Multi-microgrid system operation optimization method and system considering distributed energy transaction
By constructing an operation optimization model for a multi-microgrid system and optimizing energy exchange using the iterative sub-gradient method, the problem of low energy exchange efficiency between microgrids was solved, thereby improving the system's stability and economy.
Patent Information
- Application Number
- CN202510682256.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-26
- Publication Date
- 2025-10-28
AI Technical Summary
Existing technologies lack operational optimization methods for multi-microgrid systems that consider distributed energy trading, resulting in low energy exchange efficiency between microgrids and high system operating costs.
By constructing an operational optimization model for a multi-microgrid system, the iterative sub-gradient method is used to decompose the problem into local sub-problems. Combined with Lagrange multipliers to optimize energy exchange, energy trading between microgrids is realized to minimize the total cost.
It improves the stability and economy of multi-micronet systems, reduces operating costs, and meets the local needs and privacy protection of each micronet.
Smart Images
Figure CN120855255A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system optimization and dispatching technology, and in particular to an operation optimization method and system for a multi-microgrid system that considers distributed energy trading. Background Technology
[0002] Microgrids, as an important carrier of distributed renewable energy, enable the synergistic complementarity of different energy sources, maximizing energy utilization, reducing carbon emissions, and ultimately achieving green and sustainable energy development. Therefore, microgrids have significant advantages in addressing the coordinated development of distributed and traditional energy sources. To improve the capabilities of smart grids, a typical approach is to consider the scenario where several microgrids exchange energy with each other, even if these microgrids are in an isolated state, i.e., disconnected from the main grid. In other words, energy flows within a group of adjacent microgrids, but there is no energy flow between the microgrids and the main grid. However, currently, there is no operational optimization method or system for multi-microgrid systems that considers distributed energy trading. Summary of the Invention
[0003] To address the problems in existing technologies, this invention provides an operation optimization method and system for a multi-microgrid system considering distributed energy trading. The multi-microgrid system consists of M microgrids, where: (1) each microgrid has associated energy generation costs; (2) the distribution network operator incurs costs for energy transmission between adjacent microgrids; and (3) each microgrid has a specific electricity demand that must be met. Under these considerations, the goal is to find the optimal energy exchange between the microgrids to minimize the total operating cost of the system (energy production and transportation costs). The specific technical solution is as follows:
[0004] An operation optimization method for a multi-microgrid system considering distributed energy trading includes the following steps:
[0005] Step S1: Construct a multi-microgrid system consisting of M interconnected microgrids;
[0006] Step S2: Under the premise that all microgrids in the multi-microgrid system interact through energy exchange, construct a system with the objective function of minimizing the total operating cost of the multi-microgrid system.
[0007] Step S3: Establish energy balance constraints and power constraints within the microgrid, and combine them with the objective function in step S2 to build an operation optimization model for the multi-microgrid system.
[0008] Step S4: Decompose the operation optimization model of the multi-microgrid system into multiple local sub-problems with reduced complexity, find their dual problems, and then use the iterative sub-gradient method to solve the dual problems to find the minimum cost of operating the multi-microgrid system and the energy exchange value between each microgrid.
[0009] Step S5: Control the operation of each microgrid based on the energy exchanged between them.
[0010] Preferably, step S1 specifically includes the following steps:
[0011] Assume that within each scheduling interval, microgrid i produces a unit energy. Consume unit energy It also allows microgrid i to sell energy E to microgrid j. i,j And purchase energy E from microgrid k k,i ;
[0012] The energy balance requirements within microgrid i are as follows:
[0013]
[0014] Among them, A T This is the transpose of the link matrix A; link matrix A = [a i,j ]M×M, representing that if there is a connection between microgrid i and microgrid j, then element a i,j =1, otherwise 0, and a is specified. i,i =0; e i Represents the i-th column of an M-order identity matrix; and It is an M-dimensional column vector. This represents the energy vector that microgrid i sells to other microgrids. This represents the energy vector that microgrid i purchases from other microgrids, i.e.:
[0015]
[0016] Preferably, the total cost of operating the multi-microgrid system in step S2 includes the cost of generating energy and the cost of transmitting energy, and the objective function is as follows:
[0017] F = min(f1 + f2); (3)
[0018]
[0019] Where F is the total system cost, f1 is the cost incurred by the microgrid in producing energy, f2 is the cost incurred by the microgrids in transferring energy between microgrids; C i Generate for the i-th microgrid The cost per unit of energy, where γ is the cost of transferring a unit of energy between interconnected microgrids.
[0020] Preferably, the energy balance constraints and power constraints within the microgrid in step S3 are as follows:
[0021]
[0022] in, This represents the energy E that microgrid i is allowed to transfer to microgrid j. i,j The maximum value.
[0023] Preferably, the operation optimization model of the multi-microgrid system in step S3 is as follows:
[0024]
[0025] in, Let this be a new variable, representing the energy sold by microgrid i; The price at which microgrid i purchases energy from other microgrids, γ(E) M,i ) represents the price at which microgrid i purchases energy from microgrid M.
[0026] Preferably, step S4, which decomposes the operation optimization model of the multi-microgrid system into multiple local sub-problems with reduced complexity and finds their dual problems, specifically includes the following steps:
[0027] For each micronet i, first use the new variables Let represent the energy sold by microgrid i, and then force it to be equal to all the energy purchased from other microgrids, thus obtaining the coupling constraint, i.e. The original problem of the operation optimization model of the multi-micronet system is then decomposed into M subproblems, which can be rewritten in the following equivalent form:
[0028]
[0029] Relax coupling constraints Solve the objective function for the following dual problem:
[0030] F = maxC(λ); (10)
[0031]
[0032] in, The objective function for locally minimizing the subproblem is expressed as follows:
[0033]
[0034] At this point, a local minimization problem with a given parameter λ is introduced:
[0035]
[0036] The local minimization problem with given parameter λ represents the contribution of microgrid i to the Lagrangian function relative to the original problem. For all i = [1,…,M], the parameter vector λ = [λ1···λ M ] T It incorporates coupling constraints All corresponding Lagrange multipliers λ i .
[0037] Preferably, the step S4 of solving the dual problem using the iterative subgradient method specifically includes the following steps:
[0038] The iterative subgradient method is used to find a sequence of optimal points {λ[k]} that converges to the dual problem; specifically, for each point λ[k], the local subproblems of microgrid i are solved. And determine the minimum point To minimize the contribution of microgrid i to the Lagrange function;
[0039] The Lagrange multipliers are updated using the following formula:
[0040]
[0041] Where α[k] is the positive step factor.
[0042] An operation optimization system for a multi-microgrid system considering distributed energy trading, using the method described above, includes: a multi-microgrid system construction module for constructing a multi-microgrid system consisting of M interconnected microgrids;
[0043] An optimization objective function construction module is used to construct a system with the objective function of minimizing the total operating cost of the multi-microgrid system, under the premise that all microgrids in the multi-microgrid system interact through energy exchange.
[0044] The operation optimization model construction module is used to establish energy balance constraints and power constraints within the microgrid, and to build an operation optimization model for the multi-microgrid system in conjunction with the objective function in step S2.
[0045] The model solving module is used to decompose the operation optimization model of the multi-microgrid system into multiple local subproblems with reduced complexity, find their dual problems, and then use the iterative sub-gradient method to solve the dual problems to find the minimum cost of operating the multi-microgrid system and the energy exchange value between each microgrid.
[0046] The operation control module is used to control the operation of each microgrid based on the energy values exchanged between them.
[0047] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the operation optimization method for a multi-microgrid system considering distributed energy trading.
[0048] A processor for running a program, wherein the program executes the aforementioned method for optimizing the operation of a multi-microgrid system considering distributed energy trading.
[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0050] This invention provides an operation optimization method and system for multi-microgrid systems considering distributed energy trading. Several microgrids interact by exchanging energy to minimize operating costs while still meeting the local needs of each microgrid. The algorithm is scalable with the number of microgrids and maintains the privacy of local cost functions and local consumption. This invention improves the stability and economy of multi-microgrid systems. Attached Figure Description
[0051] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.
[0052] Figure 1 This is a flowchart of the method of the present invention.
[0053] Figure 2 The diagram shows the microgrid topology: (a) fully connected, (b) ring, and (c) linear.
[0054] Figure 3 The local load is E ( c ) =[11,11,11,E4 ( c ) The local cost curve for MWh using a fully connected topology.
[0055] Figure 4 The local load is E (c) =[11,11,11,E4 (c) A graph showing the energy sold by G-4 at MWh (left), its quantity, and its unit price (right).
[0056] Figure 5 The local load is E (c) =[11,11,11,E4 (c) The local cost curve for MWh using a ring topology.
[0057] Figure 6 The local load is E (c) =[11,11,11,E4 (c) The local cost curve for MWh using a linear topology.
[0058] Figure 7 This is a system schematic diagram of the present invention. Detailed Implementation
[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0060] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.
[0061] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.
[0062] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.
[0063] Example 1:
[0064] like Figure 1 As shown, this embodiment provides an operation optimization method for a multi-microgrid system considering distributed energy trading, including the following steps:
[0065] Step S1: Construct a multi-microgrid system consisting of M interconnected microgrids. This includes the following steps:
[0066] Assume that within each scheduling interval, microgrid i produces a unit energy. Consume unit energy It also allows microgrid i to sell energy E to microgrid j. i,j And purchase energy E from microgrid k k,i ;
[0067] The energy balance requirements within microgrid i are as follows:
[0068]
[0069] Among them, A T This is the transpose of the link matrix A; link matrix A = [a i,j]M×M, representing that if there is a connection between microgrid i and microgrid j, then element a i,j =1, otherwise 0, and a is specified. i,i =0; e i Represents the i-th column of an M-order identity matrix; and It is an M-dimensional column vector. This represents the energy vector that microgrid i sells to other microgrids. This represents the energy vector that microgrid i purchases from other microgrids, i.e.:
[0070]
[0071] Step S2: Under the premise that all microgrids in the multi-microgrid system interact through energy exchange, construct a system with the objective function of minimizing the total operating cost of the multi-microgrid system.
[0072] The total operating cost of a multi-microgrid system includes the cost of generating energy and the cost of transmitting energy. The specific objective function is as follows:
[0073] F = min(f1 + f2); (3)
[0074]
[0075] Where F is the total system cost, f1 is the cost incurred by the microgrid in producing energy, f2 is the cost incurred by the microgrids in transferring energy between microgrids; C i Generate for the i-th microgrid The cost per unit of energy, where γ is the cost of transferring a unit of energy between interconnected microgrids.
[0076] Step S3: Establish energy balance constraints and power constraints within the microgrid, and combine them with the objective function in step S2 to build an operation optimization model for the multi-microgrid system.
[0077] The specific energy balance constraints and power constraints within the microgrid are as follows:
[0078]
[0079] in, This represents the energy E that microgrid i is allowed to transfer to microgrid j. i,j The maximum value.
[0080] The specific operation optimization model for a multi-microgrid system is as follows:
[0081]
[0082] in, Let this be a new variable, representing the energy sold by microgrid i; The price at which microgrid i purchases energy from other microgrids, γ(E) M,i ) represents the price at which microgrid i purchases energy from microgrid M.
[0083] Step S4: Decompose the operation optimization model of the multi-microgrid system into multiple local subproblems with reduced complexity, find their dual problems, and then use the iterative sub-gradient method to solve the dual problems to find the minimum cost of operating the multi-microgrid system and the energy exchange value between each microgrid.
[0084] The optimization model of a multi-microgrid system is decomposed into multiple local sub-problems with reduced complexity, and its dual problem is found. Specifically, this involves the following steps:
[0085] For each micronet i, first use the new variables Let represent the energy sold by microgrid i, and then force it to be equal to all the energy purchased from other microgrids, thus obtaining the coupling constraint, i.e. The original problem of the operation optimization model of the multi-micronet system is then decomposed into M subproblems, which can be rewritten in the following equivalent form:
[0086]
[0087] Relax coupling constraints Solve the objective function for the following dual problem:
[0088] F = maxC(λ); (10)
[0089]
[0090] in, The objective function for locally minimizing the subproblem is expressed as follows:
[0091]
[0092] At this point, a local minimization problem with a given parameter λ is introduced:
[0093]
[0094] The local minimization problem with given parameter λ represents the contribution of microgrid i to the Lagrangian function relative to the original problem. For all i = [1,…,M], the parameter vector λ = [λ1···λ M ] T It incorporates coupling constraints All corresponding Lagrange multipliers λ i .
[0095] Solving the dual problem using the iterative subgradient method specifically includes the following steps:
[0096] The iterative subgradient method is used to find a sequence of optimal points {λ[k]} that converges to the dual problem; specifically, for each point λ[k], the local subproblem C of microgrid i is solved. i (l) (λ) and determine the minimum point. To minimize the contribution of microgrid i to the Lagrange function;
[0097] The Lagrange multipliers are updated using the following formula:
[0098]
[0099] Where α[k] is the positive step factor.
[0100] All necessary data in this process is calculated using microgrid i, without requiring an external centralized control unit. The information exchanged between microgrids is limited to Lagrange multipliers {λ}. i} and the required energy value {E} j，i These calculations are performed at micronet i and only passed to the corresponding micronet j. Therefore, both privacy and traffic restrictions are satisfied.
[0101] Step S5: Control the operation of each microgrid based on the energy exchanged between them.
[0102] For a system consisting of four microgrids, for simplicity, it is assumed that all microgrids have the same generation cost function. More specifically, its generator quadratic cost function is C(x) = a + bx + cx. 2 The coefficients are a = 86.3852, b = 56.5640 / MWh, and c = 0.3284 / (MWh). 2 Furthermore, the original objective cost function has been multiplied by the function... In order to include a soft constraint that specifies the maximum power generation Regarding the transfer cost function, it is modeled as a cubic polynomial γ(x) = x + x 3 , where x is megawatt-hours and γ(x) is dollars. The following is given. Figure 2 Numerical results for three topologies: fully connected, ring-shaped, and linear.
[0103] To gain a deeper understanding of the evolution of price and energy flow, consider a scenario where all local load remains constant at 11 MWh (slightly higher). ), except that the load of G-4 varies from 1 to 11 MWh. Figure 3 , Figure 5 and Figure 6 In the report, for the four microgrids, the local costs after convergence are presented. This refers to the minimum "net expenditure cost." For benchmarking purposes, the cost of each microgrid under disconnection conditions is also described (i.e., represented by dashed lines when no transactions are conducted). It can be seen that the optimal transaction always brings some benefit (cost reduction) to all microgrids.
[0104] exist Figure 3 As can be seen, the cost gained after the G-4 transaction initially follows the cost of disconnecting the microgrid. The gain only becomes significant when the local load exceeds 6 MWh. It reaches its maximum value at a certain time, and then decreases again until... The time becomes zero. This is because at this point all microgrids have the same internal demand, and due to symmetry, there is no energy exchange. In reality, the gain is the result of G-4 selling energy to other microgrids; the quantity, unit price, and corresponding revenue are as follows: Figure 4 As shown. G-4 in The gain gained at that time is almost negligible, because it results in selling a large amount of energy at a very low price. However, when At that time, microgrids sell less energy, but the unit price increases quickly enough to boost revenue (e.g., Figure 4 (As shown in the "income" curve); as mentioned above, when At that time, the microgrid achieves the optimal trade-off between the energy it sells and its unit price. When the value is large, the higher selling price cannot compensate for the reduced sales capacity, and the revenue tends to zero. Besides fully connected topologies, ring-connected topologies (see...) Figure 2 It exhibits certain specific characteristics. For example, Figure 5 Showing the ring topology The local overhead. It's possible that G-2 and G-3 gain some additional benefits by acting as intermediaries between G-4 and G-1. More precisely, after the transaction process, the energy at G-{2,3} is not only purchased to meet internal needs but also resold to G-1. By doing so, G-{2,3} can significantly reduce its local costs. Furthermore, linear topology significantly impacts costs; in this case, the microgrid is connected via a linear topology at the end of the line through G-4. Therefore, G-3 can be considered the bottleneck of the system, as all energy from G-4 to G-{1,2} inevitably passes through it. From Figure 6 As can be seen, this situation is advantageous to G-3.
[0105] This invention addresses a problem where several microgrids interact by exchanging energy to minimize system operating costs while still meeting their local needs. In this context, an iterative distributed algorithm is proposed that scales with the number of microgrids while preserving the privacy of local cost functions and local consumption. More specifically, each algorithm iteration includes a local minimization step and a market-clearing process. In the first step, each microgrid calculates its local energy bid and displays it to potential sellers. Secondly, during the market-clearing process, energy prices are adjusted according to demand patterns. Finally, numerical results confirm that the algorithm converges after a reasonable number of iterations and that gains do indeed exist in disconnected microgrids, largely depending on energy demand and network topology.
[0106] Example 2:
[0107] like Figure 7 As shown, based on the same inventive concept as Embodiment 1, this embodiment provides an operation optimization system for a multi-microgrid system considering distributed energy trading, and the method described includes:
[0108] The multi-microgrid system construction module is used to construct a multi-microgrid system consisting of M interconnected microgrids.
[0109] An optimization objective function construction module is used to construct a system with the objective function of minimizing the total operating cost of the multi-microgrid system, under the premise that all microgrids in the multi-microgrid system interact through energy exchange.
[0110] The operation optimization model construction module is used to establish energy balance constraints and power constraints within the microgrid, and to build an operation optimization model for the multi-microgrid system in conjunction with the objective function in step S2.
[0111] The model solving module is used to decompose the operation optimization model of the multi-microgrid system into multiple local subproblems with reduced complexity, find their dual problems, and then use the iterative sub-gradient method to solve the dual problems to find the minimum cost of operating the multi-microgrid system and the energy exchange value between each microgrid.
[0112] The operation control module is used to control the operation of each microgrid based on the energy values exchanged between them.
[0113] Example 3:
[0114] Based on the same inventive concept as Embodiment 1, this embodiment provides a computer-readable storage medium, which includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to execute the aforementioned method for optimizing the operation of a multi-microgrid system considering distributed energy trading.
[0115] Example 4:
[0116] Based on the same inventive concept as Embodiment 1, this embodiment provides a processor for running a program, wherein the program executes the aforementioned method for optimizing the operation of a multi-microgrid system considering distributed energy trading.
[0117] Those skilled in the art will recognize that the modules of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.
[0118] In the embodiments provided by this invention, it should be understood that the division of modules is only a logical functional division. In actual implementation, there may be other division methods, such as multiple modules can be combined into one module, one module can be split into multiple modules, or some features can be ignored.
[0119] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.
[0120] If the integrated module is implemented as a software functional module and sold or used as an independent product, it 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 all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0121] 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 them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.
Claims
1. A method for optimizing the operation of a multi-microgrid system considering distributed energy trading, characterized in that, Includes the following steps: Step S1: Construct a multi-microgrid system consisting of M interconnected microgrids; Step S2: Under the premise that all microgrids in the multi-microgrid system interact through energy exchange, construct a system with the objective function of minimizing the total operating cost of the multi-microgrid system. Step S3: Establish energy balance constraints and power constraints within the microgrid, and combine them with the objective function in step S2 to build an operation optimization model for the multi-microgrid system. Step S4: Decompose the operation optimization model of the multi-microgrid system into multiple local sub-problems with reduced complexity, find their dual problems, and then use the iterative sub-gradient method to solve the dual problems to find the minimum cost of operating the multi-microgrid system and the energy exchange value between each microgrid. Step S5: Control the operation of each microgrid based on the energy exchanged between them.
2. The method for optimizing the operation of a multi-microgrid system considering distributed energy trading as described in claim 1, characterized in that, Step S1 specifically includes the following steps: Assume that within each scheduling interval, microgrid i produces a unit energy. Consume unit energy It also allows microgrid i to sell energy E to microgrid j. i,j And purchase energy E from microgrid k k,i ; The energy balance requirements within microgrid i are as follows: Among them, A T This is the transpose of the link matrix A; link matrix A = pa i,j ]M×M, representing that if there is a connection between microgrid i and microgrid j, then element a i,j =1, otherwise 0, and a is specified. i,i =0; e i Represents the i-th column of an M-order identity matrix; and It is an M-dimensional column vector. This represents the energy vector that microgrid i sells to other microgrids. This represents the energy vector that microgrid i purchases from other microgrids, i.e.:
3. The method for optimizing the operation of a multi-microgrid system considering distributed energy trading according to claim 2, characterized in that, The total cost of operating the multi-microgrid system in step S2 includes the cost of generating energy and the cost of transmitting energy. The specific objective function is as follows: F = min(f1 + f2); (3) Where F is the total system cost, f1 is the cost incurred by the microgrid in producing energy, f2 is the cost incurred by the microgrids in transferring energy between microgrids; C i Generate for the i-th microgrid The cost per unit of energy, where γ is the cost of transferring a unit of energy between interconnected microgrids.
4. The method for optimizing the operation of a multi-microgrid system considering distributed energy trading according to claim 3, characterized in that, The specific energy balance constraints and power constraints within the microgrid in step S3 are as follows: in, This represents the energy E that microgrid i is allowed to transfer to microgrid j. i,j The maximum value.
5. The method for optimizing the operation of a multi-microgrid system considering distributed energy trading according to claim 4, characterized in that, The specific operation optimization model of the multi-microgrid system in step S3 is as follows: in, Let this be a new variable, representing the energy sold by microgrid i; The price at which microgrid i purchases energy from other microgrids, γ(E) M,i ) represents the price at which microgrid i purchases energy from microgrid M.
6. The method for optimizing the operation of a multi-microgrid system considering distributed energy trading according to claim 5, characterized in that, Step S4, which decomposes the operation optimization model of the multi-microgrid system into multiple local sub-problems with reduced complexity and finds their dual problems, specifically includes the following steps: For each micronet i, first use the new variables Let represent the energy sold by microgrid i, and then force it to be equal to all the energy purchased from other microgrids, thus obtaining the coupling constraint, i.e. The original problem of the operation optimization model of the multi-micronet system is then decomposed into M subproblems, which can be rewritten in the following equivalent form: Relax coupling constraints Solve the objective function for the following dual problem: F = maxC(λ); (10) in, The objective function for locally minimizing the subproblem is expressed as follows: At this point, a local minimization problem with a given parameter λ is introduced: The local minimization problem with given parameter λ represents the contribution of microgrid i to the Lagrangian function relative to the original problem. For all i = [1,…,M], the parameter vector λ = [λ1···λ M ] T It incorporates coupling constraints All corresponding Lagrange multipliers λ i .
7. The method for optimizing the operation of a multi-microgrid system considering distributed energy trading according to claim 6, characterized in that, The step S4, which uses the iterative sub-gradient method to solve the dual problem, specifically includes the following steps: The iterative subgradient method is used to find a sequence of optimal points {λ[k]} that converges to the dual problem; specifically, for each point λ[k], the local subproblems of microgrid i are solved. And determine the minimum point To minimize the contribution of microgrid i to the Lagrange function; The Lagrange multipliers are updated using the following formula: Where α[k] is the positive step factor.
8. An operation optimization system for a multi-microgrid system considering distributed energy trading, characterized in that, The method described by any one of claims 1 to 7 includes: The multi-microgrid system construction module is used to construct a multi-microgrid system consisting of M interconnected microgrids. An optimization objective function construction module is used to construct a system with the objective function of minimizing the total operating cost of the multi-microgrid system, under the premise that all microgrids in the multi-microgrid system interact through energy exchange. The operation optimization model construction module is used to establish energy balance constraints and power constraints within the microgrid, and to build an operation optimization model for the multi-microgrid system in conjunction with the objective function in step S2. The model solving module is used to decompose the operation optimization model of the multi-microgrid system into multiple local subproblems with reduced complexity, find their dual problems, and then use the iterative sub-gradient method to solve the dual problems to find the minimum cost of operating the multi-microgrid system and the energy exchange value between each microgrid. The operation control module is used to control the operation of each microgrid based on the energy values exchanged between them.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform an operation optimization method for a multi-microgrid system considering distributed energy trading, as described in any one of claims 1 to 7.
10. A processor, characterized in that, The processor is used to run a program, wherein the program executes an operation optimization method for a multi-microgrid system considering distributed energy trading as described in any one of claims 1 to 7.