Microgrid planning method and system for multi-microgrid coupling

Abstract

The present invention relates to the technical field of microgrid system energy management, and specifically to a microgrid planning method and system for multi-microgrid coupling. The method comprises the following steps: establishing a capability construction dimension diagram for each microgrid, setting a quantitative standard to quantify the dimension diagram into a capability construction index, evaluating the existing capability of each microgrid based on the capability construction index, calculating the power transmission loss during the coupling process, performing a benefit-cost analysis based on the benefit improvement and loss cost brought about by the coupling, generating an intelligent scheduling strategy, establishing a resource constraint function and an objective constraint function, generating multiple coupling schemes for each microgrid based on a multi-microgrid coordination algorithm, and comparing the benefit overflow values ​​of each scheme through the multi-microgrid coordination algorithm. The present invention provides a microgrid planning method and system for multi-microgrid coupling, which realizes coordinated operation and optimal resource allocation among multiple microgrids, improves the energy utilization efficiency of the overall system, reduces operating costs, and enhances power supply reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid system energy management, and in particular to a microgrid planning method and system for multi-microgrid coupling. Background Art

[0002] With the rapid development of distributed energy and microgrid technologies, microgrids, as miniaturized power systems, play a vital role in improving energy efficiency, enhancing power supply reliability, and facilitating the integration of renewable energy. However, the limited capacity and resources of a single microgrid make it difficult to meet the increasingly complex and changing power demands. Therefore, multi-microgrid coupling technology has emerged. By interconnecting and coordinating microgrids, it enables resource and capacity complementarity, improving the flexibility and cost-effectiveness of the overall system.

[0003] Currently, multi-microgrid coupling technology faces numerous challenges, including inaccurate capacity assessments between microgrids, unscientific coupling strategies, inefficient scheduling decisions, and irrational energy allocation. These issues lead to significant energy losses during the coupling process, high system operating costs, and poor power supply reliability, severely restricting the widespread application of multi-microgrid coupling technology.

[0004] For example, patent publication number CN119093364A, titled "A Method and System for Real-Time Coordinated Dispatching of Main and Distribution Microgrids," includes the following steps: making dispatch decisions based on dynamic programming within a preset energy window size; obtaining microgrid dispatch data and resource configuration data; analyzing the microgrid dispatch data and resource configuration data based on the BP neural network algorithm to construct an energy window size impact model; performing arithmetic processing based on the preset energy window size and obtaining the corresponding energy window size impact model; constructing an energy window size impact fluctuation curve, performing data analysis on the energy window size impact fluctuation curve, obtaining an optimized energy window size, and applying it to the real-time coordinated dispatch of the main and distribution microgrids. The disadvantages are: inaccurate capacity assessment between microgrids, unscientific coupling strategies, inefficient dispatch decisions, and unreasonable energy distribution. Summary of the Invention

[0005] In response to the problems in the prior art of multi-microgrid coupling, such as inaccurate capacity assessment between microgrids, unscientific coupling strategies, inefficient scheduling decisions, and unreasonable energy distribution, the present invention provides a microgrid planning method and system for multi-microgrid coupling, which achieves coordinated operation and optimal resource allocation among multiple microgrids, improves the energy utilization efficiency of the overall system, reduces operating costs, and enhances power supply reliability.

[0006] To achieve the above technical objectives, the present invention provides a technical solution, which is a microgrid planning method for multi-microgrid coupling, comprising the following steps:

[0007] S1, based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, establish a capability construction dimension diagram for each microgrid, set quantitative standards to quantify the dimension diagram into capability construction indicators, and evaluate the existing capabilities of each microgrid based on the capability construction indicators;

[0008] S2, based on the capability assessment results, selects microgrids that can complement each other's shortcomings for coupling, calculates the power transmission loss during the coupling process, and conducts a benefit-cost analysis based on the benefits and loss costs brought by the coupling;

[0009] S3 generates an intelligent dispatching strategy based on multi-source information such as real-time power demand, energy storage status, and weather forecasts. It establishes resource constraint functions and objective constraint functions, generates multiple coupling schemes for each microgrid based on the multi-microgrid coordination algorithm, compares the benefit spillover values ​​of each scheme through the multi-microgrid coordination algorithm, and selects the coupling scheme with the largest benefit spillover value as the optimal scheme.

[0010] S4 dynamically adjusts energy distribution according to real-time load changes and microgrid capabilities.

[0011] In this technical solution, a capacity building dimension diagram was first established for each microgrid and quantified into specific capacity building indicators. This accurately assessed the existing capacity of each microgrid, providing a basis for subsequent coupling selection. Next, based on the capacity assessment results, microgrids that complement each other's shortcomings were selected for coupling. The energy transmission losses during the coupling process were calculated, and a benefit-cost analysis was performed to ensure the economic and feasibility of the coupling. When generating the intelligent scheduling strategy, multiple sources of information, such as real-time power demand, energy storage status, and weather forecasts, were fully considered. Resource and objective constraint functions were established, and multiple coupling schemes were generated for each microgrid. These schemes were comprehensively compared using a multi-microgrid coordination algorithm, and the coupling scheme with the highest benefit spillover value was selected as the optimal one, further improving the overall efficiency of the system. Finally, energy allocation was dynamically adjusted based on real-time load changes and microgrid capabilities, ensuring system flexibility and adaptability.

[0012] The present invention is further configured as follows: establishing a capability dimension diagram for each microgrid according to the power supply capability, load characteristics, and energy storage capability of each microgrid includes:

[0013] Evaluate the maximum power supply capacity of each microgrid based on the type, capacity, and operating efficiency of the power generation equipment in each microgrid, taking into account the impact of peak hours, off-peak hours, and seasonal changes;

[0014] Evaluate the flexibility and responsiveness of load regulation based on the load type and load characteristics of each microgrid, including residential, commercial, and industrial types, and the characteristics including load profile, peak demand, and interruptibility;

[0015] Evaluate the energy storage regulation capability and backup power supply support capability in the event of power supply and demand imbalance based on the type, capacity, and charge / discharge efficiency of the energy storage equipment;

[0016] The evaluation results are integrated into a multi-dimensional graph, where each dimension represents power supply capacity, load characteristics and energy storage capacity.

[0017] This technical solution considers the type, capacity, and operating efficiency of power generation equipment, with particular attention paid to the impact of peak, off-peak, and seasonal variations on power supply capacity. Load types, such as residential, commercial, and industrial, are detailed, with analysis of load curves, peak demand, and interruptibility to assess load regulation flexibility and responsiveness. Energy storage equipment is evaluated for type, capacity, and charge / discharge efficiency, as well as its backup power supply capabilities in the event of power supply and demand imbalances. By integrating the evaluation results of various aspects into a multidimensional diagram, a systematic and comprehensive capability-building dimensional diagram is formed. This dimensional diagram not only demonstrates the capabilities of each microgrid in a single aspect but also, through the combination of multiple dimensions, reveals the complementarity and differences between microgrids, providing strong support for subsequent microgrid coupling and optimization.

[0018] The present invention is further configured such that: setting a quantitative standard to quantify the dimension graph into a capability building indicator comprises:

[0019] Quantify power supply capabilities based on maximum output power or power supply reliability;

[0020] Quantify load characteristics in terms of load regulation flexibility and response speed;

[0021] Energy storage capabilities are quantified based on energy storage capacity and charge and discharge efficiency.

[0022] In this technical solution, power supply capacity is quantified by maximum output power or power supply reliability; load characteristics are quantified by load regulation flexibility and response speed; and energy storage capacity is quantified by energy storage capacity and charge-discharge efficiency. Maximum output power and power supply reliability can be obtained through actual testing or historical data; load regulation flexibility and response speed can also be evaluated through load curves and actual demand changes; energy storage capacity and charge-discharge efficiency are basic performance parameters of energy storage equipment and can be directly obtained. The proposed quantitative standards are all operational indicators that can be directly used for actual evaluation and measurement.

[0023] The present invention is further configured such that: the selecting of a microgrid capable of complementing a shortcoming for coupling based on the capability evaluation result comprises:

[0024] Automatically calculate the comprehensive complementary index of each combination based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, as well as the preset complementary strategy;

[0025] Conduct feasibility analysis on the combination based on geographical location, transmission lines and policy restrictions, and select the optimal coupling combination based on the physical connection conditions, economic costs and technical feasibility between microgrids;

[0026] For the selected coupling combination, calculate the line loss and transformer loss during the coupling process;

[0027] Evaluate the economic and feasibility of coupling based on the benefit improvement and loss cost brought by coupling.

[0028] In this technical solution, the microgrids complement each other based on their different capabilities in terms of peak power supply and off-peak energy storage. This not only conforms to the actual operating characteristics of the microgrids, but also effectively utilizes the advantageous resources of each microgrid to improve the efficiency and reliability of the overall system. When selecting the coupling combination, not only the complementary capabilities between microgrids are taken into account, but also practical factors such as geographical location, transmission lines, and policy restrictions are fully considered. Through a comprehensive analysis of physical connection conditions, economic costs, and technical feasibility, the feasibility of the selected combination in actual operation is ensured. For the selected coupling combination, the line losses and transformer losses during the coupling process are also carefully calculated. This is crucial for evaluating the economic and feasibility of the coupling, and directly affects the efficiency and operating costs of the coupled system.

[0029] The present invention is further configured as follows: Step S3 also includes: cleaning and data fusion processing of multi-source information to form a unified data view, and establishing a resource constraint function and a target constraint function based on the processed data, wherein the resource constraint function includes power supply and demand balance constraints, energy storage constraints, distributed power output constraints and power transmission constraints, and the target constraint function includes economic goals, reliability goals and environmental protection goals.

[0030] This technical solution first cleans and fuses multi-source information to ensure data accuracy and consistency, forming a unified data view and providing a solid foundation for subsequent analysis and decision-making. Next, based on the processed data, resource constraint functions and objective constraint functions are established, comprehensively considering constraints such as power supply and demand balance, energy storage status, distributed power generation output, and power transmission, ensuring the feasibility and rationality of the scheduling plan. Furthermore, the establishment of the objective constraint function clarifies the economic, reliability, and environmental goals to be pursued, allowing the scheduling strategy to more accurately meet the overall needs of the system.

[0031] The present invention is further configured as follows: generating multiple coupling schemes for each microgrid according to the multi-microgrid coordination algorithm includes: defining the state variable of each microgrid as the difference between its power supply and load demand, exchanging information between each microgrid and its adjacent microgrids, and after receiving the power difference status of the adjacent microgrids, each microgrid updates its status according to the consistency algorithm; repeating the above process, continuously exchanging information and updating status, each microgrid will continuously adjust its own supply and demand balance according to its own status and the status of the adjacent microgrids, and generate multiple coupling schemes.

[0032] In generating coupling scenarios, this technical solution utilizes a multi-microgrid coordination algorithm. Through meticulous design and innovative methods, multiple feasible coupling scenarios are generated for each microgrid. Specifically, the state variable of each microgrid is defined as the difference between its power supply and load demand. This setting intuitively reflects the supply and demand status of the microgrid and provides a basis for subsequent information exchange and status updates. Next, each microgrid interacts with its neighboring microgrids and shares power difference status. This allows each microgrid to promptly understand the supply and demand status of its neighbors, providing a basis for its own adjustments. After receiving the power difference status from neighboring microgrids, each microgrid updates its status according to a consensus algorithm. This algorithm ensures coordination and consistency among microgrids, gradually optimizing the supply and demand balance of the entire system. Through repeated information exchange and status updates, each microgrid can flexibly adjust its supply and demand balance based on its own status and that of neighboring microgrids, thereby generating multiple coupling scenarios.

[0033] The present invention is further configured such that: the state update includes: each microgrid calculates a new state variable value according to a weighted average algorithm based on the power difference state received from the adjacent microgrid and its own current supply and demand state.

[0034] This technical solution uses a weighted average algorithm, enabling each microgrid to calculate new state variable values ​​based on the power differential status of neighboring microgrids and its own current supply and demand status. This algorithm fully considers the influence of neighboring microgrids and, by assigning appropriate weights to different microgrids, makes state variable updates more accurate and reasonable.

[0035] The present invention is further configured as follows: the comparison of the benefit spillover values ​​of each scheme through the multi-microgrid coordination algorithm includes: establishing an evaluation model based on multi-source information such as real-time power demand, energy storage status, and weather forecast, the cost of each scheme during operation, and the benefits brought by each scheme, quantifying each benefit indicator and assigning a weight, calculating the weighted sum of the benefits according to the weight, and subtracting the cost from the total benefit of the weighted sum to obtain the benefit spillover value of each scheme.

[0036] In this technical solution, a comprehensive evaluation model was established to compare the benefit spillover values ​​of various schemes using a multi-microgrid coordination algorithm. This model fully considers multiple sources of information, such as real-time power demand, energy storage status, and weather forecasts, as well as the costs and benefits of each scheme during operation. Each benefit indicator was quantified and weighted according to its importance, ensuring the accuracy and fairness of the evaluation. Next, the benefits were weighted and summed according to these weights to obtain the total benefit of each scheme. Finally, the cost was subtracted from the total benefit to determine the benefit spillover value of each scheme. This calculation process not only intuitively reflects the economic feasibility of each scheme but also provides a scientific basis for selecting the optimal coupling scheme, ensuring the coordinated operation of the multi-microgrid system and optimal resource allocation.

[0037] Another technical solution provided by the present invention is a microgrid planning system for multi-microgrid coupling, comprising:

[0038] Data acquisition and processing module, used to collect and process real-time operation data of the microgrid;

[0039] The capacity assessment and demand analysis module is used to establish a capacity construction dimension diagram for each microgrid based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, set quantitative standards to quantify the dimension diagram into capacity construction indicators, and evaluate the existing capacity of each microgrid based on the capacity construction indicators;

[0040] The coupling strategy and loss calculation module is used to select microgrids that can complement each other's shortcomings based on the capability assessment results, calculate the power transmission loss during the coupling process, and conduct a benefit-cost analysis based on the benefits and loss costs brought by the coupling.

[0041] The scheduling decision and algorithm design module is used to generate intelligent scheduling strategies based on multi-source information such as real-time power demand, energy storage status, and weather forecasts, establish resource constraint functions and objective constraint functions, generate multiple coupling schemes for each microgrid based on the multi-microgrid coordination algorithm, compare the benefit spillover values ​​of each scheme through the multi-microgrid coordination algorithm, establish an evaluation model based on multi-source information such as real-time power demand, energy storage status, and weather forecasts, the cost of each scheme during operation, and the benefits brought by each scheme, quantify each benefit indicator and assign a weight to it, calculate the weighted sum of the benefits based on the weights, subtract the cost from the total benefit of the weighted sum to obtain the benefit spillover value of each scheme, and select the coupling scheme with the largest benefit spillover value as the optimal scheme;

[0042] Energy distribution and optimization module, used to dynamically adjust energy distribution according to real-time load changes and microgrid capacity;

[0043] The collaborative control and execution module is used to achieve collaborative control and energy distribution between microgrids based on the scheduling decision results.

[0044] In this technical solution, the microgrid planning system for multi-microgrid coupling is a highly integrated, comprehensive, and intelligent system. Through the close collaboration of multiple modules, the system achieves comprehensive microgrid planning and management. This close collaboration between the modules creates an efficient and intelligent microgrid planning and management system, providing strong technical support and assurance for the coupled operation of multiple microgrids.

[0045] The present invention is further configured as follows: the system also includes a user interface module for interacting with the user, receiving parameters and instructions input by the user, and displaying system operation results.

[0046] In this technical solution, the multi-microgrid coupled microgrid planning system not only provides comprehensive planning and management capabilities but also incorporates a user interface module. This module acts as a bridge between the system and the user, enabling efficient interaction. It not only receives user inputs of parameters and commands, ensuring customized system operation based on the user's specific needs, but also displays operational results, allowing users to intuitively understand the system's operating status and effectiveness.

[0047] The beneficial effects of the present invention are as follows: (1) it realizes coordinated operation and optimal resource allocation among multiple microgrids, improves the energy utilization efficiency of the overall system, reduces operating costs, and enhances power supply reliability; (2) first, a capability construction dimension diagram is established for each microgrid and quantified into specific capability construction indicators, thereby accurately evaluating the existing capabilities of each microgrid and providing a basis for subsequent coupling selection. Then, based on the capability evaluation results, microgrids that can complement each other's shortcomings are selected for coupling, and the power transmission loss during the coupling process is calculated. Through benefit-cost analysis, the economy and feasibility of the coupling are ensured. When generating the intelligent scheduling strategy, multi-source information such as real-time power demand, energy storage status, and weather forecast are fully considered, and resource constraint functions and target constraint functions are established. Multiple coupling schemes are generated for each microgrid. Through the multi-microgrid coordination algorithm, these schemes are comprehensively compared, and the coupling scheme with the largest benefit spillover value is selected as the optimal scheme, further improving the overall benefit of the system. Finally, energy distribution is dynamically adjusted according to real-time load changes and microgrid capabilities to ensure the flexibility and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flowchart of a microgrid planning method for multi-microgrid coupling according to the present invention;

[0049] Figure 2 This is a schematic diagram of the structure of the present invention with a single substation as the core of the network;

[0050] Figure 3 This is a schematic diagram of the structure of the present invention with multiple substations as the core of the network. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0052] like Figures 1 to 3 As shown in the figure, as a first embodiment of the present invention, a microgrid planning method for multi-microgrid coupling includes the following steps:

[0053] S1, based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, establish a capability construction dimension diagram for each microgrid, set quantitative standards to quantify the dimension diagram into capability construction indicators, and evaluate the existing capabilities of each microgrid based on the capability construction indicators;

[0054] S2, based on the capability assessment results, selects microgrids that can complement each other's shortcomings for coupling, calculates the power transmission loss during the coupling process, and conducts a benefit-cost analysis based on the benefits and loss costs brought by the coupling;

[0055] S3 generates an intelligent dispatching strategy based on multi-source information such as real-time power demand, energy storage status, and weather forecasts. It establishes resource constraint functions and objective constraint functions, generates multiple coupling schemes for each microgrid based on the multi-microgrid coordination algorithm, compares the benefit spillover values ​​of each scheme through the multi-microgrid coordination algorithm, and selects the coupling scheme with the largest benefit spillover value as the optimal scheme.

[0056] S4 dynamically adjusts energy distribution according to real-time load changes and microgrid capabilities.

[0057] In this technical solution, a capacity building dimension diagram was first established for each microgrid and quantified into specific capacity building indicators. This accurately assessed the existing capacity of each microgrid, providing a basis for subsequent coupling selection. Next, based on the capacity assessment results, microgrids that complement each other's shortcomings were selected for coupling. Energy transmission losses during the coupling process were calculated, and a benefit-cost analysis was conducted to ensure the economic and feasibility of the coupling. When generating the intelligent scheduling strategy, multiple sources of information, such as real-time power demand, energy storage status, and weather forecasts, were fully considered. Resource and objective constraint functions were established, and multiple coupling schemes were generated for each microgrid. These schemes were comprehensively compared using a multi-microgrid coordination algorithm, and the coupling scheme with the highest benefit spillover value was selected as the optimal one, further improving the overall efficiency of the system. Finally, energy allocation was dynamically adjusted based on real-time load changes and microgrid capabilities, ensuring system flexibility and adaptability.

[0058] It can be understood that the power supply capacity includes the total installed capacity of power generation equipment, the single-unit capacity, operating efficiency, availability and historical power generation data of various power generation equipment (such as photovoltaic, wind power, and gas turbines); load characteristics include load type (such as industrial, commercial, and residential loads), load curves (daily load, weekly load, and annual load characteristics), peak and valley time division, load forecasting model and historical load data; energy storage capacity includes the type of energy storage equipment (such as battery energy storage, supercapacitor energy storage, and hydrogen energy storage), total capacity, charging and discharging efficiency, charging and discharging power limit, cycle life, and current energy storage status.

[0059] As you can understand, quantification of power supply capacity indicators includes the availability rate, average power output, maximum power output, and power generation stability (such as standard deviation and coefficient of variation) of power generation equipment. Quantification of load characteristics includes the peak-to-valley difference rate, average load rate, load forecast accuracy, and load response speed. Quantification of energy storage capacity includes the charge and discharge efficiency, charge and discharge power, energy density, cycle life utilization, and energy storage cost of energy storage equipment. In a multi-microgrid coupled system, due to the differences and uncertainties in energy production and consumption among microgrids, intelligent scheduling strategies are developed to improve system energy utilization. These scheduling strategies not only consider the internal balance of individual microgrids but also the coordination and complementarity between multiple microgrids. During the coupling planning process, the microgrids to be coupled, as well as their connection methods and parameters, are determined. Based on this, the scheduling strategy further optimizes energy flow and distribution among microgrids based on multiple sources of information, such as real-time power demand, energy storage status, and weather forecasts. Real-time load fluctuations are a key factor in formulating scheduling strategies. By monitoring and analyzing load changes across microgrids in real time, we can more accurately predict future energy demand and supply, enabling the development of more effective scheduling strategies. The impact of real-time load changes on coupling effectiveness also needs to be considered in multi-microgrid coupling planning. For example, during peak load periods, the coupling strength and frequency between microgrids may need to be increased to meet greater power demand; during low load periods, the coupling strength and frequency can be appropriately reduced to conserve energy and reduce operating costs.

[0060] The capability construction dimension diagram for each microgrid is established based on the power supply capability, load characteristics, and energy storage capability of each microgrid, including:

[0061] Evaluate the maximum power supply capacity of each microgrid based on the type, capacity, and operating efficiency of the power generation equipment in each microgrid, taking into account the impact of peak hours, off-peak hours, and seasonal changes;

[0062] Evaluate the flexibility and responsiveness of load regulation based on the load type and load characteristics of each microgrid, including residential, commercial, and industrial types, and the characteristics including load profile, peak demand, and interruptibility;

[0063] Evaluate the energy storage regulation capability and backup power supply support capability in the event of power supply and demand imbalance based on the type, capacity, and charge / discharge efficiency of the energy storage equipment;

[0064] The evaluation results are integrated into a multi-dimensional graph, where each dimension represents power supply capacity, load characteristics and energy storage capacity.

[0065] In this example, the type, capacity, and operating efficiency of power generation equipment are considered, with special attention paid to the impact of peak hours, off-peak hours, and seasonal variations on power supply capacity. For loads, residential, commercial, and industrial types are listed in detail, and characteristics such as load curves, peak demand, and interruptibility are analyzed to assess the flexibility and responsiveness of load regulation. For energy storage equipment, attention is paid to its type, capacity, and charge / discharge efficiency, as well as its backup power supply support capabilities in the event of an imbalance in power supply and demand. By integrating the evaluation results of various aspects into a multidimensional diagram, a systematic and comprehensive capability-building dimensional diagram is formed. This dimensional diagram not only demonstrates the capabilities of each microgrid in a single aspect, but also, through the combination of multiple dimensions, reveals the complementarity and differences between microgrids, providing strong support for subsequent microgrid coupling and optimization.

[0066] The quantitative standards set to quantify the dimension diagram into capability building indicators include:

[0067] Quantify power supply capabilities based on maximum output power or power supply reliability;

[0068] Quantify load characteristics in terms of load regulation flexibility and response speed;

[0069] Energy storage capabilities are quantified based on energy storage capacity and charge and discharge efficiency.

[0070] In this technical solution, power supply capacity is quantified by maximum output power or power supply reliability; load characteristics are quantified by load regulation flexibility and response speed; and energy storage capacity is quantified by energy storage capacity and charge-discharge efficiency. Maximum output power and power supply reliability can be obtained through actual testing or historical data; load regulation flexibility and response speed can also be evaluated based on load curves and actual demand changes; energy storage capacity and charge-discharge efficiency are basic performance parameters of energy storage equipment and can be directly obtained. The proposed quantitative standards are all operational indicators that can be directly used for practical evaluation and measurement. Multiple dimensions, including power supply capacity, load characteristics, and energy storage capacity, are integrated into a capability-building dimension diagram. These dimensions are then quantified into specific capability-building indicators through the use of established quantitative standards. When considering power supply capacity, not only maximum output power but also power supply reliability are considered, and these two indicators are combined for a comprehensive assessment. The capability-building dimension diagram organically links these three relatively independent dimensions: power supply capacity, load characteristics, and energy storage capacity.

[0071] The selection of microgrids that can complement each other's shortcomings for coupling based on the capability assessment results includes:

[0072] Automatically calculate the comprehensive complementary index of each combination based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, as well as the preset complementary strategy;

[0073] Conduct feasibility analysis on the combination based on geographical location, transmission lines and policy restrictions, and select the optimal coupling combination based on the physical connection conditions, economic costs and technical feasibility between microgrids;

[0074] For the selected coupling combination, calculate the line loss and transformer loss during the coupling process;

[0075] Evaluate the economic and feasibility of coupling based on the benefit improvement and loss cost brought by coupling.

[0076] This technical solution complements each microgrid based on its capabilities in peak power supply and off-peak energy storage. This not only meets the actual operating characteristics of microgrids, but also effectively utilizes the advantageous resources of each microgrid, improving the efficiency and reliability of the overall system. When selecting a coupling combination, not only the complementary capabilities between microgrids are considered, but also practical factors such as geographical location, transmission lines, and policy restrictions are fully considered. A comprehensive analysis of physical connection conditions, economic costs, and technical feasibility ensures the feasibility of the selected combination in actual operation. For the selected coupling combination, the line losses and transformer losses during the coupling process are also carefully calculated. This is crucial for evaluating the economic and feasibility of the coupling, and directly affects the efficiency and operating costs of the coupled system.

[0077] It can be understood that the complementary strategy includes that one microgrid provides power during peak hours if it has a strong power supply capability during peak hours, and the other microgrid provides power during valley hours if it has a strong energy storage capability during valley hours.

[0078] In step S2, the calculation formula for the power transmission loss during the coupling process is:

[0079] P loss =I 2 *R total +P additional ;

[0080] R total =R wire +R contact +X inductance ;

[0081] Where I is the transmission current, R total is the total line impedance, P additional is the additional loss including corona loss, skin effect loss and dielectric loss, R wire is the wire resistance, R contact is the contact resistance, X inductance The reactance generated by the line inductance.

[0082] In step S2, the calculation formula for the benefit-cost analysis is:

[0083] ;

[0084] Among them, C t is the cash inflow in year t, O t is the cash outflow in year t, r is the discount rate, n is the project life, and t is the number of years.

[0085] Step S3 also includes: cleaning and data fusion processing of multi-source information to form a unified data view, and establishing resource constraint functions and target constraint functions based on the processed data. The resource constraint functions include power supply and demand balance constraints, energy storage constraints, distributed power output constraints and power transmission constraints. The target constraint functions include economic targets, reliability targets and environmental protection targets.

[0086] This technical solution first cleans and fuses multi-source information to ensure data accuracy and consistency, forming a unified data view and providing a solid foundation for subsequent analysis and decision-making. Next, based on the processed data, resource constraint functions and objective constraint functions are established, comprehensively considering constraints such as power supply and demand balance, energy storage status, distributed power generation output, and power transmission, ensuring the feasibility and rationality of the scheduling plan. Furthermore, the establishment of the objective constraint function clarifies the economic, reliability, and environmental goals to be pursued, allowing the scheduling strategy to more accurately meet the overall needs of the system.

[0087] The method of generating multiple coupling schemes for each microgrid according to the multi-microgrid coordination algorithm includes: defining the state variable of each microgrid as the difference between its power supply and load demand, exchanging information between each microgrid and its neighboring microgrids, and updating the state of each microgrid according to a consensus algorithm after receiving the power difference state of the neighboring microgrids; repeating the above process, continuously exchanging information and updating the state, so that each microgrid continuously adjusts its own supply and demand balance according to its own state and the state of the neighboring microgrids, thereby generating multiple coupling schemes.

[0088] In this embodiment, a multi-microgrid coordination algorithm is employed to generate coupling schemes. Through meticulous design and innovative methods, multiple feasible coupling schemes are generated for each microgrid. Specifically, the state variable of each microgrid is defined as the difference between its power supply and load demand. This setting intuitively reflects the supply and demand status of the microgrid and provides a basis for subsequent information exchange and status updates. Next, each microgrid is allowed to exchange information with its neighboring microgrids and share power difference status. The multi-microgrid system implements data exchange via a wired or wireless network, allowing each microgrid to promptly understand the supply and demand status of surrounding microgrids, providing a basis for its own adjustments. After receiving the power difference status of neighboring microgrids, each microgrid updates its status according to a consensus algorithm. A consensus algorithm is a distributed control algorithm used to ensure that multiple nodes reach a consistent state during information exchange. In a multi-microgrid system, this algorithm can be used to achieve supply and demand balance and coordinated operation among microgrids. This algorithm ensures coordination and consistency among microgrids, thereby gradually optimizing the supply and demand balance of the entire system. By continuously repeating the process of information interaction and status update, each microgrid can flexibly adjust its own supply and demand balance according to its own status and the status of neighboring microgrids, thereby generating multiple coupling schemes.

[0089] The state update includes: each microgrid calculates a new state variable value according to the received power difference state of the adjacent microgrid and its own current supply and demand state using a weighted average algorithm.

[0090] In this embodiment, a weighted average algorithm is used, enabling each microgrid to calculate new state variable values ​​based on the power differential status of neighboring microgrids and its own current supply and demand status. This algorithm fully considers the influence of neighboring microgrids and, by assigning appropriate weights to different microgrids, makes state variable updates more accurate and reasonable.

[0091] It can be understood that the new state variable value will serve as the basis for the next round of information interaction and state update.

[0092] In addition, the comparison of the benefit spillover values ​​of each scheme through the multi-microgrid coordination algorithm includes: establishing an evaluation model based on multi-source information such as real-time power demand, energy storage status, and weather forecast, the cost of each scheme during operation, and the benefits brought by each scheme, quantifying each benefit indicator and assigning a weight, calculating the weighted sum of the benefits according to the weight, and subtracting the cost from the total benefit of the weighted sum to obtain the benefit spillover value of each scheme.

[0093] The present invention establishes a comprehensive evaluation model when comparing the benefit spillover values ​​of various schemes through a multi-microgrid coordination algorithm. This model fully considers multiple sources of information, such as real-time power demand, energy storage status, and weather forecasts, as well as the costs and benefits of each scheme during operation. Each benefit indicator is quantified and assigned a corresponding weight based on its importance, ensuring the accuracy and fairness of the evaluation. Next, the benefits are weighted and summed according to these weights to obtain the total benefit of each scheme. Finally, the cost is subtracted from the total benefit to obtain the benefit spillover value of each scheme. This calculation process not only intuitively reflects the economic feasibility of each scheme but also provides a scientific basis for selecting the optimal coupling scheme, ensuring the coordinated operation and optimal resource allocation of the multi-microgrid system.

[0094] It can be understood that the benefit spillover value includes economic benefits, environmental benefits and social benefits. Economic benefits include reduced electricity bill expenditure, increased electricity sales revenue, and cost savings of energy storage equipment. Environmental benefits include reduced carbon emissions and increased renewable energy utilization. Social benefits include improved power supply reliability, reduced power outage time, and improved user experience.

[0095] like Figures 2 to 3 As shown in the figure, as a second embodiment of the present invention, a microgrid planning system for multi-microgrid coupling includes:

[0096] Data acquisition and processing module, used to collect and process real-time operation data of the microgrid;

[0097] The capacity assessment and demand analysis module is used to establish a capacity construction dimension diagram for each microgrid based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, set quantitative standards to quantify the dimension diagram into capacity construction indicators, and evaluate the existing capacity of each microgrid based on the capacity construction indicators;

[0098] The coupling strategy and loss calculation module is used to select microgrids that can complement each other's shortcomings based on the capability assessment results, calculate the power transmission loss during the coupling process, and conduct a benefit-cost analysis based on the benefits and loss costs brought by the coupling.

[0099] The scheduling decision and algorithm design module is used to generate intelligent scheduling strategies based on multi-source information such as real-time power demand, energy storage status, and weather forecasts, establish resource constraint functions and objective constraint functions, generate multiple coupling schemes for each microgrid based on the multi-microgrid coordination algorithm, compare the benefit spillover values ​​of each scheme through the multi-microgrid coordination algorithm, establish an evaluation model based on multi-source information such as real-time power demand, energy storage status, and weather forecasts, the cost of each scheme during operation, and the benefits brought by each scheme, quantify each benefit indicator and assign a weight to it, calculate the weighted sum of the benefits based on the weights, subtract the cost from the total benefit of the weighted sum to obtain the benefit spillover value of each scheme, and select the coupling scheme with the largest benefit spillover value as the optimal scheme;

[0100] Energy distribution and optimization module, used to dynamically adjust energy distribution according to real-time load changes and microgrid capacity;

[0101] The collaborative control and execution module is used to achieve collaborative control and energy distribution between microgrids based on the scheduling decision results.

[0102] In this technical solution, the microgrid planning system for multi-microgrid coupling is a highly integrated, fully functional intelligent system. Through the close collaboration of multiple modules, the system achieves comprehensive planning and management of microgrids. The close collaboration between the modules of the entire system forms an efficient and intelligent microgrid planning and management system, providing strong technical support and guarantees for the coupled operation of multiple microgrids. Real-time operating data provides detailed information on the current status of the microgrid to the capacity assessment and demand analysis modules. This data includes the actual performance of key indicators such as power supply capacity, load characteristics, and energy storage capacity, enabling the system to accurately assess the current capacity and potential demand of each microgrid. By analyzing real-time data, the system can dynamically adjust the capacity assessment standards and models to adapt to the performance changes of the microgrid under different operating conditions, thereby more accurately reflecting its actual operating status.

[0103] Understandably, Figure 2 、 Figure 3 Both are schematic diagrams of the primary and secondary systems of the terminal-guaranteed microgrid, among which: Figure 2 With a single substation as the core of the network, Figure 3 The network core is composed of multiple substations.

[0104] The system also includes a user interface module for interacting with users, receiving user input parameters and commands, and displaying system operation results. The microgrid planning system for multi-microgrid coupling not only provides comprehensive planning and management capabilities, but also incorporates a user interface module. This module serves as a bridge between the system and the user, enabling efficient interaction. It not only receives user input parameters and commands, ensuring customized system operation based on the user's actual needs, but also displays system operation results, allowing users to intuitively understand the system's operating status and effectiveness.

[0105] The specific description of the present invention in the above embodiments is only used to further illustrate the present invention and cannot be understood as limiting the scope of protection of the present invention. Technical engineers in this field may make some non-essential improvements and adjustments to the present invention based on the contents of the above invention, which fall within the scope of protection of the present invention.

Claims

1. A microgrid planning method for multi-microgrid coupling, characterized by: The following steps are involved: S1, based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, establish a capability construction dimension diagram for each microgrid, set quantitative standards to quantify the dimension diagram into capability construction indicators, and evaluate the existing capabilities of each microgrid based on the capability construction indicators; S2, based on the capability assessment results, selects microgrids that can complement each other's shortcomings for coupling, calculates the power transmission loss during the coupling process, and conducts a benefit-cost analysis based on the benefits and loss costs brought by the coupling; S3 generates an intelligent dispatching strategy based on multi-source information such as real-time power demand, energy storage status, and weather forecasts. It establishes resource constraint functions and objective constraint functions, generates multiple coupling schemes for each microgrid based on the multi-microgrid coordination algorithm, compares the benefit spillover values ​​of each scheme through the multi-microgrid coordination algorithm, and selects the coupling scheme with the largest benefit spillover value as the optimal scheme. S4, dynamically adjusts energy distribution based on real-time load changes and microgrid capabilities; The selection of microgrids that can complement each other's shortcomings for coupling based on the capability assessment results includes: Automatically calculate the comprehensive complementary index of each combination based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, as well as the preset complementary strategy; Conduct feasibility analysis on the combination based on geographical location, transmission lines and policy restrictions, and select the optimal coupling combination based on the physical connection conditions, economic costs and technical feasibility between microgrids; For the selected coupling combination, calculate the line loss and transformer loss during the coupling process; Evaluate the economic and feasibility of coupling based on the benefit improvement and loss cost brought by coupling.

2. The microgrid planning method for multi-microgrid coupling according to claim 1, characterized in that: The capability construction dimension diagram for each microgrid is established based on the power supply capability, load characteristics, and energy storage capability of each microgrid, including: Evaluate the maximum power supply capacity of each microgrid based on the type, capacity, and operating efficiency of the power generation equipment in each microgrid, taking into account the impact of peak hours, off-peak hours, and seasonal changes; Evaluate the flexibility and responsiveness of load regulation based on the load type and load characteristics of each microgrid, including residential, commercial, and industrial types, and the characteristics including load profile, peak demand, and interruptibility; Evaluate the energy storage regulation capability and backup power supply support capability in the event of power supply and demand imbalance based on the type, capacity, and charge / discharge efficiency of the energy storage equipment; The evaluation results are integrated into a multi-dimensional graph, where each dimension represents power supply capacity, load characteristics and energy storage capacity.

3. The microgrid planning method for multi-microgrid coupling according to claim 2, characterized in that: The quantitative standards set to quantify the dimension diagram into capability building indicators include: Quantify power supply capabilities based on maximum output power or power supply reliability; Quantify load characteristics in terms of load regulation flexibility and response speed; Energy storage capabilities are quantified based on energy storage capacity and charge and discharge efficiency.

4. The microgrid planning method for multi-microgrid coupling according to claim 1, 2 or 3, characterized in that: Step S3 also includes: cleaning and data fusion processing of multi-source information to form a unified data view, and establishing resource constraint functions and target constraint functions based on the processed data. The resource constraint functions include power supply and demand balance constraints, energy storage constraints, distributed power output constraints and power transmission constraints. The target constraint functions include economic targets, reliability targets and environmental protection targets.

5. The microgrid planning method for multi-microgrid coupling according to claim 4, characterized in that: The method of generating multiple coupling schemes for each microgrid according to the multi-microgrid coordination algorithm includes: defining the state variable of each microgrid as the difference between its power supply and load demand, exchanging information between each microgrid and its neighboring microgrids, and updating the state of each microgrid according to a consensus algorithm after receiving the power difference state of the neighboring microgrids; repeating the above process, continuously exchanging information and updating the state, so that each microgrid continuously adjusts its own supply and demand balance according to its own state and the state of the neighboring microgrids, thereby generating multiple coupling schemes.

6. The microgrid planning method for multi-microgrid coupling according to claim 5, characterized in that: The state update includes: each microgrid calculates a new state variable value according to the received power difference state of the adjacent microgrid and its own current supply and demand state using a weighted average algorithm.

7. The microgrid planning method for multi-microgrid coupling according to claim 6, characterized in that: The method of comparing the benefit spillover values ​​of each scheme through a multi-microgrid coordination algorithm includes: establishing an evaluation model based on multi-source information such as real-time power demand, energy storage status, and weather forecast, the cost of each scheme during operation, and the benefits brought by each scheme, quantifying each benefit indicator and assigning a weight, calculating a weighted sum of the benefits based on the weight, and subtracting the cost from the total benefit of the weighted sum to obtain the benefit spillover value of each scheme.

8. A microgrid planning system for multi-microgrid coupling, characterized by: include: Data acquisition and processing module, used to collect and process real-time operation data of the microgrid; The capacity assessment and demand analysis module is used to establish a capacity construction dimension diagram for each microgrid based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, set quantitative standards to quantify the dimension diagram into capacity construction indicators, and evaluate the existing capacity of each microgrid based on the capacity construction indicators; The coupling strategy and loss calculation module is used to select microgrids that can complement each other's shortcomings based on the capability assessment results, calculate the power transmission loss during the coupling process, and conduct a benefit-cost analysis based on the benefits and loss costs brought by the coupling. The scheduling decision and algorithm design module is used to generate intelligent scheduling strategies based on multi-source information such as real-time power demand, energy storage status, and weather forecasts, establish resource constraint functions and objective constraint functions, generate multiple coupling schemes for each microgrid based on the multi-microgrid coordination algorithm, compare the benefit spillover values ​​of each scheme through the multi-microgrid coordination algorithm, establish an evaluation model based on multi-source information such as real-time power demand, energy storage status, and weather forecasts, the cost of each scheme during operation, and the benefits brought by each scheme, quantify each benefit indicator and assign a weight to it, calculate the weighted sum of the benefits based on the weights, subtract the cost from the total benefit of the weighted sum to obtain the benefit spillover value of each scheme, and select the coupling scheme with the largest benefit spillover value as the optimal scheme; Energy distribution and optimization module, used to dynamically adjust energy distribution according to real-time load changes and microgrid capacity; The collaborative control and execution module is used to achieve collaborative control and energy distribution between microgrids based on the scheduling decision results; The selection of microgrids that can complement each other's shortcomings for coupling based on the capability assessment results includes: Automatically calculate the comprehensive complementary index of each combination based on the power supply capacity, load characteristics and energy storage capacity of each microgrid, as well as the preset complementary strategy; Conduct feasibility analysis on the combination based on geographical location, transmission lines and policy restrictions, and select the optimal coupling combination based on the physical connection conditions, economic costs and technical feasibility between microgrids; For the selected coupling combination, calculate the line loss and transformer loss during the coupling process; Evaluate the economic and feasibility of coupling based on the benefit improvement and loss cost brought by coupling.

9. The microgrid planning system for multi-microgrid coupling according to claim 8, characterized in that: The system also includes a user interface module for interacting with the user, receiving parameters and instructions input by the user, and displaying the system operation results.

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