Micro-grid multi-energy coordinated scheduling method and device

Abstract

The invention provides a micro-grid multi-energy coordinated scheduling method and device, and the method comprises the steps: building a multi-energy coordinated scheduling optimization model containing time-varying constraints through obtaining the data of a photovoltaic power generation system, an energy storage system and a load system in a micro-grid, and carrying out the optimization solving through employing an improved particle swarm optimization algorithm, and designing a gridding vector current control strategy to realize fault ride-through control, executing multi-energy coordinated scheduling control, and realizing stable operation of the micro-grid through hierarchical execution. According to the method, the problem that a traditional micro-grid dispatching method is insufficient in stability and economical efficiency in the face of renewable energy source uncertainty and power grid faults is solved, and the reliability and economical efficiency of micro-grid operation are improved.

Description

Technical Field

[0001] This invention relates to the field of power systems, and in particular to a multi-energy coordinated dispatch method and apparatus for microgrids, applicable to energy management and control of microgrids containing photovoltaic power generation systems, energy storage systems and load systems. Background Technology

[0002] As a crucial component of the energy internet, microgrids integrate various energy forms, including distributed generation, energy storage systems, and controllable loads, offering advantages such as high flexibility, strong reliability, and environmental friendliness. However, with the large-scale integration of renewable energy sources, microgrid energy management faces challenges such as generation uncertainty, load volatility, and safe operation under various fault conditions, necessitating efficient multi-energy coordinated dispatch strategies.

[0003] Currently, energy management in microgrids mainly employs two types of methods: traditional optimization algorithms and heuristic algorithms. Traditional optimization methods, such as linear programming and mixed-integer programming, are accurate but computationally complex and difficult to adapt to the dynamic optimization needs of microgrids. On the other hand, basic heuristic algorithms, such as genetic algorithms and standard particle swarm optimization, have high search efficiency but are prone to getting trapped in local optima and lack adaptive mechanisms for the dynamic characteristics and fault conditions of microgrids.

[0004] Existing microgrid energy management systems typically employ a hierarchical control architecture, with the upper layer performing economic dispatch and the lower layer executing power control. A typical power control strategy uses PQ-based control to maintain microgrid stability by adjusting active and reactive power output. However, this type of control lacks effective current vector control capabilities when facing grid faults, especially asymmetrical faults, resulting in poor fault ride-through performance and severely impacting system stability.

[0005] The existing technologies have the following main problems: First, the standard particle swarm optimization algorithm is prone to premature convergence and is difficult to find the global optimal solution under multiple constraints in microgrids; second, traditional control strategies fail to effectively handle symmetric and asymmetric fault conditions, especially lacking a gridded vector current control mechanism during faults, which leads to a decrease in the stability and reliability of microgrids under fault conditions; third, the coordination mechanism among multiple energy sources is imperfect, making it difficult to achieve the optimal balance between economy and reliability based on changes in electricity prices and fluctuations in renewable energy. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-energy coordinated dispatch method and device for microgrids, which aims to solve the problems of insufficient stability and economy of existing microgrid dispatch methods when facing the uncertainty of renewable energy and grid faults.

[0007] To achieve the above objectives, the present invention provides a multi-energy coordinated dispatch method for microgrids, comprising the following steps:

[0008] Data on irradiance and temperature of photovoltaic power generation system, state of charge and charge / discharge efficiency of energy storage system, and historical electricity consumption data of load system in microgrid are obtained. Mathematical models are then performed on the photovoltaic power generation system, energy storage system and load system respectively to establish a multi-energy coordinated scheduling optimization model for microgrid with time-varying constraints.

[0009] Based on the microgrid multi-energy coordinated scheduling optimization model, an improved particle swarm optimization algorithm is used to solve the optimization problem. The improved particle swarm optimization algorithm includes chaotic sequence initialization, adaptive weight strategy and chaotic disturbance mechanism to obtain the optimal scheduling scheme of microgrid multi-energy.

[0010] Based on the microgrid multi-energy optimal dispatch scheme, a gridded vector current control strategy is designed. Fault ride-through control parameters are obtained through fault type identification and coordinated control of positive and negative sequence current components.

[0011] Based on the fault ride-through control parameters and the microgrid multi-energy optimal scheduling scheme, multi-energy coordinated scheduling control is executed to allocate power and adjust the operating status of the photovoltaic power generation system, energy storage system and load system, thereby obtaining microgrid operation control commands.

[0012] The microgrid operation control commands are executed in layers: the sensing layer collects real-time data, the control layer processes scheduling commands, and the execution layer controls the operation of energy units to obtain the stable operating state of the microgrid.

[0013] Furthermore, mathematical models are performed on the photovoltaic power generation system, energy storage system, and load system respectively, establishing a microgrid multi-energy coordinated dispatch optimization model with time-varying constraints, including:

[0014] Based on the irradiance and temperature data of the photovoltaic power generation system, a mathematical model of the photovoltaic cell current-voltage characteristic curve is established, the maximum output power curve of photovoltaic under different operating conditions is calculated, and a photovoltaic power generation prediction model is obtained.

[0015] Based on the state of charge and charge / discharge efficiency data of the energy storage system, a dynamic model of the energy storage system is established, and the SOC state equation and power constraint equation are constructed to obtain the energy storage system operation constraint model.

[0016] Based on the historical electricity consumption data of the load system, a load demand model is established using a prediction algorithm, distinguishing between critical loads and adjustable loads, to obtain a load demand prediction model.

[0017] Based on the photovoltaic power generation prediction model, the energy storage system operation constraint model, and the load demand prediction model, and taking into account the photovoltaic power generation cost, energy storage charging and discharging cost, grid power purchase cost, and reliability indicators, a multi-objective function for microgrid economic dispatch is constructed, resulting in the microgrid multi-energy coordinated dispatch optimization model.

[0018] Furthermore, based on the microgrid multi-energy coordinated scheduling optimization model, the improved particle swarm optimization algorithm includes chaotic sequence initialization, including:

[0019] Based on the decision variables of the microgrid multi-energy coordinated dispatch optimization model, a multi-dimensional particle coding scheme including photovoltaic power allocation, energy storage charging and discharging power and grid exchange power is designed. The photovoltaic power allocation is encoded as a T-dimensional vector, the energy storage charging and discharging power is encoded as a T-dimensional vector, and the grid exchange power is encoded as a T-dimensional vector, resulting in a 3T-dimensional particle coding vector.

[0020] Based on the 3T-dimensional particle encoding vector, a chaotic sequence is generated using Logistic mapping, and the chaotic sequence value is calculated using the chaotic mapping formula, where the mapping parameter μ = 4, to obtain the chaotic initialization sequence.

[0021] Particle swarm initialization is performed on the chaotic initialization sequence, mapping the chaotic sequence to the particle position and velocity space, thereby increasing the diversity of the initial solution and obtaining an initial particle swarm for multi-energy optimization solutions of microgrids.

[0022] Furthermore, based on the improved particle swarm optimization algorithm, the adaptive weight strategy includes:

[0023] Based on the iterative stage and convergence performance of the improved particle swarm optimization algorithm, the basic inertial weight is calculated using the inertial weight calculation formula, where the upper and lower limits of the weight are preset values, and the current iteration number and the maximum iteration number are algorithm parameters, thus obtaining the basic inertial weight.

[0024] Based on the aforementioned basic inertia weights and particle fitness evaluation, a weight adjustment formula is used to adjust the weights, where the current global optimal fitness, the current particle fitness, and the fitness range are the calculation parameters to obtain the adaptive adjustment weights.

[0025] The particle velocity is updated by adaptively adjusting the weights, so that particles with poor fitness are given a larger inertial weight to enhance their exploration ability, while particles with good fitness are given a smaller inertial weight to enhance their development ability, thus achieving a dynamic balance between global search and local development capabilities.

[0026] Furthermore, based on the improved particle swarm optimization algorithm, the chaotic perturbation mechanism includes:

[0027] The particle position changes in the improved particle swarm algorithm are monitored, the change in particle position over N consecutive iterations is calculated, and it is determined whether the particle meets the stagnation condition to obtain the particle stagnation state judgment result.

[0028] Based on the particle stagnation state judgment result, chaotic perturbation is applied to the particles trapped in local optima. The position perturbation formula is used to perform position perturbation, where the perturbation intensity coefficient and the chaotic sequence are perturbation parameters, and the perturbed particle position is obtained.

[0029] The perturbed particle positions are subjected to dimension-selective processing, the sensitivity of each dimension is calculated, and the 30%-50% of dimensions with the greatest impact are selected for perturbation to obtain enhanced global search capabilities.

[0030] Furthermore, the gridded vector current control strategy includes:

[0031] The three-phase voltage signal of the microgrid is acquired, and the three-phase voltage is decomposed into positive sequence, negative sequence and zero sequence components by symmetrical component transformation. The positive sequence voltage descent depth and sequence component ratio are calculated to obtain fault characteristic parameters.

[0032] Based on the fault characteristic parameters, a fault type discrimination criterion is designed to distinguish between three-phase symmetrical faults, two-phase short-circuit faults, single-phase ground faults, and two-phase ground faults, and to obtain the fault type identification result.

[0033] Based on the fault type identification results, a positive sequence current component control strategy is adopted for symmetrical faults, and a positive and negative sequence current component coordinated control strategy is adopted for asymmetrical faults. The corresponding current reference value is calculated to obtain the vector current control command.

[0034] Based on the vector current control command, a gridded control parameter matrix is ​​designed, and the current control gain is adaptively adjusted according to the fault severity and system state to obtain optimized fault ride-through control performance.

[0035] The vector current control command is subjected to current limiting protection processing. A priority-based current component limiting strategy is adopted to prioritize the protection of key control objectives while meeting the inverter current capacity limit, thereby obtaining the fault ride-through control parameters.

[0036] Furthermore, the fault type identification and positive / negative sequence current component coordinated control include:

[0037] Three-phase voltage signals of a microgrid are acquired, and positive-sequence, negative-sequence, and zero-sequence voltage components are calculated through symmetrical component transformation. High-frequency interference is filtered out using a low-pass filter to obtain the filtered sequence component signals.

[0038] Based on the filtered sequence component signal, the positive sequence voltage sag depth and sequence component ratio are calculated. A time delay judgment condition is set to require the fault characteristic to last for 2-5ms, and a reliable fault type judgment result is obtained.

[0039] Based on the fault type judgment result, the positive sequence current reference value is calculated using the positive sequence current reference value calculation formula for symmetrical faults, and the positive and negative sequence current coordinated control is used to calculate the current reference value for asymmetrical faults, thus obtaining a current control strategy that adapts to different fault types.

[0040] Furthermore, the gridded control parameter matrix includes:

[0041] Define a fault severity index FS and a system state index SS. The fault severity index FS is calculated based on the positive sequence voltage sag depth, negative sequence voltage ratio and zero sequence voltage ratio. The system state index SS is calculated based on the current active power, energy storage state of charge and system frequency to obtain state assessment parameters.

[0042] Based on the aforementioned state assessment parameters, the fault severity index FS is discretized into n levels, and the system state index SS is discretized into m levels. An n×m state space grid is constructed to obtain the gridded state space.

[0043] Based on the gridded state space, an optimal set of control parameters is preset for each grid point, including positive sequence current controller parameters, negative sequence current controller parameters, target weights and reactive power injection coefficients, to obtain a control parameter lookup table;

[0044] Based on the control parameter lookup table, the corresponding grid position is calculated according to the real-time fault severity index FS and the system status index SS. The optimal control parameters are obtained through bilinear interpolation, and the adaptively adjusted control gain is obtained.

[0045] The adaptively adjusted control gain is fine-tuned online, and the parameters are updated using a parameter update formula based on control performance feedback to obtain the gridded control parameter matrix.

[0046] Furthermore, the multi-energy coordinated scheduling control includes:

[0047] Based on the microgrid multi-energy optimal dispatch scheme, a day-ahead dispatch strategy is designed. Using photovoltaic power generation forecast, load forecast and electricity price information, a preliminary dispatch plan is allocated to each energy unit to obtain the day-ahead dispatch plan.

[0048] Based on the day-ahead scheduling plan, a real-time scheduling strategy is designed. A rolling time-domain optimization method is used to update the scheduling plan according to real-time data to cope with prediction deviations and system fluctuations, and to obtain real-time scheduling instructions.

[0049] Based on the real-time scheduling instructions, an energy storage optimization operation strategy is designed, and charging and discharging periods are reasonably arranged considering battery life and electricity price factors to maximize the economic benefits of energy storage and obtain energy storage operation control parameters.

[0050] Based on the energy storage operation control parameters, a coordinated control of photovoltaic power generation, energy storage system and controllable load is designed. The operating status of each energy unit is adjusted according to the system status and optimization objectives to obtain coordinated operation instructions.

[0051] An economic benefit assessment is performed on the coordinated operation command, calculating the operating cost, battery loss cost, and reliability cost, quantifying the economic performance of the scheduling strategy, and obtaining the microgrid operation control command.

[0052] Furthermore, the layered execution includes a perception layer, a control layer, and an execution layer, comprising:

[0053] Based on the microgrid operation control commands, real-time operation data of the microgrid is collected through the energy metering device, environmental monitoring device and status monitoring device of the sensing layer. The real-time operation data is preprocessed and fused to obtain standardized system status data.

[0054] Based on the standardized system status data, the scheduling instructions are decomposed by the central controller and local controllers of the control layer, and the microgrid operation control instructions are decomposed into specific control instructions for each energy unit to obtain distributed control instructions.

[0055] Based on the distributed control commands, power control operations are performed by the photovoltaic inverters, energy storage converters, and load controllers in the execution layer to adjust the output power and operating status of each energy unit, thereby obtaining the stable operating state of the microgrid.

[0056] The present invention also provides a microgrid multi-energy coordinated dispatch device, comprising:

[0057] The data acquisition module is used to acquire irradiance and temperature data of photovoltaic power generation systems, state of charge and charge / discharge efficiency data of energy storage systems, and historical electricity consumption data of load systems in microgrids.

[0058] The modeling and optimization module is used to perform mathematical modeling on the photovoltaic power generation system, energy storage system and load system respectively, establish a microgrid multi-energy coordinated scheduling optimization model with time-varying constraints, and use an improved particle swarm optimization algorithm to optimize and solve the model to obtain the optimal multi-energy scheduling scheme of the microgrid.

[0059] The control strategy module is used to design a gridded vector current control strategy based on the multi-energy optimal scheduling scheme of the microgrid, and obtain fault ride-through control parameters through fault type identification and coordinated control of positive and negative sequence current components.

[0060] The scheduling execution module is used to execute multi-energy coordinated scheduling control based on the fault ride-through control parameters and the microgrid multi-energy optimal scheduling scheme to obtain microgrid operation control commands;

[0061] The system management module is used to execute the microgrid operation control commands in layers. Through the coordinated work of the sensing layer, control layer and execution layer, the stable operation state of the microgrid is obtained.

[0062] The beneficial effects of this invention are:

[0063] By improving the chaotic sequence initialization, adaptive weight strategy, and chaotic perturbation mechanism in the particle swarm optimization algorithm, the problem of the standard particle swarm optimization algorithm easily getting trapped in local optima is effectively solved, and the global search capability and convergence speed of multi-energy coordinated scheduling optimization of microgrids are improved.

[0064] The designed gridded vector current control strategy can accurately distinguish between symmetrical and asymmetrical faults, and adopts corresponding positive and negative sequence current component control methods, which significantly improves the ride-through capability and stability of the microgrid under various fault conditions.

[0065] Through multi-energy coordinated scheduling control and hierarchical execution mechanism, a seamless connection from optimized scheduling to execution control is achieved, ensuring the accurate execution of scheduling strategies and stable system operation, effectively reducing microgrid operating costs and improving system reliability. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart of the microgrid multi-energy coordinated scheduling method of the present invention;

[0068] Figure 2 This is a flowchart illustrating the modeling of a multi-energy microgrid system according to the present invention;

[0069] Figure 3 This is a flowchart of the improved particle swarm algorithm of the present invention;

[0070] Figure 4 This is a flowchart of the gridded vector current control strategy of the present invention;

[0071] Figure 5 This is a flowchart of the multi-energy coordinated scheduling control of the present invention;

[0072] Figure 6 This is a diagram of the microgrid energy management system architecture of the present invention. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0074] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0075] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0076] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] like Figure 1 As shown, the microgrid multi-energy coordinated scheduling method provided by the present invention includes the following steps:

[0078] Step S1: Obtain irradiance and temperature data of photovoltaic power generation system, state of charge and charge / discharge efficiency data of energy storage system, and historical electricity consumption data of load system in microgrid. Perform mathematical modeling on photovoltaic power generation system, energy storage system and load system respectively, and establish microgrid multi-energy coordinated scheduling optimization model with time-varying constraints.

[0079] In the first step of multi-energy coordinated dispatching in a microgrid, the system needs to comprehensively acquire operational data from each energy unit. First, irradiance and temperature data are collected from the photovoltaic power generation system, as these data directly affect the output characteristics of the photovoltaic cells. Irradiance data is typically measured using a radiometer, with units of W / m². 2 Temperature data is acquired via temperature sensors. These parameters will be used to establish a mathematical model of the photovoltaic cell's current-voltage characteristic curve, thereby predicting the maximum output power under different environmental conditions.

[0080] Simultaneously, the system acquires the state of charge (SOC) and charge / discharge efficiency data of the energy storage system. SOC represents the ratio of the battery's current charge to its rated capacity, usually expressed as a percentage, and is a key parameter for energy storage system scheduling. Charge / discharge efficiency data reflects the energy losses during the energy conversion process; typically, both charging and discharging efficiencies are less than 100%. This data will be used to construct a dynamic model of the energy storage system, including the SOC state equation and power constraint equations.

[0081] In addition, the system needs to acquire historical electricity consumption data of the load system, including electricity consumption, load characteristics, and fluctuation patterns at different times. This data is collected over a long period through energy metering devices and will be used for load forecasting and classification. During the modeling process, loads are divided into two categories: critical loads and adjustable loads. The former must be satisfied, while the latter can be adjusted according to the system status.

[0082] Based on the above data, the system performs mathematical modeling for each energy unit. The photovoltaic power generation system model describes the relationship between irradiance, temperature, and output power; the energy storage system model describes the state of charge (SOC) variation and power constraints; and the load model predicts future electricity demand. Finally, considering the costs of photovoltaic power generation, energy storage charging and discharging, grid purchase costs, and reliability indicators, a multi-objective function for microgrid economic dispatch is constructed, forming a microgrid multi-energy coordinated dispatch optimization model that includes time-varying constraints.

[0083] Step S2: Based on the microgrid multi-energy coordinated scheduling optimization model, the improved particle swarm optimization algorithm is used to solve the optimization problem. The improved particle swarm optimization algorithm includes chaotic sequence initialization, adaptive weight strategy and chaotic disturbance mechanism to obtain the optimal scheduling scheme of microgrid multi-energy.

[0084] After obtaining the optimized model, the system uses an improved particle swarm optimization (PSO) algorithm to solve it. Traditional PSO algorithms are prone to getting trapped in local optima. To overcome this drawback, this step introduces three key improvements: chaotic sequence initialization, adaptive weight strategy, and chaotic perturbation mechanism.

[0085] Chaotic sequence initialization generates ergodic and random chaotic sequences through Logistic mapping, replacing traditional random initialization methods. Specifically, the decision variables of the microgrid scheduling problem are first encoded as a 3T-dimensional vector (T being the scheduling period) containing photovoltaic power allocation, energy storage charging and discharging power, and grid exchange power. Then, an initial particle swarm is generated using chaotic mapping. This initialization method results in a more uniform distribution of initial solutions in the solution space, improving the diversity of initial solutions.

[0086] The adaptive weighting strategy dynamically adjusts the inertia weights based on the algorithm's iteration stage and particle fitness. In the early stages of iteration, the system maintains a larger inertia weight, which facilitates extensive exploration of the solution space by particles. As iterations progress, the inertia weight decreases non-linearly, gradually enhancing local exploitation capabilities. Simultaneously, particles with poor fitness receive larger inertia weights to enhance their exploration capabilities, while particles with better fitness receive smaller inertia weights to enhance their exploitation capabilities, thus achieving a dynamic balance between global search and local exploitation capabilities.

[0087] The chaotic perturbation mechanism perturbs particles trapped in a stagnant state, helping them escape local optima. The system determines whether a particle is trapped in a local optimum by calculating the change in its position over N consecutive iterations. For particles that meet the stagnant condition, chaotic perturbation based on a logistic mapping is used, and dimensionality-selective processing is employed, choosing the 30%-50% of dimensions with the greatest influence for perturbation to improve perturbation efficiency and enhance the algorithm's global search capability.

[0088] Through the above-mentioned improved strategies, the algorithm can effectively avoid premature convergence while maintaining convergence efficiency, and finally obtain the optimal multi-energy scheduling scheme of the microgrid that satisfies all constraints, providing a foundation for the implementation of subsequent control strategies.

[0089] Step S3: Based on the microgrid multi-energy optimal dispatch scheme, design a gridded vector current control strategy, and obtain fault ride-through control parameters through fault type identification and coordinated control of positive and negative sequence current components;

[0090] Based on the optimal scheduling scheme obtained in step S2, a gridded vector current control strategy is designed to address various fault conditions that may occur during microgrid operation. The core of this strategy is to improve the fault ride-through capability of the microgrid through fault type identification and coordinated control of positive and negative sequence current components.

[0091] First, the system acquires the three-phase voltage signals of the microgrid and decomposes them into positive-sequence, negative-sequence, and zero-sequence components through symmetrical component transformation. The positive-sequence component represents the balanced portion of the three phases, while the negative-sequence and zero-sequence components reflect the degree of three-phase imbalance. Based on these sequence components, the positive-sequence voltage descent depth and the sequence component ratio are calculated as fault characteristic parameters. To improve identification reliability, the system uses a low-pass filter to filter out high-frequency interference and requires that the fault characteristic persist for 2-5 ms before triggering fault determination.

[0092] Then, based on the fault characteristic parameters, the system designs fault type discrimination criteria: if the positive sequence voltage drops significantly and the ratio of negative sequence and zero sequence voltage is very small, it is judged as a three-phase symmetrical fault; if the positive sequence voltage drops significantly and the ratio of negative sequence voltage is large but the ratio of zero sequence voltage is very small, it is judged as a two-phase short circuit fault; if the positive sequence voltage drops significantly and the ratios of negative sequence and zero sequence voltage are both large, it is judged as a single-phase ground fault or a two-phase ground fault, depending on the magnitude of the zero sequence voltage ratio.

[0093] Different current control strategies are employed for different types of faults. For symmetrical faults, a positive-sequence current component control strategy is used, which provides reactive power support and maintains active power output by adjusting the reference values ​​of the positive-sequence d-axis and q-axis currents. For asymmetrical faults, a coordinated positive and negative-sequence current component control strategy is used, which achieves multi-objective control such as power fluctuation suppression, current balance, and voltage support by optimizing the positive and negative-sequence current components.

[0094] To adapt to different fault conditions and system states, the system is designed with a gridded control parameter matrix, discretizing the fault severity and system state into several intervals, and pre-setting optimal control parameters for each interval combination. During operation, the optimal control parameters are obtained through bilinear interpolation based on the real-time status, achieving precise adaptive adjustment of the control parameters.

[0095] Finally, the system performs current control command limiting protection, employing a priority-based current component limiting strategy to prioritize the achievement of key control objectives while meeting inverter current capacity limitations. These measures result in optimized fault ride-through control parameters, ensuring the safe and stable operation of the microgrid under various fault conditions.

[0096] Step S4: Based on the fault ride-through control parameters and the microgrid multi-energy optimal dispatch scheme, perform multi-energy coordinated dispatch control to allocate power and adjust the operating status of the photovoltaic power generation system, energy storage system and load system, and obtain the microgrid operation control command;

[0097] After acquiring the fault ride-through control parameters and the optimal scheduling scheme, the system executes multi-energy coordinated scheduling control to allocate power and adjust the operating status of each energy unit in the microgrid. This step adopts a day-ahead-real-time rolling optimization framework to achieve an optimal balance between economy and reliability.

[0098] First, the system designs a day-ahead dispatch strategy based on the optimal dispatch scheme. This strategy utilizes photovoltaic power generation forecasts, load forecasts, and electricity price information to allocate preliminary dispatch plans for each energy unit for the next 24 hours. Day-ahead dispatch primarily considers economic objectives, minimizing total operating costs while satisfying system constraints to form the day-ahead dispatch plan.

[0099] Then, the system designs a real-time scheduling strategy, employing a rolling time-domain optimization method to dynamically update the scheduling plan based on real-time collected system data and short-term forecast results. Typically, an optimization window of 1-2 hours is used, with the plan rolling every 15-30 minutes to effectively address forecast deviations and system fluctuations, thereby improving the accuracy and adaptability of scheduling.

[0100] Based on real-time scheduling commands, the system designs an optimized energy storage operation strategy, comprehensively considering battery life and electricity price factors to rationally schedule charging and discharging periods. This strategy avoids frequent deep charging and discharging, controls the charging and discharging rate, and extends battery life; at the same time, it utilizes the peak-valley electricity price difference, charging during off-peak hours and discharging during peak hours to maximize the economic benefits of energy storage.

[0101] The system also incorporates a multi-energy coordinated control mechanism to coordinate and adjust the operating states of photovoltaic power generation, energy storage systems, and controllable loads based on system status and optimization objectives. When photovoltaic power generation fluctuates, the energy storage system provides rapid power compensation; when the system load is too high, the controllable load is appropriately reduced; and when a grid fault occurs, each energy unit operates in coordination according to fault ride-through control parameters to ensure system stability.

[0102] Finally, the system performs an economic benefit assessment on the coordinated operation commands, calculating operating costs, battery loss costs, and reliability costs, quantifying the economic performance of the scheduling strategy, and forming the final microgrid operation control commands. Through this multi-level coordinated scheduling control, the system can maximize economic benefits while ensuring reliability.

[0103] Step S5: The microgrid operation control commands are executed in layers. The sensing layer collects real-time data, the control layer processes scheduling commands, and the execution layer controls the operation of energy units to obtain the stable operating state of the microgrid.

[0104] After receiving microgrid operation control commands, the system transforms them into actual control operations through a hierarchical execution mechanism, achieving a seamless transition from optimized scheduling to execution control. Hierarchical execution comprises three layers: a sensing layer, a control layer, and an execution layer. These layers work collaboratively to ensure the precise execution of scheduling strategies.

[0105] The sensing layer collects real-time operating data of the microgrid through various sensors and metering devices, including electrical parameters (such as voltage, current, power, and frequency), environmental parameters (such as temperature, irradiance, and wind speed), and equipment status parameters (such as switch status and SOC value). The collected data undergoes preprocessing, including filtering, calibration, and anomaly detection. Then, data fusion technology integrates multi-source data to form standardized system status data, providing an accurate information foundation for control decisions.

[0106] The control layer comprises a central controller and multiple local controllers, forming a hierarchical control architecture. The central controller is responsible for global optimization and coordination, receiving system status data, executing optimization algorithms, generating scheduling strategies, and decomposing microgrid operation control commands into specific control commands for each energy unit. Local controllers are responsible for controlling individual energy units, receiving commands from the central controller, combining them with local status data, and generating detailed control signals to form distributed control commands. This hierarchical control architecture ensures the effectiveness of global optimization while improving system response speed and reliability.

[0107] The execution layer includes various power control devices, such as photovoltaic inverters, energy storage converters, and load controllers. These devices receive distributed control commands from the control layer, execute corresponding power control operations, and adjust the output power and operating status of each energy unit. Photovoltaic inverters control the power output of the photovoltaic system, potentially including maximum power point tracking or power limiting; energy storage converters control the charging and discharging power and direction of the batteries; and load controllers manage the connection and disconnection of controllable loads. Through these coordinated control operations, the stable operation of the microgrid is achieved.

[0108] The advantages of the layered execution mechanism are: the perception layer provides accurate real-time data, the control layer realizes optimized decision-making and instruction decomposition, and the execution layer precisely executes control operations. The three layers are seamlessly connected through standardized interfaces and communication protocols. This architecture not only ensures the accurate execution of scheduling strategies but also has the ability to respond quickly to system disturbances and faults, ultimately achieving stable, economical, and reliable operation of the microgrid.

[0109] like Figure 2 As shown, in step S1, mathematical models are performed on the photovoltaic power generation system, energy storage system, and load system respectively to establish a microgrid multi-energy coordinated dispatch optimization model with time-varying constraints, specifically including:

[0110] Step S11: Based on the irradiance and temperature data of the photovoltaic power generation system, establish a mathematical model of the photovoltaic cell current-voltage characteristic curve, calculate the maximum output power curve of the photovoltaic under different operating conditions, and obtain the photovoltaic power generation prediction model.

[0111] In step S11, the photovoltaic power generation prediction model mainly considers the impact of solar irradiance and ambient temperature on photovoltaic output power. First, a mathematical model of the photovoltaic cell's current-voltage characteristic curve is established, describing the relationship between voltage and current under different irradiance and temperature conditions. Then, based on this model, the maximum power point under different operating conditions is calculated, forming the maximum photovoltaic output power curve. Finally, combining historical meteorological data and weather forecast information, the photovoltaic power generation power for future periods is predicted, resulting in the photovoltaic power generation prediction model.

[0112] Step S12: Based on the state of charge and charge / discharge efficiency data of the energy storage system, establish a dynamic model of the energy storage system, construct the SOC state equation and power constraint equation, and obtain the operating constraint model of the energy storage system.

[0113] In step S12, the energy storage system operation constraint model mainly describes the dynamic characteristics and operational limitations of the energy storage system. First, the SOC (State of Charge) equation of the energy storage system is established, which describes the dynamic process of the system's state of charge changing with charging and discharging power. Then, considering the charging and discharging efficiency, power limitations, and capacity limitations of the energy storage system, a power constraint equation is constructed. Finally, taking into account the lifespan characteristics and safe operation requirements of the energy storage system, a complete energy storage system operation constraint model is formed.

[0114] Step S13: Based on the historical electricity consumption data of the load system, a load demand model is established using a prediction algorithm, distinguishing between critical loads and adjustable loads to obtain a load demand prediction model;

[0115] In step S13, the load demand forecasting model uses historical electricity consumption data and employs time series analysis, regression analysis, or artificial intelligence algorithms to forecast loads. This model categorizes loads into critical loads and adjustable loads. Critical loads must be satisfied, while adjustable loads can be adjusted based on system conditions and economic considerations. By analyzing the temporal distribution characteristics and influencing factors of the load, an accurate load demand forecasting model is obtained.

[0116] Step S14: Based on the photovoltaic power generation prediction model, the energy storage system operation constraint model, and the load demand prediction model, and taking into account the photovoltaic power generation cost, energy storage charging and discharging cost, grid power purchase cost, and reliability indicators, construct a multi-objective function for microgrid economic dispatch, and obtain a multi-energy coordinated dispatch optimization model for microgrid.

[0117] In step S14, the microgrid multi-energy coordinated dispatch optimization model comprehensively considers the system's economy and reliability. The economic objectives include minimizing photovoltaic power generation costs, energy storage charging and discharging costs, and grid purchase costs; the reliability objectives include minimizing load shedding and system operation risks. By constructing a multi-objective function and considering various operational constraints, such as power balance constraints, energy storage system SOC constraints, and ramp-up constraints, a complete microgrid multi-energy coordinated dispatch optimization model is formed.

[0118] like Figure 3 As shown, in step S2, based on the microgrid multi-energy coordinated scheduling optimization model, an improved particle swarm optimization algorithm is used for optimization. The improved particle swarm optimization algorithm includes chaotic sequence initialization, adaptive weighting strategy, and chaotic perturbation mechanism, specifically including:

[0119] Step S21: Based on the decision variables of the microgrid multi-energy coordinated dispatch optimization model, design a multi-dimensional particle coding scheme that includes photovoltaic power allocation, energy storage charging and discharging power and grid exchange power. Encode the photovoltaic power allocation as a T-dimensional vector, the energy storage charging and discharging power as a T-dimensional vector, and the grid exchange power as a T-dimensional vector to obtain a 3T-dimensional particle coding vector.

[0120] In the multi-energy coordinated scheduling optimization problem of microgrids, particle coding is a key step in mapping optimization variables into a form that the algorithm can handle. The system first designs a multi-dimensional particle coding scheme that includes photovoltaic power allocation, energy storage charging and discharging power, and grid exchange power. Assuming the scheduling period is T (typically 24 hours) and the time interval is 1 hour, the photovoltaic power allocation variable can be represented as a T-dimensional vector. Where PV represents the actual output power of the photovoltaic system at time t, which is limited by the predicted maximum power. The energy storage charging and discharging power is encoded as a T-dimensional vector. Where PESS represents the energy storage system power at time t, with positive values ​​indicating discharge and negative values ​​indicating charging. The grid exchange power is encoded as a T-dimensional vector. Where Pgrid represents the power exchanged with the main grid at time t, with positive values ​​indicating power purchases and negative values ​​indicating power sales.

[0121] This encoding method allows each particle to be represented as a 3T-dimensional vector X = [PV, PESS, Pgrid], fully describing the energy allocation scheme within a scheduling cycle. The advantage of this encoding scheme lies in its intuitiveness and clarity; each dimension corresponds to a decision variable with a clearly defined physical meaning, facilitating subsequent constraint processing and result interpretation. Furthermore, the encoding scheme considers the characteristics of a microgrid's multi-energy system, using photovoltaic power generation, energy storage systems, and grid interaction as the main decision variables, thus covering the core aspects of microgrid energy management.

[0122] Step S22: Based on the 3T-dimensional particle encoding vector, a chaotic sequence is generated using the Logistic mapping. The chaotic sequence value is calculated using the chaotic mapping formula, where the mapping parameter μ = 4, to obtain the chaotic initialization sequence.

[0123] Traditional particle swarm optimization (PSO) algorithms typically employ random initialization, which can easily lead to uneven distribution of initial solutions and negatively impact algorithm performance. To overcome this problem, the system innovatively introduces a chaotic sequence initialization strategy, specifically using a Logistic mapping to generate chaotic sequences. The Logistic mapping is a typical chaotic system, with its iterative formula z(k+1)=μz(k)(1-z(k)), where μ is a control parameter. When μ=4, the system exhibits completely chaotic characteristics. The initial value z(0) must be within the interval (0,1) and not equal to special values ​​such as 0.25, 0.5, or 0.75 to avoid the system getting trapped in periodicity or fixed points.

[0124] In the chaotic sequence generation process, the system first selects an appropriate initial value z(0), and then iteratively calculates a sufficient number of sequence values ​​using the Logistic mapping formula to form a chaotic initialization sequence. Since the sequence generated by the Logistic mapping possesses ergodicity (able to traverse the entire domain) and randomness (the sequence values ​​exhibit a quasi-random distribution), it can make the initial particles more evenly distributed in the solution space while maintaining a certain degree of randomness. This characteristic is significant for improving the diversity of initial solutions and preventing the algorithm from prematurely converging to a local optimum.

[0125] Step S23: Initialize the chaotic initialization sequence with particle swarm optimization, map the chaotic sequence to the particle position and velocity space, improve the diversity of the initial solution, and obtain the initial particle swarm for multi-energy optimization solution of microgrid;

[0126] After obtaining the chaotic initialization sequence, the system maps it to the particle position and velocity space, completing the particle swarm initialization. For position initialization, the system maps the chaotic sequence z-values ​​to the range of values ​​for each decision variable: X i = Lb + (Ub - Lb) × zi, where X i Let V represent the j-th position of the i-th particle, Lb and Ub be the lower and upper bounds of the j-th decision variable, respectively, and zi be the corresponding chaotic sequence value. For velocity initialization, the system typically sets the initial velocity to a small random value, or generates it based on another set of chaotic sequences: V i =Vmin+(Vmax-Vmin)×z' i Where Vmin and Vmax are the lower and upper limits of the velocity, respectively, z' i For another set of chaotic sequence values.

[0127] In this way, the system generates a particle swarm with a uniform initial position distribution and a certain degree of randomness. Compared with traditional stochastic initialization methods, chaotic initialization can more effectively explore the solution space and improve the quality and diversity of initial solutions. Especially for complex optimization problems such as multi-energy coordinated scheduling in microgrids, a good initial solution distribution has a significant impact on the algorithm's convergence performance and the quality of the final solution. After initialization, the system evaluates the fitness value of each particle, determines its historical optimal position and global optimal position, laying the foundation for subsequent iterative optimization.

[0128] Step S24: Based on the iterative stage and convergence performance of the improved particle swarm optimization algorithm, the basic inertial weight is calculated using the inertial weight calculation formula, where the upper and lower limits of the weight are preset values, and the current iteration number and the maximum iteration number are algorithm parameters, thus obtaining the basic inertial weight;

[0129] In particle swarm optimization (PSO), the inertia weight *w* is a key parameter affecting the balance between the algorithm's global search and local exploitation capabilities. An excessively large *w* is beneficial for global search but may lead to slow convergence, while an excessively small *w* is beneficial for fine-grained local search but is prone to getting trapped in local optima. To achieve a dynamic balance, the system designs an inertia weight calculation strategy based on the iteration stage and convergence performance. Specifically, the formula *w* = *wmax* - (*wmax* - *wmin*) × (*k* / *kmax*) is used. 2 , where wmax and wmin are the upper and lower limits of the weights (usually set to 0.9 and 0.4), k is the current iteration number, and kmax is the maximum iteration number.

[0130] This calculation method causes the inertia weight to decrease non-linearly with iteration. In the early stages of iteration, the inertia weight is close to wmax, giving particles strong inertia, which is beneficial for extensive exploration in the solution space and enhances global search capabilities. As iteration progresses, the inertia weight gradually decreases, and particle motion is more influenced by individual and global optimal positions, enhancing local exploration capabilities. A squared term (k / kmax) is used. 2 This makes the weight changes smoother and provides more detailed local search capabilities in the later stages of iteration, helping the algorithm find more accurate solutions.

[0131] Step S25: Based on the basic inertia weight and particle fitness evaluation, the weight is adjusted using the weight adjustment formula, where the current global optimal fitness, the current particle fitness, and the fitness range are the calculation parameters to obtain the adaptive adjustment weight;

[0132] The basic inertia weight only considers factors in the iteration stage. To further improve the algorithm performance, the system introduces an adaptive adjustment mechanism based on particle fitness evaluation. Specifically, the formula w = w × (1 + α × (fbest - fi) / (fmax - fmin)) is used, where w is the basic inertia weight calculated in step S24, fbest is the current global optimal fitness, fi is the current particle fitness, fmax and fmin are the current maximum and minimum fitness of the population, respectively, and α is the adjustment coefficient (usually taken as 0.2).

[0133] The core idea of ​​this adaptive adjustment mechanism is to dynamically adjust the inertia weight of a particle based on its fitness performance. For particles with poor fitness (fi is much larger than fbest, assuming a minimization problem), the value inside the parentheses on the right side of the formula is positive, leading to an increase in the inertia weight, enhancing its exploration ability and helping it escape local optima. For particles with good fitness (fi is close to fbest), the value inside the parentheses on the right side of the formula is close to zero, the inertia weight remains basically unchanged or decreases slightly, enhancing its exploration ability and helping it to perform fine-grained searches in promising regions.

[0134] Through this adaptive adjustment mechanism, the system achieves "division of labor and cooperation" within the particle swarm: particles with poor fitness are responsible for exploring new regions, while particles with better fitness are responsible for developing currently promising regions. This accelerates algorithm convergence while maintaining diversity, increasing the probability of finding the global optimum. This strategy is particularly suitable for complex optimization problems with multiple local optima, such as multi-energy coordinated scheduling in microgrids.

[0135] Step S26: Update the particle velocity by adaptively adjusting the weights, so that particles with poor fitness receive a larger inertial weight to enhance their exploration ability, and particles with good fitness receive a smaller inertial weight to enhance their development ability, thus obtaining a dynamic balance between global search and local development capabilities.

[0136] Based on adaptive weight adjustment, the system updates particle velocities, achieving dynamic equilibrium through global search and local exploitation capabilities. The particle velocity update uses an improved formula: V i k+1 =w·V i k +c1·r1·(P i -X i k )+c2·r2·(Pg-X i k )+c3·r3·(Pl-X i k ), where w is the adaptively adjusted inertia weight, P i$P_{best}$ is the historical best position of the particle, $P_g$ is the global best position, $P_l$ is the local neighborhood best position, $c_1$, $c_2$ and $c_3$ are learning factors, and $r_1$, $r_2$ and $r_3$ are random numbers between [0, 1]. Compared with the traditional PSO, a learning term for the local neighborhood best position is added, enhancing the information exchange between particles.

[0137] The particle position is updated using the standard formula: $X$ i k+1 $= X$ i k $+ V$ i k+1 . The updated position may exceed the boundary of the search space, and the system adopts a boundary absorption strategy to handle it: if $X$ i k+1 $< L_b$, then $X$ i k+1 $= L_b$; if $X$ i k+1 $> U_b$, then $X$ i k+1 $= U_b$. At the same time, the velocity in the corresponding dimension is set to zero or reversed to prevent the particle from continuously rushing towards the boundary.

[0138] Through the adaptive weight strategy, particles with poor fitness obtain a larger inertia weight to enhance their ability to explore new regions; particles with good fitness obtain a smaller inertia weight to enhance their ability to develop in the current promising region. This mechanism achieves a dynamic balance between global search and local development capabilities, improving the convergence efficiency and solution quality of the algorithm.

[0139] Step S27: Monitor the change in the particle position in the improved particle swarm optimization algorithm, calculate the change in the particle position in consecutive N iterations, determine whether the particle meets the stagnation condition, and obtain the particle stagnation state judgment result;

[0140] To promptly detect particles trapped in local optima, the system continuously monitors the change in particle position. The specific method is to calculate the change in the particle position in consecutive N iterations (usually taking 5 - 10 times) and determine whether it meets the stagnation condition: $\|X$ i k $- X$ i k-N $\| < \epsilon$, where $\|\cdot\|$ represents the Euclidean norm and $\epsilon$ is a preset threshold (usually set to 0.1% - 1% of the decision space diameter).

[0141] The core idea of ​​this monitoring mechanism is that if a particle's position changes very little across multiple iterations, it may be trapped in a local optimum. In practice, the system maintains a historical position record for each particle, storing its position over the last N iterations. After each iteration, the distance between the current position and its position N iterations ago is calculated and compared with a threshold ε to determine if the stagnation condition is met.

[0142] To improve the reliability of the judgment, the system also considers changes in particle fitness. If the particle position changes little and the fitness does not improve significantly in consecutive iterations (|f i k -f i k-N If |<δ, where δ is the fitness change threshold, then it is more likely to get stuck in a local optimum. This comprehensive judgment mechanism can more accurately identify particles that need to be perturbed and avoid unnecessary interference to particles that are converging normally.

[0143] Step S28: Based on the particle stagnation state judgment result, implement chaotic perturbation on the particles trapped in local optima, and use the position perturbation formula to perform position perturbation, where the perturbation intensity coefficient and the chaotic sequence are perturbation parameters, and obtain the position of the perturbed particles;

[0144] For particles that meet the stagnation condition, the system implements chaotic perturbations to help them escape local optima. Specifically, formula X is used. i k =X i k +β×(Ub d -Lb d )×(2z d -1) Perform position perturbation, where Ub d and Lb d These are the upper and lower bounds of the d-th dimension decision variable, z. d represents the chaotic sequence value generated by the Logistic mapping, and β is the perturbation intensity coefficient.

[0145] The perturbation strength coefficient β is a key parameter controlling the perturbation amplitude. To balance exploration and exploitation capabilities, the system adopts a β value that decreases linearly with iteration: β = βmax - (βmax - βmin) × (k / kmax), where βmax and βmin are the upper and lower limits of the perturbation strength (usually set to 0.5 and 0.1), respectively, k is the current iteration number, and kmax is the maximum iteration number. This design allows for larger perturbations in the early stages of iteration to enhance global exploration capabilities, while smaller perturbations are implemented in the later stages of iteration to avoid disrupting existing good solutions.

[0146] The chaotic perturbation uses a chaotic sequence generated by a Logistic mapping, rather than simple random numbers, primarily leveraging the ergodic and deterministic characteristics of the chaotic sequence. Ergodicity ensures the perturbation covers the entire solution space, enhancing its ability to escape local optima; determinism makes the algorithm repeatable, facilitating debugging and analysis. The expression (2z) d -1) Map the chaotic sequence values ​​from [0,1] to [-1,1] so that the perturbation can be positive or negative, increasing the diversity of the perturbation.

[0147] Step S29: Perform dimension-selective processing on the perturbed particle positions, calculate the sensitivity of each dimension, and select the 30%-50% of dimensions with the greatest impact for perturbation to obtain enhanced global search capability;

[0148] To improve perturbation efficiency, the system performs dimensionality-selective processing on the perturbed particle positions, rather than perturbing all dimensions simultaneously. First, the system calculates the sensitivity of each dimension to measure the degree of influence of each dimension on the objective function. Dimension sensitivity can be calculated as follows: S d =|f(X+Δe) d )-f(X)| / |Δ|, where e d Let f(X) be the unit vector of dimension d, Δ be the small perturbation value, and f(X) be the fitness function.

[0149] Based on computational sensitivity, the system selects the 30%-50% of dimensions with the greatest impact for perturbation. This selective perturbation strategy has two main advantages: first, it improves computational efficiency and reduces unnecessary perturbation calculations; second, it preserves good values ​​for some dimensions, avoiding complete destruction of the existing solution structure and achieving a balance between perturbation and preservation.

[0150] In practical implementation, the system can employ more efficient approximation methods to estimate dimensional sensitivity, such as utilizing gradient information of each dimension in historical iterations, or pre-determining key dimensions based on problem characteristics. For the multi-energy coordinated scheduling problem in microgrids, the energy storage charging and discharging power at different times is usually a highly sensitive dimension because it directly affects the system's power balance and economy.

[0151] By selectively altering dimensions, the chaotic perturbation mechanism can more effectively change the key dimensions of particles, enhance global search capabilities, and maintain the algorithm's convergence efficiency, thus balancing exploration and development.

[0152] Step S210: Determine whether the algorithm termination condition is met, such as reaching the maximum number of iterations or the change of the global optimal value for multiple consecutive generations being less than a preset threshold; if met, output the optimal solution, otherwise return to step S24 to continue iterating.

[0153] After completing a full iteration, the system determines whether the algorithm's termination conditions are met. Common termination conditions include: reaching the maximum number of iterations kmax (usually set to 100-200); the change in the global optimum over multiple consecutive iterations (usually 10-20 iterations) being less than a preset threshold ε (e.g., 0.1% of the objective function value); the global optimum meeting a predetermined accuracy requirement; and the computation time reaching a preset limit.

[0154] The system typically employs a multi-condition termination strategy, where the algorithm terminates when any one condition is met. This strategy ensures the algorithm has sufficient iterations to explore the solution space while stopping promptly upon finding a satisfactory solution, avoiding unnecessary computation. For applications with high real-time requirements, such as multi-energy coordinated dispatching in microgrids, computation time constraints are also an important termination condition.

[0155] If the termination condition is met, the system outputs the current global optimal solution as the final multi-energy optimal scheduling scheme for the microgrid, and proceeds to the subsequent control strategy design stage. If the termination condition is not met, the system returns to step S24 and enters the next iteration. Before returning, the system updates the individual optimal position and global optimal position of each particle to provide a basis for the next iteration.

[0156] To improve the robustness of the algorithm, the system also designed a premature convergence judgment mechanism: if the population diversity (such as the standard deviation of particle positions) is lower than the threshold and the global optimal solution has not improved for a long time, it is considered that the algorithm may fall into a premature convergence state. At this time, a re-initialization operation can be triggered to retain a small number of elite particles and re-initialize the remaining particles through a chaotic sequence to increase the population diversity and give the algorithm a "second chance" to explore new regions.

[0157] Through this robust termination judgment and anti-premature convergence mechanism, the system can improve the computational efficiency and reliability of the algorithm while ensuring solution quality, providing a high-quality optimization solution for multi-energy coordinated scheduling of microgrids.

[0158] like Figure 4 As shown, in step S3, based on the microgrid multi-energy optimal dispatch scheme, a gridded vector current control strategy is designed. Through fault type identification and coordinated control of positive and negative sequence current components, fault ride-through control parameters are obtained, specifically including:

[0159] Step S31: Collect the three-phase voltage signal of the microgrid, decompose the three-phase voltage into positive sequence, negative sequence and zero sequence components through symmetrical component transformation, calculate the positive sequence voltage sag depth and sequence component ratio, and obtain fault characteristic parameters;

[0160] In step S31, the three-phase voltage is decomposed into positive-sequence, negative-sequence, and zero-sequence components through symmetrical component transformation. The positive-sequence voltage descent depth and the ratio of the sequence components are calculated. These parameters are key features for fault type identification. To improve the reliability of identification, a low-pass filter is used to filter out high-frequency interference, and the fault characteristic is required to last for 2-5 ms before triggering fault judgment to avoid misjudgment caused by transient interference.

[0161] Step S32: Based on the fault characteristic parameters, design fault type discrimination criteria to distinguish between three-phase symmetrical faults, two-phase short-circuit faults, single-phase ground faults and two-phase ground faults, and obtain the fault type identification results;

[0162] In step S32, a fault type discrimination criterion is designed based on the sequence component characteristics: if the positive sequence voltage drops significantly and the ratio of negative sequence and zero sequence voltage is very small, it is judged as a three-phase symmetrical fault; if the positive sequence voltage drops significantly and the ratio of negative sequence voltage is large but the ratio of zero sequence voltage is very small, it is judged as a two-phase short circuit fault; if the positive sequence voltage drops significantly and the ratios of negative sequence and zero sequence voltage are both large, it is judged as a single-phase ground fault or a two-phase ground fault, depending on the magnitude of the zero sequence voltage ratio.

[0163] Step S33: Based on the fault type identification results, a positive sequence current component control strategy is adopted for symmetrical faults, and a positive and negative sequence current component coordinated control strategy is adopted for asymmetrical faults. The corresponding current reference value is calculated to obtain the vector current control command.

[0164] In step S33, different current control strategies are adopted for different types of faults. For symmetrical faults, a positive-sequence current component control strategy is adopted, which provides reactive power support and maintains active power output by adjusting the reference values ​​of the positive-sequence d-axis and q-axis currents. For asymmetrical faults, a positive- and negative-sequence current component coordinated control strategy is adopted, which achieves multi-objective control such as power fluctuation suppression, current balance, and voltage support by optimizing the positive- and negative-sequence current components.

[0165] Step S34: Based on the vector current control command, design a gridded control parameter matrix, and adaptively adjust the current control gain according to the fault severity and system state to obtain optimized fault ride-through control performance;

[0166] In step S34, a gridded control parameter matrix is ​​designed, discretizing the fault severity and system state into several intervals, and pre-setting optimal control parameters for each interval combination. During operation, the optimal control parameters are obtained through bilinear interpolation based on the real-time state, achieving precise adaptive adjustment of the control parameters and improving the accuracy and robustness of control.

[0167] Step S35: Perform current limiting protection processing on the vector current control command, and adopt a priority-based current component limiting strategy to prioritize the protection of key control objectives while meeting the inverter current capacity limit, thereby obtaining fault ride-through control parameters.

[0168] In step S35, a priority-based current component limiting strategy is designed to prioritize the achievement of key control objectives while meeting inverter current capacity limits. Under asymmetrical fault conditions, negative-sequence current control is crucial for suppressing power fluctuations and balancing three-phase currents, and therefore is given the highest priority; positive-sequence reactive current component is important for grid voltage support, and is given the second priority; although positive-sequence active current component is important for maintaining power output, it can be appropriately reduced during fault periods, and therefore is given the lowest priority.

[0169] like Figure 5 As shown, in step S4, based on the fault ride-through control parameters and the microgrid multi-energy optimal dispatch scheme, multi-energy coordinated dispatch control is executed to allocate power and adjust the operating status of the photovoltaic power generation system, energy storage system, and load system, thereby obtaining microgrid operation control commands, specifically including:

[0170] Step S41: Based on the microgrid multi-energy optimal dispatch scheme, design the day-ahead dispatch strategy, and use photovoltaic power generation forecast, load forecast and electricity price information to allocate preliminary dispatch plans to each energy unit to obtain the day-ahead dispatch plan;

[0171] In step S41, the day-ahead dispatch strategy allocates preliminary dispatch plans for each energy unit for the next 24 hours based on photovoltaic power generation forecasts, load forecasts, and electricity price information. This strategy primarily considers economic objectives, minimizing total operating costs while satisfying system constraints to form the day-ahead dispatch plan.

[0172] Step S42: Based on the day-ahead scheduling plan, design a real-time scheduling strategy, and use a rolling time-domain optimization method to update the scheduling plan according to real-time data to cope with prediction deviations and system fluctuations, and obtain real-time scheduling instructions;

[0173] In step S42, the real-time scheduling strategy employs a rolling time-domain optimization method, dynamically updating the scheduling plan based on real-time collected system data and short-term forecast results. Typically, an optimization window of 1-2 hours is used, with the plan rolling every 15-30 minutes to effectively address forecast biases and system fluctuations, thereby improving the accuracy and adaptability of scheduling.

[0174] Step S43: Based on real-time scheduling instructions, design an energy storage optimization operation strategy, take into account battery life and electricity price factors to reasonably arrange charging and discharging periods, maximize the economic benefits of energy storage, and obtain energy storage operation control parameters;

[0175] In step S43, the energy storage optimization operation strategy comprehensively considers battery life and electricity price factors to rationally arrange charging and discharging periods. This strategy avoids frequent deep charging and discharging, controls the charging and discharging rate, and extends battery life; at the same time, it utilizes the peak-valley electricity price difference, charging during off-peak hours and discharging during peak hours to maximize the economic benefits of energy storage.

[0176] Step S44: Based on the energy storage operation control parameters, design the coordinated control of photovoltaic power generation, energy storage system and controllable load, adjust the operation status of each energy unit according to the system status and optimization objectives, and obtain coordinated operation instructions;

[0177] The multi-energy coordinated control mechanism coordinates and adjusts the operating status of photovoltaic power generation, energy storage system, and controllable load according to system status and optimization objectives. When photovoltaic power generation fluctuates, the energy storage system provides rapid power compensation; when the system load is too high, the controllable load is appropriately reduced; when a grid fault occurs, each energy unit operates in coordination according to the fault ride-through control parameters to ensure system stability.

[0178] Step S45: Evaluate the economic benefits of the coordinated operation command, calculate the operating cost, battery loss cost and reliability cost, quantify the economic performance of the scheduling strategy, and obtain the microgrid operation control command.

[0179] The economic performance evaluation model calculates the economic performance of the dispatch strategy, including operating costs (such as generation costs and electricity purchase costs), battery depreciation costs (considering the impact of charge-discharge cycles and depth on battery life), and reliability costs (such as load shedding costs and system risk costs). By quantifying these costs, the economic performance of the dispatch strategy is evaluated, resulting in the final microgrid operation control commands.

[0180] like Figure 6 As shown, in step S5, the microgrid operation control commands are executed in layers. This involves the sensing layer collecting real-time data, the control layer processing scheduling commands, and the execution layer controlling the operation of energy units to obtain the stable operating state of the microgrid. Specifically, this includes:

[0181] Step S51: Based on the microgrid operation control command, collect real-time operation data of the microgrid through the energy metering device, environmental monitoring device and status monitoring device of the sensing layer, preprocess the real-time operation data and perform data fusion to obtain standardized system status data;

[0182] Step S52: Based on standardized system status data, the dispatch instructions are decomposed through the central controller and local controllers of the control layer, and the microgrid operation control instructions are decomposed into specific control instructions for each energy unit to obtain distributed control instructions;

[0183] Step S53: Based on distributed control commands, power control operations are performed through the photovoltaic inverter, energy storage converter and load controller in the execution layer to adjust the output power and operating status of each energy unit, thereby obtaining a stable operating state of the microgrid.

[0184] In step S51, the sensing layer collects real-time operating data of the microgrid through various sensors and metering devices, including electrical parameters (such as voltage, current, power, and frequency), environmental parameters (such as temperature, irradiance, and wind speed), and equipment status parameters (such as switch status and SOC value). The collected data is preprocessed, including filtering, calibration, and anomaly detection, and then multi-source data is integrated through data fusion technology to form standardized system status data.

[0185] In step S52, the control layer includes a central controller and multiple local controllers, forming a hierarchical control architecture. The central controller is responsible for global optimization and coordination, receiving system status data, executing optimization algorithms, generating scheduling strategies, and decomposing microgrid operation control commands into specific control commands for each energy unit. The local controllers are responsible for controlling individual energy units, receiving commands from the central controller, combining them with local status data, generating detailed control signals, and forming distributed control commands.

[0186] In step S53, the execution layer includes various power control devices, such as photovoltaic inverters, energy storage converters, and load controllers. These devices receive distributed control commands from the control layer, execute corresponding power control operations, and adjust the output power and operating status of each energy unit. The photovoltaic inverter controls the power output of the photovoltaic system, which may include maximum power point tracking or power limiting; the energy storage converter controls the charging and discharging power and direction of the batteries; and the load controller manages the connection and disconnection of controllable loads. Through these coordinated control operations, the stable operation of the microgrid is achieved.

[0187] The present invention also provides a microgrid multi-energy coordinated dispatch device, comprising:

[0188] The data acquisition module is used to acquire irradiance and temperature data of photovoltaic power generation systems, state of charge and charge / discharge efficiency data of energy storage systems, and historical electricity consumption data of load systems in microgrids.

[0189] The modeling and optimization module is used to perform mathematical modeling of the photovoltaic power generation system, energy storage system and load system respectively, establish a microgrid multi-energy coordinated scheduling optimization model with time-varying constraints, and use an improved particle swarm optimization algorithm to solve the optimization problem and obtain the optimal multi-energy scheduling scheme of the microgrid.

[0190] The control strategy module is used to design a gridded vector current control strategy based on the multi-energy optimal dispatch scheme of microgrids. It obtains fault ride-through control parameters through fault type identification and coordinated control of positive and negative sequence current components.

[0191] The scheduling execution module is used to execute multi-energy coordinated scheduling control based on fault ride-through control parameters and the optimal multi-energy scheduling scheme of the microgrid, and obtain microgrid operation control commands.

[0192] The system management module is used to execute microgrid operation control commands in a layered manner. Through the coordinated work of the sensing layer, control layer and execution layer, the stable operating status of the microgrid is obtained.

[0193] Example 1:

[0194] This invention's method is applied to a microgrid system in an industrial park. This microgrid comprises a 500kW photovoltaic power generation system, a 200kWh energy storage system, and an average load of 400kW. First, irradiance and temperature data for the photovoltaic power generation system, state of charge and charge / discharge efficiency data for the energy storage system, and historical electricity consumption data for the load system are acquired to establish a multi-energy coordinated scheduling optimization model for the microgrid. Then, an improved particle swarm optimization algorithm is used to solve the optimization problem, obtaining the optimal multi-energy scheduling scheme for the microgrid. Based on this scheme, a gridded vector current control strategy is designed to execute multi-energy coordinated scheduling control, and stable operation of the microgrid is achieved through hierarchical execution.

[0195] Through actual operation tests, compared with traditional methods, the method of the present invention reduces operating costs by about 15% in terms of economy, increases fault ride-through success rate by about 20% in terms of reliability, and reduces system fluctuations by about 25% in terms of stability, significantly improving the overall performance of microgrids.

[0196] Example 2:

[0197] The method of this invention is applied to a microgrid system in a residential community. This microgrid includes a 300kW photovoltaic power generation system, a 150kWh energy storage system, and an average load of 250kW. First, system data is acquired and an optimization model is established. Then, an improved particle swarm optimization algorithm is used for optimization. The chaotic sequence is initialized using a Logistic mapping, and an adaptive weighting strategy dynamically adjusts the inertial weights based on the iteration stage and particle fitness. A chaotic perturbation mechanism perturbs particles trapped in local optima. Based on the optimization results, a gridded vector current control strategy is designed, discretizing the fault severity and system state into an 8×6 state space grid, and pre-setting optimal control parameters for each grid point.

[0198] In actual operation, when a two-phase short-circuit fault occurs, the system quickly identifies the fault type and adopts a coordinated control strategy for positive and negative sequence current components, successfully achieving fault ride-through. Compared with traditional control methods, the method of this invention reduces power fluctuations by approximately 30%, improves voltage recovery speed by approximately 25%, and significantly enhances the fault adaptability of the microgrid.

[0199] Example 3:

[0200] The method of this invention is applied to a microgrid system in a commercial building. This microgrid includes a 400kW photovoltaic power generation system, a 250kWh energy storage system, and an average load of 350kW. For multi-energy coordinated dispatch and control, a day-ahead-real-time rolling optimization framework is adopted. Day-ahead dispatch is based on 24-hour forecast data to formulate a preliminary plan, while real-time dispatch uses a 1-hour optimization window that rolls over every 15 minutes. The energy storage optimization operation strategy considers battery life and peak-valley electricity price differences, charging during off-peak hours and discharging during peak hours, controlling the depth of charge / discharge in a single cycle to not exceed 60%.

[0201] In terms of layered execution, the perception layer uses a high-precision sensor network to collect system data in real time, the control layer adopts a distributed control architecture to coordinate various energy units, and the execution layer executes control commands precisely through standardized interfaces. Through a month-long operational test, the method of this invention improved photovoltaic utilization by approximately 12%, extended battery lifespan by approximately 15%, and reduced electricity costs by approximately 18% compared to traditional methods, achieving an optimal balance between economy and reliability.

[0202] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0203] It should be noted that those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of this invention. If such modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include such modifications and variations.

[0204] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the microgrid multi-energy coordinated scheduling method described in the above-described method embodiments. The storage medium can be either volatile or non-volatile computer-readable storage.

[0205] In addition, this disclosure also provides a computer program product, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the microgrid multi-energy coordinated scheduling method provided in any of the above embodiments of this disclosure. For details, please refer to the above method embodiments, which will not be repeated here.

[0206] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium, which can be a volatile or non-volatile computer-readable storage medium. In another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0207] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices and apparatuses described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0208] The units described 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.

[0209] In addition, the functional units in the various embodiments of this disclosure 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.

[0210] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0211] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A micro-grid multi-energy coordinated scheduling method, characterized in that, The method comprises the following steps: obtaining irradiance and temperature data of a photovoltaic power generation system, state of charge and charging and discharging efficiency data of an energy storage system, and historical power consumption data of a load system, respectively modeling the photovoltaic power generation system, the energy storage system and the load system, and establishing a micro-grid multi-energy coordinated scheduling optimization model containing time-varying constraints; based on the micro-grid multi-energy coordinated scheduling optimization model, an improved particle swarm algorithm is used for optimization and solution, wherein the improved particle swarm algorithm comprises chaotic sequence initialization, adaptive weight strategy and chaotic disturbance mechanism, and a micro-grid multi-energy optimal scheduling scheme is obtained; based on the micro-grid multi-energy optimal scheduling scheme, a grid vector current control strategy is designed, fault type identification and positive and negative sequence current component coordinated control are performed, and fault ride-through control parameters are obtained; based on the fault ride-through control parameters and the micro-grid multi-energy optimal scheduling scheme, multi-energy coordinated scheduling control is performed, power distribution and operating state adjustment are performed on the photovoltaic power generation system, the energy storage system and the load system, and micro-grid operation control instructions are obtained; the micro-grid operation control instructions are executed in layers, real-time data are collected through a perception layer, scheduling instructions are processed through a control layer, and energy unit operation is controlled through an execution layer, and a micro-grid stable operation state is obtained.

2. The method of claim 1, wherein, The photovoltaic power generation system, the energy storage system and the load system are respectively modeled, and a micro-grid multi-energy coordinated scheduling optimization model containing time-varying constraints is established, comprising: based on the irradiance and temperature data of the photovoltaic power generation system, a photovoltaic cell volt-ampere characteristic curve mathematical model is established, the photovoltaic maximum output power curve under different working conditions is calculated, and a photovoltaic power generation prediction model is obtained; based on the state of charge and charging and discharging efficiency data of the energy storage system, an energy storage system dynamic model is established, an SOC state equation and a power constraint equation are constructed, and an energy storage system operation constraint model is obtained; based on the historical power consumption data of the load system, a load demand model is established by using a prediction algorithm, key loads and adjustable loads are distinguished, and a load demand prediction model is obtained; based on the photovoltaic power generation prediction model, the energy storage system operation constraint model and the load demand prediction model, the photovoltaic power generation cost, the energy storage charging and discharging cost, the grid power purchase cost and the reliability index are comprehensively considered, a micro-grid economic dispatch multi-objective function is constructed, and the micro-grid multi-energy coordinated scheduling optimization model is obtained.

3. The method of claim 1, wherein, Based on the micro-grid multi-energy coordinated scheduling optimization model, the improved particle swarm algorithm comprises chaotic sequence initialization, comprising: based on the decision variables of the micro-grid multi-energy coordinated scheduling optimization model, a multi-dimensional particle coding scheme containing photovoltaic power distribution, energy storage charging and discharging power and grid exchange power is designed, the photovoltaic power distribution is coded as a T-dimensional vector, the energy storage charging and discharging power is coded as a T-dimensional vector, and the grid exchange power is coded as a T-dimensional vector, and a 3T-dimensional particle coding vector is obtained; based on the 3T-dimensional particle coding vector, a chaotic sequence is generated by using a Logistic mapping, and a chaotic sequence value is calculated by using a formula z(k+1)=μz(k)(1-z(k)), wherein μ=4, and a chaotic initialization sequence is obtained. The chaotic initialization sequence is subjected to particle swarm initialization, the chaotic sequence is mapped to particle position and velocity space, the diversity of initial solution is improved, and an initial particle swarm for solving a multi-energy optimization of a micro-grid is obtained.

4. The method of claim 1, wherein, Based on the improved particle swarm algorithm, the adaptive weight strategy comprises: Based on the iteration stage and convergence performance of the improved particle swarm algorithm, w = wmax- (wmax-wmin) × (k / kmax) 2 The inertia weight is calculated by formula, wherein wmax and wmin are upper and lower limits of the weight respectively, k is the current iteration number, kmax is the maximum iteration number, and a basic inertia weight is obtained. Based on the basic inertia weight and particle fitness evaluation, a weight adjustment is performed using a formula w=w×(1+α×(fbest-fi) / (fmax-fmin)), where fbest is a current global optimal fitness, fi is a current particle fitness, and an adaptive adjustment weight is obtained; Particle velocity update is performed on the adaptive adjustment weight, a particle with poor fitness obtains a larger inertia weight to enhance exploration ability, a particle with good fitness obtains a smaller inertia weight to enhance development ability, and a dynamic balance between global search and local development ability is obtained.

5. The method of claim 1, wherein, Based on the improved particle swarm algorithm, the chaotic disturbance mechanism comprises: The particle position change in the improved particle swarm algorithm is monitored, the particle position change amount in continuous N times of iterations is calculated, whether the particle satisfies the condition of ||X i k -X i k N||<ε, and a particle stagnation state judgment result is obtained. Based on the particle stagnation state judgment result, chaos disturbance is implemented on the particle trapped in local optimum, and the position of the particle is disturbed by using the formula of X i k = X i k + β × (Ub d - Lb d ) × (2z d - 1) formula, wherein β is a disturbance intensity coefficient, z d is a chaos sequence, and a disturbed particle position is obtained. The disturbed particle position is subjected to dimensional selective processing, each dimension sensitivity is calculated, 30%-50% of the most influential dimensions are selected for disturbance, and enhanced global search ability is obtained.

6. The method of claim 1, wherein, The grid vector current control strategy comprises: Three-phase voltage signals of the micro-grid are collected, the three-phase voltage is decomposed into positive, negative and zero sequence components through symmetrical component transformation, positive sequence voltage step depth AV1 and sequence component ratio are calculated, and fault characteristic parameters are obtained; Based on the fault characteristic parameters, a fault type discrimination criterion is designed to distinguish three-phase symmetric fault, two-phase short circuit fault, single-phase ground fault and two-phase ground fault, and a fault type identification result is obtained; Based on the fault type identification result, a positive sequence current component control strategy is used for symmetric fault, and a positive and negative sequence current component coordinated control strategy is used for asymmetric fault, corresponding current reference values are calculated, and vector current control instructions are obtained; Based on the vector current control instructions, a grid control parameter matrix is designed, current control gain is adaptively adjusted according to fault severity and system state, and optimized fault ride-through control performance is obtained; The vector current control instructions are subjected to current limiting protection processing, a current component limiting strategy based on priority is used to preferentially ensure key control targets while meeting the inverter current capacity limit, and the fault ride-through control parameters are obtained.

7. The method of claim 1, wherein, The fault type identification and positive and negative sequence current component coordinated control comprise: Three-phase voltage signals of the micro-grid are collected, positive, negative and zero sequence voltage components are calculated through symmetrical component transformation, and high-frequency interference is filtered out using a low-pass filter, and filtered sequence component signals are obtained; Based on the filtered sequence component signals, positive sequence voltage step depth AV1 and sequence component ratio |V2| / |V1|, |V0| / |V1| are calculated, a time delay judgment condition is set to require that the fault characteristics last for 2-5 ms, and a reliable fault type judgment result is obtained; Based on the fault type judgment result, Id + = Id0 + · (1 - k · AV1) and Iq + = k · AV1· In The positive sequence current reference value is calculated by the formula, the positive and negative sequence current coordination control is used to calculate the current reference value for asymmetric faults, and the current control strategy suitable for different fault types is obtained.

8. The method of claim 6, wherein, The grid control parameter matrix comprises: define a fault severity indicator FS and a system state indicator SS, calculate the fault severity indicator FS based on the positive sequence voltage sag depth, the negative sequence voltage ratio and the zero sequence voltage ratio, calculate the system state indicator SS based on the current active power, the energy storage state of charge and the system frequency, obtain a state evaluation parameter; based on the state evaluation parameter, discretize the fault severity indicator FS into n levels and discretize the system state indicator SS into m levels, construct an n x m state space grid, and obtain a grid state space; Based on the grid state space, preset an optimal control parameter set for each grid point, including positive sequence current controller parameters Kp + and Ki + , negative sequence current controller parameters Kp - and Ki - , target weights w1-w3 and reactive power injection coefficient k, to obtain a control parameter lookup table; based on the control parameter lookup table, calculate the corresponding grid position according to the real-time fault severity indicator FS and the system state indicator SS, obtain the optimal control parameter by the bilinear interpolation method, and obtain the self-adaptive adjustment control gain; The adaptively adjusted control gain is fine-tuned online, and control performance feedback is used as the basis for this adjustment. The formula is used to update the parameters, resulting in the gridded control parameter matrix.

9. The method of claim 1, wherein, the multi-energy coordinated dispatching control includes: based on the microgrid multi-energy optimal scheduling scheme, design a day-ahead scheduling strategy, allocate a preliminary scheduling plan for each energy unit using photovoltaic power generation prediction, load prediction and price information, and obtain a day-ahead scheduling plan; based on the day-ahead scheduling plan, design a real-time scheduling strategy, and update the scheduling plan according to real-time data using a rolling horizon optimization method to cope with prediction bias and system fluctuations, and obtain real-time scheduling instructions; based on the real-time scheduling instructions, design an energy storage optimization operation strategy, consider battery life and price factors to reasonably arrange the charging and discharging period, maximize the economic benefit of energy storage, and obtain energy storage operation control parameters; based on the energy storage operation control parameters, design the coordinated control of photovoltaic power generation, energy storage system and controllable load, adjust the operation state of each energy unit according to the system state and optimization target, and obtain coordinated operation instructions; evaluate the economic benefit of the coordinated operation instructions, calculate the operation cost, battery loss cost and reliability cost, quantify the economic performance of the scheduling strategy, and obtain the microgrid operation control instructions.

10. The method of claim 1, wherein, The hierarchical execution includes a perception layer, a control layer and an execution layer, including: based on the microgrid operation control instructions, collect microgrid real-time operation data through the energy metering device, environmental monitoring device and state monitoring device of the perception layer, preprocess and data fuse the real-time operation data, and obtain standardized system state data; based on the standardized system state data, decompose the scheduling instructions through the central controller and local controller of the control layer, decompose the microgrid operation control instructions into specific control instructions for each energy unit, and obtain distributed control instructions; based on the distributed control instructions, perform power control operations through the photovoltaic inverter, energy storage converter and load controller of the execution layer, adjust the output power and operation state of each energy unit, and obtain the stable operation state of the microgrid.

11. A micro-grid multi-energy coordinated dispatching device, characterized in that, including: a data acquisition module for acquiring irradiance and temperature data of a photovoltaic power generation system, state of charge and charging and discharging efficiency data of an energy storage system, and historical electricity consumption data of a load system in a microgrid; The modeling optimization module is configured to respectively model the photovoltaic power generation system, the energy storage system and the load system, establish a micro-grid multi-energy coordinated dispatching optimization model containing time-varying constraints, and solve the model by using an improved particle swarm algorithm to obtain a micro-grid multi-energy optimal dispatching scheme. The control strategy module is configured to design a grid vector current control strategy based on the micro-grid multi-energy optimal dispatching scheme, identify a fault type, and coordinate control of positive and negative sequence current components to obtain fault ride-through control parameters. The dispatching execution module is configured to execute multi-energy coordinated dispatching control based on the fault ride-through control parameters and the micro-grid multi-energy optimal dispatching scheme to obtain micro-grid operation control instructions. The system management module is configured to hierarchically execute the micro-grid operation control instructions, and obtain a micro-grid stable operation state through coordinated work of a perception layer, a control layer and an execution layer.

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