A multi-level micro-grid-based optimal scheduling method, system, device and medium

Abstract

The present application belongs to the technical field of power distribution network optimal scheduling, and discloses a kind of optimal scheduling method, system, equipment and medium based on multistage microgrid, the method includes: based on the equipment parameter information of each microgrid, combined with the purchase and sale electricity price of microgrid public connection point, constructs microgrid operation optimization model, obtains microgrid optimal scheduling strategy;Microgrid group operation optimization model is constructed and solved, until the difference of two times of solution is in predetermined range or reaches preset iteration number, and microgrid group optimal scheduling strategy is obtained;Distribution network operation optimization model is established, and distribution network operation optimization model is iteratively solved, until the difference of two times of solution is in predetermined range or reaches preset iteration number, and distribution network optimal scheduling strategy is obtained.The present application realizes the optimal scheduling of maximum autonomy of each layer and each level of microgrid-microgrid group-distribution network, and improves the safety and reliability of each level of distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution network optimization scheduling technology, and in particular to an optimization scheduling method, system, equipment and medium based on multi-level microgrids. Background Technology

[0002] With the deepening of the global energy transition and the rapid development of renewable and distributed energy technologies, traditional centralized power systems are accelerating their transformation towards distributed, intelligent, and regionally autonomous systems. Microgrids, as a crucial component of modern power systems, integrate distributed power sources, energy storage systems, and electrical loads to achieve efficient utilization and flexible dispatch of local energy resources. Against this backdrop, the coordinated optimization of multi-level energy systems has become a key direction for technological innovation in the power industry. How to achieve intelligent dispatch, resource sharing, and energy balance at the microgrid, microgrid cluster, and distribution network levels has become a critical scientific issue driving the intelligent development of power systems.

[0003] Microgrids are the basic units of distributed energy systems, consisting of distributed generation, energy storage, loads, and control equipment. They can operate in both grid-connected and islanded modes. Existing microgrid optimization and dispatch technologies mainly focus on improving energy utilization efficiency and reducing operating costs. This is achieved by coordinating the output of distributed power sources, the charging and discharging strategies of energy storage systems, and load response to achieve dynamic energy balance in local areas. At the microgrid cluster level, multiple microgrids explore the possibilities of resource sharing and collaborative optimization through energy and information interaction. Distribution network-level optimization and dispatch aim to coordinate distributed energy resources, load demand, and energy storage systems from a global perspective, balancing regional energy supply and demand and ensuring the safety and economy of system operation.

[0004] However, existing multi-level energy system optimization and scheduling methods have many limitations. First, due to the intermittent and random nature of renewable energy, microgrid-level scheduling faces significant uncertainty challenges. Second, resource coordination and goal consistency among microgrid groups are difficult to achieve effectively, limiting the depth and breadth of resource sharing. Third, distribution network-level scheduling algorithms often lack precise characterization of the operating characteristics of lower-level microgrids and microgrid groups, making it difficult to achieve true multi-level collaborative optimization. These technical shortcomings prevent existing methods from fully realizing the potential of distributed energy systems, hindering the in-depth development of power systems towards intelligence and decarbonization.

[0005] Therefore, how to provide a multi-level autonomous and optimized scheduling solution that maximizes the autonomy of microgrids, microgrid groups, and distribution networks has become an urgent problem to be solved. Summary of the Invention

[0006] This invention provides an optimized scheduling method, system, device, and medium based on multi-level microgrids to address the challenges of significant uncertainty in microgrid-level scheduling, the difficulty in effectively achieving resource coordination and goal consistency among microgrid groups, and the fact that distribution network-level scheduling algorithms often lack accurate characterization of the operating characteristics of lower-level microgrids and microgrid groups, making it difficult to achieve true multi-level collaborative optimization.

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or to describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0008] According to a first aspect of the present invention, an optimized scheduling method based on a multi-level microgrid is provided.

[0009] In one embodiment, the multi-level microgrid-based optimal scheduling method includes:

[0010] Based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial sales price of electricity at the microgrid's point of common connection, a microgrid operation optimization model is constructed. The optimal scheduling strategy of the microgrid is obtained by solving the microgrid operation optimization model, and the microgrid planning characteristics are determined and uploaded to the microgrid group control center.

[0011] Based on the microgrid planning characteristics received by the microgrid control center, a microgrid operation optimization model is constructed. The microgrid operation optimization model is iteratively solved until the absolute difference between the results of two consecutive iterations is within the predetermined difference range of the microgrid or reaches the preset number of iterations of the microgrid. The optimal scheduling strategy of the microgrid is obtained, the microgrid planning characteristics are determined, and the results are uploaded to the distribution network control center.

[0012] Based on the microgrid group planning characteristics uploaded by the microgrid group control center and the purchase and sale prices of electricity at the common connection points of the distribution network, a distribution network operation optimization model is established. The distribution network operation optimization model is iteratively solved until the absolute difference between the two iterations is within the predetermined difference range of the distribution network or reaches the preset number of iterations of the distribution network, thus obtaining the optimal dispatch strategy of the distribution network.

[0013] In one embodiment, based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial sales price of electricity at the microgrid's point of common coupling, a microgrid operation optimization model is constructed. Solving the microgrid operation optimization model yields the optimal dispatch strategy for the microgrid, and the microgrid's planned characteristics are determined. This process is then uploaded to the microgrid cluster control center, including:

[0014] Initialize the equipment parameter information of each microgrid, and set the initial purchase price and initial sales price of electricity at the microgrid's point of common connection;

[0015] Based on the initial purchase price and initial sales price of electricity at the microgrid's point of common connection, a minimum operating cost objective function for the microgrid is constructed with the goal of minimizing the microgrid's electricity purchase cost. In conjunction with the microgrid's active power balance constraints, microgrid energy storage device constraints, constraints on the shifting time of transferable loads, and constraints on the maximum power of transferable loads, a microgrid operation optimization model is constructed.

[0016] Solve the microgrid operation optimization model to obtain the optimal scheduling strategy for different microgrids within each microgrid group;

[0017] Based on the optimal dispatch strategy for microgrids, the planning characteristics of microgrids are determined and uploaded to the microgrid group control center;

[0018] The planned features of a microgrid include the power curve of an equivalent power unit, aggregated energy storage parameters of the microgrid, charging power curve of the microgrid, and discharging power curve of the microgrid.

[0019] In one embodiment, the objective function for minimizing the operating cost of a microgrid is expressed as follows:

[0020]

[0021] In the formula, F MG,i,j ρ represents the electricity purchase cost of the microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; and These refer to the purchase price and sales price of electricity from the microgrid to the microgrid cluster, respectively. Support price for microgrids; and These are the support prices received and the support prices provided for microgrids, respectively. Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

[0022] In one embodiment, based on the microgrid planning characteristics received by the microgrid control center, a microgrid operation optimization model is constructed, and the model is iteratively solved until the absolute difference between the results of two consecutive iterations is within a predetermined difference range for the microgrid or reaches a preset number of iterations. This yields the optimal scheduling strategy for the microgrid and determines the microgrid planning characteristics, which are then uploaded to the distribution network control center.

[0023] Based on the microgrid planning characteristics uploaded by each microgrid in the microgrid group and the purchase and sale price of electricity at the common connection point set by the upper-level power grid, a target function for maximizing the autonomous operation of the microgrid group is constructed. Combined with the active power balance constraint of the microgrid group, a corresponding microgrid group operation optimization model is constructed.

[0024] The optimal scheduling strategy for the microgrid group is obtained by iteratively solving the microgrid group operation optimization model.

[0025] Calculate the absolute difference between the solution results of the two iterations of the microgrid operation optimization model, and compare the absolute difference with the predetermined difference range of the microgrid.

[0026] If the comparison result shows that the absolute difference is within the predetermined difference range of the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group, and the microgrid group planning characteristics are uploaded to the distribution network control center.

[0027] If the comparison result shows that the absolute difference is outside the predetermined difference range of the microgrid group, it is determined whether the number of iterations of the microgrid group operation optimization model exceeds the preset number of iterations for the microgrid group. If the number of iterations is less than or equal to the preset number of iterations for the microgrid group, the support power of each microgrid is updated and the microgrid operation optimization model is constructed. If the number of iterations is greater than the preset number of iterations for the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group and uploaded to the distribution network control center.

[0028] The microgrid cluster plan features include the power curves of the aggregated equivalent power units of the microgrid cluster, the aggregated energy storage parameters of the microgrid cluster, the charging power curves of the microgrid cluster, and the discharging power curves of the microgrid cluster.

[0029] In one embodiment, the expression for the objective function of maximizing autonomous operation of a microgrid group is:

[0030] Min FM Gs,i =FP CC,i +FM G

[0031]

[0032] In the formula, F MGs,i The operating cost of microgrid group i; F PCC,i For the electricity purchase cost of microgrid i; F MG ρ represents the aggregated power purchase cost of each microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; ρ represents the inter-microgrid support power at time t. t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; Support price for microgrids; Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

[0033] In one embodiment, based on the microgrid group planning characteristics uploaded by the microgrid group control center and the electricity purchase and sale prices at the common connection points of the distribution network, a distribution network operation optimization model is established. The distribution network operation optimization model is iteratively solved until the absolute difference between two consecutive iterations is within a predetermined difference range of the distribution network or reaches a preset number of iterations. The optimal dispatch strategy for the distribution network is then obtained, including:

[0034] Based on the microgrid group planning characteristics uploaded by each microgrid group in the distribution network and the power purchase and sale price of the common connection point set by the distribution network, a target function for maximizing the autonomous operation of the distribution network is constructed, and combined with the active power balance constraint of the distribution network, a corresponding distribution network operation optimization model is constructed.

[0035] The optimal dispatching strategy for the distribution network is obtained by iteratively solving the distribution network operation optimization model.

[0036] Calculate the absolute difference between the solution results of the two iterations of the distribution network operation optimization model, and compare the absolute difference with the predetermined difference range of the distribution network.

[0037] If the comparison result shows that the absolute difference is within the predetermined difference range of the distribution network, the distribution network planning characteristics are determined according to the optimal dispatching strategy of the distribution network.

[0038] If the comparison result shows that the absolute difference is outside the predetermined difference range of the distribution network, it is determined whether the number of iterations of the distribution network operation optimization model exceeds the preset number of iterations of the distribution network. If the number of iterations is less than or equal to the preset number of iterations of the distribution network, the support power of each microgrid is updated and a microgrid group operation optimization model is constructed. If the number of iterations is greater than the preset number of iterations of the distribution network, the distribution network planning characteristics are determined according to the optimal scheduling strategy of the distribution network.

[0039] Among them, the planned characteristics of the distribution network include the power purchased and the power sold by the distribution network.

[0040] In one embodiment, the expression for the objective function of maximizing autonomous operation of the distribution network is:

[0041] Min FDN = FPCC + FMGs

[0042]

[0043] In the formula, F DNFor the operating costs of the distribution network; F PCC For the power purchase cost of the distribution network; F MGs ρ represents the aggregated electricity purchase cost of each microgrid group; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t The purchase and sale price of electricity at the point of common connection of the distribution network at time t; ρ represents the planned input and output power of the distribution network at time t; t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; The inter-microgrid support power at time t.

[0044] According to a second aspect of the present invention, an optimized scheduling system based on a multi-level microgrid is provided.

[0045] In one embodiment, the multi-level microgrid-based optimized dispatch system includes:

[0046] The microgrid operation optimization module is used to construct a microgrid operation optimization model based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial sales price of electricity at the microgrid's point of common coupling, solve the microgrid operation optimization model to obtain the optimal scheduling strategy of the microgrid, determine the microgrid planning characteristics, and upload them to the microgrid group control center.

[0047] The microgrid group operation optimization module is used to construct a microgrid group operation optimization model based on the microgrid plan characteristics received by the microgrid group control center, iteratively solve the microgrid group operation optimization model until the absolute difference between the results of two consecutive iterations is within the predetermined difference range of the microgrid group or reaches the preset number of iterations of the microgrid group, obtain the optimal scheduling strategy of the microgrid group, determine the microgrid group plan characteristics, and upload them to the distribution network control center.

[0048] The distribution network operation optimization module is used to establish a distribution network operation optimization model based on the microgrid group planning characteristics uploaded by the microgrid group control center and the purchase and sale prices of electricity at the common connection points set by the distribution network. The model is iteratively solved until the absolute difference between the two iterations is within the predetermined difference range of the distribution network or the preset number of iterations is reached, thus obtaining the optimal dispatch strategy of the distribution network.

[0049] In one embodiment, the microgrid operation optimization module constructs a microgrid operation optimization model based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial retail price of electricity at the microgrid's point of common coupling (PCC). It solves the microgrid operation optimization model to obtain the optimal scheduling strategy for the microgrid and determines the microgrid's planned characteristics. When uploading this information to the microgrid group control center, it initializes the equipment parameter information of each microgrid and sets the initial purchase price and initial retail price of electricity at the PCC. Based on the initial purchase price and initial retail price of electricity at the PCC, and with the objective of minimizing the microgrid's electricity purchase cost, it constructs the microgrid... The minimum operating cost objective function is used, and a microgrid operation optimization model is constructed by combining the active power balance constraint, microgrid energy storage device constraint, shifting time constraint of transferable load, and maximum power constraint of transferable load. The microgrid operation optimization model is solved to obtain different optimal scheduling strategies for each microgrid group. Based on the optimal scheduling strategy, the microgrid planning characteristics are determined and uploaded to the microgrid group control center. The microgrid planning characteristics include the power curve of the equivalent power unit, the aggregated energy storage parameters of the microgrid, the charging power curve of the microgrid, and the discharging power curve of the microgrid.

[0050] In one embodiment, the objective function for minimizing the operating cost of a microgrid is expressed as follows:

[0051]

[0052] In the formula, F MG,i,j ρ represents the electricity purchase cost of the microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; and These refer to the purchase price and sales price of electricity from the microgrid to the microgrid cluster, respectively. Support price for microgrids; and These are the support prices received and the support prices provided for microgrids, respectively. Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

[0053] In one embodiment, the microgrid operation optimization module constructs a microgrid operation optimization model based on the microgrid plan characteristics received from the microgrid control center, iteratively solves the microgrid operation optimization model until the absolute difference between the results of two consecutive iterations is within a predetermined difference range for the microgrid or reaches a preset number of iterations, thus obtaining the optimal scheduling strategy for the microgrid and determining the microgrid plan characteristics. When this is uploaded to the distribution network control center, the module constructs a microgrid maximizing autonomous operation objective function based on the microgrid plan characteristics uploaded by each microgrid within the microgrid and the purchase and sale price of electricity at the common connection point set by the upper-level power grid. Combined with the active power balance constraints of the microgrid, a corresponding microgrid operation optimization model is constructed. The module iteratively solves the microgrid operation optimization model to obtain the optimal scheduling strategy for the microgrid. It calculates the absolute difference between the results of two consecutive iterations of the microgrid operation optimization model and compares this absolute difference with the predetermined difference range for the microgrid. The process involves several steps: First, a comparison is performed. If the absolute difference in the comparison result is within the predetermined difference range for the microgrid group, the microgrid group's planned characteristics are determined according to the optimal scheduling strategy, and these characteristics are uploaded to the distribution network control center. Second, if the absolute difference in the comparison result is outside the predetermined difference range, it is determined whether the number of iterations in the microgrid group's operation optimization model exceeds the preset iteration number. If the number of iterations is less than or equal to the preset iteration number, the support power of each microgrid is updated, and the microgrid operation optimization model is constructed. If the number of iterations is greater than the preset iteration number, the microgrid group's planned characteristics are determined according to the optimal scheduling strategy, and these characteristics are uploaded to the distribution network control center. The microgrid group's planned characteristics include the power curves of the aggregated equivalent power units, the aggregated energy storage parameters, the charging power curve, and the discharging power curve.

[0054] In one embodiment, the expression for the objective function of maximizing autonomous operation of a microgrid group is:

[0055] Min FM Gs,i =FP CC,i +FM G

[0056]

[0057] In the formula, F MGs,i The operating cost of microgrid group i; F PCC,i For the electricity purchase cost of microgrid i; F MG ρ represents the aggregated power purchase cost of each microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; ρ represents the inter-microgrid support power at time t. t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; Support price for microgrids; Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

[0058] In one embodiment, the distribution network operation optimization module establishes a distribution network operation optimization model based on the microgrid group planning characteristics uploaded by the microgrid group control center and the purchase and sale prices of electricity at the common connection points of the distribution network. It iteratively solves the distribution network operation optimization model until the absolute difference between two consecutive iterations is within a predetermined difference range or reaches a preset number of iterations, thus obtaining the optimal dispatch strategy for the distribution network. Then, based on the microgrid group planning characteristics uploaded by each microgrid group within the distribution network and the purchase and sale prices of electricity at the common connection points of the distribution network, it constructs a distribution network maximizing autonomous operation objective function. Combined with the active power balance constraints of the distribution network, it constructs a corresponding distribution network operation optimization model. Iteratively solves the distribution network operation optimization model to obtain the optimal dispatch strategy for the distribution network. Finally, it calculates the distribution network operation optimization model before and after iterations. The absolute difference between the results of two iterations is calculated and compared with the predetermined difference range of the distribution network. If the comparison result shows that the absolute difference is within the predetermined difference range of the distribution network, the planned characteristics of the distribution network are determined according to the optimal dispatch strategy of the distribution network. If the comparison result shows that the absolute difference is outside the predetermined difference range of the distribution network, it is determined whether the number of iterations of the distribution network operation optimization model exceeds the preset number of iterations of the distribution network. If the number of iterations is less than or equal to the preset number of iterations of the distribution network, the support power of each microgrid is updated and a microgrid group operation optimization model is constructed. If the number of iterations is greater than the preset number of iterations of the distribution network, the planned characteristics of the distribution network are determined according to the optimal dispatch strategy of the distribution network. The planned characteristics of the distribution network include the power purchased and the power sold by the distribution network.

[0059] In one embodiment, the expression for the objective function of maximizing autonomous operation of the distribution network is:

[0060] Min FDN = FPCC + FMGs

[0061]

[0062] In the formula, F DN For the operating costs of the distribution network; F PCC For the power purchase cost of the distribution network; F MGs ρ represents the aggregated electricity purchase cost of each microgrid group; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ tThe purchase and sale price of electricity at the point of common connection of the distribution network at time t; ρ represents the planned input and output power of the distribution network at time t; t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; The inter-microgrid support power at time t.

[0063] According to a third aspect of the present invention, a computer device is provided.

[0064] In one embodiment, the computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0065] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0066] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the method described above.

[0067] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0068] This invention achieves optimized scheduling with maximum autonomy at each level of the microgrid, microgrid group, and distribution network. Based on the maximum autonomous operation at each level, it achieves the goal of improving the security, reliability, and flexibility of each level of the distribution network.

[0069] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0070] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0071] Figure 1 This is a flowchart illustrating an optimized scheduling method based on a multi-level microgrid, according to an exemplary embodiment.

[0072] Figure 2 This is a specific implementation diagram illustrating an optimized scheduling method based on a multi-level microgrid, according to an exemplary embodiment.

[0073] Figure 3 This is an exemplary embodiment illustrating a method for optimizing the scheduling of a multi-level microgrid, where each level of the three-layer distribution network maximizes its autonomous scheduling.

[0074] Figure 4 This is an equivalent aggregation diagram within a microgrid in an optimized scheduling method based on a multi-level microgrid, as illustrated in an exemplary embodiment.

[0075] Figure 5 This is a diagram illustrating the relationship between microgrids within a microgrid group in an optimized scheduling method based on a multi-level microgrid, according to an exemplary embodiment.

[0076] Figure 6 This is a structural block diagram of an optimized scheduling system based on a multi-level microgrid, according to an exemplary embodiment.

[0077] Figure 7 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment. Detailed Implementation

[0078] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some embodiments may include or substitute parts and features of other embodiments. The scope of the embodiments herein encompasses the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0079] The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" used in this document to indicate orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings. They are used solely for the convenience of describing the document and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description herein, unless otherwise specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two elements; they can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0080] In this document, unless otherwise stated, the term "multiple" means two or more.

[0081] In this article, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0082] In this article, the term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0083] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the diagram may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0084] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0085] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0086] Figures 1-5An embodiment of an optimized scheduling method based on a multi-level microgrid according to the present invention is shown.

[0087] In this optional embodiment, the optimized scheduling method based on multi-level microgrids includes:

[0088] Step S101: Based on the equipment parameter information of each microgrid, and combined with the initial purchase price and initial sales price of electricity at the microgrid common connection point, construct a microgrid operation optimization model, solve the microgrid operation optimization model to obtain the optimal scheduling strategy of the microgrid, determine the microgrid planning characteristics, and upload it to the microgrid group control center.

[0089] Specifically, based on the day-ahead power forecast and equipment parameter information of the sources, loads, and storage devices within each microgrid, as well as the initial purchase and sale prices of electricity at the PCC point, an optimization model for maximizing autonomous operation is constructed, and the optimal scheduling result is obtained. The purchase and sale plans, aggregated energy storage models, and charging and discharging plans that meet the loads within the microgrid are determined and uploaded to the microgrid group control center.

[0090] Step S103: Based on the microgrid planning characteristics received by the microgrid group control center, construct a microgrid group operation optimization model, iteratively solve the microgrid group operation optimization model until the absolute difference between the results of two consecutive iterations is within the predetermined difference range of the microgrid group or reaches the preset number of iterations of the microgrid group, obtain the optimal scheduling strategy of the microgrid group, determine the microgrid group planning characteristics, and upload them to the distribution network control center.

[0091] Specifically, based on various plans and energy storage models, the microgrid control center constructs a microgrid-maximizing autonomous operation optimization model based on the information uploaded by each microgrid and the PCC point purchase and sales prices set by the upper-level power grid, and obtains the optimal scheduling result. If the difference between the calculation results of two iterations is within a certain range, or if the preset number of iterations is reached, the microgrid purchase and sales plans, aggregated energy storage models, and charging and discharging plans are transmitted to the distribution network control center. Otherwise, the updated inter-microgrid support power and supported power are transmitted to each microgrid to reconstruct the microgrid optimization model and obtain the microgrid scheduling strategy.

[0092] Step S105: Based on the microgrid group planning characteristics uploaded by the microgrid group control center and the purchase and sale prices of electricity at the common connection points set up in the distribution network, establish a distribution network operation optimization model, iteratively solve the distribution network operation optimization model until the absolute difference between the two iterations is within the predetermined difference range of the distribution network or reaches the preset number of iterations of the distribution network, and obtain the optimal scheduling strategy of the distribution network.

[0093] Specifically, based on various plans and energy storage models, the distribution network control center constructs an optimization model for maximizing the autonomous operation of the distribution network according to the information uploaded by each microgrid group control center and the PCC point purchase and sales prices set by the upper-level power grid. The optimal scheduling result is obtained. If the difference between the calculation results of the two iterations is within a certain range, or if the preset number of iterations is reached, the optimal scheduling strategy of the distribution network is output. Otherwise, the updated support power between each microgrid group is transmitted to each microgrid group to reconstruct the microgrid group optimization model and obtain the optimal scheduling strategy of the distribution network.

[0094] In this optional embodiment, based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial sales price of electricity at the microgrid's point of common coupling, a microgrid operation optimization model is constructed. Solving the microgrid operation optimization model yields the optimal microgrid scheduling strategy, and the microgrid planning characteristics are determined. This process is then uploaded to the microgrid cluster control center, including:

[0095] Step S1011: Initialize the equipment parameter information of each microgrid and set the initial purchase price and initial sales price of the microgrid's point of common connection;

[0096] Specifically, information within each microgrid (MG) is initialized, including day-ahead power forecasts for sources, loads, and storage, as well as equipment parameter information. The price set by the distribution network is used as the initial purchase and sale price of electricity at the microgrid's point of common coupling (PCC).

[0097] Step S1013: Based on the initial purchase price and initial sales price of electricity at the microgrid's point of common connection, construct the objective function for minimizing the microgrid's electricity purchase cost, and combine the microgrid's active power balance constraints, microgrid energy storage device constraints, load transfer time constraints, and maximum power constraints of transferable loads to construct a microgrid operation optimization model;

[0098] Specifically, an optimization model is constructed based on the aggregation of day-ahead power forecasts and parameter information for the source, load, and storage of the i-th microgrid within the i-th microgrid group, as well as the PCC point-of-use and electricity sales prices.

[0099] Specifically, the electricity generated by the new energy power generation equipment in the microgrid is given priority to supply its internal loads, and the remaining electricity is stored through energy storage devices. When the microgrid cannot fully absorb the electricity or the electricity required by the load is too large, the microgrid trades with the microgrid cluster (MGs), which is a mode of maximizing autonomous operation. The objective function of minimizing the operating cost of the microgrid is constructed.

[0100] Step S1015: Solve the microgrid operation optimization model to obtain the optimal scheduling strategy for different microgrids within each microgrid group;

[0101] Specifically, in this embodiment, the CPLEX solver toolbox is used to solve the mixed-integer linear programming problem to obtain the optimal scheduling strategy for the j-th microgrid within the i-th microgrid group. This strategy includes the power curves of the microgrid's equivalent power units, the aggregated energy storage parameters of the microgrid, and the charging and discharging power curves of the microgrid.

[0102] Step S1017: Determine the microgrid planning characteristics according to the microgrid optimal scheduling strategy, and upload the microgrid planning characteristics to the microgrid group control center;

[0103] The planned features of a microgrid include the power curve of an equivalent power unit, aggregated energy storage parameters of the microgrid, charging power curve of the microgrid, and discharging power curve of the microgrid.

[0104] Specifically, based on the optimal dispatch strategy for microgrids, the power curves of the equivalent power units of the microgrid are obtained. Microgrid aggregated energy storage parameters and microgrid charging and discharging power curves And upload it to the microgrid group control center;

[0105] Specifically, In the formula, The input and output power of the microgrid at time t (with positive input and negative output) are the decision variables in the microgrid optimization problem.

[0106] In this optional embodiment, the objective function for minimizing the operating cost of the microgrid is expressed as follows:

[0107]

[0108]

[0109] In the formula, F MG,i,j ρ represents the electricity purchase cost of the microgrid; i and j represent the j-th microgrid within the i-th microgrid group (i = 1, 2, 3, ..., N; j = 1, 2, 3, ..., M); T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle (t = 1, 2, 3, ..., T); ρ t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; and These refer to the purchase price and sales price of electricity from the microgrid to the microgrid cluster, respectively. Support price for microgrids; and These are the support prices received and the support prices provided for microgrids, respectively. Let the planned input and output power of the microgrid at time t (with positive input and negative output) be the decision variables for the microgrid optimization problem. The inter-microgrid support power at time t is positive when supported and negative when output.

[0110] Specifically, the above formula satisfies

[0111] Specifically, the expression for the active power balance constraint of a microgrid is:

[0112]

[0113] In the formula, These represent the charging and discharging power of the energy storage device used for the internal loads of the microgrid at time t, respectively. These represent the charging power and discharging power of the energy storage device used for inter-microgrid support at time t; This represents the photovoltaic power generation at time t; This represents the power required by the normal load at time t; This represents the power required to transfer the load at time t.

[0114] Specifically, the expression for the constraints of microgrid energy storage devices is as follows:

[0115]

[0116] In the formula, These represent the planned state of charge and upper and lower limits of the energy storage at time t, respectively. These are the charging and discharging power limits of the energy storage device at time t; These represent the charge and discharge efficiencies of the energy storage device at time t; E i,j Indicates the rated capacity of the energy storage device; These represent the charging and discharging states of the energy storage device at time t, respectively, and are 0-1 variables. These represent the state of charge of the energy storage device at time t0 and time T, respectively.

[0117] Specifically, the expression for the constraint on the shifting time period of the shiftable load is as follows:

[0118]

[0119] In the formula, Indicates the start time of operation of the k-th type of transferable load; This represents the runtime of the k-th type of transferable load; Indicates the number of transferable loads of type k;

[0120] Specifically, the expression for the maximum power constraint of transferable load is:

[0121]

[0122] In the formula, This represents the maximum power of the transferable load at time t.

[0123] In this optional embodiment, based on the microgrid planning characteristics received by the microgrid control center, a microgrid operation optimization model is constructed. The model is iteratively solved until the absolute difference between two consecutive iterations is within a predetermined range or reaches a preset number of iterations. This yields the optimal microgrid scheduling strategy, determines the microgrid planning characteristics, and uploads them to the distribution network control center.

[0124] Step S1031: Based on the microgrid planning characteristics uploaded by each microgrid in the microgrid group and the purchase and sale price of electricity at the common connection point set by the upper-level power grid, construct the objective function for maximizing the autonomous operation of the microgrid group, and combine the active power balance constraint of the microgrid group to construct the corresponding microgrid group operation optimization model;

[0125] Specifically, in this method, the microgrid group adopts a maximized autonomous operation mode. Based on the information uploaded by each microgrid in the microgrid group and the purchase and sale price of electricity at the common connection point set by the upper-level power grid, the i-th corresponding microgrid group operation optimization model is constructed.

[0126] Specifically, in this method, the microgrid cluster constructs an objective function to maximize autonomous operation of the microgrid cluster by aggregating the plans and models uploaded by each microgrid.

[0127] Step S1033: Iteratively solve the microgrid group operation optimization model to obtain the optimal scheduling strategy for the microgrid group;

[0128] Specifically, the microgrid group operation optimization model is solved iteratively. In this embodiment, the mixed integer linear programming problem is solved using the CPLEX solver toolbox to obtain the optimal scheduling result for microgrid group i.

[0129] Step S1035: Calculate the absolute difference between the solution results of the two iterations of the microgrid group operation optimization model, and compare the absolute difference with the predetermined difference range of the microgrid group;

[0130] If the comparison result shows that the absolute difference is within the predetermined difference range of the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group, and the microgrid group planning characteristics are uploaded to the distribution network control center.

[0131] If the comparison result shows that the absolute difference is outside the predetermined difference range of the microgrid group, it is determined whether the number of iterations of the microgrid group operation optimization model exceeds the preset number of iterations for the microgrid group. If the number of iterations is less than or equal to the preset number of iterations for the microgrid group, the support power of each microgrid is updated, and the microgrid operation optimization model is constructed. If the number of iterations is greater than the preset number of iterations for the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group, and the microgrid group planning characteristics are uploaded to the distribution network control center.

[0132] The microgrid cluster plan features include the power curve of the aggregated equivalent power unit of the microgrid cluster, the aggregated energy storage parameters of the microgrid cluster, the charging power curve of the microgrid cluster, and the discharging power curve of the microgrid cluster.

[0133] Specifically, based on the absolute difference between the calculation results of two iterations of the i-th microgrid group, if its value is within the predetermined difference range ε ​​of the microgrid group... MGs Within the network, based on the optimal scheduling results, the power curves of the aggregated equivalent power units of the microgrid cluster are obtained. Microgrid cluster aggregated energy storage parameters ( E i ) and microgrid group charging and discharging power curves Uploaded to the power distribution network control center.

[0134] Specifically, based on the absolute difference between the calculation results of two iterations of the i-th microgrid group, if its value is within the predetermined difference range ε ​​of the microgrid group... MGs In addition, it further determines whether the number of iterations exceeds the preset number of iterations K for the microgrid group. MGs If the power level is less than or equal to the preset iteration number of the microgrid group, then update the supported power of each microgrid. Then return to step S1013 to reconstruct the optimization model of each microgrid group; if the number of iterations exceeds the preset number of iterations for the microgrid group, obtain the power curve of the aggregated equivalent power unit of the microgrid group based on its optimal scheduling result. Microgrid cluster aggregated energy storage parameters ( E i ) and microgrid group charging and discharging power curves Uploaded to the power distribution network control center.

[0135] Specifically, the above formula satisfies

[0136] In this optional embodiment, the expression for the objective function of maximizing autonomous operation of the microgrid group is:

[0137] Min F MGs,i =F PCC,i +F MG

[0138]

[0139] In the formula, and These represent the purchase price and sales price of electricity from the distribution network for the microgrid group, respectively. and These represent the support price accepted by the microgrid and the support price, respectively; F MGs,i The operating cost of microgrid group i; F PCC,i For the electricity purchase cost of microgrid i; F MG ρ represents the aggregated power purchase cost of each microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group (input is positive, output is negative); Price support for micro-network groups; ρ represents the inter-microgrid support power at time t. t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; Support price for microgrids; Let t be the planned input and output power of the microgrid (positive when supported, negative when output); Let be the inter-microgrid support power at time t; where and It is a decision variable in the microgrid group optimization problem.

[0140] Specifically, the above formula satisfies

[0141] Specifically, the expression for the active power balance constraint of a microgrid group is:

[0142]

[0143] In this optional embodiment, based on the microgrid group planning characteristics uploaded by the microgrid group control center and the electricity purchase and sale prices at the common connection points of the distribution network, a distribution network operation optimization model is established. The distribution network operation optimization model is iteratively solved until the absolute difference between two consecutive iterations is within a predetermined difference range of the distribution network or reaches a preset number of iterations. The optimal dispatch strategy for the distribution network is obtained, including:

[0144] Step S1051: Based on the microgrid group planning characteristics uploaded by each microgrid group in the distribution network and the power purchase and sale price of the common connection point set by the distribution network, construct the objective function for maximizing the autonomous operation of the distribution network, and combine it with the active power balance constraint of the distribution network to construct the corresponding distribution network operation optimization model;

[0145] Specifically, in this method, the distribution network adopts a maximized autonomous operation mode, and constructs a corresponding optimization model for the distribution network based on the information uploaded by each microgrid group and the PCC point purchase and sales electricity prices.

[0146] Specifically, in this method, the distribution network constructs an objective function to maximize autonomous operation by aggregating the plans and models uploaded by each microgrid group.

[0147] Step S1053: Iteratively solve the distribution network operation optimization model to obtain the optimal dispatch strategy for the distribution network;

[0148] Specifically, the distribution network operation optimization model is solved iteratively. In this embodiment, the mixed integer linear programming problem is solved based on the CPLEX solver toolbox to obtain the optimal scheduling result of the distribution network.

[0149] Step S1055: Calculate the absolute difference between the solution results of the two iterations of the distribution network operation optimization model, and compare the absolute difference with the predetermined difference range of the distribution network.

[0150] If the comparison result shows that the absolute difference is within the predetermined difference range of the distribution network, the distribution network planning characteristics are determined according to the optimal dispatching strategy of the distribution network.

[0151] If the comparison result shows that the absolute difference is outside the predetermined difference range of the distribution network, it is determined whether the number of iterations of the distribution network operation optimization model exceeds the preset number of iterations of the distribution network. If the number of iterations is less than or equal to the preset number of iterations of the distribution network, the support power of each microgrid is updated and the microgrid group operation optimization model is constructed. If the number of iterations is greater than the preset number of iterations of the distribution network, the distribution network planning characteristics are determined according to the optimal scheduling strategy of the distribution network.

[0152] The planned characteristics of the distribution network include the power purchased and the power sold by the distribution network.

[0153] Specifically, based on the absolute difference between the results of two iterations of calculation for the distribution network, if its value is within the predetermined difference range ε ​​of the distribution network... DN Internally, based on the power purchased and sold in the distribution network, the system outputs the optimal dispatch strategy for the distribution network.

[0154] Specifically, based on the absolute difference between the results of two iterations of calculation for the distribution network, if its value is within the predetermined difference range ε ​​of the distribution network... DN If the problem is internal or external, then it is further determined whether the number of iterations exceeds the preset number of iterations K for the distribution network. DN If the number of iterations is less than or equal to the preset number of iterations for the distribution network, then update the supported power of each microgrid group. Then return to step S1033 to reconstruct the optimization model of each microgrid group; otherwise, output the optimal dispatch strategy of the distribution network based on the power purchased and sold by the distribution network.

[0155] In this optional embodiment, the expression for the objective function of maximizing the autonomous operation of the distribution network is:

[0156] Min FDN = FPCC + FMGs

[0157]

[0158] In the formula, These represent the electricity purchase and sale prices at point PCC in the distribution network at time t; F DN For the operating costs of the distribution network; F PCC For the power purchase cost of the distribution network; F MGs ρ represents the aggregated electricity purchase cost of each microgrid group; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t The purchase and sale price of electricity at the point of common connection of the distribution network at time t; Let ρ be the planned input and output power of the distribution network point of common coupling at time t (positive when purchasing electricity, negative when selling electricity); t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; The inter-microgrid support power at time t; and These are decision variables in the power distribution network optimization problem.

[0159] Specifically, the expression for the active power balance constraint in the distribution network is:

[0160]

[0161] Figure 6 An embodiment of an optimized scheduling system based on a multi-level microgrid according to the present invention is shown.

[0162] In this optional embodiment, the multi-level microgrid-based optimized scheduling system includes:

[0163] The microgrid operation optimization module 201 is used to construct a microgrid operation optimization model based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial sales price of the microgrid's common connection point, solve the microgrid operation optimization model to obtain the optimal scheduling strategy of the microgrid, determine the microgrid planning characteristics, and upload them to the microgrid group control center.

[0164] The microgrid group operation optimization module 203 is used to construct a microgrid group operation optimization model based on the microgrid plan characteristics received by the microgrid group control center, iteratively solve the microgrid group operation optimization model until the absolute difference between the results of two consecutive iterations is within the predetermined difference range of the microgrid group or reaches the preset number of iterations of the microgrid group, obtain the optimal scheduling strategy of the microgrid group, determine the microgrid group plan characteristics, and upload them to the distribution network control center.

[0165] The distribution network operation optimization module 205 is used to establish a distribution network operation optimization model based on the microgrid group planning characteristics uploaded by the microgrid group control center and the purchase and sale prices of electricity at the common connection points set by the distribution network. The model is iteratively solved until the absolute difference between the two iterations is within the predetermined difference range of the distribution network or the preset number of iterations is reached, thus obtaining the optimal scheduling strategy of the distribution network.

[0166] In this optional embodiment, the microgrid operation optimization module 201 constructs a microgrid operation optimization model based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial sales price of the microgrid's point of common connection (PCC). It solves the microgrid operation optimization model to obtain the optimal microgrid scheduling strategy and determines the microgrid's planning characteristics. When uploading this information to the microgrid group control center, it initializes the equipment parameter information of each microgrid and sets the initial purchase price and initial sales price of the PCC. Based on the initial purchase price and initial sales price of the PCC, and with the objective of minimizing the microgrid's electricity purchase cost, it constructs a microgrid operation optimization model. The objective function for minimizing the grid operating cost is used, and a microgrid operation optimization model is constructed by combining the active power balance constraints of the microgrid, the constraints of the microgrid energy storage device, the constraints of the shifting time of the shiftable load, and the constraints of the maximum power of the shiftable load. The microgrid operation optimization model is solved to obtain the optimal scheduling strategy for each microgrid group. Based on the optimal scheduling strategy, the microgrid planning characteristics are determined and uploaded to the microgrid group control center. The microgrid planning characteristics include the power curve of the equivalent power unit, the aggregated energy storage parameters of the microgrid, the charging power curve of the microgrid, and the discharging power curve of the microgrid.

[0167] In this optional embodiment, the objective function for minimizing the operating cost of the microgrid is expressed as follows:

[0168]

[0169] In the formula, F MG,i,j ρ represents the electricity purchase cost of the microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; and These refer to the purchase price and sales price of electricity from the microgrid to the microgrid cluster, respectively. Support price for microgrids; and These are the support prices received and the support prices provided for microgrids, respectively. Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

[0170] In this optional embodiment, the microgrid group operation optimization module 203 constructs a microgrid group operation optimization model based on the microgrid plan characteristics received by the microgrid group control center, iteratively solves the microgrid group operation optimization model until the absolute difference between the results of two consecutive iterations is within the predetermined difference range of the microgrid group or reaches the preset number of iterations of the microgrid group, thus obtaining the optimal scheduling strategy of the microgrid group and determining the microgrid group plan characteristics. When the microgrid group plan characteristics are determined and uploaded to the distribution network control center, the module constructs a microgrid group objective function to maximize autonomous operation based on the microgrid plan characteristics uploaded by each microgrid in the microgrid group and the purchase and sale price of the common connection point set by the upper-level power grid. Combined with the active power balance constraint of the microgrid group, the module constructs a corresponding microgrid group operation optimization model; iteratively solves the microgrid group operation optimization model to obtain the optimal scheduling strategy of the microgrid group; calculates the absolute difference between the results of two consecutive iterations of the microgrid group operation optimization model, and compares the absolute difference with the predetermined difference range of the microgrid group. The comparison is performed on the following: If the absolute difference in the comparison result is within the predetermined difference range of the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group, and the microgrid group planning characteristics are uploaded to the distribution network control center; if the absolute difference in the comparison result is outside the predetermined difference range of the microgrid group, it is determined whether the number of iterations of the microgrid group operation optimization model exceeds the preset number of iterations of the microgrid group. If the number of iterations is less than or equal to the preset number of iterations of the microgrid group, the support power of each microgrid is updated, and the microgrid operation optimization model is constructed; if the number of iterations is greater than the preset number of iterations of the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group, and the microgrid group planning characteristics are uploaded to the distribution network control center. Among them, the microgrid group planning characteristics include the power curve of the aggregated equivalent power unit of the microgrid group, the aggregated energy storage parameters of the microgrid group, the charging power curve of the microgrid group, and the discharging power curve of the microgrid group.

[0171] In this optional embodiment, the expression for the objective function of maximizing autonomous operation of the microgrid group is:

[0172] Min FM Gs,i =FP CC,i +FM G

[0173]

[0174] In the formula, F MGs,iThe operating cost of microgrid group i; F PCC,i For the electricity purchase cost of microgrid i; F MG ρ represents the aggregated power purchase cost of each microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; ρ represents the inter-microgrid support power at time t. t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; Support price for microgrids; Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

[0175] In this optional embodiment, the distribution network operation optimization module 205 establishes a distribution network operation optimization model based on the microgrid group planning characteristics uploaded by the microgrid group control center and the purchase and sale prices of electricity at the common connection points of the distribution network. It iteratively solves the distribution network operation optimization model until the absolute difference between two consecutive iterations is within a predetermined range or reaches a preset number of iterations, thus obtaining the optimal dispatch strategy for the distribution network. Then, based on the microgrid group planning characteristics uploaded by each microgrid group within the distribution network and the purchase and sale prices of electricity at the common connection points of the distribution network, it constructs a distribution network maximizing autonomous operation objective function. Combined with the active power balance constraints of the distribution network, it constructs a corresponding distribution network operation optimization model; iteratively solves the distribution network operation optimization model to obtain the optimal dispatch strategy for the distribution network; and calculates the distribution network operation optimization model. The absolute difference between the results of two consecutive iterations is calculated and compared with the predetermined difference range of the distribution network. If the comparison result shows that the absolute difference is within the predetermined difference range of the distribution network, the planned characteristics of the distribution network are determined according to the optimal dispatch strategy of the distribution network. If the comparison result shows that the absolute difference is outside the predetermined difference range of the distribution network, it is determined whether the number of iterations of the distribution network operation optimization model exceeds the preset number of iterations of the distribution network. If the number of iterations is less than or equal to the preset number of iterations of the distribution network, the support power of each microgrid is updated and a microgrid group operation optimization model is constructed. If the number of iterations is greater than the preset number of iterations of the distribution network, the planned characteristics of the distribution network are determined according to the optimal dispatch strategy of the distribution network. The planned characteristics of the distribution network include the power purchased and the power sold by the distribution network.

[0176] In this optional embodiment, the expression for the objective function of maximizing the autonomous operation of the distribution network is:

[0177] Min FDN = FPCC + FMGs

[0178]

[0179] In the formula, F DN For the operating costs of the distribution network; F PCC For the power purchase cost of the distribution network; F MGs ρ represents the aggregated electricity purchase cost of each microgrid group; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t The purchase and sale price of electricity at the point of common connection of the distribution network at time t; ρ represents the planned input and output power of the distribution network at time t; t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; The inter-microgrid support power at time t.

[0180] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0181] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0182] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0183] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0184] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0185] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. An optimized scheduling method based on a multi-level microgrid, characterized in that, The optimal scheduling method based on multi-level microgrids includes: Based on the equipment parameter information of each microgrid, and combined with the initial purchase price and initial sales price of electricity at the microgrid's point of common coupling, a microgrid operation optimization model is constructed. Solving the microgrid operation optimization model yields the optimal dispatch strategy for the microgrid, determines the microgrid's planned characteristics, and uploads the results to the microgrid group control center. The microgrid operation optimization model, based on the initial purchase price and initial sales price of electricity at the microgrid's point of common coupling, aims to minimize the microgrid's electricity purchase cost. It constructs a minimum operating cost objective function for the microgrid, incorporating constraints related to the microgrid's active power balance, energy storage devices, the shifting time period of transferable loads, and the maximum power constraint of transferable loads. Based on the microgrid planning characteristics received by the microgrid control center, a microgrid operation optimization model is constructed. The model is iteratively solved until the absolute difference between the results of two consecutive iterations is within a predetermined difference range or the preset number of iterations is reached. This yields the optimal scheduling strategy for the microgrid and determines the microgrid planning characteristics, which are then uploaded to the distribution network control center. The microgrid operation optimization model is constructed by maximizing the autonomous operation objective function of the microgrid based on the microgrid planning characteristics uploaded by each microgrid within the microgrid and the purchase and sale price of electricity at the common connection point set by the upper-level power grid, and is further constructed in conjunction with the active power balance constraints of the microgrid. Based on the microgrid group planning characteristics uploaded by the microgrid group control center and the purchase and sale prices of electricity at the common connection points of the distribution network, a distribution network operation optimization model is established. The distribution network operation optimization model is iteratively solved until the absolute difference between two consecutive iterations is within a predetermined difference range of the distribution network or reaches the preset number of iterations, thus obtaining the optimal dispatch strategy of the distribution network. The distribution network operation optimization model is constructed by building an objective function for maximizing the autonomous operation of the distribution network based on the microgrid group planning characteristics uploaded by each microgrid group in the distribution network and the purchase and sale prices of electricity at the common connection points of the distribution network, and combined with the active power balance constraints of the distribution network.

2. The optimized scheduling method based on a multi-level microgrid according to claim 1, characterized in that, Based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial sales price of electricity at the microgrid's point of common coupling, a microgrid operation optimization model is constructed. Solving the microgrid operation optimization model yields the optimal scheduling strategy for the microgrid, and the microgrid's planned characteristics are determined. This process is then uploaded to the microgrid cluster control center, including: Initialize the equipment parameter information of each microgrid, and set the initial purchase price and initial sales price of electricity at the microgrid's point of common connection; Construct a microgrid operation optimization model; Solve the microgrid operation optimization model to obtain the optimal scheduling strategy for different microgrids within each microgrid group; Based on the optimal dispatch strategy for microgrids, the planning characteristics of microgrids are determined and uploaded to the microgrid group control center; The microgrid planning features include the power curve of the equivalent power unit, the aggregated energy storage parameters of the microgrid, the charging power curve of the microgrid, and the discharging power curve of the microgrid.

3. The optimized scheduling method based on a multi-level microgrid according to claim 2, characterized in that, The objective function for minimizing the operating cost of the microgrid is expressed as follows: In the formula, F MG,i,j ΔT represents the electricity purchase cost of the microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period. t represents a specific moment within the scheduling period; ρ t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; and These refer to the purchase price and sales price of electricity from the microgrid to the microgrid cluster, respectively. Support price for microgrids; and These are the support prices received and the support prices provided for microgrids, respectively. Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

4. The optimized scheduling method based on a multi-level microgrid according to claim 1, characterized in that, The process of constructing a microgrid operation optimization model based on the microgrid planning characteristics received by the microgrid control center, iteratively solving the microgrid operation optimization model until the absolute difference between the results of two consecutive iterations is within a predetermined difference range for the microgrid or reaches a preset number of iterations, thereby obtaining the optimal scheduling strategy for the microgrid, determining the microgrid planning characteristics, and uploading them to the distribution network control center includes: Construct a corresponding microgrid cluster operation optimization model; The optimal scheduling strategy for the microgrid group is obtained by iteratively solving the microgrid group operation optimization model. Calculate the absolute difference between the solution results of the two iterations of the microgrid group operation optimization model, and compare the absolute difference with the predetermined difference range of the microgrid group; If the comparison result shows that the absolute difference is within the predetermined difference range of the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group, and the microgrid group planning characteristics are uploaded to the distribution network control center. If the comparison result shows that the absolute difference is outside the predetermined difference range of the microgrid group, it is determined whether the number of iterations of the microgrid group operation optimization model exceeds the preset number of iterations for the microgrid group. If the number of iterations is less than or equal to the preset number of iterations for the microgrid group, the support power of each microgrid is updated, and the microgrid operation optimization model is constructed. If the number of iterations is greater than the preset number of iterations for the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group, and the microgrid group planning characteristics are uploaded to the distribution network control center. The microgrid cluster plan features include the power curve of the aggregated equivalent power unit of the microgrid cluster, the aggregated energy storage parameters of the microgrid cluster, the charging power curve of the microgrid cluster, and the discharging power curve of the microgrid cluster.

5. The optimized scheduling method based on a multi-level microgrid according to claim 4, characterized in that, The expression for the objective function of maximizing autonomous operation of the microgrid group is: Min F MGs,i =F PCC,i +F MG In the formula, F MGs,i The operating cost of microgrid group i; F PCC,i For the electricity purchase cost of microgrid i; F MG denoted as the aggregated electricity purchase cost of each microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period. t represents a specific moment within the scheduling period; ρ t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; ρ represents the inter-microgrid support power at time t. t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; Support price for microgrids; Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

6. The optimized scheduling method based on a multi-level microgrid according to claim 4, characterized in that, Based on the microgrid group planning characteristics uploaded by the microgrid group control center and the electricity purchase and sale prices of the common connection points set in the distribution network, a distribution network operation optimization model is established. The distribution network operation optimization model is iteratively solved until the absolute difference between two consecutive iterations is within a predetermined difference range of the distribution network or reaches the preset number of iterations. The optimal dispatch strategy of the distribution network is obtained, including: Construct a corresponding power distribution network operation optimization model; The optimal dispatching strategy for the distribution network is obtained by iteratively solving the distribution network operation optimization model. Calculate the absolute difference between the solution results of the two iterations of the distribution network operation optimization model, and compare the absolute difference with the predetermined difference range of the distribution network. If the comparison result shows that the absolute difference is within the predetermined difference range of the distribution network, the distribution network planning characteristics are determined according to the optimal dispatching strategy of the distribution network. If the comparison result shows that the absolute difference is outside the predetermined difference range of the distribution network, it is determined whether the number of iterations of the distribution network operation optimization model exceeds the preset number of iterations of the distribution network. If the number of iterations is less than or equal to the preset number of iterations of the distribution network, the support power of each microgrid is updated and the microgrid group operation optimization model is constructed. If the number of iterations is greater than the preset number of iterations of the distribution network, the distribution network planning characteristics are determined according to the optimal scheduling strategy of the distribution network. The planned characteristics of the distribution network include the power purchased and the power sold by the distribution network.

7. The optimized scheduling method based on a multi-level microgrid according to claim 6, characterized in that, The expression for the objective function of maximizing autonomous operation of the distribution network is: Min F DN =F PCC +F MGs In the formula, F DN For the operating costs of the distribution network; F PCC For the power purchase cost of the distribution network; F MGs ρ represents the aggregated electricity purchase cost of each microgrid group; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t P represents the purchase and sale price of electricity at the point of common coupling of the distribution network at time t; t pcc ρ represents the planned input and output power of the distribution network at time t; t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; The inter-microgrid support power at time t.

8. An optimized dispatching system based on a multi-level microgrid, characterized in that, The optimized scheduling system based on multi-level microgrids includes: The microgrid operation optimization module is used to construct a microgrid operation optimization model based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial sales price of electricity at the microgrid's point of common coupling (PCC). The module then solves the microgrid operation optimization model to obtain the optimal dispatch strategy for the microgrid, determines the microgrid's planning characteristics, and uploads the solution to the microgrid group control center. The microgrid operation optimization model, based on the initial purchase price and initial sales price of electricity at the PCC, aims to minimize the microgrid's electricity purchase cost. It constructs a minimum operating cost objective function for the microgrid, incorporating constraints related to active power balance, energy storage devices, the shifting time period of transferable loads, and the maximum power constraint of transferable loads. The microgrid group operation optimization module is used to construct a microgrid group operation optimization model based on the microgrid planning characteristics received by the microgrid group control center, iteratively solve the microgrid group operation optimization model until the absolute difference between the results of two consecutive iterations is within the predetermined difference range of the microgrid group or reaches the preset number of iterations of the microgrid group, obtain the optimal scheduling strategy of the microgrid group, determine the microgrid group planning characteristics, and upload them to the distribution network control center; the microgrid group operation optimization model is constructed by constructing the objective function of maximizing the autonomous operation of the microgrid group based on the microgrid planning characteristics uploaded by each microgrid in the microgrid group and the purchase and sale price of electricity at the common connection point set by the upper-level power grid, and combined with the active power balance constraints of the microgrid group; The distribution network operation optimization module is used to establish a distribution network operation optimization model based on the microgrid group planning characteristics uploaded by the microgrid group control center and the purchase and sale prices of electricity at the common connection points set by the distribution network. The module iteratively solves the distribution network operation optimization model until the absolute difference between two consecutive iterations is within a predetermined difference range of the distribution network or reaches the preset number of iterations, thus obtaining the optimal dispatch strategy for the distribution network. The distribution network operation optimization model is constructed by building an objective function for maximizing the autonomous operation of the distribution network based on the microgrid group planning characteristics uploaded by each microgrid group in the distribution network and the purchase and sale prices of electricity at the common connection points set by the distribution network, and combining this objective function with the active power balance constraints of the distribution network.

9. An optimized scheduling system based on a multi-level microgrid according to claim 8, characterized in that, The microgrid operation optimization module constructs a microgrid operation optimization model based on the equipment parameter information of each microgrid, combined with the initial purchase price and initial sales price of the microgrid's point of common coupling (PCC). It solves the microgrid operation optimization model to obtain the optimal dispatch strategy for the microgrid and determines the microgrid's planned characteristics. When uploading this information to the microgrid group control center, it initializes the equipment parameter information of each microgrid and sets the initial purchase price and initial sales price of the PCC. It then constructs the microgrid operation optimization model, solves it to obtain different optimal dispatch strategies for each microgrid group, and determines the microgrid's planned characteristics based on the optimal dispatch strategy. These planned characteristics are then uploaded to the microgrid group control center. The microgrid planned characteristics include the power curve of the equivalent power unit, the aggregated energy storage parameters of the microgrid, the charging power curve of the microgrid, and the discharging power curve of the microgrid.

10. An optimized scheduling system based on a multi-level microgrid according to claim 9, characterized in that, The objective function for minimizing the operating cost of the microgrid is expressed as follows: In the formula, F MG,i,j ΔT represents the electricity purchase cost of the microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period. t represents a specific moment within the scheduling period; ρ t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; and These refer to the purchase price and sales price of electricity from the microgrid to the microgrid cluster, respectively. Support price for microgrids; and These are the support prices received and the support prices provided for microgrids, respectively. Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

11. An optimized scheduling system based on a multi-level microgrid according to claim 8, characterized in that, The microgrid group operation optimization module constructs a microgrid group operation optimization model based on the microgrid plan characteristics received by the microgrid group control center, iteratively solves the microgrid group operation optimization model until the absolute difference between the results of two consecutive iterations is within the predetermined difference range of the microgrid group or reaches the preset number of iterations of the microgrid group, obtains the optimal scheduling strategy of the microgrid group, determines the microgrid group plan characteristics, and uploads them to the distribution network control center, thus constructing the corresponding microgrid group operation optimization model. The optimal scheduling strategy for the microgrid group is obtained by iteratively solving the microgrid group operation optimization model. Calculate the absolute difference between the solution results of the two iterations of the microgrid group operation optimization model, and compare the absolute difference with the predetermined difference range of the microgrid group; If the comparison result shows that the absolute difference is within the predetermined difference range of the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group, and the microgrid group planning characteristics are uploaded to the distribution network control center. If the comparison result shows that the absolute difference is outside the predetermined difference range of the microgrid group, it is determined whether the number of iterations of the microgrid group operation optimization model exceeds the preset number of iterations of the microgrid group. If the number of iterations is less than or equal to the preset number of iterations of the microgrid group, the support power of each microgrid is updated and the microgrid operation optimization model is constructed. If the number of iterations exceeds the preset number of iterations for the microgrid group, the microgrid group planning characteristics are determined according to the optimal scheduling strategy of the microgrid group, and the microgrid group planning characteristics are uploaded to the distribution network control center; wherein, the microgrid group planning characteristics include the power curve of the aggregated equivalent power unit of the microgrid group, the aggregated energy storage parameters of the microgrid group, the charging power curve of the microgrid group, and the discharging power curve of the microgrid group.

12. The optimized scheduling system based on a multi-level microgrid according to claim 11, characterized in that, The expression for the objective function of maximizing autonomous operation of the microgrid group is: Min F MGs,i =F PCC,i +F MG In the formula, F MGs,i The operating cost of microgrid group i; F PCC,i For the electricity purchase cost of microgrid i; F MG denoted as the aggregated electricity purchase cost of each microgrid; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period. t represents a specific moment within the scheduling period; ρ t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; ρ represents the inter-microgrid support power at time t. t,i,j The price for purchasing and selling electricity from microgrids to microgrid groups; Support price for microgrids; Let t be the planned input and output power of the microgrid. Let t be the inter-microgrid support power at time t.

13. The optimized scheduling system based on a multi-level microgrid according to claim 11, characterized in that, The distribution network operation optimization module establishes a distribution network operation optimization model based on the microgrid group planning characteristics uploaded by the microgrid group control center and the electricity purchase and sale price of the common connection point set by the distribution network. It iteratively solves the distribution network operation optimization model until the absolute difference between the two iterations is within the predetermined difference range of the distribution network or reaches the preset number of iterations of the distribution network. When the optimal scheduling strategy of the distribution network is obtained, the corresponding distribution network operation optimization model is constructed. The optimal dispatching strategy for the distribution network is obtained by iteratively solving the distribution network operation optimization model. Calculate the absolute difference between the solution results of the two iterations of the distribution network operation optimization model, and compare the absolute difference with the predetermined difference range of the distribution network. If the comparison result shows that the absolute difference is within the predetermined difference range of the distribution network, the distribution network planning characteristics are determined according to the optimal dispatching strategy of the distribution network. If the comparison result shows that the absolute difference is outside the predetermined difference range of the distribution network, it is determined whether the number of iterations of the distribution network operation optimization model exceeds the preset number of iterations of the distribution network. If the number of iterations is less than or equal to the preset number of iterations of the distribution network, the support power of each microgrid is updated, and the microgrid group operation optimization model is constructed. If the number of iterations is greater than the preset number of iterations of the distribution network, the distribution network planning characteristics are determined according to the optimal scheduling strategy of the distribution network. The distribution network planning characteristics include the power purchased and the power sold by the distribution network.

14. The optimized scheduling system based on a multi-level microgrid according to claim 13, characterized in that, The expression for the objective function of maximizing autonomous operation of the distribution network is: Min F DN =F PCC +F MGs In the formula, F DN For the operating costs of the distribution network; F PCC For the power purchase cost of the distribution network; F MGs ρ represents the aggregated electricity purchase cost of each microgrid group; i and j represent the j-th microgrid within the i-th microgrid group; T represents the number of time periods within a scheduling cycle; ΔT represents the duration of a scheduling time period; t represents a specific moment within the scheduling cycle; ρ t P represents the purchase and sale price of electricity at the point of common coupling of the distribution network at time t; t pcc ρ represents the planned input and output power of the distribution network at time t; t,i The purchase and sale price of electricity between the microgrid group and the distribution network; Let t be the planned input and output power of the microgrid group; Price support for micro-network groups; The inter-microgrid support power at time t.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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