System energy-saving structure optimization control method based on dynamic balance strategy

Abstract

The invention discloses a system energy-saving structure optimization control method based on a dynamic balance strategy, and particularly relates to the technical field of energy management systems, which comprises the steps of dynamic model construction, LASSO regression decoupling, transient compensation, multi-machine system coordination control, power distribution network parameter optimization, multi-target control model construction and improved sparrow algorithm. And source-load-storage coordinated control of the new energy microgrid is realized, and short-term and long-term electric quantity balance is optimized. According to the system energy-saving structure optimization control method based on the dynamic balance strategy, through the optimization control method, the adaptive capacity of the system to rapid changes is enhanced, stable operation of a power grid under various changes is ensured, the stability and economical efficiency of the system are improved, a user is effectively guided to carry out energy adjustment, and the user experience is improved. The energy utilization efficiency is improved, the global optimization capability of the system is enhanced, the efficient and stable operation of the micro-grid is realized, the energy consumption is reduced, and the energy-saving optimization is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management systems, and in particular to a system energy-saving structure optimization control method based on a dynamic balance strategy. Background Art

[0002] With the acceleration of industrialization and the increasing energy consumption, energy efficiency and system stability have become the focus of global attention. In the field of power systems, especially in the context of microgrids and smart grids, how to achieve efficient use of energy and dynamic balance of the system has become the key to technological development.

[0003] At present, the realization of energy conservation and dynamic balance of power systems mainly relies on some basic control strategies, such as PID control, frequency control and simple economic dispatch. These methods can achieve energy management and system stability to a certain extent, but they usually have problems such as slow response speed, poor adaptability and limited optimization ability. For example, although the traditional PID control strategy is simple to implement, it is difficult to adapt to changes in system parameters and the influence of external disturbances; the frequency control method is difficult to achieve accurate power balance when facing large-scale power grids; and simple economic dispatch often ignores the dynamic characteristics of the system, resulting in dispatch results that do not meet actual needs.

[0004] The main defects of the existing technology are the lack of accurate description and control of the system dynamic characteristics, as well as the ability to optimize the system parameters. These defects lead to low energy efficiency and insufficient system stability. Due to the lack of an accurate system dynamic model, traditional control methods are difficult to achieve accurate prediction and control of system responses; due to the lack of optimization of system parameters, the existing technology is difficult to adapt to changes in system parameters and the impact of external disturbances, resulting in low system operation efficiency and energy waste. Summary of the invention

[0005] The main purpose of the present invention is to provide a system energy-saving structure optimization control method based on a dynamic balance strategy, which can effectively solve the problems of low energy efficiency and insufficient system stability caused by the lack of accurate system dynamic model and optimized control parameters in the prior art.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A system energy-saving structure optimization control method based on a dynamic balance strategy comprises the following steps:

[0008] S1: Establish the dynamic model of the system, including virtual damping and virtual inertia;

[0009] S2: Decouple virtual damping and virtual inertia through ridge estimation LASSO regression to solve the optimal virtual damping coefficient and virtual moment of inertia of the system under dynamic and static conditions;

[0010] S3: Introduce transient compensation to optimally control the system damping inertia, based on dynamic performance and optimal second-order system criteria;

[0011] S4: Set the virtual inertia and virtual damping coefficient of each synchronous voltage source to keep the transition time of the multi-machine system consistent and perform dynamic and static coordinated control of multiple self-synchronous voltage sources;

[0012] S5: Obtain distribution network parameters, use the steady-state safety domain to adjust the responsibility adjustment amount of each node; perform responsibility adjustment based on the responsibility adjustment amount;

[0013] S6: construct a distribution network optimization model and a user optimization model based on the optimization objectives and security domain constraints; solve the distribution network optimization model and the user optimization model to obtain the target regulation amount and reward price information, and send the solution information to the user for energy regulation;

[0014] S7: Define GDRGA to analyze the interaction between APFs, take the minimum integral of the difference between the output current of each APF and the reference current within the simulation time as the objective function, and establish a multi-objective coordinated control model with the upper and lower limits of each parameter as constraints;

[0015] S8: Aiming at the defects of the traditional sparrow algorithm in the later stage of search, such as poor global optimization ability and easy to fall into local optimum, tent chaos and dynamic random Cauchy mutation are introduced to improve it, and the improved sparrow algorithm is used to coordinate and optimize the control parameters of APF;

[0016] S9: Source-load-storage coordinated control method for off-grid operation of new energy microgrids. The control objects include energy storage systems, new energy units and load switches in each substation. The control architecture includes equipment local control layer, microgrid coordination control layer and distribution network master station control layer.

[0017] S10: The local control layer of the equipment adopts a power balancing strategy based on dynamic compensation of energy storage, and dynamically compensates the power difference between the new energy unit and the load through the energy storage converter to achieve instantaneous power balance;

[0018] S11: The microgrid coordination control layer adopts a short-term power balance strategy based on source-storage collaborative control, and adjusts the output of new energy units through the microgrid controller to achieve short-term power balance; the distribution network master station control layer adopts a long-term power balance strategy based on load regulation, and adjusts the load through the distribution master station to achieve long-term power balance.

[0019] Preferably, the method for solving the optimal virtual damping coefficient and virtual moment of inertia of the system under dynamic and static conditions described in S2 further includes:

[0020] S2.1: Construct the design matrix: Create an n×m design matrix X, where each row represents a sample and each column represents a feature. Add a bias term with a value of 1 to the design matrix so that the model includes an intercept term.

[0021] S2.2: Parameter estimation: Parameter estimation of ridge regression is achieved by minimizing the loss function, and the parameters are calculated using the formula θ = (XTX + λI)-1XTy, where X is the design matrix, y is the target value vector, λ is the regularization parameter, and I is the identity matrix;

[0022] S2.3: Model evaluation and hyperparameter selection: Use mean square error (MSE) or coefficient of determination (R 2 ) indicator to evaluate the model performance and select the best regularization parameter λ value through cross-validation.

[0023] Preferably, the dynamic and static coordinated control of multiple self-synchronous voltage sources described in S4 further includes:

[0024] S4.1: real-time monitoring of the output power of each synchronous voltage source and grid demand;

[0025] S4.2: dynamically adjust the virtual inertia and virtual damping coefficient of each synchronous voltage source according to the monitoring results to adapt to the changes in the power grid;

[0026] S4.3: Ensure the dynamic balance between the output power of each synchronous voltage source and the grid demand through a closed-loop control strategy.

[0027] Preferably, the responsibility adjustment described in S5 further includes:

[0028] S5.1: Determine the responsible regulation amount of each node according to the real-time operation status of the distribution network;

[0029] S5.2: Use advanced optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, to optimize the responsibility adjustment amount;

[0030] S5.3: Apply the optimized responsibility regulation amount to the distribution network to improve the stability and economy of the system.

[0031] Preferably, the energy regulation described in S6 further comprises:

[0032] S6.1: Collect users’ electricity consumption data and demand information;

[0033] S6.2: Calculate the energy regulation potential of the user based on the user optimization model;

[0034] S6.3: Through smart contracts or incentive mechanisms, users are encouraged to adjust energy based on the adjustment amount and reward price information.

[0035] Preferably, the multi-objective coordinated control model described in S7 further includes:

[0036] S7.1: Define the interaction between APFs and establish the objective function and constraints;

[0037] S7.2: Use swarm intelligence optimization algorithms, such as the grey wolf optimization algorithm or the whale optimization algorithm, to solve multi-objective coordinated control models;

[0038] S7.3: Update model parameters in real time to adapt to changes in grid operation status.

[0039] Preferably, the improved sparrow algorithm described in S8 further includes:

[0040] S8.1: Introduce tent chaos and dynamic random Cauchy mutation to enhance the global search capability of the algorithm;

[0041] S8.2: Design an adaptive parameter adjustment mechanism to dynamically adjust algorithm parameters according to the search process;

[0042] S8.3: Verify the effectiveness and superiority of the improved sparrow algorithm through simulation experiments.

[0043] Preferably, the source-load-storage coordinated control method described in S9 further comprises:

[0044] S9.1: Real-time monitoring of the status of the energy storage system, including energy storage capacity and charging and discharging status;

[0045] S9.2: Dynamically adjust the on / off status of the load switch according to the status of the energy storage system and the output of the new energy unit;

[0046] S9.3: Achieve optimal coordinated control of source, load and storage through intelligent dispatching system.

[0047] 9. According to the system energy-saving structure optimization control method based on dynamic balancing strategy in claim 1, preferably, the power balancing strategy based on energy storage dynamic compensation in S10 further comprises:

[0048] S10.1: Real-time monitoring of the power difference between the new energy unit and the load;

[0049] S10.2: According to the power difference, dynamically adjust the output of the energy storage converter to compensate for the power difference;

[0050] S10.3: Ensure the stability and response speed of power balance through closed-loop control strategy.

[0051] Preferably, the short-term power balance strategy based on source-storage coordinated control and the long-term power balance strategy based on load regulation described in S11 further include:

[0052] S11.1: In the short-term power balance strategy, the output of the new energy units is adjusted through the microgrid controller to adapt to short-term load changes;

[0053] S11.2: In the long-term power balance strategy, the load is adjusted through the distribution master station to adapt to the long-term load changes and the uncertainty of renewable energy output;

[0054] S11.3: Combine short-term and long-term power balancing strategies to achieve efficient and stable operation of the microgrid.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The present invention improves the dynamic response and energy-saving efficiency of the system. Through accurate description of system characteristics, optimized system parameters, transient compensation strategy, coordinated control of multiple self-synchronous voltage sources, optimization of responsibility regulation, incentive mechanism for user energy regulation, multi-objective coordinated control model and improved sparrow algorithm, as well as coordinated control of source, load and storage and power balancing strategy based on dynamic compensation of energy storage, the system's adaptability to rapid changes is enhanced, the stable operation of the power grid under various changes is ensured, the stability and economy of the system are improved, users are effectively guided to perform energy regulation, energy utilization efficiency is improved, the global optimization capability of the system is enhanced, the efficient and stable operation of the microgrid is realized, energy consumption is reduced, and energy-saving optimization is achieved.

[0057] 2. The present invention significantly optimizes system performance through precise control. In steps S2.1-S2.3, by constructing a design matrix and parameter estimation, precise decoupling of virtual damping and virtual inertia is achieved, and the dynamic and static performance of the system is optimized. In steps S4.1-S4.3, the virtual inertia and virtual damping coefficient of the synchronous voltage source are monitored and dynamically adjusted in real time to maintain the consistency and stability of the multi-machine system. In steps S6.1-S6.3, by collecting user data and optimizing models, the user's energy regulation potential is calculated, and users are encouraged to participate in energy regulation, thereby improving energy utilization efficiency. The comprehensive application of these steps enables the system to achieve more precise control under various operating conditions and improve overall performance.

[0058] 3. The present invention enhances the flexibility and responsiveness of the power grid by defining the interaction between APFs and establishing a multi-objective coordinated control model. In steps S8.1-S8.3, the sparrow algorithm improved by introducing chaos and dynamic random Cauchy mutation is improved to enhance the global search capability of the algorithm and optimize the APF control parameters, so that the system can quickly adapt to changes in the power grid. In steps S9.1-S9.3, the state of the energy storage system is monitored in real time and the load switch is adjusted dynamically. The optimal coordinated control of source, load and storage is achieved through the intelligent dispatching system, further improving the adaptability and response speed of the power grid to fluctuations in new energy.

[0059] 4. The present invention uses a power balancing strategy based on dynamic compensation of energy storage to monitor and compensate the power difference between the new energy unit and the load in real time, thereby ensuring instantaneous power balance and improving the economy and reliability of the microgrid. In steps S11.1-S11.3, the output and load of the new energy unit are adjusted through the microgrid controller and the distribution main station in combination with the short-term and long-term power balancing strategies to adapt to short-term and long-term load changes, thereby achieving efficient and stable operation of the microgrid. These measures not only improve energy utilization efficiency, but also reduce operating costs, and enhance the stability and economy of the microgrid in the face of uncertainty in the output of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0061] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0062] Example 1, see Figure 1 , a system energy-saving structure optimization control method based on a dynamic balance strategy, comprising the following steps:

[0063] S1: Establish the dynamic model of the system, including virtual damping and virtual inertia;

[0064] In this step, we need to build a mathematical model that can accurately describe the dynamic behavior of the system. This model will include the system's virtual damping and virtual inertia, which are key parameters for simulating the system's response. Virtual damping represents the system's resistance to change, while virtual inertia describes the system's inertia to change. To build this model, we need to collect historical data on the system, including inputs and outputs, and how the system performs under different operating conditions. Through data analysis and system identification techniques, we can determine the parameters of the model and verify the accuracy of the model.

[0065] S2: Decouple virtual damping and virtual inertia through ridge estimation LASSO regression to solve the optimal virtual damping coefficient and virtual moment of inertia of the system under dynamic and static conditions;

[0066] S3: Introduce transient compensation to optimally control the system damping inertia, based on dynamic performance and optimal second-order system criteria;

[0067] S4: Set the virtual inertia and virtual damping coefficient of each synchronous voltage source to keep the transition time of the multi-machine system consistent and perform dynamic and static coordinated control of multiple self-synchronous voltage sources;

[0068] S5: Obtain distribution network parameters, use the steady-state safety domain to adjust the responsibility adjustment amount of each node; perform responsibility adjustment based on the responsibility adjustment amount;

[0069] S6: construct a distribution network optimization model and a user optimization model based on the optimization objectives and security domain constraints; solve the distribution network optimization model and the user optimization model to obtain the target regulation amount and reward price information, and send the solution information to the user for energy regulation;

[0070] S7: Define GDRGA to analyze the interaction between APFs, take the minimum integral of the difference between the output current of each APF and the reference current within the simulation time as the objective function, and establish a multi-objective coordinated control model with the upper and lower limits of each parameter as constraints;

[0071] S8: Aiming at the defects of the traditional sparrow algorithm in the later stage of search, such as poor global optimization ability and easy to fall into local optimum, tent chaos and dynamic random Cauchy mutation are introduced to improve it, and the improved sparrow algorithm is used to coordinate and optimize the control parameters of APF;

[0072] S9: Source-load-storage coordinated control method for off-grid operation of new energy microgrids. The control objects include energy storage systems, new energy units and load switches in each substation. The control architecture includes equipment local control layer, microgrid coordination control layer and distribution network master station control layer.

[0073] S10: The local control layer of the equipment adopts a power balancing strategy based on dynamic compensation of energy storage, and dynamically compensates the power difference between the new energy unit and the load through the energy storage converter to achieve instantaneous power balance;

[0074] S11: The microgrid coordination control layer adopts a short-term power balance strategy based on source-storage collaborative control, and adjusts the output of new energy units through the microgrid controller to achieve short-term power balance; the distribution network master station control layer adopts a long-term power balance strategy based on load regulation, and adjusts the load through the distribution master station to achieve long-term power balance.

[0075] Embodiment 2: Based on Embodiment 1, this embodiment further discloses the specific practical operation mode of each process therein:

[0076] Furthermore, the method for solving the optimal virtual damping coefficient and virtual moment of inertia of the system in dynamic and static conditions in S2 further includes:

[0077] S2.1: Construct the design matrix: Create an n×m design matrix X, where each row represents a sample and each column represents a feature. Add a bias term with a value of 1 to the design matrix so that the model includes an intercept term.

[0078] S2.2: Parameter estimation: Parameter estimation of ridge regression is achieved by minimizing the loss function, and the parameters are calculated using the formula θ = (XTX + λI)-1XTy, where X is the design matrix, y is the target value vector, λ is the regularization parameter, and I is the identity matrix;

[0079] S2.3: Model evaluation and hyperparameter selection: Use mean square error (MSE) or coefficient of determination (R 2 ) indicator to evaluate the model performance and select the best regularization parameter λ value through cross-validation.

[0080] Furthermore, the dynamic and static coordinated control of multiple self-synchronous voltage sources in S4 further includes:

[0081] S4.1: real-time monitoring of the output power of each synchronous voltage source and grid demand;

[0082] S4.2: dynamically adjust the virtual inertia and virtual damping coefficient of each synchronous voltage source according to the monitoring results to adapt to the changes in the power grid;

[0083] S4.3: Ensure the dynamic balance between the output power of each synchronous voltage source and the grid demand through a closed-loop control strategy.

[0084] Furthermore, the responsibility adjustment in S5 further includes:

[0085] S5.1: Determine the responsible regulation amount of each node according to the real-time operation status of the distribution network;

[0086] S5.2: Use advanced optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, to optimize the responsibility adjustment amount;

[0087] S5.3: Apply the optimized responsibility regulation amount to the distribution network to improve the stability and economy of the system.

[0088] Furthermore, the energy regulation in S6 further includes:

[0089] S6.1: Collect users’ electricity consumption data and demand information;

[0090] S6.2: Calculate the energy regulation potential of the user based on the user optimization model;

[0091] S6.3: Through smart contracts or incentive mechanisms, users are encouraged to adjust energy based on the adjustment amount and reward price information.

[0092] Furthermore, the multi-objective coordinated control model in S7 further includes:

[0093] S7.1: Define the interaction between APFs and establish the objective function and constraints;

[0094] S7.2: Use swarm intelligence optimization algorithms, such as the grey wolf optimization algorithm or the whale optimization algorithm, to solve multi-objective coordinated control models;

[0095] S7.3: Update model parameters in real time to adapt to changes in grid operation status.

[0096] Furthermore, the improved sparrow algorithm in S8 further includes:

[0097] S8.1: Introduce tent chaos and dynamic random Cauchy mutation to enhance the global search capability of the algorithm;

[0098] S8.2: Design an adaptive parameter adjustment mechanism to dynamically adjust algorithm parameters according to the search process;

[0099] S8.3: Verify the effectiveness and superiority of the improved sparrow algorithm through simulation experiments.

[0100] Furthermore, the source-load-storage coordinated control method in S9 further includes:

[0101] S9.1: Real-time monitoring of the status of the energy storage system, including energy storage capacity and charging and discharging status;

[0102] S9.2: Dynamically adjust the on / off status of the load switch according to the status of the energy storage system and the output of the new energy unit;

[0103] S9.3: Achieve optimal coordinated control of source, load and storage through intelligent dispatching system.

[0104] 9. According to the system energy-saving structure optimization control method based on dynamic balancing strategy in claim 1, further, the power balancing strategy based on energy storage dynamic compensation in S10 further includes:

[0105] S10.1: Real-time monitoring of the power difference between the new energy unit and the load;

[0106] S10.2: According to the power difference, dynamically adjust the output of the energy storage converter to compensate for the power difference;

[0107] S10.3: Ensure the stability and response speed of power balance through closed-loop control strategy.

[0108] Furthermore, the short-term power balance strategy based on source-storage coordinated control and the long-term power balance strategy based on load regulation in S11 further include:

[0109] S11.1: In the short-term power balance strategy, the output of the new energy units is adjusted through the microgrid controller to adapt to short-term load changes;

[0110] S11.2: In the long-term power balance strategy, the load is adjusted through the distribution master station to adapt to the long-term load changes and the uncertainty of renewable energy output;

[0111] S11.3: Combine short-term and long-term power balancing strategies to achieve efficient and stable operation of the microgrid.

[0112] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A system energy-saving structure optimization control method based on a dynamic balance strategy, characterized in that: The following steps are involved: S1: Establish the dynamic model of the system, including virtual damping and virtual inertia; S2: Decouple virtual damping and virtual inertia through ridge estimation LASSO regression to solve the optimal virtual damping coefficient and virtual moment of inertia of the system under dynamic and static conditions; S3: Introduce transient compensation to optimally control the system damping inertia, based on dynamic performance and optimal second-order system criteria; S4: Set the virtual inertia and virtual damping coefficient of each synchronous voltage source to keep the transition time of the multi-machine system consistent and perform dynamic and static coordinated control of multiple self-synchronous voltage sources; S5: Obtain distribution network parameters, use the steady-state safety domain to adjust the responsibility adjustment amount of each node; perform responsibility adjustment based on the responsibility adjustment amount; S6: construct a distribution network optimization model and a user optimization model based on the optimization objectives and security domain constraints; solve the distribution network optimization model and the user optimization model to obtain the target regulation amount and reward price information, and send the solution information to the user for energy regulation; S7: Define GDRGA to analyze the interaction between APFs, take the minimum integral of the difference between the output current of each APF and the reference current within the simulation time as the objective function, and establish a multi-objective coordinated control model with the upper and lower limits of each parameter as constraints; S8: Aiming at the defects of the traditional sparrow algorithm in the later stage of search, such as poor global optimization ability and easy to fall into local optimum, tent chaos and dynamic random Cauchy mutation are introduced to improve it, and the improved sparrow algorithm is used to coordinate and optimize the control parameters of APF; S9: Source-load-storage coordinated control method for off-grid operation of new energy microgrids. The control objects include energy storage systems, new energy units and load switches in each substation. The control architecture includes equipment local control layer, microgrid coordination control layer and distribution network master station control layer. S10: The local control layer of the equipment adopts a power balancing strategy based on dynamic compensation of energy storage, and dynamically compensates the power difference between the new energy unit and the load through the energy storage converter to achieve instantaneous power balance; S11: The microgrid coordination control layer adopts a short-term power balance strategy based on source-storage collaborative control, and adjusts the output of new energy units through the microgrid controller to achieve short-term power balance; the distribution network master station control layer adopts a long-term power balance strategy based on load regulation, and adjusts the load through the distribution master station to achieve long-term power balance.

2. The system energy-saving structure optimization control method based on the dynamic balance strategy according to claim 1 is characterized in that: The method for solving the optimal virtual damping coefficient and virtual moment of inertia of the system under dynamic and static conditions described in S2 further includes: S2.1: Construct the design matrix: Create an n×m design matrix X, where each row represents a sample and each column represents a feature. Add a bias term with a value of 1 to the design matrix so that the model includes an intercept term. S2.2: Parameter estimation: Parameter estimation of ridge regression is achieved by minimizing the loss function, and the parameters are calculated using the formula θ = (XTX + λI)-1XTy, where X is the design matrix, y is the target value vector, λ is the regularization parameter, and I is the identity matrix; S2.3: Model evaluation and hyperparameter selection: Use mean square error (MSE) or coefficient of determination (R 2 ) indicator to evaluate the model performance and select the best regularization parameter λ value through cross-validation.

3. The system energy-saving structure optimization control method based on the dynamic balance strategy according to claim 1 is characterized in that: The dynamic and static coordinated control of multiple self-synchronous voltage sources described in S4 further includes: S4.1: real-time monitoring of the output power of each synchronous voltage source and grid demand; S4.2: dynamically adjust the virtual inertia and virtual damping coefficient of each synchronous voltage source according to the monitoring results to adapt to the changes in the power grid; S4.3: Ensure the dynamic balance between the output power of each synchronous voltage source and the grid demand through a closed-loop control strategy.

4. The system energy-saving structure optimization control method based on the dynamic balance strategy according to claim 1 is characterized in that: The liability adjustment described in S5 further includes: S5.1: Determine the responsible regulation amount of each node according to the real-time operation status of the distribution network; S5.2: Use advanced optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, to optimize the responsibility adjustment amount; S5.3: Apply the optimized responsibility regulation amount to the distribution network to improve the stability and economy of the system.

5. The system energy-saving structure optimization control method based on the dynamic balance strategy according to claim 1 is characterized in that: The energy regulation described in S6 further includes: S6.1: Collect users’ electricity consumption data and demand information; S6.2: Calculate the energy regulation potential of the user based on the user optimization model; S6.3: Through smart contracts or incentive mechanisms, users are encouraged to adjust energy based on the adjustment amount and reward price information.

6. The system energy-saving structure optimization control method based on the dynamic balance strategy according to claim 1 is characterized by: The multi-objective coordinated control model described in S7 further includes: S7.1: Define the interaction between APFs and establish the objective function and constraints; S7.2: Use swarm intelligence optimization algorithms, such as the grey wolf optimization algorithm or the whale optimization algorithm, to solve multi-objective coordinated control models; S7.3: Update model parameters in real time to adapt to changes in grid operation status.

7. The system energy-saving structure optimization control method based on the dynamic balance strategy according to claim 1 is characterized by: The improved sparrow algorithm described in S8 further includes: S8.1: Introduce tent chaos and dynamic random Cauchy mutation to enhance the global search capability of the algorithm; S8.2: Design an adaptive parameter adjustment mechanism to dynamically adjust algorithm parameters according to the search process; S8.3: Verify the effectiveness and superiority of the improved sparrow algorithm through simulation experiments.

8. The system energy-saving structure optimization control method based on the dynamic balance strategy according to claim 1 is characterized by: The source-load-storage coordinated control method described in S9 further includes: S9.1: Real-time monitoring of the status of the energy storage system, including energy storage capacity and charging and discharging status; S9.2: Dynamically adjust the on / off status of the load switch according to the status of the energy storage system and the output of the new energy unit; S9.3: Achieve optimal coordinated control of source, load and storage through intelligent dispatching system.

9. The system energy-saving structure optimization control method based on dynamic balance strategy according to claim 1 is characterized by: The power balancing strategy based on dynamic compensation of energy storage described in S10 further includes: S10.1: Real-time monitoring of the power difference between the new energy unit and the load; S10.2: According to the power difference, dynamically adjust the output of the energy storage converter to compensate for the power difference; S10.3: Ensure the stability and response speed of power balance through closed-loop control strategy.

10. The system energy-saving structure optimization control method based on dynamic balance strategy according to claim 1 is characterized by: The short-term power balance strategy based on source-storage coordinated control and the long-term power balance strategy based on load regulation described in S11 further include: S11.1: In the short-term power balance strategy, the output of the new energy units is adjusted through the microgrid controller to adapt to short-term load changes; S11.2: In the long-term power balance strategy, the load is adjusted through the distribution master station to adapt to the long-term load changes and the uncertainty of renewable energy output; S11.3: Combine short-term and long-term power balancing strategies to achieve efficient and stable operation of the microgrid.

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