Operation control method and device of distributed energy power generation system

Abstract

The invention provides an operation control method and device for a distributed energy power generation system, and belongs to the technical field of energy power generation, and the method comprises the steps: obtaining an environment parameter in a preset time, a first electrical parameter of the distributed energy power generation system, and a second electrical parameter of load equipment; inputting the parameters into the three-dimensional dynamic digital twin, and obtaining an influence source of voltage fluctuation, a sensitive node of the voltage fluctuation and an influence level corresponding to the sensitive node according to a simulation result; adjusting the duty ratio of each port of the multi-port solid-state transformer based on the influence source of the voltage fluctuation, the sensitive node of the voltage fluctuation and the influence level corresponding to the sensitive node; and regulating and controlling the operation state of the distributed energy power generation system based on the duty ratio of each port of the multi-port solid-state transformer. According to the invention, when the voltage fluctuates, the duty ratio of each port can be rapidly adjusted, the voltage is kept stable, and the power utilization safety of the load is ensured.

Description

Technical Field

[0001] The present application belongs to the field of energy generation technology, and more specifically, relates to an operation control method and device for a distributed energy generation system. Background Art

[0002] As the global energy transition accelerates, distributed energy generation systems have become a core component of smart grids and microgrids due to their advantages of cleanliness, flexibility, and local consumption. However, the complementary nature of distributed energy sources (such as the intermittent power supply of wind and photovoltaic power) and their multi-node interconnected structure make system operation susceptible to environmental parameters and load equipment, leading to abnormal voltage fluctuations and seriously compromising power supply quality and equipment safety. Summary of the Invention

[0003] To solve the above problems, the present application provides an operation control method and device for a distributed energy generation system, which can quickly reduce voltage fluctuations and ensure smooth operation of the system by adjusting the duty cycle of each port of the solid-state transformer.

[0004] In a first aspect of an embodiment of the present application, a method for controlling the operation of a distributed energy generation system is provided. The method is applied to a distributed energy generation system, wherein the system includes a multi-port solid-state transformer and a plurality of energy modules of different energy types; each input port of the multi-port solid-state transformer is connected to an energy module; and the method includes: Obtaining environmental parameters within a preset time, first electrical parameters of the distributed energy generation system, and second electrical parameters of the load device; the first electrical parameters include electrical parameters corresponding to multiple energy modules of different energy types; Inputting the environmental parameters, the first electrical parameters, and the second electrical parameters into the three-dimensional dynamic digital twin, and obtaining the influencing source of the voltage fluctuation, the sensitive nodes of the voltage fluctuation, and the impact levels corresponding to the sensitive nodes based on the simulation results of the three-dimensional dynamic digital twin; Based on the influencing sources of voltage fluctuations, the sensitive nodes of voltage fluctuations and the impact levels corresponding to the sensitive nodes, the operation strategy of the distributed energy generation system is determined to be adjusting the duty cycle of each port of the multi-port solid-state transformer; The operating status of the distributed energy generation system is regulated based on the operating strategy of the distributed energy generation system.

[0005] In a second aspect of an embodiment of the present application, an operation control device for a distributed energy generation system is provided. The device is applied to a distributed energy generation system, wherein the system includes a multi-port solid-state transformer and a plurality of energy modules of different energy types; each input port of the multi-port solid-state transformer is connected to an energy module; the device includes: The data acquisition unit is configured to acquire environmental parameters, first electrical parameters of the distributed energy generation system, and second electrical parameters of the load device within a preset time period, wherein the first electrical parameters include electrical parameters corresponding to energy modules of different energy types respectively; The first data processing unit is configured to input the environmental parameters, the first electrical parameters, and the second electrical parameters into a three-dimensional dynamic digital twin, and obtain an influence source of voltage fluctuation, a sensitive node of voltage fluctuation, and an influence level corresponding to the sensitive node according to a simulation result of the three-dimensional dynamic digital twin. The second data processing unit is configured to determine, based on the influence source of voltage fluctuation, the sensitive node of voltage fluctuation, and the influence level corresponding to the sensitive node, an operation strategy of the distributed energy generation system as adjusting duty cycles of each port of the multi-port solid-state transformer. The control unit is configured to control an operation state of the distributed energy generation system based on the operation strategy of the distributed energy generation system.

[0006] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the operation control method of the distributed energy generation system are implemented.

[0007] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the operation control method of the distributed energy generation system are implemented.

[0008] The operation control method and device of the distributed energy generation system provided by the embodiments of the present application have the following advantages: The embodiments of the present application reproduce the operation state of the distributed energy system through the three-dimensional dynamic digital twin, effectively predict the voltage fluctuation amplitude, duration, and propagation path within the first time period according to the input multi-source data, and then quickly locate the influence source and the sensitive node, and evaluate the influence level of the sensitive node. Based on the influence source, the sensitive node of voltage fluctuation, and the influence level of the sensitive node, the duty cycles of each port of the multi-port solid-state transformer are dynamically adjusted to balance the power distribution, stabilize the load-side voltage, and reduce the influence of voltage fluctuation on the load-side electrical equipment. The embodiments of the present application can significantly improve the power supply quality of the distributed energy system, maintain the stable operation and anti-interference ability of the system, and optimize the energy utilization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0010] Figure 1 A schematic diagram of the structure of a distributed energy generation system provided in one embodiment of the present application; Figure 2 A flow chart of an operation control method of a distributed energy generation system provided in one embodiment of the present application; Figure 3 A structural block diagram of an operation control device for a distributed energy generation system provided in one embodiment of the present application; Figure 4 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0011] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0012] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0013] Figure 1 A structural diagram of a distributed energy generation system 100 provided in one embodiment of the present application. The distributed energy generation system 100 is used to supply power to a load device 200. The load device 200 can be an electrical device in the power grid, or a single electrical device that exists independently of the power grid. The power demand of these devices is fluctuating and non-constant. For example, the load of the civil power grid has obvious seasonal and periodic characteristics. The power consumption in summer and daytime is significantly greater than that in winter and nighttime, showing an obvious peak and trough effect. Similarly, a single electrical device may have various states such as startup, stable operation, and standby, and the power load in different states will also be different. Therefore, the above-mentioned load device 200 is a fluctuating load, and the distributed energy generation system 100 is required to meet this fluctuation while saving costs as much as possible.

[0014] For this purpose, refer to Figure 1The present application provides a distributed energy generation system 100, which includes a multi-port solid-state transformer 110, a control module 120, and multiple energy modules of different energy types. The multiple energy modules of different energy types include, but are not limited to, at least two of a photovoltaic power generation module 130, a wind power generation module 140, a gas power generation module 150, a fuel oil power generation module 160, or an energy storage module 170. Figure 1 The system shown in the figure includes five energy modules of different energy types: photovoltaic power generation module 130, wind power generation module 140, gas power generation module 150, oil power generation module 160 and energy storage module 170. Among them, each input port of the multi-port solid-state transformer 110 is connected to an energy module, that is, the multiple input ports of the multi-port solid-state transformer 110 are respectively connected to the photovoltaic power generation module 130, wind power generation module 140, gas power generation module 150, oil power generation module 160 and energy storage module 170, the control port of the multi-port solid-state transformer 110 is connected to the control module 120, and the output port is connected to the load device 200. The control module 120 is also connected to multiple data acquisition devices ( Figure 1 The distributed energy generation system 100 is connected to a plurality of data acquisition devices (not shown) for collecting environmental data, first electrical parameters of the distributed energy generation system 100, and second electrical parameters of the load device 200. The first electrical parameters include electrical parameters corresponding to multiple energy modules of different energy types. The distributed energy generation system 100 can stably supply the load device 200 with the electrical energy output by the energy modules.

[0015] The photovoltaic power generation module 130 is used to convert solar energy into electrical energy using the photovoltaic effect. Photovoltaic power generation utilizes solar panels composed of multiple solar cells containing photovoltaic materials. Multiple photovoltaic modules containing solar panels are connected in series and parallel, along with a supporting structure and inverter, to form a photovoltaic array.

[0016] The wind power generation module 140 converts wind energy into mechanical energy through aerodynamic principles, and then converts it into electrical energy through a generator. It mainly consists of a wind wheel unit, a transmission unit, a generator and a control unit.

[0017] The gas-fired power generation module 150 uses natural gas, biogas, and other combustible gases as fuel, generating electricity through an internal combustion engine or gas turbine that drives a generator. It boasts fast startup and low pollution. The gas-fired power generation module 150 primarily consists of a gas supply unit, a combustion unit, a generator, and a waste heat recovery unit.

[0018] The fuel-fired power generation module 160 uses fuel (such as diesel, gasoline, or heavy oil) as fuel. It converts the fuel's chemical energy into mechanical energy through a power unit such as an internal combustion engine or gas turbine, and then converts the mechanical energy into electrical energy through a generator. This module features rapid startup, a small footprint, and relatively easy fuel acquisition, making it widely used in scenarios such as emergency power supply.

[0019] The energy storage module 170 is connected to the power grid and is used to store electrical energy. The energy storage module 170 can be composed of one or more of the following: a lithium-ion battery; a lead-acid battery; or a supercapacitor.

[0020] Corresponding to a distributed energy generation system in the above embodiment, Figure 2 This is a flow chart of the operation control method of the distributed energy generation system provided in one embodiment of the present application, with reference to Figure 2 , the operation control method may include S101~S104.

[0021] S101: Acquire environmental parameters within a preset time, first electrical parameters of a distributed energy generation system, and second electrical parameters of a load device.

[0022] In this embodiment, the preset time refers to a time interval set in advance before performing the data collection operation, which is used to limit the time range of the environmental parameters, the first electrical parameters, and the second electrical parameters that need to be obtained, to ensure the timeliness of the data and its relevance to the current operating status. For example, it can be set to "within the past 10 minutes", and the specific duration can be set according to seasonal characteristics, periodic characteristics of the power load, or other conditions. The parameter data in this embodiment can be collected by multiple data collection devices, wherein the multiple data collection devices include temperature and humidity sensors, light intensity sensors, and anemometers that can collect environmental data; voltage sensors, current sensors, and temperature sensors that can collect the first electrical parameters of the distributed energy system; and power monitoring meters that can collect the second electrical parameters of the load device.

[0023] The environmental parameters in this embodiment are external conditions that affect the operation of the distributed energy system, including but not limited to light intensity, wind speed, temperature, humidity, and pressure. The first electrical parameter reflects the operating status of the energy modules within the system, including but not limited to the output voltage and output power of each energy module. The second electrical parameter describes the power demand and power quality on the load side, including but not limited to the harmonic components of voltage / current, active power, reactive power, and power factor. Load devices on the load side include industrial loads, commercial loads, residential loads, and special loads (such as hospitals, communication base stations, etc.).

[0024] S102: input the environmental parameters, the first electrical parameters and the second electrical parameters into the three-dimensional dynamic digital twin, and obtain the influence source of voltage fluctuation, the sensitive node of voltage fluctuation and the influence level corresponding to the sensitive node according to the simulation result of the three-dimensional dynamic digital twin.

[0025] In the embodiment, the three-dimensional dynamic digital twin is a virtual mirror image of the physical distributed energy generation system. By fusing multi-source data (environmental parameters, first electrical parameters and second electrical parameters), physical mechanism models (such as electromagnetic transient simulation models) and data-driven models, real-time simulation and prediction of the system operation state are realized. The electromagnetic transient simulation model is a mathematical model for simulating electromagnetic phenomena in the generation system in a short time scale (microsecond to millisecond level), which is used to reproduce the dynamic behavior of power equipment in the transient process (such as voltage surge / drop). The data-driven model is a model for obtaining predicted data within a first time period based on the above multi-source data, and obtaining the influence source of voltage fluctuation, the sensitive node of voltage fluctuation and the influence level corresponding to the sensitive node according to the predicted data.

[0026] That is, after inputting the environmental parameters, the first electrical parameters and the second electrical parameters into the three-dimensional dynamic digital twin, the digital twin reproduces the voltage fluctuation phenomenon of the distributed energy generation system through virtual simulation, predicts the voltage fluctuation amplitude, duration and propagation path within a first time period based on multi-source data, and finally obtains the influence source of voltage fluctuation and the sensitive node of voltage fluctuation based on the above predicted data. According to the device characteristics corresponding to the sensitive node and the voltage fluctuation amplitude, the influence level corresponding to the sensitive node can be obtained. The above parameters are beneficial to timely adjust the control strategy of the system, reduce the influence of voltage fluctuation on the load device, and significantly improve the power supply quality and operation stability of the system.

[0027] S103: based on the influence source of voltage fluctuation, the sensitive node of voltage fluctuation and the influence level corresponding to the sensitive node, the operation strategy of the distributed energy generation system is determined as adjusting the duty cycle of each port of the multi-port solid-state transformer.

[0028] In the embodiment, the influence source of voltage fluctuation can be an internal influence source or an external influence source. The internal influence source can be the switching operation of the energy module and the multi-port solid-state transformer, and the external influence source can be the mutation of environmental parameters, the increase of load devices, etc.

[0029] For example, if the influence source of voltage fluctuation is a photovoltaic power generation module (when sunlight is blocked by clouds, the power output of the photovoltaic power generation module is affected), the output power of other energy modules can be increased or other energy modules can be used to provide energy for the load. If the influence source of voltage fluctuation is a wind power generation module, the same applies. The influence source of voltage fluctuation can also be a load device. When a high-power device is suddenly connected to the load device for a period of time, the voltage required on the load side increases.

[0030] Voltage fluctuation-sensitive nodes are the locations in the system that respond most significantly to voltage fluctuations, typically manifesting as large voltage deviation amplitudes, high fluctuation frequencies, or long durations. Voltage fluctuation-sensitive nodes can be port nodes of multi-port solid-state transformers, critical load busbar nodes, edge load nodes, or energy storage module interface nodes, among others. Based on the voltage fluctuation-sensitive nodes, the device corresponding to the node can be accurately identified, and the node's impact level can be calculated based on the node's importance coefficient and the corresponding voltage fluctuation amplitude. After determining the source of voltage fluctuations, the voltage fluctuation-sensitive nodes, and the corresponding impact level of the sensitive nodes, a "duty cycle-port power-node voltage" mapping relationship can be established using electromagnetic transient models or historical data fitting methods. This allows for timely adjustments to the output power of each energy module and the duty cycle of each port connected to the multi-port solid-state transformer to ensure stable voltage on the load side.

[0031] S104: Regulating the operating state of the distributed energy generation system based on the operating strategy of the distributed energy generation system.

[0032] In this embodiment, after determining the operational strategy for the distributed energy generation system, the control module sends instructions to a multi-port solid-state transformer to dynamically adjust the conduction time ratio (i.e., duty cycle) of the switching devices at each port, thereby changing the power distribution at each port. For example, if a sudden drop in light intensity at a photovoltaic power generation module causes a low primary load bus voltage (a sensitive node with a high impact level), the duty cycle of the photovoltaic port of the multi-port solid-state transformer needs to be reduced or set to zero, stopping the photovoltaic power generation module from supplying power to the load device and reducing voltage fluctuations. Simultaneously, the duty cycle of the gas-fired power generation port is increased to compensate for the power shortfall. If an increase in wind speed causes a DC bus overvoltage at a wind power generation module, the duty cycle of the wind power generation port is reduced to limit input, and the charging duty cycle of the energy storage port is increased to absorb excess power.

[0033] From the above, it can be concluded that the embodiment of the present application reproduces the operating status of the distributed energy system through a three-dimensional dynamic digital twin, and effectively predicts the voltage fluctuation amplitude, duration and propagation path within the first time period based on the input multi-source data, thereby quickly locating the influencing source and sensitive nodes, and evaluating the impact level of the sensitive nodes. Based on the influencing source, the sensitive node of the voltage fluctuation and the impact level of the sensitive node, the duty cycle of each port of the multi-port solid-state transformer is dynamically adjusted to achieve balanced power distribution, stabilize the load-side voltage, and reduce the impact of voltage fluctuations on load-side electrical equipment. The embodiment of the present application can significantly improve the power supply quality of the distributed energy system, maintain the smooth operation and anti-interference ability of the system, and optimize energy utilization efficiency.

[0034] In one embodiment of the present application, regulating the operating state of the distributed energy generation system based on the operating strategy of the distributed energy generation system includes: The sources of voltage fluctuation, the sensitive nodes of voltage fluctuation, and the corresponding impact levels of the sensitive nodes are input into the reinforcement learning model to obtain the power distribution of each port of the multi-port solid-state transformer; Adjust the operation strategy of the distributed energy generation system based on the power allocation to obtain the target optimization strategy; The operating status of the distributed energy generation system is regulated based on the target optimization strategy.

[0035] In this embodiment, the distributed energy generation system's initial operational strategy may prioritize addressing voltage fluctuations, but may neglect other issues. For example, to reduce losses in multi-port solid-state transformers, the initial strategy may limit the duty cycle adjustment range, but this may not completely suppress voltage fluctuations. Optimization is required to find a global optimal solution among multiple objectives, such as "voltage quality," "equipment lifespan," and "system efficiency." Therefore, the initial strategy needs to be optimized.

[0036] In this embodiment, the sources of voltage fluctuation, the sensitive nodes of voltage fluctuation, and the impact levels corresponding to the sensitive nodes are input into the reinforcement learning model to obtain the power distribution of each port of the multi-port solid-state transformer, including: The sources of voltage fluctuation, the sensitive nodes of voltage fluctuation, and the corresponding impact levels of the sensitive nodes are input into the global coordinator of the reinforcement learning model, the evaluation value of the objective function is calculated, and the combination of weight coefficients that minimizes the evaluation value is determined as the target weight coefficient combination; Determine the priority of each port of a multi-port solid-state transformer based on a combination of target weight coefficients; The priority of each port of the multi-port solid-state transformer and the real-time status information of each port are input into the local controller of the reinforcement learning model to obtain the power distribution of each port of the multi-port solid-state transformer; the real-time status information of each port includes the electrical parameters of each energy module.

[0037] In this embodiment, the reinforcement learning model is a machine learning model that achieves dynamic optimization through "state perception-action decision-making-reward feedback." Its core is to gradually adjust strategies to maximize long-term cumulative rewards through interactive learning between the agent and the environment. In this embodiment, the reinforcement learning model is a two-layer reinforcement learning structure (global coordinator + local controller) used to solve the dynamic optimization problem of distributed energy generation systems across multiple objectives such as "voltage quality," "equipment lifespan," and "system efficiency." The model learns the mapping relationship between "influence source-sensitive node-influence level" and "power allocation" to ultimately generate an optimal duty cycle adjustment strategy for multi-port solid-state transformers.

[0038] The global coordinator takes as input the sources of voltage fluctuations, sensitive nodes, and corresponding impact levels output by the three-dimensional dynamic digital twin. It uses total power balance constraints and equipment safety constraints as global constraints to output the priorities of each port in a multi-port solid-state transformer. The total power balance constraint requires that the sum of the output power / voltage of the multiple energy modules in the system equals the total power / voltage requirements of the load devices. Equipment safety constraints require that each energy module remain within a safe range. For example, the energy storage module's state of charge (SOC) safety range is [SOC1, SOC2] (e.g., [20%, 80%)), and the gas-fired power generation module's minimum operating power exceeds a preset percentage of the rated power (e.g., 10%).

[0039] The optimization goal of the global coordinator is to minimize the evaluation value of the objective function. The expression of the objective function is:

[0040] in, is the objective function, is the weight coefficient of voltage stability, is the total number of sensitive nodes, is the actual voltage deviation of the i-th sensitive node, is the maximum voltage deviation of the i-th sensitive node, is the weight coefficient of power balance, is the total number of ports, is the actual charge and discharge power of the jth port, is the rated power of the jth port.

[0041] When the evaluation value of the objective function is minimum, we can get and The value of and The value of determines the contribution of each port to the objective function evaluation value. The priority of each port in a multi-port solid-state transformer is determined based on the order of the contribution values. For example, a multi-port solid-state transformer includes a photovoltaic power generation port, an energy storage port, and a gas power generation port. The contributions of these ports to the objective function evaluation value are a1, a2, and a3, respectively, and a2 > a1 > a3. Therefore, the priority of the energy storage port is > the priority of the photovoltaic power generation port > the priority of the gas power generation port.

[0042] The local controller takes the priority of each port of the multi-port solid-state transformer and the real-time state information of each port as input, encodes the multi-dimensional real-time state information of each port into a feature vector, and outputs the power distribution of each port through a deep reinforcement learning strategy network (such as DDPG or PPO). For example, when the weight coefficient of voltage stability is greater than the weight coefficient of power balance, the local controller preferentially adjusts the duty cycle of the photovoltaic power generation port to suppress voltage fluctuations, and the energy storage port adjusts the charging / discharging power according to the SOC; at the same time, the action is corrected through a hard constraint (such as a duty cycle range limit, which cannot exceed the range [0, 1]) to ensure compliance with the physical limitations of the equipment; and finally the power distribution of each port (i.e. the corrected action) is output.

[0043] The embodiment obtains the power distribution of each port based on the global coordinator and the local controller, and adjusts the duty cycle of each port to obtain a target optimization strategy according to the power distribution of each port. Based on the target optimization strategy, the running state of the distributed energy generation system is regulated and controlled, which provides a clear control direction for the dynamic and adaptive control of the multi-port solid-state transformer in a complex power grid environment, and improves the stability of the system.

[0044] In an embodiment of the present application, the operation control method further comprises: If the power distribution output by the local controller does not satisfy the constraint condition, the power distribution is corrected by a gradient descent method to obtain a corrected power distribution.

[0045] In the embodiment, the gradient descent method is a local optimization algorithm for correcting the power distribution. The constraint condition refers to the physical operation limit or safety threshold of the multi-port solid-state transformer and each energy module. The constraint condition includes but is not limited to the duty cycle range of each port and the power threshold of each energy module, etc. The duty cycle range of each port refers to the duty cycle of each port of the multi-port solid-state transformer, which needs to be limited in the interval [0, 1] (determined by the hardware circuit characteristics, and exceeding it will cause equipment damage); the power limit refers to the charging / discharging power of each energy module (such as a photovoltaic power generation module or an energy storage module) cannot exceed its rated power.

[0046] If the power distribution output by the local controller does not satisfy the constraint condition, , first, the power distribution output by the local controller is checked port by port to determine whether the above constraint condition is violated. For the power distribution that violates the constraint condition, a loss function of the deviation of the power distribution from the constraint boundary is constructed. For example, if the constraint condition is the power threshold of each energy module, the loss function is:

[0047] is the maximum power threshold of the jth port, and the total loss function is the sum L of the loss functions of each port.

[0048] The steps of gradient calculation and iterative correction are: Step 1: Calculate the gradient of the total loss function L with respect to the power allocation P :

[0049] Step 2: Adjust the power distribution in the opposite direction of the gradient:

[0050] in is the learning rate (controls the adjustment step size to avoid overcorrection).

[0051] Step 3: Repeat steps 1 and 2 until the corrected power distribution is obtained. All constraints are met, that is, L is 0.

[0052] will satisfy the constraints As the final power distribution.

[0053] In this embodiment, when the power allocation output by the local controller violates the constraint conditions, the gradient of the loss function composed of the "deviation between the power allocation amount and the constraint boundary" is calculated, and the power allocation amount is gradually adjusted in the opposite direction of the gradient, so that the corrected power allocation amount finally meets the constraint conditions, thereby ensuring stable operation of the system.

[0054] In one embodiment of the present application, a method for identifying nodes sensitive to voltage fluctuations includes: Calculate the sensitivity coefficient of each node voltage to the power of each port of the multi-port solid-state transformer, and determine the absolute value of the voltage deviation amplitude and the duration of the fluctuation in the disturbance scenario based on the influencing source of the voltage fluctuation; The sensitivity coefficient, the absolute value of the voltage deviation amplitude, and the duration of the fluctuation are input into the node evaluation formula to obtain the evaluation score of each node; The evaluation score of each node is compared with a first preset threshold value, and the node sensitive to voltage fluctuation is determined according to the comparison result.

[0055] In this embodiment, the sensitivity coefficient quantifies the sensitivity of the node voltage to changes in the power at each port of a multi-port solid-state transformer. It reflects the rate of change of the node voltage when the port power fluctuates. A larger coefficient indicates that the node voltage is more significantly affected by the corresponding port power. The node evaluation formula is a mathematical expression used to comprehensively quantify the sensitivity of the node to voltage fluctuations and can be expressed as:

[0056] represents the evaluation score of the i-th node, 、 and They represent the sensitivity coefficient, absolute value of voltage deviation amplitude and duration of fluctuation corresponding to the i-th node, respectively. 、 and is a normalized value, dimensionless, , and They are 、 and The corresponding weight coefficient.

[0057] In this embodiment, if there is a node whose evaluation score is greater than a first preset threshold among the nodes, all nodes whose evaluation scores are greater than the first preset threshold are regarded as nodes sensitive to voltage fluctuations.

[0058] From the above, it can be concluded that by comprehensively considering the sensitivity coefficient, voltage deviation amplitude, and duration, a quantitative evaluation of sensitive nodes can be achieved, thus avoiding misjudgment caused by a single indicator and ensuring that the identified nodes are truly key locations that respond significantly to voltage fluctuations, providing an important basis for subsequent regulation.

[0059] In one embodiment of the present application, a method for determining an impact level corresponding to a sensitive node includes: Determining the level of the absolute value of the voltage deviation amplitude based on a preset standard; Determine the level of sensitive nodes based on their importance; The level of the sensitive node is multiplied by the level of the absolute value of the voltage deviation amplitude to obtain the impact level corresponding to the sensitive node.

[0060] In this embodiment, first, a voltage deviation amplitude range can be pre-set (for example, 0-5V is Level 1, 5-10V is Level 2, etc.). Based on this standard, the absolute value of the voltage deviation amplitude can be determined to quantify the intensity of voltage fluctuations. Second, the sensitive node's individual level is preset based on its importance (e.g., whether it is a critical load node such as a hospital or data center) (for example, ordinary nodes are Level 1, important nodes are Level 2, and core nodes are Level 3). Finally, the sensitive node's individual level is multiplied by the absolute value of the voltage deviation amplitude. The result is the corresponding impact level of the sensitive node, which comprehensively reflects the impact of the node's importance and the intensity of the voltage fluctuation.

[0061] From the above, it can be concluded that this embodiment achieves accurate assessment of the impact of sensitive nodes by hierarchically quantifying the voltage deviation amplitude and node importance, avoiding the limitation of single-dimensional judgment.

[0062] In an embodiment of the present application, the operation control method further comprises: Based on the load type of each sensitive node connection and the position of the sensitive node in the topological relationship of the three-dimensional dynamic digital twin, the importance of the node is obtained.

[0063] In the present embodiment, the demand for power supply stability of different loads is significantly different: for example, life support equipment in hospitals, core machine rooms of communication base stations, etc. belong to primary load, and have extremely low tolerance to voltage fluctuation, and the node importance of the connection is high. The sensitivity of ordinary residential lighting load and the like to fluctuation is low, and the corresponding node importance is low. From the topological relationship of the three-dimensional dynamic digital twin, the position of the sensitive node in the system determines its influence range: when the sensitive node is the node at the position of the energy module and the main load hub, once voltage fluctuation occurs, it may cause serious consequences of load damage, and therefore the importance is high. The fluctuation influence range of the end branch node is limited, and the importance is relatively low.

[0064] The present embodiment can objectively quantify the importance of each sensitive node by comprehensively considering the above two dimensions.

[0065] The operation control method of the distributed energy generation system corresponding to the above embodiment, Figure 3 The structure block diagram of the operation control device of the distributed energy generation system provided in an embodiment of the present application is shown in FIG. 2. For the sake of convenience, only the parts related to the embodiments of the present application are shown. For reference Figure 3 The operation control device 20 of the distributed energy generation system is applied to a distributed energy generation system, which includes a multi-port solid-state transformer and a plurality of energy modules of different energy types; each input port of the multi-port solid-state transformer is connected with an energy module. The device 20 includes a data acquisition unit 21, a first data processing unit 22, a second data processing unit 23 and a control unit 24.

[0066] The data acquisition unit 21 is configured to acquire environmental parameters, first electrical parameters of the distributed energy generation system and second electrical parameters of the load device within a preset time; the first electrical parameters include electrical parameters corresponding to each of the plurality of energy modules of different energy types; The first data processing unit 22 is configured to input the environmental parameters, the first electrical parameters and the second electrical parameters into a three-dimensional dynamic digital twin, and obtain an influence source of voltage fluctuation, a sensitive node of voltage fluctuation and an influence level corresponding to the sensitive node according to a simulation result of the three-dimensional dynamic digital twin; The second data processing unit 23 is configured to determine, based on the influence source of voltage fluctuation, the sensitive node of voltage fluctuation and the influence level corresponding to the sensitive node, an operation strategy of the distributed energy generation system as adjusting the duty cycle of each port of the multi-port solid-state transformer; The regulating unit 24 is configured to regulate the operation state of the distributed energy generation system based on the operation strategy of the distributed energy generation system.

[0067] In an embodiment of the present application, the regulating unit 24 is specifically configured to: input the influence source of the voltage fluctuation, the sensitive node of the voltage fluctuation, and the influence level corresponding to the sensitive node into the reinforcement learning model to obtain the power distribution amount of each port of the multi-port solid-state transformer; adjust the operation strategy of the distributed energy generation system based on the power distribution amount to obtain a target optimization strategy; regulate the operation state of the distributed energy generation system based on the target optimization strategy.

[0068] In an embodiment of the present application, the regulating unit 24 is specifically configured to: input the influence source of the voltage fluctuation, the sensitive node of the voltage fluctuation, and the influence level corresponding to the sensitive node into the global coordinator of the reinforcement learning model, calculate the evaluation value of the target function, and determine the combination of the weight coefficients that minimizes the evaluation value as the target weight coefficient combination; determine the priority of each port of the multi-port solid-state transformer based on the target weight coefficient combination; input the priority of each port of the multi-port solid-state transformer and the real-time state information of each port into the local controller of the reinforcement learning model to obtain the power distribution amount of each port of the multi-port solid-state transformer; the real-time state information of each port includes the electrical parameters of each energy module.

[0069] In an embodiment of the present application, the second data processing unit 23 is specifically configured to: calculate the sensitivity coefficient of each node voltage to the power of each port of the multi-port solid-state transformer, and determine the absolute value of the voltage deviation amplitude and the fluctuation duration under the disturbance scenario based on the influence source of the voltage fluctuation; input the sensitivity coefficient, the absolute value of the voltage deviation amplitude, and the fluctuation duration into the node evaluation formula to obtain the evaluation score of each node; compare the evaluation score of each node with the first preset threshold respectively, and determine the sensitive node of the voltage fluctuation according to the comparison result.

[0070] In an embodiment of the present application, the second data processing unit 23 is specifically configured to: determine the level of the absolute value of the voltage deviation amplitude based on a preset standard; determine the level of the sensitive node based on the importance of the sensitive node; multiply the level of the sensitive node and the level of the absolute value of the voltage deviation amplitude to obtain the influence level corresponding to the sensitive node.

[0071] In one embodiment of the present application, the second data processing unit 23 is further configured to: Based on the load type connected to each sensitive node and the position of the sensitive node in the topological relationship of the three-dimensional dynamic digital twin, the importance of the node is obtained.

[0072] In one embodiment of the present application, the objective function is expressed as:

[0073] in, is the objective function, is the weight coefficient of voltage stability, is the total number of sensitive nodes, is the actual voltage deviation of the i-th sensitive node, is the maximum voltage deviation of the i-th sensitive node, is the weight coefficient of power balance, is the total number of ports, is the actual charge and discharge power of the jth port, is the rated power of the jth port.

[0074] In one embodiment of the present application, the control unit 24 is further configured to: If the power distribution output by the local controller does not meet the constraint conditions, the power distribution is corrected by the gradient descent method to obtain the corrected power distribution.

[0075] In one embodiment of the present application, the plurality of energy modules of different energy types include at least two of the following: Photovoltaic power generation module, wind power generation module, gas power generation module, oil power generation module or energy storage module.

[0076] See also Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 4 The electronic device 300 in the illustrated embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each unit in the above-mentioned device embodiments, such as the functions of the data acquisition unit 21, the first data processing unit 22, the second data processing unit 23, and the control unit 24.

[0077] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0078] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0079] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a nonvolatile random access memory.

[0080] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present application can execute the implementation method described in the operation control method of the distributed energy generation system provided in the embodiment of the present application, and can also execute the implementation method of the electronic device described in the embodiment of the present application, which will not be repeated here.

[0081] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0082] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0083] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0084] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0086] The units described as separate components may or may not be physically separate, and 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 may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0087] In addition, each of the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0088] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for controlling the operation of a distributed energy generation system, characterized in that: Applied to a distributed energy generation system, the system comprises a multi-port solid-state transformer and a plurality of energy modules of different energy types; each input port of the multi-port solid-state transformer is connected to the energy module; The method comprises: Acquire environmental parameters within a preset time, first electrical parameters of the distributed energy generation system, and second electrical parameters of the load device; the first electrical parameters include electrical parameters corresponding to each of the energy modules of the multiple different energy types; Inputting the environmental parameters, the first electrical parameters, and the second electrical parameters into a three-dimensional dynamic digital twin, and obtaining, based on simulation results of the three-dimensional dynamic digital twin, an influencing source of voltage fluctuations, sensitive nodes of voltage fluctuations, and impact levels corresponding to the sensitive nodes; Based on the influencing source of the voltage fluctuation, the sensitive nodes of the voltage fluctuation, and the impact levels corresponding to the sensitive nodes, determining that the operating strategy of the distributed energy generation system is to adjust the duty cycle of each port of the multi-port solid-state transformer; The operating state of the distributed energy generation system is regulated based on the operating strategy of the distributed energy generation system.

2. The method according to claim 1, wherein The regulating the operating state of the distributed energy generation system based on the operating strategy of the distributed energy generation system includes: Inputting the influencing source of the voltage fluctuation, the sensitive nodes of the voltage fluctuation, and the impact levels corresponding to the sensitive nodes into a reinforcement learning model to obtain the power distribution amount of each port of the multi-port solid-state transformer; Adjusting the operation strategy of the distributed energy generation system based on the power allocation to obtain a target optimization strategy; The operating state of the distributed energy generation system is regulated based on the target optimization strategy.

3. The method according to claim 2, wherein The inputting the influencing source of the voltage fluctuation, the sensitive nodes of the voltage fluctuation, and the impact levels corresponding to the sensitive nodes into the reinforcement learning model to obtain the power distribution amount of each port of the multi-port solid-state transformer includes: Inputting the influencing source of the voltage fluctuation, the sensitive nodes of the voltage fluctuation, and the impact levels corresponding to the sensitive nodes into a global coordinator of a reinforcement learning model, calculating an evaluation value of an objective function, and determining a combination of weight coefficients that minimizes the evaluation value as a target weight coefficient combination; Determining the priority of each port of the multi-port solid-state transformer based on the target weight coefficient combination; The priority of each port of the multi-port solid-state transformer and the real-time status information of each port are input into the local controller of the reinforcement learning model to obtain the power distribution amount of each port of the multi-port solid-state transformer; the real-time status information of each port includes the electrical parameters of each energy module.

4. The method according to any one of claims 1 to 3, wherein The method for identifying nodes sensitive to voltage fluctuations includes: Calculate the sensitivity coefficient of each node voltage to the power of each port of the multi-port solid-state transformer, and determine the absolute value of the voltage deviation amplitude and the duration of the fluctuation in the disturbance scenario based on the influencing source of the voltage fluctuation; Inputting the sensitivity coefficient, the absolute value of the voltage deviation amplitude, and the duration of the fluctuation into a node evaluation formula to obtain an evaluation score for each node; The evaluation scores of the nodes are respectively compared with a first preset threshold, and the nodes sensitive to voltage fluctuations are determined according to the comparison results.

5. The method according to claim 4, wherein The method for determining the impact level corresponding to the sensitive node includes: Determining the level of the absolute value of the voltage deviation amplitude based on a preset standard; Determining the level of the sensitive node based on the importance of the sensitive node; The level of the sensitive node and the level of the absolute value of the voltage deviation amplitude are multiplied to obtain the impact level corresponding to the sensitive node.

6. The method according to claim 5, wherein Also includes: Based on the load type connected to each sensitive node and the position of the sensitive node in the topological relationship of the three-dimensional dynamic digital twin, the importance of the node is obtained.

7. The method according to claim 3, wherein The expression of the objective function is: in, is the objective function, is the weight coefficient of voltage stability, is the total number of sensitive nodes, is the actual voltage deviation of the i-th sensitive node, is the maximum voltage deviation of the i-th sensitive node, is the weight coefficient of power balance, is the total number of ports, is the actual charge and discharge power of the jth port, is the rated power of the jth port.

8. The method according to claim 3, wherein Also includes: If the power distribution amount output by the local controller does not meet the constraint conditions, the power distribution amount is corrected by a gradient descent method to obtain a corrected power distribution amount.

9. The method according to claim 1, wherein The plurality of energy modules of different energy types include at least two of the following: Photovoltaic power generation module, wind power generation module, gas power generation module, oil power generation module or energy storage module.

10. An operation control device for a distributed energy generation system, characterized in that: Applied to a distributed energy generation system, the system comprises a multi-port solid-state transformer and a plurality of energy modules of different energy types; each input port of the multi-port solid-state transformer is connected to the energy module; The device comprises: A data acquisition unit, configured to acquire environmental parameters within a preset time, a first electrical parameter of the distributed energy generation system, and a second electrical parameter of a load device; the first electrical parameter includes electrical parameters corresponding to each of the energy modules of the plurality of different energy types; a first data processing unit, configured to input the environmental parameters, the first electrical parameters, and the second electrical parameters into a three-dimensional dynamic digital twin, and obtain, based on simulation results of the three-dimensional dynamic digital twin, an influencing source of voltage fluctuations, sensitive nodes of voltage fluctuations, and impact levels corresponding to the sensitive nodes; a second data processing unit, configured to determine, based on the influencing source of the voltage fluctuation, the sensitive nodes of the voltage fluctuation, and the impact levels corresponding to the sensitive nodes, that the operating strategy of the distributed energy generation system is to adjust the duty cycle of each port of the multi-port solid-state transformer; A control unit is used to control the operating state of the distributed energy generation system based on the operating strategy of the distributed energy generation system.

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