A multi-micronet energy coordination optimization method and system

Abstract

The application provides a multi-micro-grid energy coordination optimization method and system, comprising the following steps: S1, establishing an energy coordination network topology structure comprising a plurality of micro-grid nodes; S2, constructing an energy balance model based on real-time power demand, new energy output prediction and power grid dispatching instructions; S3, taking the minimization of the total operation cost of the multi-micro-grid system as the target, forming an optimization function; S4, solving the optimization function by using an improved beluga whale optimization algorithm, and obtaining the energy scheduling scheme of each micro-grid in each period; S5, according to the energy scheduling scheme, adjusting the energy storage devices, charging piles and adjustable loads in each micro-grid, and coordinating the energy flow direction and transmission power among the micro-grids; and S6, monitoring the actual operation state and energy interaction effect of each micro-grid, and when it is detected that the prediction deviation exceeds the preset threshold, re-executing S2 to S5 based on real-time data. The application can realize energy coordination among the multi-micro-grids, improve the new energy consumption capacity, reduce the operation cost, and enhance the economy and stability of the power grid operation.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a method and system for coordinated energy optimization of multiple microgrids. Background Technology

[0002] With the global energy crisis, distributed energy systems centered on distributed generation of renewable energy sources (such as wind power and photovoltaics) have developed rapidly. Microgrids, as an important technological means to effectively integrate distributed energy, improve energy utilization efficiency, and enhance regional power supply reliability, have received widespread attention. A microgrid can be viewed as a small-scale power generation and distribution system containing various distributed power sources, energy storage systems, loads, and control devices, capable of grid-connected or independent operation. Due to its limited scale, resource types and capacity, and limited regulation capabilities, a single microgrid exhibits significant randomness, volatility, and intermittency in its renewable energy output. Coupled with the uncertainty of user load demand, this makes it difficult for a single microgrid to balance source-load power fluctuations when operating independently. This can lead to problems such as decreased power supply reliability and high operating costs, especially when power source or load failures occur within the microgrid, posing a significant challenge to its safe and stable operation.

[0003] To address the limitations of single microgrids, the concept of multi-microgrid systems emerged. A multi-microgrid system refers to a complex energy system that connects multiple independently operating microgrids within a geographically proximate area through a common coupling point and integrates them into the distribution network. However, existing multi-microgrid systems suffer from deficiencies in energy coordination among the microgrids, resulting in low renewable energy absorption capacity, persistently high overall system operating costs, and unstable grid operation. Summary of the Invention

[0004] Based on the above-mentioned problems, this invention proposes a multi-microgrid energy collaborative optimization method and system. By establishing a multi-microgrid collaborative optimization framework and a multi-dimensional constraint model, and combining it with the improved White Whale algorithm for solving, intelligent energy collaboration among multiple microgrids is realized, which significantly improves the renewable energy absorption capacity, reduces the overall system operating cost, and enhances the economy and stability of power grid operation.

[0005] In view of this, one aspect of the present invention proposes a multi-microgrid energy collaborative optimization method, comprising:

[0006] S1: Establish an energy collaborative network topology containing multiple microgrid nodes. Each microgrid node includes distributed photovoltaic devices, wind power devices, energy storage devices, and charging piles. Each microgrid node realizes bidirectional flow of electricity through an energy interaction interface.

[0007] S2: Based on real-time electricity demand, new energy output forecasts and grid dispatch instructions, a multi-microgrid energy balance model integrating demand response mechanism and carbon trading constraints is constructed. The demand response mechanism dynamically adjusts the energy storage charging and discharging strategies in each microgrid according to time-of-use electricity prices and load characteristics. The carbon trading constraints limit the scale of energy interaction based on the carbon emission quotas of each microgrid.

[0008] S3: With the goal of minimizing the total operating cost of the multi-microgrid system, a comprehensive objective function is established that includes electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. An energy complementarity benefit term between microgrids is also introduced to form a multi-objective optimization function that considers both economic and environmental factors.

[0009] S4: An improved Whale Optimization Algorithm that integrates adaptive weight adjustment and elite retention strategies is adopted to solve the multi-objective optimization function and obtain the optimal energy scheduling scheme for each microgrid in each time period, including energy storage charging and discharging power, energy interaction power between microgrids and power allocation of charging piles.

[0010] S5: According to the optimal energy dispatch scheme, the energy management system controls the energy storage devices, charging piles and adjustable loads in each microgrid in real time, while coordinating the energy flow and transmission power between microgrids to achieve the coordinated operation of multiple microgrid systems.

[0011] S6: Monitor the actual operating status and energy interaction effect of each microgrid. When the prediction deviation is detected to exceed the preset threshold, re-execute steps S2 to S5 based on real-time data to achieve dynamic optimization and adjustment of energy coordination among multiple microgrids.

[0012] Optionally, S5: Based on the optimal energy dispatch scheme, the energy management system controls the energy storage devices, charging piles, and adjustable loads within each microgrid in real time, while coordinating the energy flow and transmission power between microgrids to achieve the coordinated operation of the multi-microgrid system, including:

[0013] Based on the optimal energy dispatch scheme and the time-of-use electricity price information for the current period, determine the charging and discharging mode of the energy storage devices in each microgrid, control the charging of energy storage devices during periods when the electricity price is lower than the first preset price, and control the discharging of energy storage devices during periods when the electricity price is higher than the second preset price.

[0014] Monitor the real-time power load of each microgrid transformer. When the load power is detected to be close to the transformer capacity set value, control the energy storage device to discharge or the photovoltaic device to reduce the power output, so that the transformer capacity is kept within the safe operating range.

[0015] When the total power demand of the charging piles exceeds the power supply capacity of the microgrid, the charging power of each charging pile will be dynamically adjusted or some charging piles will be suspended according to the preset first priority principle.

[0016] By setting the PCC point power control target value, when the output power of the distributed power source is greater than the load power, the energy storage device is controlled to charge or the charging pile load is increased; when the output power of the distributed power source is insufficient, the energy storage device is controlled to discharge to supplement the power supply.

[0017] Optionally, S6: Monitoring the actual operating status and energy interaction effect of each microgrid, and when the prediction deviation is detected to exceed a preset threshold, re-executing steps S2 to S5 based on real-time data to achieve dynamic optimization and adjustment of multi-microgrid energy coordination, including:

[0018] Real-time monitoring of the load power change rate of each microgrid; when the load power change rate exceeds the preset threshold, control the energy storage device to discharge in order to smooth load fluctuations and reduce the impact on the power grid.

[0019] When a high-power short-term load demand is detected in the microgrid, the energy storage device is prioritized to discharge and supplement the power, so as to avoid transformer overload and realize dynamic expansion of system capacity.

[0020] Based on the real-time changes in the power factor of each microgateway port, the energy storage PCS is controlled to continuously adjust the reactive power output to maintain the system power factor within a reasonable range.

[0021] The actual operating data of each microgrid is compared and analyzed with the predicted data. When the deviation exceeds the allowable range, the optimization parameters are recalculated and the scheduling strategy is updated.

[0022] Optionally, S2: The steps for constructing a multi-microgrid energy balance model integrating demand response mechanisms and carbon trading constraints based on real-time electricity demand, renewable energy output forecasts, and grid dispatch instructions include:

[0023] Real-time collection of power demand data from each microgrid, power generation forecast data from new energy equipment, and dispatch instructions issued by the upper-level power grid; establishment of a unified data time synchronization mechanism and data quality verification mechanism to ensure consistency of various types of data in terms of time dimension and accuracy requirements;

[0024] Based on time-of-use electricity price signals and historical load characteristic curves of each microgrid, a dynamic charging and discharging response strategy for energy storage devices is formulated. During periods of low electricity price, the charging power of energy storage is increased to store cheap electricity, and during periods of high electricity price, the discharging power of energy storage is increased to reduce electricity purchase costs. At the same time, the technical constraints and service life of energy storage devices are taken into account.

[0025] Based on carbon emission policies and carbon emission quotas allocated to each microgrid, a carbon emission accounting system is established to compare the actual carbon emissions of each microgrid with the quota. When the carbon emissions of a microgrid are close to the upper limit of the quota, its electricity purchase from the grid is restricted or it is required to increase the proportion of clean energy use. The overall carbon emission control of the system is achieved through carbon quota trading between microgrids.

[0026] By integrating demand response mechanisms and carbon trading constraints into the traditional supply and demand balance equations, a comprehensive energy balance constraint model is established that considers electricity price response, carbon emission limits, and fluctuations in renewable energy output, ensuring that each microgrid achieves dynamic supply and demand balance while meeting environmental protection requirements.

[0027] When multiple constraints conflict, a constraint priority coordination mechanism is established to prioritize ensuring the security of electricity supply and demand, followed by carbon emission constraints, and finally optimizing economic constraints. Constraint relaxation and compensation strategies are used to ensure the solvability and practicality of the constraint model.

[0028] Optionally, S1: Establish an energy collaborative network topology containing multiple microgrid nodes. Each microgrid node includes distributed photovoltaic devices, wind power devices, energy storage devices, and charging piles. Each microgrid node achieves bidirectional power flow through an energy interaction interface, including:

[0029] Each microgrid node is configured with a unified internal architecture, including inverter access points for distributed photovoltaic devices, converter access points for wind power devices, bidirectional converter access points for energy storage devices, and AC / DC conversion access points for charging piles. Each device is uniformly controlled through a local energy management unit.

[0030] Standardized energy interaction interfaces are deployed between microgrid nodes, including power transmission channels and information communication channels. The power transmission channels are responsible for enabling bidirectional power flow between microgrids, while the information communication channels are responsible for transmitting energy dispatch commands and status feedback information.

[0031] Establish a unified multi-microgrid communication protocol to specify the data exchange format, communication timing and fault handling mechanism between microgrid nodes, and ensure that each node can share key operational information such as load forecasting, power generation plan and energy storage status in real time.

[0032] Based on the geographical location, electrical distance and load characteristics of each microgrid, establish physical and logical connections between microgrids to form a redundant network topology that supports multipath energy transmission;

[0033] Each microgrid node is assigned a unique network identifier, initial energy interaction permissions and transmission capacity limits are set, and a database of device parameters and an operational status monitoring mechanism for each node are established.

[0034] Optionally, S3: With the goal of minimizing the total operating cost of the multi-microgrid system, a comprehensive objective function is established, including electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. An energy complementarity benefit term between microgrids is also introduced, forming a multi-objective optimization function considering both economic and environmental factors, including:

[0035] Cost calculation matrices are established for electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. The electricity purchase cost is based on the time-of-use electricity price for each period and the electricity purchase demand of each microgrid. The carbon trading cost is based on carbon emissions and carbon price fluctuations. The equipment operation and maintenance cost is based on the operating time and maintenance frequency of each piece of equipment. The energy transmission loss cost is based on the transmission distance between microgrids and the line loss rate.

[0036] Establish a mechanism for evaluating the benefits of energy complementarity among microgrids. By analyzing the load time-series characteristics and renewable energy output complementarity of each microgrid, identify the time and spatial differences in energy supply and demand among microgrids, and transform the complementary effect into quantifiable economic benefits.

[0037] Based on the operational goals and policy guidance of the multi-microgrid system, dynamic weight coefficients are set for each cost item and benefit item. The economic weight is adjusted according to electricity price fluctuations and market environment, while the environmental weight is set according to carbon emission reduction targets and policy incentives.

[0038] Establish a correlation between each cost item and benefit item and the system operation constraints to ensure that the changes in equipment operating parameters, energy transmission power and energy storage status of each microgrid meet the technical and safety constraints during the objective function optimization process.

[0039] By minimizing each cost item and maximizing the complementary benefit item, and combining them with set weight coefficients, a unified multi-objective optimization function is constructed to achieve a coordinated balance between minimizing economic costs and maximizing environmental benefits.

[0040] Optionally, S4: An improved beluga optimization algorithm incorporating adaptive weight adjustment and elite retention strategies is used to solve the multi-objective optimization function to obtain the optimal energy scheduling scheme for each microgrid in each time period, including:

[0041] Based on the equipment parameters and operational constraints of each microgrid, multidimensional decision variables representing energy storage charging and discharging power, energy interaction power between microgrids, and power allocation of charging piles are generated. An initial population that meets the constraints is constructed, and a fitness evaluation index is assigned to each individual.

[0042] Based on the algorithm iteration process and / or the population convergence status, the exploration weight and development weight in the Whale Algorithm are dynamically adjusted. Specifically, when the number of iterations is less than one-third of the preset total number of iterations, and / or when the individual fitness dispersion of the population is higher than the preset convergence threshold, the exploration weight is increased to expand the search range; when the number of iterations exceeds two-thirds of the preset total number of iterations, and / or when the individual fitness dispersion of the population is lower than the preset convergence threshold, the development weight is increased to refine the optimization and achieve a balance between global search and local optimization.

[0043] In each iteration, identify and retain several elite individuals with the best fitness in the current population, and pass on the excellent characteristics of these elite individuals to the next generation of the population through crossover and mutation operations to prevent the loss of excellent solutions in the evolution process.

[0044] The algorithm solution process is divided into a coarse search stage and a fine search stage. In the coarse search stage, a position update strategy with a large step size is used to quickly locate the optimal solution region, and in the fine search stage, a local search strategy with a small step size is used to accurately solve the optimal scheduling scheme.

[0045] When the algorithm meets the convergence condition or reaches the maximum number of iterations, the decision variable values ​​corresponding to the optimal individual are extracted and converted into specific scheduling instructions for each microgrid in each time period, including the charging and discharging power settings of each energy storage device, the energy exchange power instructions between each microgrid, and the power allocation scheme of each charging pile.

[0046] Optionally, in step S1, a dynamic weight allocation mechanism is used to determine the coordination priority of each microgrid node, specifically calculated using the following formula:

[0047]

[0048] in, Let be the collaborative weight coefficient of the mg-th microgrid; This represents the renewable energy installed capacity of the mgth microgrid; This represents the total installed capacity of renewable energy in all microgrids within the system. Let mg be the energy storage capacity of the mg-th microgrid; This represents the total energy storage capacity of all microgrids within the system. The communication delay time between the mg-th microgrid and the energy coordination center; Used as the baseline communication delay time; , , Assign factors to the weights, satisfying .

[0049] Optionally, when establishing the demand response mechanism in step S2, an adaptive response depth adjustment model is adopted, and the response level of each microgrid is dynamically adjusted using the following formula:

[0050] ;

[0051] in, Let mg be the demand response depth of the mg-th microgrid at time t; As the baseline demand response depth; This is the response sensitivity adjustment coefficient; Let t be the total load power of the power grid at time t; Demand response trigger threshold power; This is a parameter for normalized load power. Let be the response capability coefficient of the mg-th microgrid at time t.

[0052] Another aspect of the present invention provides a multi-microgrid energy collaborative optimization system for executing a multi-microgrid energy collaborative optimization method, comprising: microgrid nodes and a server;

[0053] The server is configured as follows:

[0054] An energy collaborative network topology is established, which includes multiple microgrid nodes. Each microgrid node contains distributed photovoltaic devices, wind power devices, energy storage devices, and charging piles. Each microgrid node realizes bidirectional flow of electricity through an energy interaction interface.

[0055] Based on real-time electricity demand, new energy output forecasts, and grid dispatch instructions, a multi-microgrid energy balance model integrating demand response mechanism and carbon trading constraints is constructed. The demand response mechanism dynamically adjusts the energy storage charging and discharging strategies within each microgrid according to time-of-use pricing and load characteristics, while the carbon trading constraints limit the scale of energy interaction based on the carbon emission quotas of each microgrid.

[0056] With the goal of minimizing the total operating cost of a multi-microgrid system, a comprehensive objective function is established that includes electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. Furthermore, an energy complementarity benefit term between microgrids is introduced to form a multi-objective optimization function that considers both economic and environmental factors.

[0057] An improved white whale optimization algorithm, which integrates adaptive weight adjustment and elite retention strategies, is used to solve the multi-objective optimization function to obtain the optimal energy scheduling scheme for each microgrid in each time period, including energy storage charging and discharging power, energy interaction power between microgrids, and charging pile power allocation.

[0058] According to the optimal energy dispatch scheme, the energy management system controls the energy storage devices, charging piles and adjustable loads in each microgrid in real time, while coordinating the energy flow and transmission power between microgrids to achieve the coordinated operation of multiple microgrid systems.

[0059] Monitor the actual operating status and energy interaction effect of each microgrid. When the prediction deviation is detected to exceed the preset threshold, re-execute steps S2 to S5 based on real-time data to achieve dynamic optimization and adjustment of energy coordination among multiple microgrids.

[0060] The multi-microgrid energy collaborative optimization method using the technical solution of this invention includes: S1: Establishing an energy collaborative network topology containing multiple microgrid nodes, each microgrid node including distributed photovoltaic devices, wind power devices, energy storage devices, and charging piles, with each microgrid node realizing bidirectional power flow through an energy interaction interface; S2: Constructing a multi-microgrid energy balance model integrating demand response mechanism and carbon trading constraints based on real-time electricity demand, new energy output forecasting, and grid dispatch instructions, wherein the demand response mechanism dynamically adjusts the energy storage charging and discharging strategies within each microgrid according to time-of-use pricing and load characteristics, and the carbon trading constraints limit the scale of energy interaction based on the carbon emission quotas of each microgrid; S3: Establishing a comprehensive objective function including electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost, with the goal of minimizing the total operating cost of the multi-microgrid system. The algorithm incorporates an inter-microgrid energy complementarity benefit term to form a multi-objective optimization function considering both economic and environmental factors. Step S4: An improved Whale Optimization Algorithm, integrating adaptive weight adjustment and elite retention strategies, solves the multi-objective optimization function to obtain the optimal energy scheduling scheme for each microgrid in each time period, including energy storage charging and discharging power, inter-microgrid energy interaction power, and charging pile power allocation. Step S5: Based on the optimal energy scheduling scheme, the energy management system dynamically regulates the energy storage devices, charging piles, and adjustable loads within each microgrid, while simultaneously coordinating the energy flow and transmission power between microgrids to achieve coordinated operation of the multi-microgrid system. Step S6: The actual operating status and energy interaction effect of each microgrid are monitored. When a prediction deviation exceeds a preset threshold, steps S2 to S5 are re-executed based on real-time data to achieve dynamic optimization and adjustment of multi-microgrid energy coordination. By establishing a multi-microgrid coordinated optimization framework and a multi-dimensional constraint model, combined with the improved Whale Algorithm, intelligent energy coordination between multi-microgrids is achieved, significantly improving the renewable energy absorption capacity, reducing the overall system operating cost, and enhancing the economic efficiency and stability of the power grid operation. Attached Figure Description

[0061] Figure 1 This is a flowchart of a multi-microgrid energy collaborative optimization method provided in one embodiment of the present invention;

[0062] Figure 2 This is a schematic block diagram of a multi-microgrid energy collaborative optimization system provided in an embodiment of the present invention. Detailed Implementation

[0063] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0064] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0065] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0066] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0067] The following reference Figures 1 to 2 This invention describes a multi-microgrid energy collaborative optimization method and system provided by some embodiments of the present invention.

[0068] like Figure 1 As shown, one embodiment of the present invention provides a multi-microgrid energy collaborative optimization method, comprising:

[0069] S1: Establish an energy collaborative network topology containing multiple microgrid nodes. Each microgrid node includes distributed photovoltaic devices, wind power devices, energy storage devices, and charging piles. Each microgrid node realizes bidirectional flow of electricity through an energy interaction interface.

[0070] S2: Based on real-time electricity demand, new energy output forecasts and grid dispatch instructions, a multi-microgrid energy balance model integrating demand response mechanism and carbon trading constraints is constructed. The demand response mechanism dynamically adjusts the energy storage charging and discharging strategies in each microgrid according to time-of-use electricity prices and load characteristics. The carbon trading constraints limit the scale of energy interaction based on the carbon emission quotas of each microgrid.

[0071] S3: With the goal of minimizing the total operating cost of the multi-microgrid system, a comprehensive objective function is established that includes electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. An energy complementarity benefit term between microgrids is also introduced to form a multi-objective optimization function that considers both economic and environmental factors.

[0072] S4: An improved Whale Optimization Algorithm that integrates adaptive weight adjustment and elite retention strategies is adopted to solve the multi-objective optimization function and obtain the optimal energy scheduling scheme for each microgrid in each time period, including energy storage charging and discharging power, energy interaction power between microgrids and power allocation of charging piles.

[0073] S5: According to the optimal energy dispatch scheme, the energy management system controls the energy storage devices, charging piles and adjustable loads in each microgrid in real time, while coordinating the energy flow and transmission power between microgrids to achieve the coordinated operation of multiple microgrid systems.

[0074] It is understandable that energy storage device regulation includes: controlling charging power, discharging power, and switching operating modes; managing the State of Charge (SOC) within a safe range; adjusting power output in response to dispatch commands; and so on. Charging pile regulation includes: dynamically adjusting charging power (e.g., reducing charging power from rated power to 50%), controlling the start-up and shutdown status of charging piles, and implementing orderly charging strategies (priority management). Adjustable load regulation includes: start-up and shutdown control of industrial loads (e.g., air conditioning and lighting systems), load shifting (shifting some electricity demand from peak hours to off-peak hours), and load reduction (temporarily reducing electricity consumption of non-critical loads).

[0075] S6: Monitor the actual operating status and energy interaction effect of each microgrid. When the prediction deviation is detected to exceed the preset threshold, re-execute steps S2 to S5 based on real-time data to achieve dynamic optimization and adjustment of energy coordination among multiple microgrids.

[0076] The technical solution of this embodiment, by establishing a multi-microgrid collaborative optimization framework and a multi-dimensional constraint model, and combining the improved White Whale algorithm for solving, realizes intelligent energy coordination among multiple microgrids, significantly improves the renewable energy absorption capacity, reduces the overall system operating cost, and enhances the economy and stability of power grid operation.

[0077] In some possible embodiments of the present invention, S5: the step of real-time regulation of energy storage devices, charging piles, and adjustable loads within each microgrid through the energy management system according to the optimal energy dispatch scheme, while coordinating the energy flow and transmission power between microgrids to achieve the coordinated operation of multiple microgrid systems, includes:

[0078] Based on the optimal energy dispatch scheme and the time-of-use electricity price information for the current period, determine the charging and discharging mode of the energy storage devices in each microgrid, control the charging of energy storage devices during periods when the electricity price is lower than the first preset price, and control the discharging of energy storage devices during periods when the electricity price is higher than the second preset price.

[0079] Monitor the real-time power load of each microgrid transformer. When the load power is detected to be close to the transformer capacity set value, control the energy storage device to discharge or the photovoltaic device to reduce the power output, so that the transformer capacity is kept within the safe operating range.

[0080] When the total power demand of the charging piles exceeds the power supply capacity of the microgrid, the charging power of each charging pile will be dynamically adjusted or some charging piles will be suspended according to the preset first priority principle.

[0081] Understandably, the first priority principle is the "last to fill, first to cut" principle.

[0082] By setting the PCC point power control target value, when the output power of the distributed power source is greater than the load power, the energy storage device is controlled to charge or the charging pile load is increased; when the output power of the distributed power source is insufficient, the energy storage device is controlled to discharge to supplement the power supply.

[0083] This embodiment realizes refined coordinated control of various devices within the microgrid, effectively improving device utilization efficiency, ensuring the safe and stable operation of the power grid, and reducing electricity costs.

[0084] In some possible embodiments of the present invention, S6: monitoring the actual operating status and energy interaction effect of each microgrid, and when a prediction deviation is detected to exceed a preset threshold, re-executing steps S2 to S5 based on real-time data to achieve dynamic optimization and adjustment of multi-microgrid energy coordination, including:

[0085] Real-time monitoring of the load power change rate of each microgrid; when the load power change rate exceeds the preset threshold, control the energy storage device to discharge in order to smooth load fluctuations and reduce the impact on the power grid.

[0086] When a high-power short-term load demand is detected in the microgrid, the energy storage device is prioritized to discharge and supplement the power, so as to avoid transformer overload and realize dynamic expansion of system capacity.

[0087] Based on the real-time changes in the power factor of each microgateway port, the energy storage PCS is controlled to continuously adjust the reactive power output to maintain the system power factor within a reasonable range.

[0088] The actual operating data of each microgrid is compared and analyzed with the predicted data. When the deviation exceeds the allowable range, the optimization parameters are recalculated and the scheduling strategy is updated.

[0089] The feedback control mechanism in this embodiment enhances the adaptability of the multi-microgrid system to load fluctuations and external interference, thereby improving the stability and reliability of system operation.

[0090] In some possible embodiments of the present invention, S2: the step of constructing a multi-microgrid energy balance model that integrates demand response mechanism and carbon trading constraints based on real-time electricity demand, renewable energy output forecasting, and grid dispatch instructions includes:

[0091] Real-time collection of power demand data from each microgrid, power generation forecast data from new energy equipment, and dispatch instructions issued by the upper-level power grid; establishment of a unified data time synchronization mechanism and data quality verification mechanism to ensure consistency of various types of data in terms of time dimension and accuracy requirements;

[0092] Based on time-of-use electricity price signals and historical load characteristic curves of each microgrid, a dynamic charging and discharging response strategy for energy storage devices is formulated. During periods of low electricity price (when the price is lower than the third preset price), the charging power of energy storage is increased to store cheap electricity. During periods of high electricity price (when the price is higher than the fourth preset price), the discharging power of energy storage is increased to reduce electricity purchase costs. At the same time, the technical constraints and service life of energy storage devices are taken into account.

[0093] Based on carbon emission policies and carbon emission quotas allocated to each microgrid, a carbon emission accounting system is established to compare the actual carbon emissions of each microgrid with the quota. When the carbon emissions of a microgrid are close to the upper limit of the quota, its electricity purchase from the grid is restricted or it is required to increase the proportion of clean energy use. The overall carbon emission control of the system is achieved through carbon quota trading between microgrids.

[0094] By integrating demand response mechanisms and carbon trading constraints into the traditional supply and demand balance equations, a comprehensive energy balance constraint model is established that considers electricity price response, carbon emission limits, and fluctuations in renewable energy output, ensuring that each microgrid achieves dynamic supply and demand balance while meeting environmental protection requirements.

[0095] When multiple constraints conflict, a constraint priority coordination mechanism is established to prioritize ensuring the security of electricity supply and demand, followed by carbon emission constraints, and finally optimizing economic constraints. Constraint relaxation and compensation strategies are used to ensure the solvability and practicality of the constraint model.

[0096] It is understood that in this embodiment, the traditional supply and demand balance equation refers to: grid power purchase + local power generation = fixed load + losses; the integrated comprehensive equation refers to: grid power purchase + photovoltaic power generation + wind power generation + energy storage discharge = base load + price response adjustment load + charging pile load + energy storage charging + losses, while simultaneously satisfying: carbon emissions ≤ carbon quota, energy storage SOC within a safe range, power of each device within technical limits, and electricity price response meeting user acceptance, etc.

[0097] The multi-dimensional constraint model in this embodiment achieves a coordinated balance between economic and environmental benefits by integrating demand response and carbon trading mechanisms. It enhances the adaptability of multi-microgrid systems to electricity price fluctuations and changes in carbon policies, providing effective technical support for building a smart grid for a low-carbon economy.

[0098] In some possible embodiments of the present invention, S1: Establish an energy collaborative network topology containing multiple microgrid nodes, each microgrid node including distributed photovoltaic devices, wind power devices, energy storage devices, and charging piles, and each microgrid node realizes bidirectional flow of electrical energy through an energy interaction interface, including:

[0099] Each microgrid node is configured with a unified internal architecture, including inverter access points for distributed photovoltaic devices, converter access points for wind power devices, bidirectional converter access points for energy storage devices, and AC / DC conversion access points for charging piles. Each device is uniformly controlled through a local energy management unit.

[0100] Standardized energy interaction interfaces are deployed between microgrid nodes, including power transmission channels and information communication channels. The power transmission channels are responsible for enabling bidirectional power flow between microgrids, while the information communication channels are responsible for transmitting energy dispatch commands and status feedback information.

[0101] Establish a unified multi-microgrid communication protocol to specify the data exchange format, communication timing and fault handling mechanism between microgrid nodes, and ensure that each node can share key operational information such as load forecasting, power generation plan and energy storage status in real time.

[0102] Based on the geographical location, electrical distance and load characteristics of each microgrid, establish physical and logical connections between microgrids to form a redundant network topology that supports multipath energy transmission;

[0103] Each microgrid node is assigned a unique network identifier, initial energy interaction permissions and transmission capacity limits are set, and a database of device parameters and an operational status monitoring mechanism for each node are established.

[0104] The network topology construction method in this embodiment realizes standardized interconnection and transparent information management among multiple micronets, improves the scalability and reliability of the system, and provides stable network infrastructure support for subsequent collaborative optimization.

[0105] In some possible embodiments of the present invention, S3: With the goal of minimizing the total operating cost of the multi-microgrid system, a comprehensive objective function is established, including electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. An energy complementarity benefit term between microgrids is introduced, forming a multi-objective optimization function considering both economic and environmental factors, including:

[0106] Cost calculation matrices are established for electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. The electricity purchase cost is based on the time-of-use electricity price for each period and the electricity purchase demand of each microgrid. The carbon trading cost is based on carbon emissions and carbon price fluctuations. The equipment operation and maintenance cost is based on the operating time and maintenance frequency of each piece of equipment. The energy transmission loss cost is based on the transmission distance between microgrids and the line loss rate.

[0107] Establish a mechanism for evaluating the benefits of energy complementarity among microgrids. By analyzing the load time-series characteristics and renewable energy output complementarity of each microgrid, identify the time and spatial differences in energy supply and demand among microgrids, and transform the complementary effect into quantifiable economic benefits.

[0108] Based on the operational goals and policy guidance of the multi-microgrid system, dynamic weight coefficients are set for each cost item and benefit item. The economic weight is adjusted according to electricity price fluctuations and market environment, while the environmental weight is set according to carbon emission reduction targets and policy incentives.

[0109] As can be understood, economic weights refer to the weighting coefficients assigned to economic cost-related terms in the objective function, including: electricity purchase cost weight, equipment operation and maintenance cost weight, energy transmission loss cost weight, and demand response participation cost weight. The mechanism of economic weights: the larger the economic weight, the more the system tends to reduce operating costs; during peak electricity prices, increasing the economic weight encourages the system to more actively utilize energy storage for discharge and reduce electricity purchases; during off-peak electricity prices, decreasing the economic weight allows for appropriate increases in electricity purchases for energy storage charging. Dynamic adjustment logic: large electricity price fluctuations → increase economic weight → more active peak-valley arbitrage; intense market competition → increase economic weight → greater focus on cost control; relatively stable electricity prices → decrease economic weight → can balance other objectives.

[0110] Environmental weights refer to the weight coefficients assigned to environmental benefit-related terms in the objective function, including: carbon trading cost weight (negative, i.e., the benefit of reducing carbon emissions), renewable energy utilization rate weight, carbon emission penalty weight, and clean energy complementarity benefit weight. The mechanism of environmental weight action: The larger the environmental weight, the more the system tends to reduce carbon emissions; prioritize the use of clean energy and reduce the purchase of fossil fuel electricity; and more actively participate in clean energy complementarity between microgrids. The dynamic adjustment logic of environmental weights: Rising carbon prices → Increase environmental weight → More proactive emission reduction; Tightening environmental policies → Increase environmental weight → Prioritize clean energy; Approaching carbon emission reduction targets → Increase environmental weight → Accelerate emission reduction actions.

[0111] Establish a correlation between each cost item and benefit item and the system operation constraints to ensure that the changes in equipment operating parameters, energy transmission power and energy storage status of each microgrid meet the technical and safety constraints during the objective function optimization process.

[0112] By minimizing each cost item and maximizing the complementary benefit item, and combining them with set weight coefficients, a unified multi-objective optimization function is constructed to achieve a coordinated balance between minimizing economic costs and maximizing environmental benefits.

[0113] The comprehensive objective function construction method in this embodiment realizes the unified quantification and trade-off optimization of multi-dimensional cost-effectiveness, which not only ensures the economic efficiency of the operation of the multi-microgrid system, but also takes into account environmental protection and complementary synergistic effects, thereby improving the scientificity and comprehensiveness of optimization decision-making.

[0114] In some possible embodiments of the present invention, S4: An improved beluga optimization algorithm incorporating adaptive weight adjustment and elite retention strategies is used to solve the multi-objective optimization function to obtain the optimal energy scheduling scheme for each microgrid in each time period, including:

[0115] Based on the equipment parameters and operational constraints of each microgrid, multidimensional decision variables representing energy storage charging and discharging power, energy interaction power between microgrids, and power allocation of charging piles are generated. An initial population that meets the constraints is constructed, and a fitness evaluation index is assigned to each individual.

[0116] Based on the algorithm iteration process and / or the population convergence status, the exploration weight and development weight in the Whale Algorithm are dynamically adjusted. Specifically, when the number of iterations is less than one-third of the preset total number of iterations, and / or when the individual fitness dispersion of the population is higher than the preset convergence threshold, the exploration weight is increased to expand the search range; when the number of iterations exceeds two-thirds of the preset total number of iterations, and / or when the individual fitness dispersion of the population is lower than the preset convergence threshold, the development weight is increased to refine the optimization and achieve a balance between global search and local optimization.

[0117] In each iteration, identify and retain several elite individuals with the best fitness in the current population, and pass on the excellent characteristics of these elite individuals to the next generation of the population through crossover and mutation operations to prevent the loss of excellent solutions in the evolution process.

[0118] The algorithm solution process is divided into a coarse search stage and a fine search stage. In the coarse search stage, a position update strategy with a large step size (first preset step size) is used to quickly locate the optimal solution region. In the fine search stage, a local search strategy with a small step size (second preset step size) is used to accurately solve the optimal scheduling scheme.

[0119] When the algorithm meets the convergence condition or reaches the maximum number of iterations, the decision variable values ​​corresponding to the optimal individual are extracted and converted into specific scheduling instructions for each microgrid in each time period, including the charging and discharging power settings of each energy storage device, the energy exchange power instructions between each microgrid, and the power allocation scheme of each charging pile.

[0120] The improved Whale Algorithm in this embodiment significantly improves the solution accuracy and convergence speed of multi-objective optimization problems by combining adaptive weight adjustment and elite retention strategies. It can obtain more stable and high-quality scheduling schemes in complex multi-micronet collaborative optimization scenarios, thereby enhancing the practicality and reliability of the algorithm.

[0121] In some possible embodiments of the present invention, in step S1, a dynamic weight allocation mechanism is used to determine the collaborative priority of each microgrid node, specifically calculated using the following formula:

[0122]

[0123] in, Let be the collaborative weight coefficient of the mg-th microgrid; This represents the renewable energy installed capacity of the mgth microgrid; This represents the total installed capacity of renewable energy in all microgrids within the system. Let mg be the energy storage capacity of the mg-th microgrid; This represents the total energy storage capacity of all microgrids within the system. The communication delay time between the mg-th microgrid and the energy coordination center; Used as the baseline communication delay time; , , Assign factors to the weights, satisfying .

[0124] The dynamic weighting mechanism in this embodiment can comprehensively consider the resource endowment, energy storage capacity and communication performance of microgrids, achieve a more reasonable allocation of collaborative priorities, and improve the overall coordination efficiency of multi-microgrid systems.

[0125] In some possible embodiments of the present invention, when establishing the demand response mechanism in step S2, an adaptive response depth adjustment model is adopted, and the response level of each microgrid is dynamically adjusted through the following formula:

[0126] ;

[0127] in, Let mg be the demand response depth of the mg-th microgrid at time t; As the baseline demand response depth; This is the response sensitivity adjustment coefficient; Let t be the total load power of the power grid at time t; Demand response trigger threshold power; This is a parameter for normalized load power. Let be the response capability coefficient of the mg-th microgrid at time t.

[0128] The adaptive response model in this embodiment can dynamically adjust the depth of demand response based on the real-time load conditions of the power grid and the response capabilities of each microgrid, thereby improving the accuracy and effectiveness of demand response.

[0129] In some possible embodiments of the present invention, when generating the multi-objective optimization function in step S3, a microgrid energy complementarity benefit quantification model is introduced, and the complementarity benefit is calculated using the following formula:

[0130]

[0131] in, M represents the total complementary benefit of the multi-microgrid system, where M is the total number of microgrids. The unit complementary benefit coefficient for energy transmission from microgrid m to microgrid n; This represents the amount of energy transferred from microgrid m to microgrid n. For microgrid m, this represents the peak load time. Let n be the peak load time of microgrid n.

[0132] The complementary benefit model in this embodiment quantifies the synergistic value generated by the load time sequence differences between microgrids, incentivizing more effective energy interaction between microgrids and improving the overall economic efficiency of the system.

[0133] In some possible embodiments of the present invention, the optimization process of the improved beluga algorithm in step S4 introduces a multi-level search strategy, updating the individual position using the following formula:

[0134]

[0135] in, The updated position vector for each individual; Let L be the vector of the individual's current position, and L be the total number of search levels. is the weight coefficient for the l-th level; The velocity vector of the l-th level; is the random perturbation factor of the l-th level, with a value range of [0,1]; G is the current iteration number; This represents the maximum number of iterations.

[0136] Simultaneously, a convergence criterion function is used:

[0137]

[0138] Where Conv is the algorithm convergence index; Let m be the position vector of the m-th individual; This represents the current optimal individual position vector; These are convergence control parameters.

[0139] The multi-level search strategy in this embodiment improves the algorithm's global search capability and convergence speed by introducing search mechanisms of different scales and convergence discrimination, thus obtaining a better multi-micronet collaborative scheduling scheme.

[0140] Please refer to Figure 2 Another embodiment of the present invention provides a multi-microgrid energy collaborative optimization system for executing a multi-microgrid energy collaborative optimization method, comprising: microgrid nodes and a server;

[0141] The server is configured as follows:

[0142] An energy collaborative network topology is established, which includes multiple microgrid nodes. Each microgrid node contains distributed photovoltaic devices, wind power devices, energy storage devices, and charging piles. Each microgrid node realizes bidirectional flow of electricity through an energy interaction interface.

[0143] Based on real-time electricity demand, new energy output forecasts, and grid dispatch instructions, a multi-microgrid energy balance model integrating demand response mechanism and carbon trading constraints is constructed. The demand response mechanism dynamically adjusts the energy storage charging and discharging strategies within each microgrid according to time-of-use pricing and load characteristics, while the carbon trading constraints limit the scale of energy interaction based on the carbon emission quotas of each microgrid.

[0144] With the goal of minimizing the total operating cost of a multi-microgrid system, a comprehensive objective function is established that includes electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. Furthermore, an energy complementarity benefit term between microgrids is introduced to form a multi-objective optimization function that considers both economic and environmental factors.

[0145] An improved white whale optimization algorithm, which integrates adaptive weight adjustment and elite retention strategies, is used to solve the multi-objective optimization function to obtain the optimal energy scheduling scheme for each microgrid in each time period, including energy storage charging and discharging power, energy interaction power between microgrids, and charging pile power allocation.

[0146] According to the optimal energy dispatch scheme, the energy management system controls the energy storage devices, charging piles and adjustable loads in each microgrid in real time, while coordinating the energy flow and transmission power between microgrids to achieve the coordinated operation of multiple microgrid systems.

[0147] Monitor the actual operating status and energy interaction effect of each microgrid. When the prediction deviation is detected to exceed the preset threshold, re-execute steps S2 to S5 based on real-time data to achieve dynamic optimization and adjustment of energy coordination among multiple microgrids.

[0148] It should be known that, Figure 2The block diagram of the multi-microgrid energy collaborative optimization system shown is for illustrative purposes only, and the number of modules shown does not limit the scope of protection of this invention. The multi-microgrid energy collaborative optimization system provided in this embodiment can be used to execute various embodiments of the corresponding multi-microgrid energy collaborative optimization method. For specific implementation details, please refer to the descriptions of the respective method embodiments, which will not be repeated here.

[0149] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0150] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0151] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0152] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0153] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0154] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0155] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0156] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0157] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A method for coordinated energy optimization across multiple microgrids, characterized in that, include: S1: Establish an energy collaborative network topology containing multiple microgrid nodes. Each microgrid node includes distributed photovoltaic devices, wind power devices, energy storage devices, and charging piles. Each microgrid node realizes bidirectional flow of electricity through an energy interaction interface. S2: Based on real-time electricity demand, new energy output forecasts and grid dispatch instructions, a multi-microgrid energy balance model integrating demand response mechanism and carbon trading constraints is constructed. The demand response mechanism dynamically adjusts the energy storage charging and discharging strategies in each microgrid according to time-of-use electricity prices and load characteristics. The carbon trading constraints limit the scale of energy interaction based on the carbon emission quotas of each microgrid. S3: With the goal of minimizing the total operating cost of the multi-microgrid system, a comprehensive objective function is established that includes electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. An energy complementarity benefit term between microgrids is also introduced to form a multi-objective optimization function that considers both economic and environmental factors. S4: An improved Whale Optimization Algorithm that integrates adaptive weight adjustment and elite retention strategies is adopted to solve the multi-objective optimization function and obtain the optimal energy scheduling scheme for each microgrid in each time period, including energy storage charging and discharging power, energy interaction power between microgrids and power allocation of charging piles. S5: According to the optimal energy dispatch scheme, the energy management system controls the energy storage devices, charging piles and adjustable loads in each microgrid in real time, while coordinating the energy flow and transmission power between microgrids to achieve the coordinated operation of multiple microgrid systems. S6: Monitor the actual operating status and energy interaction effect of each microgrid. When the prediction deviation is detected to exceed the preset threshold, re-execute steps S2 to S5 based on real-time data to achieve dynamic optimization and adjustment of energy coordination among multiple microgrids. In step S1, a dynamic weight allocation mechanism is used to determine the collaborative priority of each microgrid node, specifically calculated using the following formula: in, Let be the collaborative weight coefficient of the mg-th microgrid; This represents the renewable energy installed capacity of the mgth microgrid; This represents the total installed capacity of renewable energy in all microgrids within the system. Let mg be the energy storage capacity of the mg-th microgrid; This represents the total energy storage capacity of all microgrids within the system. The communication delay time between the mg-th microgrid and the energy coordination center; Used as the baseline communication delay time; , , Assign factors to the weights, satisfying ; In step S2, when establishing the demand response mechanism, an adaptive response depth adjustment model is adopted, which dynamically adjusts the response level of each microgrid using the following formula: in, Let mg be the demand response depth of the mg-th microgrid at time t; As the baseline demand response depth; The response sensitivity adjustment coefficient; Let t be the total load power of the power grid at time t; Demand response trigger threshold power; This is a parameter for normalized load power; Let be the response capability coefficient of the mg-th microgrid at time t.

2. The multi-microgrid energy collaborative optimization method according to claim 1, characterized in that, S5: Based on the optimal energy dispatch scheme, the steps of using the energy management system to real-time regulate energy storage devices, charging piles, and adjustable loads within each microgrid, while coordinating energy flow and transmission power between microgrids, to achieve coordinated operation of the multi-microgrid system include: Based on the optimal energy dispatch scheme and the time-of-use electricity price information for the current period, determine the charging and discharging mode of the energy storage devices in each microgrid, control the charging of energy storage devices during periods when the electricity price is lower than the first preset price, and control the discharging of energy storage devices during periods when the electricity price is higher than the second preset price. Monitor the real-time power load of each microgrid transformer. When the load power is detected to be close to the transformer capacity set value, control the energy storage device to discharge or the photovoltaic device to reduce the power output, so that the transformer capacity is kept within the safe operating range. When the total power demand of the charging piles exceeds the power supply capacity of the microgrid, the charging power of each charging pile will be dynamically adjusted or some charging piles will be suspended according to the preset first priority principle. By setting the PCC point power control target value, when the output power of the distributed power source is greater than the load power, the energy storage device is controlled to charge or the charging pile load is increased; when the output power of the distributed power source is insufficient, the energy storage device is controlled to discharge to supplement the power supply.

3. The multi-microgrid energy collaborative optimization method according to claim 2, characterized in that, S6: Monitor the actual operating status and energy interaction effect of each microgrid. When the predicted deviation is detected to exceed the preset threshold, re-execute steps S2 to S5 based on real-time data to achieve dynamic optimization and adjustment of multi-microgrid energy coordination, including: Real-time monitoring of the load power change rate of each microgrid; when the load power change rate exceeds the preset threshold, control the energy storage device to discharge in order to smooth load fluctuations and reduce the impact on the power grid. When a high-power short-term load demand is detected in the microgrid, the energy storage device is prioritized to discharge and supplement the power, so as to avoid transformer overload and realize dynamic expansion of system capacity. Based on the real-time changes in the power factor of each microgateway port, the energy storage PCS is controlled to continuously adjust the reactive power output to maintain the system power factor within a reasonable range. The actual operating data of each microgrid is compared and analyzed with the predicted data. When the deviation exceeds the allowable range, the optimization parameters are recalculated and the scheduling strategy is updated.

4. The multi-microgrid energy collaborative optimization method according to claim 3, characterized in that, S2: The steps for constructing a multi-microgrid energy balance model that integrates demand response mechanisms and carbon trading constraints based on real-time electricity demand, renewable energy output forecasting, and grid dispatch instructions include: Real-time collection of power demand data from each microgrid, power generation forecast data from new energy equipment, and dispatch instructions issued by the upper-level power grid; establishment of a unified data time synchronization mechanism and data quality verification mechanism to ensure consistency of various types of data in terms of time dimension and accuracy requirements; Based on time-of-use electricity price signals and historical load characteristic curves of each microgrid, a dynamic charging and discharging response strategy for energy storage devices is formulated. During periods of low electricity price, the charging power of energy storage is increased to store cheap electricity, and during periods of high electricity price, the discharging power of energy storage is increased to reduce electricity purchase costs. At the same time, the technical constraints and service life of energy storage devices are taken into account. Based on carbon emission policies and carbon emission quotas allocated to each microgrid, a carbon emission accounting system is established to compare the actual carbon emissions of each microgrid with the quota. When the carbon emissions of a microgrid are close to the upper limit of the quota, its electricity purchase from the grid is restricted or it is required to increase the proportion of clean energy use. The overall carbon emission control of the system is achieved through carbon quota trading between microgrids. By integrating demand response mechanisms and carbon trading constraints into the traditional supply and demand balance equations, a comprehensive energy balance constraint model is established that considers electricity price response, carbon emission limits, and fluctuations in renewable energy output, ensuring that each microgrid achieves dynamic supply and demand balance while meeting environmental protection requirements. When multiple constraints conflict, a constraint priority coordination mechanism is established to prioritize ensuring the security of electricity supply and demand, followed by carbon emission constraints, and finally optimizing economic constraints. Constraint relaxation and compensation strategies are used to ensure the solvability and practicality of the constraint model.

5. The multi-microgrid energy collaborative optimization method according to claim 4, characterized in that, S1: Establish an energy-coordinated network topology comprising multiple microgrid nodes. Each microgrid node includes distributed photovoltaic (PV) devices, wind power devices, energy storage devices, and charging piles. Energy flows bidirectionally between microgrid nodes through energy interaction interfaces, including: Each microgrid node is configured with a unified internal architecture, including inverter access points for distributed photovoltaic devices, converter access points for wind power devices, bidirectional converter access points for energy storage devices, and AC / DC conversion access points for charging piles. Each device is uniformly controlled through a local energy management unit. Standardized energy interaction interfaces are deployed between microgrid nodes, including power transmission channels and information communication channels. The power transmission channels are responsible for enabling bidirectional power flow between microgrids, while the information communication channels are responsible for transmitting energy dispatch commands and status feedback information. Establish a unified multi-microgrid communication protocol to specify the data exchange format, communication timing and fault handling mechanism between microgrid nodes, and ensure that each node can share key operational information such as load forecasting, power generation plan and energy storage status in real time. Based on the geographical location, electrical distance and load characteristics of each microgrid, establish physical and logical connections between microgrids to form a redundant network topology that supports multipath energy transmission; Each microgrid node is assigned a unique network identifier, initial energy interaction permissions and transmission capacity limits are set, and a database of device parameters and an operational status monitoring mechanism for each node are established.

6. The multi-microgrid energy collaborative optimization method according to claim 5, characterized in that, S3: With the goal of minimizing the total operating cost of the multi-microgrid system, a comprehensive objective function is established, including electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. An energy complementarity benefit term between microgrids is also introduced, forming a multi-objective optimization function considering both economic and environmental factors, including: Cost calculation matrices are established for electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. The electricity purchase cost is based on the time-of-use electricity price for each period and the electricity purchase demand of each microgrid. The carbon trading cost is based on carbon emissions and carbon price fluctuations. The equipment operation and maintenance cost is based on the operating time and maintenance frequency of each piece of equipment. The energy transmission loss cost is based on the transmission distance between microgrids and the line loss rate. Establish a mechanism for evaluating the benefits of energy complementarity among microgrids. By analyzing the load time-series characteristics and renewable energy output complementarity of each microgrid, identify the time and spatial differences in energy supply and demand among microgrids, and transform the complementary effect into quantifiable economic benefits. Based on the operational goals and policy guidance of the multi-microgrid system, dynamic weight coefficients are set for each cost item and benefit item. The economic weight is adjusted according to electricity price fluctuations and market environment, while the environmental weight is set according to carbon emission reduction targets and policy incentives. Establish a correlation between each cost item and benefit item and the system operation constraints to ensure that the changes in equipment operating parameters, energy transmission power and energy storage status of each microgrid meet the technical and safety constraints during the objective function optimization process. By minimizing each cost item and maximizing the complementary benefit item, and combining them with set weight coefficients, a unified multi-objective optimization function is constructed to achieve a coordinated balance between minimizing economic costs and maximizing environmental benefits.

7. The multi-microgrid energy collaborative optimization method according to claim 6, characterized in that, S4: An improved white whale optimization algorithm, incorporating adaptive weight adjustment and elite retention strategies, is used to solve the multi-objective optimization function to obtain the optimal energy scheduling scheme for each microgrid in each time period, including: Based on the equipment parameters and operational constraints of each microgrid, multidimensional decision variables representing energy storage charging and discharging power, energy interaction power between microgrids, and power allocation of charging piles are generated. An initial population that meets the constraints is constructed, and a fitness evaluation index is assigned to each individual. Based on the algorithm iteration process and / or the population convergence status, the exploration weight and development weight in the Whale Algorithm are dynamically adjusted. Specifically, when the number of iterations is less than one-third of the preset total number of iterations, and / or when the individual fitness dispersion of the population is higher than the preset convergence threshold, the exploration weight is increased to expand the search range; when the number of iterations exceeds two-thirds of the preset total number of iterations, and / or when the individual fitness dispersion of the population is lower than the preset convergence threshold, the development weight is increased to refine the optimization and achieve a balance between global search and local optimization. In each iteration, identify and retain several elite individuals with the best fitness in the current population, and pass on the excellent characteristics of these elite individuals to the next generation of the population through crossover and mutation operations to prevent the loss of excellent solutions in the evolution process. The algorithm solution process is divided into a coarse search stage and a fine search stage. In the coarse search stage, a position update strategy with a large step size is used to quickly locate the optimal solution region, and in the fine search stage, a local search strategy with a small step size is used to accurately solve the optimal scheduling scheme. When the algorithm meets the convergence condition or reaches the maximum number of iterations, the decision variable values ​​corresponding to the optimal individual are extracted and converted into specific scheduling instructions for each microgrid in each time period, including the charging and discharging power settings of each energy storage device, the energy exchange power instructions between each microgrid, and the power allocation scheme of each charging pile.

8. A multi-microgrid energy collaborative optimization system, used to execute the multi-microgrid energy collaborative optimization method as described in any one of claims 1 to 7, characterized in that, include: Microgrid nodes and servers; The server is configured as follows: An energy collaborative network topology is established, which includes multiple microgrid nodes. Each microgrid node contains distributed photovoltaic devices, wind power devices, energy storage devices, and charging piles. Each microgrid node realizes bidirectional flow of electricity through an energy interaction interface. Based on real-time electricity demand, new energy output forecasts, and grid dispatch instructions, a multi-microgrid energy balance model integrating demand response mechanism and carbon trading constraints is constructed. The demand response mechanism dynamically adjusts the energy storage charging and discharging strategies within each microgrid according to time-of-use pricing and load characteristics, while the carbon trading constraints limit the scale of energy interaction based on the carbon emission quotas of each microgrid. With the goal of minimizing the total operating cost of a multi-microgrid system, a comprehensive objective function is established that includes electricity purchase cost, carbon trading cost, equipment operation and maintenance cost, and energy transmission loss cost. Furthermore, an energy complementarity benefit term between microgrids is introduced to form a multi-objective optimization function that considers both economic and environmental factors. An improved white whale optimization algorithm, which integrates adaptive weight adjustment and elite retention strategies, is used to solve the multi-objective optimization function to obtain the optimal energy scheduling scheme for each microgrid in each time period, including energy storage charging and discharging power, energy interaction power between microgrids, and charging pile power allocation. According to the optimal energy dispatch scheme, the energy management system controls the energy storage devices, charging piles and adjustable loads in each microgrid in real time, while coordinating the energy flow and transmission power between microgrids to achieve the coordinated operation of multiple microgrid systems. Monitor the actual operating status and energy interaction effect of each microgrid. When the prediction deviation is detected to exceed the preset threshold, re-execute steps S2 to S5 based on real-time data to achieve dynamic optimization and adjustment of energy coordination among multiple microgrids.

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