fuel,iDenote the fuel cost of MT in the \(i\)-th microgrid as \(C\). DE&Waste,i Denote the pollutant treatment cost of MT in the \(i\)-th microgrid as \(K\). MT Denote the operation and maintenance coefficient of MT as \(C\). f Denote the natural gas price, taking 2.53 yuan / m. 3 , where \(LHV\) represents the lower heating value of natural gas, taking 9.73 kWh / m. 3 , \(C\). MT,k Denote the cost coefficient of MT for treating the \(k\)-th type of polluting gas as \(\gamma\). MT,k Denote the emission coefficient of the \(k\)-th type of polluting gas of the micro gas turbine.

[0064] Furthermore, the load side model:

[0065] Among the microgrid loads involved in this paper, there are fixed loads, shiftable loads, and interruptible loads, and incentive demand side response (DR) is adopted; the adjusted revenue model is expressed as:

[0066] \(C\). DR,i = \(r\). i,t \(P\). DR,i,t

[0067] Among them, \(C\). DR,i Denote the adjusted revenue after DR of the microgrid user as \(P\). DR,i,t Denote the DR power in the microgrid as \(r\). i,t Denote the DR compensation price in the microgrid;

[0068] Energy storage side model:

[0069] This paper considers the adjustment cost of energy storage (ES), and the specific model is expressed as:

[0070]

[0071] Among them, \(C\). ES,i Denote the adjustment cost of the microgrid energy storage unit, \(\eta_0\) represents the depreciation coefficient. Denote the charging power of the battery. Denote the discharging power of the storage battery.

[0072] S2: According to the source network load-storage side model, further establish the lower layer microgrid layer of the microgrid distributed network two-layer collaborative economic optimal scheduling model.

[0073] It should be noted that as Figure 1 shown in S2, the goal of the microgrid layer is to minimize the total cost, and the lower layer microgrid layer of the microgrid distributed network two-layer collaborative economic optimal scheduling model is expressed as:

[0074]

[0075] Among them, T represents the total number of hours of power grid dispatching, N represents the number of microgrids, C PV,i,t represents the cost of photovoltaic panels, C DE,i,t represents the cost of diesel generators, C MT,i,t represents the cost of micro gas turbines, C DR,i,t represents the cost of DR in the microgrid, C ES,i,t represents the cost of ES, C PV&OM,i,t represents the operation and maintenance cost of the photovoltaic panels of the i-th microgrid at time t, C DE&OM,i,t represents the operation and maintenance cost of DE in the i-th microgrid at time t, C DE&Fuel,i,t represents the fuel cost of DE in the i-th microgrid at time t, C DE&Waste,i,t represents the pollution treatment cost of DE in the i-th microgrid at time t, C MT&OM,i,t represents the operation and maintenance cost of MT in the i-th microgrid at time t, C MT&Fuel,i,t represents the fuel cost of MT in the i-th microgrid at time t, C MT&Waste,i,t represents the pollution treatment cost of MT in the i-th microgrid at time t, C Net,i,t represents the electricity purchase cost and sales revenue of the microgrid from the distribution network, expressed as:

[0076]

[0077] Among them, P Net,i,t represents the purchase and sale of electricity from the distribution network in the microgrid, ρ1 represents the purchase price, when P Net,i,t is positive, the microgrid purchases electricity from the distribution network at this time, ρ2 represents the electricity price, when P Net,i,t is negative, it means that the microgrid sells electricity to the distribution network.

[0078] S3: According to the source network load-storage side model, further establish the upper distribution network layer of the microgrid distributed network double-layer collaborative economic optimization scheduling model.

[0079] It should be noted that, as Figure 1 shown in S3, for the upper distribution network layer of the distributed network double-layer collaborative economic optimization scheduling model, in order to optimize the revenue of the distribution network layer, the objective function is expressed as:

[0080]

[0081] The constraint conditions are expressed as:

[0082]

[0083] Among them, ρ set represents the fixed electricity price, taking 0.35, ρ t represents the time-of-use price.

[0084] S4: Update and iterate the algorithm to increase the revenue of the distribution network.

[0085] It should be noted that, as Figure 1 shown in S4, as Figure 2 shown, the enhanced adaptive DE algorithm is used to update the purchase and sales prices of the upper-layer distribution network, and the objective of the upper-layer model is input into the lower-layer model as known quantities. In the lower-layer model, the CPLEX solver is used to minimize the total expenditure and power consumption of the microgrid under the guidance of the upper-layer input; then the optimization results are communicated back to the upper layer, and the final results are obtained through iterative optimization.

[0086] The above is a schematic solution of a two-layer economic optimal scheduling method for a microgrid with source-network load storage coordination in this embodiment. It should be noted that the technical solution of the system of the two-layer economic optimal scheduling method for a microgrid with source-network load storage coordination belongs to the same concept as the technical solution of the above two-layer economic optimal scheduling method for a microgrid with source-network load storage coordination. For the details not described in detail in the technical solution of the two-layer economic optimal scheduling system for a microgrid with source-network load storage coordination in this embodiment, reference can be made to the description of the technical solution of the above two-layer economic optimal scheduling method for a microgrid with source-network load storage coordination.

[0087] Embodiment 2 is an embodiment of the present invention. This embodiment provides a two-layer economic optimal scheduling method for a microgrid with source-network load storage coordination, including: a source network model module, a lower-layer microgrid layer module, an upper-layer distribution network layer module, and an iterative optimization module;

[0088] The source network model module establishes a source network load-storage side model;

[0089] The lower-layer microgrid layer module further establishes a lower-layer microgrid layer of a two-layer collaborative economic optimal scheduling model for the microgrid distributed network according to the source network load-storage side model;

[0090] The upper-layer distribution network layer module further establishes an upper-layer distribution network layer of a two-layer collaborative economic optimal scheduling model for the microgrid distributed network according to the source network load-storage side model;

[0091] The iterative optimization module updates and iterates the algorithm to increase the revenue of the distribution network.

[0092] This embodiment also provides a computing device applicable to the case of the two-layer economic optimal scheduling method for a microgrid with source-network load storage coordination, including:

[0093] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the two-layer economic optimal scheduling method for a microgrid with source-network load storage coordination proposed in the above embodiment.

[0094] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the microgrid two-layer economic optimal scheduling method for source-grid-load storage coordination proposed in the above embodiment.

[0095] The storage medium proposed in this embodiment and the microgrid two-layer economic optimal scheduling method for source-grid-load storage coordination proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be referred to in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0096] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0097] Logic and / or steps described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0098] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0099] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A two-layer economic optimization dispatching method for microgrids with source-grid-load-storage coordination, characterized by: include: Establish source network load-storage side model; According to the source network load-storage side model, the lower microgrid layer of the two-layer collaborative economic optimization dispatch model of the microgrid distributed network is further established; According to the source network load-storage side model, the upper distribution network layer of the two-layer collaborative economic optimization dispatch model of the microgrid distributed network is further established; The algorithm is updated and iterated to increase the revenue of the distribution network.

2. The source-grid-load-storage coordinated two-layer economic optimization scheduling method for a microgrid according to claim 1, characterized in that: The source network load-storage side model includes a source end model, a load side model and an energy storage side model.

3. The source-grid-load-storage coordinated two-layer economic optimization scheduling method for a microgrid as claimed in claim 2, characterized in that: The source end model includes photovoltaic power generation, diesel generator and micro gas turbine; Among them, photovoltaic power generation is expressed as: C PV&OM,i =K PV P PV,i,t T PV Among them, C PV&OM,i represents the operation and maintenance cost of the ith microgrid photovoltaic panel, K PV Represents the operation and maintenance factor of the photovoltaic panel, P PV,i,t represents the output of the ith PV microgrid at time t, T PV Indicates the time of photovoltaic power generation in the total scheduling time; Among them, the diesel generator is expressed as: Among them, C DE&OM,i represents the operation and maintenance cost of DE in the i-th microgrid, C DE&Fuel,i represents the fuel cost of DE in the i-th microgrid, C DE&Waste,i represents the pollutant treatment cost of DE in the i-th microgrid, P DE,i,t represents the DE output of the microgrid at time t, K DE represents the DE operation and maintenance coefficient, α DE , β DE , γ DE Denotes DE fuel coefficient, C DE,k represents the cost coefficient of DE for treating type k polluted gas, γ DE,k Indicates the emission coefficient of pollutant gas from diesel generators; Among them, the micro gas turbine is expressed as: Among them, η MT Indicates the operating efficiency of MT, P MT,i,t represents the output power of the i-th MT microgrid at time t; Among them, C DE&OM,i represents the operation and maintenance cost of MT in the i-th microgrid, C DE&Fuel,i represents the fuel cost of MT in the i-th microgrid, C DE&Waste,i represents the pollutant treatment cost of MT in the i-th microgrid, K MT represents the MT operation and maintenance coefficient, C f represents the natural gas price, LHV represents the lower heating value of natural gas, C MT,k represents the cost coefficient of MT treatment of type k polluted gas, γ MT,k It represents the emission coefficient of type k pollutant gas from micro gas turbine.

4. The source-grid-load-storage coordinated two-layer economic optimization scheduling method for a microgrid as claimed in claim 3, characterized in that: The load side model is expressed as: C DR,i =r i,t P DR,i,t Among them, C DR,i represents the adjusted income of microgrid users after DR, P DR,i,t represents the DR power in the microgrid, r i,t represents the DR compensation price in the microgrid; The energy storage side model is expressed as: Among them, C ES,i represents the adjustment cost of the microgrid energy storage unit, η0 represents the depreciation coefficient, Indicates the charging power of the battery. Indicates the discharge power of the battery.

5. The source-grid-load-storage coordinated two-layer economic optimization scheduling method of microgrid according to claim 4, characterized in that: The lower microgrid layer of the distributed network two-layer collaborative economic optimization dispatch model is expressed as: Where T represents the total number of hours of grid dispatch, N represents the number of microgrids, and C PV,i,t represents the cost of photovoltaic panels, C DE,i,t represents the diesel generator cost, C MT,i,t represents the cost of the micro gas turbine, C DR,i,t represents the cost of DR in microgrid, C ES,i,t represents the cost of ES, C PV&OM,i,t represents the operation and maintenance cost of the ith microgrid photovoltaic panel at time t, C DE&OM,i,t represents the operation and maintenance cost of DE in the i-th microgrid at time t, C DE&Fuel,i,t represents the fuel cost of DE in the i-th microgrid at time t, C DE&Waste,t,t represents the pollution treatment cost of DE in the i-th microgrid at time t, C MT&OM,i,t represents the operation and maintenance cost of MT in the i-th microgrid at time t, C MT&Fuel,i,t represents the fuel cost of MT in the i-th microgrid at time t, C MT&Waste,i,t represents the pollution treatment cost of MT in the i-th microgrid at time t, C Net,i,t It represents the electricity purchase cost and sales revenue of the microgrid from the distribution network, which is expressed as: Among them, P Net,i,t represents the purchase and sale of electricity from the distribution network in the microgrid, ρ1 represents the purchase price, and when P Net,i,t When P is positive, the microgrid buys electricity from the distribution network. ρ2 represents the electricity price. Net,i,t When it is negative, it means that the microgrid sells electricity to the distribution grid.

6. The source-grid-load-storage coordinated two-layer economic optimization dispatching method for a microgrid as claimed in claim 5, characterized in that: The upper distribution network layer of the distributed network two-layer collaborative economic optimization dispatch model is expressed as: The constraints are expressed as: Among them, ρ set represents the fixed electricity price, ρ t Indicates the price of usage time.

7. The source-grid-load-storage coordinated two-layer economic optimization dispatching method for a microgrid according to claim 6, characterized in that: The updating and iterating of the algorithm includes using an enhanced adaptive DE algorithm to update the purchase and sales prices of the upper distribution network, and the target of the upper model is input into the lower model as a known quantity; in the lower model, the CPLEX solver is used to minimize the overall expenditure and power consumption of the microgrid under the guidance of the upper input; the optimization results are communicated back to the upper layer, and the final results are obtained through iterative optimization.

8. A system for two-layer economic optimization scheduling of a microgrid based on the source-grid-load-storage collaboration according to any one of claims 1 to 7, characterized in that: include: Source network model module, lower microgrid layer module, upper distribution network layer module and iterative optimization module; The source network model module establishes a source network load-storage side model; The lower microgrid layer module further establishes the lower microgrid layer of the microgrid distributed network double-layer collaborative economic optimization scheduling model according to the source network load-storage side model; The upper distribution network layer module further establishes the upper distribution network layer of the microgrid distributed network double-layer collaborative economic optimization dispatch model according to the source network load-storage side model; The iterative optimization module updates and iterates the algorithm to increase the revenue of the distribution network.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the source-grid-load-storage coordinated microgrid two-layer economic optimization scheduling method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the source-grid-load-storage coordinated microgrid two-layer economic optimization scheduling method described in any one of claims 1 to 7 are implemented.

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