Digital twin simulation control platform for network-constructed optical storage and charging system

Abstract

This invention discloses a digital twin simulation control platform for a grid-type photovoltaic-storage-charging system, comprising: a data acquisition layer, a control layer, a digital twin simulation layer, and an execution layer. The data acquisition layer is used to collect real-time operating data of the grid-type photovoltaic-storage-charging system. The real-time operating data includes photovoltaic array output data, energy storage system status data, charging pile load data, grid connection point data, and environmental data. The control layer is communicatively connected to the data acquisition layer and is used to preprocess the real-time operating data and generate initial control parameters based on the grid-type control strategy. This invention relies on the digital twin simulation layer to load the initial control parameters and real-time data for pre-simulation. Combined with an iterative optimization mechanism of "parameter adjustment - simulation - result feedback," it can identify risks such as transient power angle deviation exceeding the standard and fault current exceeding the limit in advance, avoid reverse current and overcapacity problems caused by blind execution of the control strategy, and reduce system operation risks and economic losses.

Description

Technical Field

[0001] This invention belongs to the technical field of simulation control platform for photovoltaic energy storage and charging systems, and particularly relates to a digital twin simulation control platform for a network-type photovoltaic energy storage and charging system. Background Technology

[0002] The transformation of the energy system towards a low-carbon structure centered on renewable energy has become an inevitable trend. As an important component of clean new energy, photovoltaic power generation has achieved large-scale application due to its clean and sustainable characteristics. However, its output power is significantly intermittent and fluctuating due to factors such as light intensity and ambient temperature. Direct grid connection can easily cause problems such as voltage fluctuations and frequency deviations. At the same time, the rapid growth of electric vehicle ownership has driven the large-scale construction of charging facilities. The randomness of charging load further exacerbates the risk of grid supply and demand imbalance. Photovoltaic-storage-charging systems, by integrating photovoltaic power generation, energy storage peak shaving, and charging services, have become a key technological direction for solving photovoltaic consumption, grid peak shaving, and charging demand.

[0003] As the proportion of new energy sources in the power system continues to increase, traditional grid-connected converters, which rely on grid voltage support and lack the inertia and damping characteristics of synchronous generators, are unable to meet the grid stability requirements under high proportion of new energy access. Grid-connected technology, with its ability to simulate the voltage / frequency support of synchronous generators, has become the core solution for photovoltaic-storage-charging systems to access weak grids and achieve autonomous operation.

[0004] When traditional photovoltaic-storage-charging systems are upgraded to "grid-type units that actively support grid stability," the lack of an integrated technical solution that combines "grid-level functions, strategy pre-verification, multi-entity collaboration, and digital simulation" leads to several core deficiencies: First, they cannot actively simulate the inertia and damping characteristics of synchronous generators, making it difficult to respond to grid voltage / frequency fluctuations and smooth transient power angle deviations, thus failing to meet the grid's stability support requirements for the grid-type unit. Second, the control strategy lacks a digital twin pre-simulation stage, making it prone to blind execution that could lead to reverse current, overcapacity, or excessive fault current, increasing system operation risks and economic losses. Third, the lack of a collaborative scheduling mechanism between photovoltaics, energy storage, charging piles, and the grid makes it impossible to achieve a balance between safety and economy under complex operating conditions involving multi-entity power coupling (such as sudden changes in sunlight or charging pile load impacts). Fourth, the lack of high-precision digital tools adapted to grid-type scenarios makes it difficult to cover full-scenario simulations such as grid-connected / off-grid switching and grid fault emergency response, hindering the design optimization and practical application of grid-type systems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a digital twin simulation control platform for a grid-type photovoltaic-storage-charging system. This platform addresses the problem that when traditional photovoltaic-storage-charging systems are upgraded to "grid-type units that actively support grid stability," the lack of an integrated technical solution that combines "grid-level functions, strategy pre-verification, multi-entity collaboration, and digital simulation" leads to the concentrated manifestation of core defects.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The digital twin simulation control platform for a grid-type photovoltaic energy storage and charging system includes: a data acquisition layer, a control layer, a digital twin simulation layer, and an execution layer; The data acquisition layer is used to collect real-time operating data of the grid-type photovoltaic-storage-charging system. The real-time operating data includes photovoltaic array output data, energy storage system status data, charging pile load data, grid connection point data, and environmental data. The control layer is communicatively connected to the data acquisition layer and is used to preprocess real-time operating data and generate initial control parameters based on the network control strategy. The digital twin simulation layer is communicatively connected to the control layer and has a built-in digital twin model of the network-type optical storage and charging system. It is used to load initial control parameters and real-time operating data, and obtain system simulation operating data through simulation. The execution layer is communicatively connected to the digital twin simulation layer and the network-type optical storage and charging system, respectively, and is used to receive the optimized control commands output by the digital twin simulation layer and drive the network-type optical storage and charging system to execute them. The digital twin model integrates virtual synchronous generator technology, which can simulate the transient response, power flow distribution and grid interaction characteristics of a grid-connected photovoltaic-storage-charging system. The control layer iteratively optimizes the initial control parameters based on the system simulation operation data until the simulation results meet the preset stability threshold, and then generates the optimized control command.

[0007] Preferably, the data acquisition layer includes several acquisition devices, including: a photovoltaic-side sensor for acquiring the output current, voltage and power of the photovoltaic array; an energy storage-side sensor for acquiring the state of charge, charging and discharging current and temperature of the energy storage battery; a charging-side sensor for acquiring the charging power, voltage and charging demand of the charging pile; a grid-side sensor for acquiring the voltage, frequency and power of the grid connection point; and an environmental sensor for acquiring light intensity, ambient temperature and humidity. The energy storage-side sensor is communicatively connected to the battery management system of the energy storage system, the charging-side sensor is communicatively connected to the control unit of the charging pile, and the grid-side sensor is communicatively connected to the electricity meter at the grid connection point. Each acquisition device transmits the real-time operating data it acquires to the control layer via Ethernet, 5G, or Bluetooth protocols.

[0008] Preferably, the digital twin model of the digital twin simulation layer is a three-dimensional simulation model, which includes a photovoltaic sub-model, an energy storage sub-model, a charging pile model, a power grid construction sub-model, and an environmental sub-model. The photovoltaic sub-model is used to simulate the power output characteristics of the photovoltaic array under different illumination and temperature conditions; the energy storage sub-model is used to simulate the charging and discharging efficiency, state of charge changes, and fault response of the energy storage battery; the charging pile model is used to simulate the power consumption and charging process under different charging demands; the power grid construction sub-model is used to simulate the voltage support, frequency regulation, and transient power angle stability characteristics of the virtual synchronous generator; the environmental sub-model is used to simulate the impact of illumination and temperature changes on system operation; the digital twin model uses computational fluid dynamics technology to process the thermal field distribution data of the environmental sub-model and uses the finite volume method to calculate the power fluctuations and current changes during the transient response process of the system.

[0009] Preferably, the network control strategy of the control layer includes a virtual synchronous generator control module, a sliding mode control module, and a damping compensation module; The virtual synchronous generator control module is used to simulate the inertia and damping characteristics of a synchronous generator, providing voltage and frequency support for the grid-type photovoltaic-storage-charging system. The sliding mode control module is used to suppress power angle fluctuations and fault currents during system transient processes. By designing a terminal complementary sliding mode surface and a super-helical switching law, it reduces power overshoot and dynamic recovery time. The damping compensation module is used to coordinate the coupling relationship between system inertia support and damping support. Based on the system frequency change rate and steady-state frequency deviation, it adjusts the damping compensation coefficient to avoid the contradiction between steady-state accuracy and dynamic response in traditional grid-type control.

[0010] Preferably, the control layer further includes an energy management submodule, which is used for: When the digital twin simulation layer detects a risk of reverse flow in the system, it instructs the energy storage system to increase its charging power. If the energy storage system fails to communicate, it instructs the photovoltaic array to reduce its output. If the photovoltaic array fails to communicate, it instructs the circuit breaker between the photovoltaic array and the grid to disconnect. When there is no risk of reverse current in the system, the controllable power is calculated. The controllable power is determined based on the rated capacity of the transformer, the power of the grid connection point, the rated power of the charging piles and the number of offline charging piles, the rated power of the energy storage converter, and the fault tolerance coefficient and the offline allocation coefficient. If the total power demand of the online charging piles is not greater than the controllable power, the charging power is allocated according to the power demand of each charging pile. If the total power demand is greater than the controllable power, the charging power is allocated according to the ratio coefficient between the power demand of each charging pile and the total power demand.

[0011] Preferably, the control layer further includes a security management submodule, which is used for: Anomaly detection is performed on key nodes of the grid-type photovoltaic-storage-charging system. These key nodes include photovoltaic inverters, energy storage converters, charging pile control units, and grid connection points. Anomaly detection includes overcurrent, overvoltage, overtemperature, and communication interruption. When a risk of thermal runaway is detected in the energy storage battery, the fire suppression system is activated. The overheated area is located using an infrared thermal imaging sensor, and the energy storage system is instructed to stop charging and discharging. Data transmitted and stored within the platform is protected by employing data encryption, access control, and trusted computing technologies to prevent data leakage and malicious attacks.

[0012] Preferably, the digital twin simulation layer further includes a visualization and interaction submodule, which is used for: The system displays the equipment layout, real-time operating status, and power flow distribution of a grid-type photovoltaic-storage-charging system in a 3D visualization format. The real-time operating status includes the voltage, current, power, and temperature of each device. It provides a human-machine interface that allows users to set simulation parameters, including light intensity variation curves, charging pile load mutation thresholds, and grid voltage drop amplitudes. It also supports historical simulation data query and analysis, generating system transient response reports, energy utilization efficiency reports, and fault simulation reports.

[0013] Preferably, a feedback optimization mechanism is provided between the digital twin simulation layer and the control layer: the digital twin simulation layer feeds back the system simulation operation data to the control layer, and the system simulation operation data includes transient power angle deviation, DC bus voltage fluctuation, fault current peak value and power regulation time; The control layer evaluates the rationality of the initial control parameters based on the system simulation operation data. If the transient power angle deviation exceeds the preset threshold, the sliding surface slope of the sliding mode control module is adjusted. If the DC bus voltage fluctuation is too large, the charging and discharging control parameters of the energy storage converter are optimized. The "parameter adjustment-simulation-result feedback" process is executed cyclically until the system simulation operation data meets the preset stability threshold. The preset stability threshold includes a power angle deviation of no more than ±0.03 rad, a voltage fluctuation of no more than ±5% of the rated value, and a fault current of no more than 1.3 times the rated value.

[0014] Preferably, the platform is adapted to the HarmonyOS distributed architecture, and the data acquisition devices of the data acquisition layer, the edge computing gateway of the control layer, and the cloud server of the digital twin simulation layer are all built on the HarmonyOS system. The HarmonyOS system supports multi-device collaborative communication, enabling plug-and-play acquisition devices, real-time data synchronization between the edge gateway and the cloud, and flexible adaptation to different computing hardware. The platform ensures the security of cross-device data transmission through the trust management and data anonymization technology of the HarmonyOS system, while utilizing the lightweight AI capabilities embedded in the HarmonyOS system to accelerate the iterative optimization efficiency of control parameters.

[0015] Preferably, it also includes a carbon accounting and revenue management submodule, which is communicatively connected to the digital twin simulation layer and the data acquisition layer, respectively, for: Based on the photovoltaic output simulation data, energy storage system loss data, and grid power consumption data output by the digital twin simulation layer, the system's carbon emissions are calculated by combining photovoltaic emission reduction factors and grid emission factors; and the carbon emission reduction benefits are evaluated based on the regional grid tiered carbon trading rules. Based on the charging records of charging piles and the power data exchanged with the power grid in the data acquisition layer, the charging revenue of charging piles and the revenue from electricity purchase and sale from the power grid are calculated; carbon accounting reports and revenue analysis reports are generated and displayed through visualization and interactive sub-modules to provide users with economic optimization suggestions.

[0016] The technical effects and advantages of the digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system of this invention are as follows: This invention simulates the inertia and damping characteristics of a synchronous generator through a virtual synchronous generator control module, and combines a sliding mode control module to suppress transient power angle fluctuations and a damping compensation module to coordinate the coupling relationship between inertia and damping. This can effectively respond to grid voltage / frequency fluctuations, smooth out transient power angle deviations, and meet the grid's requirements for stable support of grid units.

[0017] This invention relies on the digital twin simulation layer to load initial control parameters and real-time data for pre-simulation. Combined with the iterative optimization mechanism of "parameter adjustment-simulation-result feedback", it can identify risks such as excessive transient power angle deviation and excessive fault current in advance, avoid reverse current and overcapacity problems caused by blind execution of control strategy, and reduce system operation risks and economic losses.

[0018] This invention establishes a hierarchical scheduling logic through an energy management submodule, which not only achieves tiered prevention and control of backflow risks (prioritizing energy storage, then photovoltaic, and finally disconnecting the circuit), but also intelligently allocates charging pile power based on controllable power (full allocation when demand is ≤ controllable power, and proportional allocation when demand exceeds the limit). Under complex operating conditions such as sudden changes in sunlight and charging pile load impacts, it enables the coordinated operation of photovoltaic, energy storage, charging piles and the power grid, balancing system safety and economy.

[0019] This invention constructs a three-dimensional digital twin model containing sub-models of photovoltaics, energy storage, charging piles, power grid construction, and the environment. It uses computational fluid dynamics to handle the thermal field and the finite volume method to calculate transient fluctuations. Combined with visualization and interactive sub-modules, it covers full-scenario simulations such as grid-connected / off-grid switching and power grid fault emergency response. It provides high-precision digital tools adapted to grid construction scenarios to help optimize the design of grid-type systems. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the overall system workflow of the digital twin simulation control platform for the grid-type optical energy storage and charging system proposed in this invention. Figure 2 This is a flowchart of the core feedback optimization mechanism of the digital twin simulation control platform for the grid-type optical storage and charging system proposed in this invention; Figure 3 This is a flowchart of the energy management submodule of the digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system proposed in this invention; Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. refer to Figure 1-3 To make the objectives, technical solutions and advantages of the present invention clearer, the following describes in detail the digital twin simulation control platform of the network-type optical storage and charging system of the present invention with reference to specific embodiments and accompanying drawings.

[0023] This embodiment uses a "park-level grid-type photovoltaic-storage-charging system" as the application scenario. The system includes a 100kW photovoltaic array, a 200kWh energy storage system, 10 60kW DC charging piles, and a 10kV grid connection point. The platform is built on the HarmonyOS distributed architecture and can realize simulation control and optimized scheduling under all operating conditions of the system.

[0024] The digital twin simulation control platform for the network-type photovoltaic energy storage and charging system in this embodiment is based on a four-layer architecture design: "data acquisition layer - control layer - digital twin simulation layer - execution layer". The hardware selection, software configuration, and communication methods of each layer need to be implemented one by one according to the actual needs of the park-level application scenario to ensure efficient collaboration among all links. The specific configuration is as follows: As the system's perception end, the data acquisition layer adopts a "distributed sensing + edge acquisition" mode to build a full-dimensional data perception network. Through multiple types of high-precision sensors, it realizes the status monitoring of five core links: photovoltaic, energy storage, charging, power grid and environment, providing raw data support for subsequent control decisions and simulation. Furthermore, the photovoltaic side sensors focus on energy output characteristic acquisition. By capturing changes in current, voltage, and active power in real time, they accurately reflect the power generation capacity of the photovoltaic array. The acquisition frequency and communication protocol settings need to match the rapid fluctuation characteristics of photovoltaic output. The energy storage side sensors are deeply integrated into the battery management system. By continuously monitoring the state of charge (SOC), charging and discharging current and the temperature of individual cells, they not only provide the status basis for energy dispatch, but also build the first line of defense for battery safety. The charging-side sensors are deployed at the output end of each charging pile to synchronously collect power, voltage and user charging mode requirements, supporting the power allocation and service priority scheduling of the charging pile group. The grid-side sensors are linked with the meters at the grid connection point to monitor voltage, frequency, and interactive power in real time, providing key data for judging the grid operating status and preventing reverse flow risks. Environmental sensors are installed around the photovoltaic array to collect parameters such as light intensity, temperature, and humidity, providing environmental references for photovoltaic output prediction and equipment heat dissipation control.

[0025] The specific parameter configurations for each sensor are shown in Table 1:

[0026] Table 1 Data Acquisition Layer Equipment Configuration Table After completing the full-dimensional configuration of the data acquisition layer, the control layer, as the core link of data processing and instruction generation, selects the HarmonyOS edge gateway equipped with a quad-core ARM Cortex-A55 processor as the core hardware. This gateway supports multiple protocols such as Ethernet, 5G, and CAN, and can establish bidirectional communication with various sensors in the data acquisition layer and the cloud server in the digital twin simulation layer to ensure the real-time performance and reliability of data transmission, providing hardware support for the subsequent implementation of control logic.

[0027] Furthermore, the control layer software integrates four core functional modules: a virtual synchronous generator (VSG) control module, a sliding mode control module, a damping compensation module, and an energy management submodule. The functions of each module revolve around the stable operation of the system and energy optimization, and are specifically implemented as follows: VSG control module: Simulates the inertia and damping characteristics of a synchronous generator. By setting the virtual inertia (J=0.8kg・m²), damping coefficient (D=20N・m・s / rad), voltage droop coefficient (Kq=500V / var), and frequency droop coefficient (Kf=1900kW / Hz), it provides stable voltage (380V±5%) and frequency (50Hz±0.2Hz) support for the system, ensuring the basic power quality during the grid construction process. Sliding mode control module: Employs a terminal complementary sliding surface design, by setting the sliding surface slope (C=1×10). 6 The super-spiral switching law coefficients (k1=50, k2=200) effectively suppress transient power angle fluctuations (target deviation ≤ ±0.03rad) and fault currents (target ≤ 1.3 times rated current), enhancing the system's ability to cope with transient disturbances. Damping compensation module: Based on the system frequency change rate (max 0.5 Hz / s) and steady-state frequency deviation (±0.2 Hz), the damping compensation coefficient Dk is dynamically adjusted, with a value range of 0.2~0.8, to avoid the problem of "coupling between inertia support and damping support" in traditional VSG control, and further optimize the dynamic response characteristics of the system; Energy Management Submodule: Receives simulation data from the digital twin simulation layer in real time and establishes a hierarchical anomaly handling logic: When a negative power value (reverse flow risk) is detected at the grid connection point, the energy storage system is given priority to increase its charging power (from 50kW to 100kW); if the energy storage converter communication is interrupted, the photovoltaic array is instructed to reduce its output (from 100kW to 60kW); if the photovoltaic inverter communication fails, the circuit breaker with shunt trip is triggered to disconnect the photovoltaic system from the grid, ensuring system safety layer by layer.

[0028] The various functions of the control layer need to be optimized through simulation verification of the digital twin simulation layer to avoid system risks caused by directly issuing commands. The digital twin simulation layer is built on a cloud server and uses the Unity 2023.1 engine to construct a 3D digital twin model. It integrates five sub-models: photovoltaic sub-model, energy storage sub-model, charging pile sub-model, power grid construction sub-model, and environmental sub-model. The parameter settings and simulation logic of each sub-model are closely aligned with the actual operating scenario to ensure the accuracy and reference value of the simulation results, as detailed below: Photovoltaic sub-model: Based on the characteristics of monocrystalline silicon photovoltaic modules, the module has a rated power of 545W and a conversion efficiency of 22.5%. It can simulate the output characteristics under different light intensity (such as sudden change from 1000W / m² to 600W / m²) and temperature (change from 25℃ to 35℃). For every 100W / m² decrease in light intensity, the output decreases by about 10kW, accurately reflecting the environmental sensitivity of photovoltaic output. Energy storage sub-model: Designed based on the characteristics of lithium iron phosphate batteries, with a battery capacity of 200kWh and a rated voltage of 512V, it can simulate charge and discharge efficiency (92%~95%), SOC change (it takes about 3.6 hours for the SOC to drop from 90% to 20% during discharge) and fault response (such as triggering charge and discharge suspension when the temperature of a single battery cell exceeds 50℃), covering the entire life cycle operation status of the energy storage system; Charging pile model: It is designed for 10 60kW DC charging piles and can simulate different charging demand combinations (such as 5 fast charging piles and 3 slow charging piles, with a total power demand of 360kW). It supports adjusting the power of a single pile according to control commands (minimum adjustment step of 1kW) to adapt to diverse user charging scenarios. Power grid sub-model: Integrating VSG technology, it can simulate the transient response under power grid voltage drop (e.g., 0.4 pu) and frequency fluctuation (e.g., 49.5 Hz). It uses the finite volume method to calculate the power angle change and fault current peak. The simulation step size is set to 10 ms to ensure the accuracy of the transient process simulation. Environmental sub-model: Based on computational fluid dynamics (CFD) technology, it can simulate the thermal field distribution of photovoltaic array (when the illumination is 1000W / m², the surface temperature of the module is 20℃ higher than the ambient temperature) and the heat dissipation effect of the energy storage battery compartment (when the fan is turned on, the temperature inside the compartment decreases by 5℃~8℃), providing simulation basis for equipment heat dissipation optimization.

[0029] The digital twin simulation layer also constructs a feedback optimization mechanism to achieve precise optimization of control parameters through multiple rounds of simulation iterations: the initial control parameters generated by the control layer are loaded for the first time (such as VSG inertia J=0.8, sliding surface slope C=5×10). 5 When the simulation results showed that the transient power angle deviation was ±0.08 rad (exceeding the preset threshold of ±0.03 rad), the control layer adjusted the sliding surface slope C to 1×10⁻⁶. 6 The second simulation power angle deviation was reduced to ±0.04 rad; the damping compensation coefficient Dk was adjusted again from 0.5 to 0.7, and the third simulation power angle deviation stabilized at ±0.02 rad, meeting the preset threshold, and finally generating optimized control commands.

[0030] The optimized instructions generated by the digital twin simulation layer need to be implemented into the actual devices through the execution layer. The execution layer covers the core execution devices of the network-type optical storage and charging system, and achieves low-latency communication (latency ≤100ms) with the digital twin simulation layer through the 5G protocol to ensure rapid instruction response. The functional positioning and execution logic of each device are as follows: Photovoltaic inverter: After receiving the optimized control command, the inverter switches adjust the photovoltaic array output by controlling the duty cycle of the inverter switching transistors through the PWM signal. For example, when the output is reduced from 100kW to 60kW, the response time is ≤100ms, ensuring the timeliness of the output adjustment. Energy storage converter: Adjusts charging and discharging power by controlling DC bus voltage according to instructions. For example, when charging from 50kW to 100kW in a reverse current scenario, the charging and discharging current fluctuation is ≤5A to avoid damage to the battery caused by sudden current changes. Charging pile controller: After receiving the power allocation command, it realizes precise power allocation according to the proportional coefficient. For example, when the total controllable power is 300kW, the power required by 3 fast charging piles is 60kW each, and the power required by 2 slow charging piles is 30kW each, the power is allocated according to the fast charging ratio of 0.2 and the slow charging ratio of 0.1, so that each fast charging pile gets 60kW and each slow charging pile gets 30kW, ensuring fair power allocation and no overcapacity. Circuit breaker: When receiving a disconnection command, the tripping time is ≤0.1s, quickly disconnecting the photovoltaic system from the grid, preventing the spread of faults such as reverse current, and ensuring the safety of the grid and system equipment.

[0031] After completing the hardware and software configuration of the four-layer architecture, the workflow of the digital twin simulation control platform for the network-type photovoltaic energy storage and charging system revolves around five core stages: "data acquisition - control parameter generation - digital twin simulation - feedback optimization - execution control". The time nodes and specific operations of each stage are closely linked to form a closed-loop control logic, ensuring that the system can operate stably and efficiently under different operating conditions. The detailed process is as follows: During the data acquisition phase (T0-T1, T1-T0=1s), each sensor in the data acquisition layer synchronously collects real-time operating data according to the acquisition frequency in Table 1, covering key states of the system in various dimensions: photovoltaic array output 100kW, energy storage SOC 80%, charging current 50A, total power demand of 5 charging piles 300kW (3 fast charging 60kW, 2 slow charging 30kW), grid connection point power -20kW (indicating reverse current risk), ambient light 1000W / m², temperature 25℃; these data are transmitted to the HarmonyOS edge gateway in the control layer through corresponding protocols (RS485, CAN, Ethernet, etc.). After data cleaning (noise filtering) and format unification, they provide high-quality basic data support for the subsequent generation of control parameters.

[0032] After completing real-time data acquisition and preprocessing, the platform enters the control parameter generation stage (T1-T2, T2-T1=0.5s): The control layer generates initial control parameters based on the preset network control strategy and combined with real-time data characteristics, specifically including: VSG inertia J=0.8, sliding surface slope C=5×10 5 The damping compensation coefficient Dk=0.5, and the energy storage charging power command is 50kW, and the photovoltaic output command is 100kW. At the same time, the energy management submodule detects that the grid connection point power is -20kW (reverse current risk) through real-time data analysis, and automatically marks the optimization direction of "prioritizing the adjustment of energy storage charging power" to ensure that the initial command can specifically address the potential risks of the system and lay the foundation for subsequent simulation optimization.

[0033] After the initial control parameters are generated, to avoid potential system instability caused by direct execution, the platform immediately enters the digital twin simulation stage (T2-T3, T3-T2=2s): The digital twin simulation layer loads the initial control parameters and real-time operating data, starts the three-dimensional simulation, and synchronously outputs the simulation results of each sub-model: The photovoltaic sub-model simulates voltage and current fluctuations under 100kW output, with a deviation ≤±2%, meeting the power quality requirements; the energy storage sub-model simulates the SOC change during 50kW charging, with the SOC rising from 80% to 80.2% within 1 second, closely matching the actual charging and discharging characteristics; the grid construction sub-model simulates the transient power angle change during reverse flow, with a power angle deviation of ±0.08rad (exceeding the threshold by ±0.03rad), and a fault current of 1.4 times the rated value (exceeding the threshold by 1.3 times), indicating that the transient performance does not meet the standard; Finally, these simulation results (system simulation operating data) are fed back to the control layer in real time, providing a clear direction for subsequent parameter optimization.

[0034] The feedback from the digital twin simulation layer showed that the system's transient performance did not reach the preset threshold. To ensure stable system operation, the platform entered the feedback optimization phase (T3-T5, T5-T3=1.5s), achieving precise parameter tuning through multiple iterations. During the first optimization (T3-T4, 0.5s), the control layer identified the core issue of "power angle deviation exceeding the threshold" by evaluating the simulation results, and then adjusted the sliding surface slope C from 5×10 5 Increased to 1×10 6 New control parameters are generated; the digital twin simulation layer performs a second simulation based on the new parameters, reducing the power angle deviation to ±0.04 rad and the fault current to 1.35 times the rated value. Although there is an improvement, it is still not fully up to standard.

[0035] In the second optimization (T4-T5, 1s), the control layer continued to evaluate and found that the fault current still exceeded the threshold. The damping compensation coefficient Dk was further adjusted from 0.5 to 0.7, and the commanded energy storage charging power was increased from 50kW to 100kW. This dual approach of "control parameter adjustment + energy dispatch optimization" improved system performance. After three simulations in the digital twin simulation layer, the power angle deviation was ±0.02rad, and the fault current was 1.2 times the rated value, both meeting the preset thresholds. At this point, the final optimized control command was generated. After two rounds of optimization, the simulation results met all preset thresholds, and the platform entered the execution control phase (T5-T6, T6-T5=0.3s). Each device in the execution layer synchronously received and executed the optimization control commands: the energy storage converter increased the charging power to 100kW, the photovoltaic inverter maintained 100kW output, and the charging piles were proportionally allocated according to the total controllable power of 300kW (60kW for fast charging and 30kW for slow charging). To verify the execution effect, the digital twin simulation layer restarted the simulation verification after 10s. The results showed that the grid connection point power became 10kW (no reverse current), the power angle deviation was ±0.02rad, the voltage fluctuation was ±3%, and all system indicators were stable within the preset range, achieving safe and efficient operation.

[0036] To fully verify the platform's core performance and security reliability, specific verifications were conducted on key functions such as transient stability control, energy management optimization, security protection, carbon accounting, and revenue management. Each verification scenario was designed based on typical operating conditions that might be encountered in actual operation. The results are analyzed below: In the transient stability control verification, an extreme scenario was simulated where the grid voltage dropped to 0.4 pu (T=10s). The control effects of traditional VSG control and this platform were compared: Under traditional VSG control, the power angle deviation was ±3.2 rad, the active power overshoot was 2.09 pu, and the recovery time was 1.8s, with large transient response fluctuations and slow recovery. However, through the synergistic effect of sliding mode control and digital twin feedback optimization, this platform controlled the power angle deviation to ±0.02 rad, the active power overshoot to 1.08 pu, and the recovery time to 0.8s, improving transient performance by more than 50%. It met the system's preset transient stability safety threshold, fully demonstrating the platform's advantages in dealing with grid transient disturbances.

[0037] The energy management optimization verification focuses on "power supply and demand balance and maximum energy utilization," and designs two typical scenarios: First, the total power demand of online charging piles is 400kW (with a controllable power of 300kW). The platform allocates power according to a proportional coefficient: fast charging piles (demand 60kW, ratio 0.2) receive 60kW, and slow charging piles (demand 30kW, ratio 0.1) receive 30kW, for a total allocated power of 300kW, which does not exceed the capacity and meets the user's core charging needs. Second, when the controllable power increases to 350kW due to the photovoltaic output, the platform automatically allocates the 50kW difference according to the original ratio (fast charging receives 30kW, slow charging receives 20kW), and the power of a single pile is adjusted to 90kW and 50kW, respectively, to achieve efficient utilization of idle energy and avoid energy waste.

[0038] Safety protection verification revolves around "equipment safety and data security," simulating a fault scenario where the temperature of a single energy storage battery cell rises to 55℃ (T=15s): After the safety management submodule detects the abnormal temperature in real time through the energy storage-side sensors, it immediately instructs the energy storage converter to stop charging and discharging, and simultaneously triggers the battery compartment cooling fan to run at full speed, reducing the temperature to 45℃ within 1 minute, effectively avoiding the risk of thermal runaway; In addition, the data encryption module uses the AES-256 algorithm to encrypt the data collected throughout the process, preventing leakage during data transmission and ensuring system data security.

[0039] The carbon accounting and revenue management verification aims to further validate the comprehensive application value of the system by leveraging the platform's full-dimensional data collection capabilities and digital twin simulation analysis capabilities. The platform acquires core data such as photovoltaic power generation, charging pile power consumption, and grid interaction power in real time through the data collection layer. Combined with long-term operation simulation data from the digital twin simulation layer, a dedicated analysis submodule completes the statistics and calculations. Taking a 100kW photovoltaic array configured in this system as an example, referencing the annual effective utilization hours of photovoltaic power in the park's location of 1500 hours, and combining the system losses simulated by the platform, the actual annual photovoltaic power generation reaches 150,000 kWh. Calculated based on a photovoltaic emission reduction factor of 0.6 tCO2 / MWh, the annual carbon emission reduction is 150,000 kWh ÷ 1000 × 0.6 = 90 tCO2. With a corresponding regional carbon trading price of 60 yuan / t, the annual carbon revenue is 5400 yuan.

[0040] Based on the platform's power allocation and scheduling simulation, 10 60kW DC charging piles achieved efficient operation, with an annual charging volume of 120,000 kWh. Calculated at a charging unit price of 1.5 yuan / kWh, the charging revenue was 180,000 yuan. At the same time, relying on the platform's photovoltaic-storage-charging collaborative control simulation optimization, the grid power purchase and sale strategy became more precise, resulting in an annual net revenue of 20,000 yuan. Combining carbon revenue, charging revenue, and grid revenue, the total annual revenue reached 205,400 yuan.

[0041] Furthermore, the platform can provide users with economic optimization suggestions linked to "grid control" based on historical data trends simulated by digital twins, such as the matching degree between photovoltaic output and load and the peak electricity consumption patterns of charging piles. For example, when the simulation data shows that the photovoltaic output margin is large, it is recommended to moderately increase the photovoltaic capacity. At the same time, the impact of the new capacity on the grid stability should be simulated through the platform to further improve the emission reduction benefits while ensuring the transient stability of the system. Alternatively, by simulating the effect of off-peak charging on the grid load smoothing, the operating time of charging piles can be optimized to reduce the cost of electricity purchase, ultimately helping users achieve the triple goal of "grid stability + environmental protection + economy".

[0042] The above embodiments can be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0043] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0044] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0045] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0046] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A digital twin simulation control platform for a grid-type photovoltaic energy storage and charging system, characterized in that, include: Data acquisition layer, control layer, digital twin simulation layer, and execution layer; The data acquisition layer is used to collect real-time operating data of the grid-type photovoltaic-storage-charging system. The real-time operating data includes photovoltaic array output data, energy storage system status data, charging pile load data, grid connection point data, and environmental data. The control layer is communicatively connected to the data acquisition layer and is used to preprocess real-time operating data and generate initial control parameters based on the network control strategy. The digital twin simulation layer is communicatively connected to the control layer and has a built-in digital twin model of the network-type optical storage and charging system. It is used to load initial control parameters and real-time operating data, and obtain system simulation operating data through simulation. The execution layer is communicatively connected to the digital twin simulation layer and the network-type optical storage and charging system, respectively, and is used to receive the optimized control commands output by the digital twin simulation layer and drive the network-type optical storage and charging system to execute them. The digital twin model integrates virtual synchronous generator technology, which can simulate the transient response, power flow distribution and grid interaction characteristics of a grid-connected photovoltaic-storage-charging system. The control layer iteratively optimizes the initial control parameters based on the system simulation operation data until the simulation results meet the preset stability threshold, and then generates the optimized control command.

2. The digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system as described in claim 1, characterized in that, The data acquisition layer includes several acquisition devices, including: a photovoltaic-side sensor for acquiring the output current, voltage and power of the photovoltaic array; an energy storage-side sensor for acquiring the state of charge, charging and discharging current and temperature of the energy storage battery; a charging-side sensor for acquiring the charging power, voltage and charging demand of the charging pile; a grid-side sensor for acquiring the voltage, frequency and power of the grid connection point; and an environmental sensor for acquiring light intensity, ambient temperature and humidity. The energy storage-side sensor is communicatively connected to the battery management system of the energy storage system, the charging-side sensor is communicatively connected to the control unit of the charging pile, and the grid-side sensor is communicatively connected to the electricity meter at the grid connection point. Each acquisition device transmits the real-time operating data it acquires to the control layer via Ethernet, 5G, or Bluetooth protocols.

3. The digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system as described in claim 1, characterized in that, The digital twin model of the digital twin simulation layer is a three-dimensional simulation model, which includes a photovoltaic sub-model, an energy storage sub-model, a charging pile model, a power grid construction sub-model, and an environmental sub-model. The photovoltaic sub-model is used to simulate the power output characteristics of the photovoltaic array under different light and temperature conditions; the energy storage sub-model is used to simulate the charging and discharging efficiency, state of charge changes, and fault response of the energy storage battery; the charging pile model is used to simulate the power consumption and charging process under different charging demands; the power grid construction sub-model is used to simulate the voltage support, frequency regulation, and transient power angle stability characteristics of the virtual synchronous generator; and the environmental sub-model is used to simulate the impact of changes in light and temperature on system operation. The digital twin model uses computational fluid dynamics to process the thermal field distribution data of the environmental sub-model and employs the finite volume method to calculate the power fluctuations and current changes during the system's transient response.

4. The digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system as described in claim 1, characterized in that, The network control strategy of the control layer includes a virtual synchronous generator control module, a sliding mode control module, and a damping compensation module. The virtual synchronous generator control module is used to simulate the inertia and damping characteristics of a synchronous generator, providing voltage and frequency support for the grid-type photovoltaic-storage-charging system. The sliding mode control module is used to suppress power angle fluctuations and fault currents during system transient processes. By designing a terminal complementary sliding mode surface and a super-helical switching law, it reduces power overshoot and dynamic recovery time. The damping compensation module is used to coordinate the coupling relationship between system inertia support and damping support. Based on the system frequency change rate and steady-state frequency deviation, it adjusts the damping compensation coefficient to avoid the contradiction between steady-state accuracy and dynamic response in traditional grid-type control.

5. The digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system as described in claim 1, characterized in that, The control layer also includes an energy management submodule, which is used for: When the digital twin simulation layer detects a risk of reverse flow in the system, it instructs the energy storage system to increase its charging power. If the energy storage system fails to communicate, it instructs the photovoltaic array to reduce its output. If the photovoltaic array fails to communicate, it instructs the circuit breaker between the photovoltaic array and the grid to disconnect. When there is no risk of reverse current in the system, the controllable power is calculated. The controllable power is determined based on the rated capacity of the transformer, the power of the grid connection point, the rated power of the charging piles and the number of offline charging piles, the rated power of the energy storage converter, and the fault tolerance coefficient and the offline allocation coefficient. If the total power demand of the online charging piles is not greater than the controllable power, the charging power is allocated according to the power demand of each charging pile. If the total power demand is greater than the controllable power, the charging power is allocated according to the ratio coefficient between the power demand of each charging pile and the total power demand.

6. The digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system as described in claim 1, characterized in that, The control layer also includes a security management submodule, which is used for: Anomaly detection is performed on key nodes of the grid-type photovoltaic-storage-charging system. These key nodes include photovoltaic inverters, energy storage converters, charging pile control units, and grid connection points. Anomaly detection includes overcurrent, overvoltage, overtemperature, and communication interruption. When a risk of thermal runaway is detected in the energy storage battery, the fire suppression system is activated. The overheated area is located using an infrared thermal imaging sensor, and the energy storage system is instructed to stop charging and discharging. Data transmitted and stored within the platform is protected by employing data encryption, access control, and trusted computing technologies to prevent data leakage and malicious attacks.

7. The digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system as described in claim 1, characterized in that, The digital twin simulation layer also includes a visualization and interaction submodule, which is used for: The system displays the equipment layout, real-time operating status, and power flow distribution of a grid-type photovoltaic-storage-charging system in a 3D visualization format. The real-time operating status includes the voltage, current, power, and temperature of each device. It also provides a human-machine interface that allows users to set simulation parameters, including light intensity variation curves, charging pile load change thresholds, and grid voltage drop amplitude. It supports querying and analyzing historical simulation data, and generates system transient response reports, energy utilization efficiency reports, and fault simulation reports.

8. The digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system as described in claim 1, characterized in that, A feedback optimization mechanism is provided between the digital twin simulation layer and the control layer: the digital twin simulation layer feeds back the system simulation operation data to the control layer, and the system simulation operation data includes transient power angle deviation, DC bus voltage fluctuation, fault current peak value and power regulation time; The control layer evaluates the rationality of the initial control parameters based on the system simulation operation data. If the transient power angle deviation exceeds the preset threshold, the sliding surface slope of the sliding mode control module is adjusted. If the DC bus voltage fluctuation is too large, the charging and discharging control parameters of the energy storage converter are optimized. The "parameter adjustment-simulation-result feedback" process is executed cyclically until the system simulation operation data meets the preset stability threshold. The preset stability threshold includes a power angle deviation of no more than ±0.03 rad, a voltage fluctuation of no more than ±5% of the rated value, and a fault current of no more than 1.3 times the rated value.

9. The digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system as described in claim 1, characterized in that, The platform is adapted to the HarmonyOS distributed architecture. The data acquisition devices in the data acquisition layer, the edge computing gateways in the control layer, and the cloud servers in the digital twin simulation layer are all built on the HarmonyOS system. The HarmonyOS system supports multi-device collaborative communication, enabling plug-and-play acquisition devices, real-time data synchronization between edge gateways and the cloud, and flexible adaptation to different computing hardware. The platform ensures the security of cross-device data transmission through the trust management and data anonymization technology of the HarmonyOS system, while leveraging the lightweight AI capabilities embedded in the HarmonyOS system to accelerate the iterative optimization efficiency of control parameters.

10. The digital twin simulation control platform for the grid-type photovoltaic energy storage and charging system as described in claim 1, characterized in that, It also includes a carbon accounting and revenue management submodule, which is communicatively connected to the digital twin simulation layer and the data acquisition layer, and is used for: Based on the photovoltaic output simulation data, energy storage system loss data, and grid power consumption data output by the digital twin simulation layer, the system's carbon emissions are calculated by combining photovoltaic emission reduction factors and grid emission factors; and the carbon emission reduction benefits are evaluated based on the regional grid tiered carbon trading rules. Based on the charging records of charging piles and the power data exchanged with the power grid in the data acquisition layer, the charging revenue of charging piles and the revenue from electricity purchase and sale from the power grid are calculated; carbon accounting reports and revenue analysis reports are generated and displayed through visualization and interactive sub-modules to provide users with economic optimization suggestions.

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