Power distribution network master station test system and method based on new energy penetration rate optimization control

Abstract

The application belongs to the technical field of power distribution system simulation and control, and discloses a power distribution network master station test system and method based on new energy penetration rate optimization control. The system comprises: a simulation module, which constructs a high-fidelity digital simulation environment consistent with the real power distribution network topology and dynamically configures a new energy access scene; an algorithm and control module, which calculates the optimal access point and penetration rate of new energy based on simulation data and generates an access strategy; a strategy evaluation module, which quantitatively evaluates and simulates the strategy to ensure that the access of new energy does not interfere with the original protection logic and fault positioning algorithm of the power distribution network; and an interface module, which realizes standardized data interaction with the power distribution automation master station and the cloud management platform. The application can realize closed-loop verification of new energy access strategy control logic and collaborative testing of power distribution master station functions in a digital twin environment, and effectively evaluate the operation control and decision-making ability of the master station under high-proportion new energy access.

Description

Technical Field

[0001] This invention belongs to the field of power distribution system simulation and control technology, specifically relating to a power distribution network master station test system and method based on optimized control of new energy penetration rate. Background Technology

[0002] With the continuous advancement of the "dual carbon" goals, the penetration rate of new energy sources such as wind power, photovoltaics, and energy storage in distribution networks continues to rise, driving the transformation of distribution networks from traditional passive networks to multi-source interactive and complex active distribution networks. Simultaneously, the distribution network master station system needs to process real-time operational data such as voltage, current, active power, and reactive power from massive distribution terminals (such as FTUs / DTUs / TTUs). The large-scale integration of diverse loads (such as electric vehicles and smart homes) further increases the complexity and uncertainty of system operation. When the penetration rate of new energy sources is too high, it can easily trigger problems such as distribution network power flow reconfiguration, voltage rise, and power quality deterioration, thereby impacting the safe and stable operation of the master station system.

[0003] However, current testing and evaluation methods for renewable energy integration still have the following significant shortcomings: First, existing testing systems have limited capabilities in simulating various types of large-scale loads. They struggle to realistically reproduce the processing performance of the main station system under high-concurrency data streams, and cannot effectively verify the alarm and handling logic of the main station system under extreme conditions such as voltage over-limit and power backflow.

[0004] Secondly, most related impact assessments are based on offline calculations and theoretical analyses, lacking an integrated testing platform capable of dynamically adjusting the penetration rate of new energy sources and providing real-time feedback on their impact on the main station system (such as state estimation, power flow calculation, and protection setting functions). Existing technologies struggle to accurately simulate system strength changes under different penetration rates in a testing environment, making it impossible to quantitatively analyze their impact on the control accuracy and response speed of the main station system.

[0005] Finally, existing technologies lack a testing system that can interact and collaborate with the master station system to dynamically verify the differences in access locations and capacity configurations of different new energy sources through algorithms. Furthermore, existing methods often fail to create a closed loop between simulation optimization results and master station control commands, leading to a disconnect between the test scenario and the actual production control process. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a distribution network master station testing system and method based on the optimized control of new energy penetration rate.

[0007] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a distribution network master station testing system based on optimized control of new energy penetration rate, comprising: The simulation module is used to build a high-fidelity digital simulation environment for the distribution network that is consistent with the real distribution network topology. It calculates and simulates the operating data of each monitoring point in real time, and supports dynamic configuration of the access location, access capacity and equipment type of new energy to simulate different penetration rate scenarios. The algorithm and control module is used to calculate the optimal access point and penetration rate of new energy sources based on the real-time operating data output by the simulation module, combined with preset targets and system constraints, and to generate an adjustable new energy access strategy. The strategy evaluation module is used to quantitatively evaluate and simulate the new energy access strategy in the digital simulation environment of the distribution network. The verification includes verifying the stable operation capability of the distribution network, the power supply efficiency of new energy and the adaptability of fault response under the access strategy, and verifying the operation control and decision-making capability of the distribution automation master station system under a high proportion of new energy access, so as to ensure that the new energy access does not interfere with the correctness of the original protection logic and fault location algorithm of the distribution network. The interface module is used to realize standardized data interaction and protocol adaptation between the distribution network master station test system, the distribution automation master station system, the cloud management platform, and various new energy / load terminals; The interface module obtains real power grid data from the distribution automation master station system and provides it to the simulation module for model initialization. It also converts the access strategy generated by the algorithm and control module into control commands and simulates them being sent to the new energy equipment nodes in the simulation model via the distribution automation master station system and cloud management platform. This completes the full-process closed-loop verification of the business control logic of the distribution automation master station system in the digital twin environment.

[0008] Furthermore, the simulation module specifically includes: The model library integration unit is used to integrate parameterizable distributed energy models such as wind power, photovoltaic, and energy storage, as well as traditional power grid component models of distribution terminals and various loads in the simulation environment to form a complete equipment model library. The model building unit is used to construct a full-level distribution network simulation model, including substations, transformers, distribution areas, feeders, buses and distribution terminals, based on the topology of the real distribution automation master station system and by calling the component models in the equipment model library. The data-driven initialization unit is used to obtain the real network structure, load distribution, power supply capacity, transformer location, load data and line parameters from the distribution automation master station system through a standard data interface, and assign the obtained real parameters to the corresponding parameters in the full-level distribution network simulation model to initialize and calibrate the simulation model. The scenario flexible configuration unit is used to flexibly and dynamically adjust the access location, access capacity and equipment type of new energy in the simulation environment after initialization and calibration. It calls the corresponding new energy equipment models from the equipment model library to access different nodes of the distribution network, and sets short-circuit fault conditions in the distribution network topology to simulate the impact of different power generation penetration rates and fault scenarios on the operation of the distribution network, providing a basis for multi-scenario analysis. The dynamic simulation and calculation unit is used to flexibly configure the initial model after the unit configuration based on the scenario. According to the physical laws of the power grid, it calculates and simulates the operating data of voltage, current, active power and reactive power at each monitoring point in real time, and outputs the calculated operating data to the algorithm and control module and the strategy evaluation module.

[0009] Furthermore, the algorithm and control module specifically includes: The access point algorithm optimization unit is used to determine the optimal location for new energy access within the distribution network and each distribution area by relying on the basic data of the distribution network and transformer area through built-in algorithm calculation, and output the access point optimization result. The selection of access points takes ensuring the stable operation of the distribution automation system as the core premise, while also taking into account key factors such as the load and power flow calculation of the distribution network system. The penetration rate algorithm optimization unit is used to receive the optimal location result output by the access point algorithm optimization unit, combine the real-time and basic data of the distribution network system load and power flow calculation, as well as the system operation indicators, preset targets and system constraints, calculate the current new energy penetration rate of the system through the built-in optimization algorithm, determine the maximum accessible capacity of each transformer area and the optimal penetration rate range and limit value for stable operation of the distribution network, output the penetration rate optimization result, and dynamically adjust the virtual access new energy power in the distribution network digital simulation environment to simulate the system state under different penetration rate levels. The control strategy output unit is used to integrate the access point optimization results output by the access point algorithm optimization unit and the penetration rate optimization results output by the penetration rate algorithm optimization unit to generate a new energy access strategy for the distribution network. The new energy access strategy is then converted into control commands and injected into the simulation module to simulate the distribution automation master station system or cloud master station's issuance and execution process in a digital twin environment, thus completing the closed-loop pre-verification of the control logic.

[0010] Furthermore, the strategy evaluation module specifically includes: The operation status analysis unit is used to calculate and monitor the key operation indicators of the distribution network under the new energy access strategy based on the real-time operation data of the simulation module. The key operation indicators include, but are not limited to, node voltage deviation, line load rate, system network loss, short-circuit current level and power quality parameters. The distribution automation master station system function verification unit is used to simulate the impact of the implementation of the new energy access strategy on various functions of the distribution automation master station system, and to verify the state estimation accuracy, power flow calculation convergence, protection setting adaptability, voltage and reactive power control response logic and feeder automation action correctness of the distribution automation master station system under the corresponding operating scenario. The risk assessment and report generation unit is used to provide early warning and graded assessment of the risks of exceeding limits, oscillation, protection malfunction or failure to operate that may occur under the new energy access strategy, and generate a test evaluation report that includes strategy performance comparison, main station system compatibility analysis and improvement suggestions.

[0011] Furthermore, it also includes: The solution application module is used to generate suggestions for expanding capacity or adjusting access points for existing distribution networks that have been connected to new energy sources, based on the new energy access strategy; and to generate planning suggestions for the scale and layout of new energy construction based on the predicted load and the new energy access strategy for newly built distribution networks, and output a planning scheme that includes the location, quantity and capacity of new energy access.

[0012] Secondly, this invention provides a distribution network master station testing method based on optimized control of new energy penetration rate, comprising the following steps: S1: Obtain actual power grid data from the distribution automation master station system through the standard data interface, construct a distribution network digital simulation environment and distribution network simulation model that are consistent with the real distribution network topology, and initialize the model parameters in the distribution network digital simulation environment based on the obtained actual power grid data; S2: In the digital simulation environment of the power distribution network, the access location, number of access devices and penetration rate of new energy devices are dynamically adjusted to simulate various access scenarios and calculate the operation data of the power distribution network in real time. S3: Based on the aforementioned operational data, combined with preset targets and system constraints, the optimal access point and penetration rate of new energy sources are calculated through optimization algorithms, and an adjustable new energy access strategy is generated. S4: Based on the new energy access strategy, perform simulation verification in the digital simulation environment of the distribution network to determine whether the new energy access strategy simultaneously meets the distribution network operation constraints, the main station system functional requirements, and the protection logic correctness requirements; if it meets the requirements, proceed to step S5; if it does not meet the requirements, return to step S3, adjust the preset target or system constraints based on the deviation information of this simulation verification, and recalculate the new energy access strategy until the verification is satisfied or the preset number of iterations is reached; S5: The new energy access strategy is converted into standard control commands, and the control commands are simulated to be issued to the new energy equipment nodes in the simulation model through the distribution automation master station system and cloud management platform. The grid data after the simulation model is adjusted is collected to complete the closed-loop verification of control command issuance and execution feedback.

[0013] Furthermore, S1 specifically includes: The system collects data from the distribution automation master station system via standard data interfaces, including the grid structure, load distribution, power supply capacity, transformer location, load data, line parameters, and operating data of voltage, current, active power, and reactive power of each distribution terminal node. The system uses file isolation to communicate with the distribution automation master station system, imports the collected data into the distribution network master station test system, constructs a distribution network digital simulation environment consistent with the real power grid topology, and initializes and calibrates the parameters of substations, transformers, distribution areas, feeders, buses and distribution terminals in the distribution network digital simulation environment.

[0014] Furthermore, in S2, The new energy equipment types include at least one of photovoltaic power generation, wind power generation and energy storage devices, and support simulation of connecting multiple new energy equipment of the same type to different nodes of the power distribution network. The adjustment range of the new energy penetration rate is from low to high. The proportion of new energy power generation to the total load of the distribution network can be adjusted manually or automatically until the preset grid stability critical condition in the simulation is reached to determine the limit penetration rate.

[0015] Furthermore, in step S3, the optimal access point and penetration rate of new energy sources are calculated using an optimization algorithm, specifically including: The generated new energy access strategy includes at least one of the following: the system's optimal new energy penetration rate, the optimal access location for newly added new energy units, the optimal access capacity and number of newly added new energy units, and the optimal access location and access capacity for new energy at newly built power stations. The preset objective is to use one or a combination of multiple objectives, such as minimizing system network loss, minimizing node voltage deviation, maximizing static voltage stability margin, or minimizing investment cost, as the preset objective. Under the system constraints of meeting node voltage limits, line current carrying capacity limits, and short-circuit current limits, the optimization algorithm iteratively calculates and finds the optimal parameter combination, which includes the optimal access point, maximum accessible capacity, penetration rate, and the optimal penetration rate range and limit value, to generate the new energy access strategy.

[0016] Furthermore, the simulation verification in S4 specifically includes: Calculate and monitor key operating indicators of the distribution network under the new energy access strategy to verify whether the operating constraints of the distribution network are met; The impact of the implementation of the new energy access strategy on various functions of the distribution automation master station system is simulated. The operation control and decision-making capabilities of the distribution automation master station system under a high proportion of new energy access are tested and verified to verify whether the functional requirements of the master station system are met. In the digital simulation environment of the power distribution network, a fault scenario is set up to verify that the withdrawal behavior of new energy sources does not affect the correctness of the original power distribution network protection logic and fault location algorithm when a fault is simulated, so as to verify whether the correctness requirements of the protection logic are met. After completing the above verification, the process also includes risk assessment and report generation steps: providing early warning and graded assessment of the risks of exceeding limits, oscillation, protection malfunction or failure to operate that may occur under the new energy access strategy, and generating a test evaluation report that includes strategy performance comparison, main station system compatibility analysis and improvement suggestions. The key operating indicators include node voltage deviation, line load rate, system network loss, short-circuit current level, and power quality parameters. The functional requirements of the master station system include verifying the accuracy of state estimation, the convergence of power flow calculation, the adaptability of protection settings, the voltage and reactive power control response logic, and the correctness of feeder automation actions.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention constructs a high-fidelity digital simulation environment consistent with the real distribution network topology and utilizes interface modules to achieve standardized data interaction with the distribution automation master station and cloud platform. It deeply integrates real data from the production system and realizes a complete business closed-loop verification of "data acquisition-decision-distribution-execution-feedback". This overcomes the limitations of traditional simulation scenarios, such as fixed scenarios, single models, and inability to verify the actual control logic of the master station. By dynamically configuring the access location, capacity, and equipment type of new energy access, it flexibly simulates multiple scenarios with different penetration rates from low to high. It uses optimization algorithms to iteratively calculate multiple objectives such as minimizing network loss and voltage deviation, and quantitatively outputs safety boundary parameters such as the optimal access point, maximum capacity, optimal penetration rate range, and limit values. This provides accurate quantitative decision-making basis for the planning, layout, operation, and control of new energy in the distribution network.

[0018] Simultaneously, the strategy evaluation module comprehensively verifies the renewable energy access strategy, ensuring that renewable energy access does not interfere with the correctness of the original protection logic and fault location algorithm, and proactively identifying risks of exceeding limits, oscillations, and protection maloperation or failure to operate. This invention deeply integrates renewable energy penetration rate optimization control with distribution station testing, completing the strategy optimization and control logic closed-loop pre-verification in a digital twin environment. It comprehensively verifies the adaptability of the master station's core functions such as state estimation, power flow calculation, protection setting adaptation, voltage and reactive power control, and feeder automation under high renewable energy access, effectively solving the pain points of "unclear visibility, inaccurate calculation, weak control, and inability to verify" in distribution networks facing high renewable energy access, and providing an integrated solution for the safe and efficient consumption of renewable energy and the reliable operation of distribution networks. Attached Figure Description

[0019] Figure 1 This is a system architecture diagram of the distribution network master station test system based on the new energy penetration rate optimization control provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the structure and flow of the distribution network master station test system based on new energy penetration rate optimization control provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the structure and flow of the distribution network master station testing method based on the optimized control of new energy penetration rate provided in Embodiment 2 of the present invention.

[0020] Among them, 1-busbar; 2-distribution terminal; 3-transformer area; 4-load; 5-wind power system; 6-wind power inverter; 7-photovoltaic system; 8-photovoltaic inverter; 9-energy storage system; 10-energy storage converter; 11-intelligent integrated terminal; 12-cloud master station; 13-property management platform. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 This embodiment provides a distribution network master station testing system based on renewable energy penetration rate optimization control. This system aims to address the current technical challenge of lacking a systematic testing tool capable of simulating large-scale renewable energy access scenarios, dynamically controlling penetration rates, and quantitatively evaluating their impact on the master station system's performance and functionality. The system employs a pure software approach to construct a distribution network digital simulation environment consistent with the topology of a real distribution automation master station. Through closed-loop data and command interaction with the actual master station system, cloud master station, and property management platform, it can dynamically simulate complex operating scenarios of various renewable energy sources such as photovoltaics, wind power, and energy storage under different access locations, access capacities, access quantities, and different power generation penetration rates. The system embeds a self-developed renewable energy penetration rate optimization algorithm. Based on multi-scenario simulation data, it can automatically iteratively calculate and output the optimal location, recommended capacity, maximum accessible capacity, safe penetration rate range, and critical limit value for renewable energy access, based on optimization objectives such as minimizing system network loss and node voltage deviation, while meeting safety constraints such as voltage and current. This forms a quantifiable and executable optimized access strategy.

[0024] After being fully verified in a simulation environment, this strategy can generate standardized control commands, which are then simulated and issued to the intelligent converged terminal via the cloud master station and property management platform. In the digital twin environment, a complete business closed-loop test of "strategy generation - command issuance - simulation execution - effect evaluation" is achieved. This fully verifies the correctness and adaptability of the distribution automation master station's core functions such as state estimation, power flow calculation, voltage control, and protection coordination under high-proportion renewable energy access conditions. In particular, it ensures that renewable energy access does not affect the correctness of the original protection logic and fault location algorithm.

[0025] See Figure 1 As shown, the distribution network master station testing system provided in this embodiment needs to work in conjunction with the distribution automation system, the cloud master station, and the property management platform. The overall architecture mainly consists of these three parts: (1) The power distribution automation system includes a power distribution automation master station system, bus 1, multiple transformer substations 3 (e.g., substation 1 to substation N), loads 4 (e.g., load 1 to load N), power distribution terminals 2, and a new energy system. The power distribution automation master station system communicates with each power distribution terminal 2 through the IEC 60870 5 104 protocol, collects real-time operating data, and issues control commands. The new energy system includes a wind power system 5, a photovoltaic system 7, and an energy storage system 9, as well as corresponding wind power inverters 6, photovoltaic inverters 8, and energy storage converters 10. The new energy system communicates with the upper-level system through an intelligent fusion terminal 11. The intelligent fusion terminal 11 collects the operating data of the new energy system and executes remote control commands through the ModBus / 645 / 698 protocol.

[0026] (2) The distribution network master station test system: As the core innovation of this embodiment, it performs bidirectional data interaction with the distribution automation master station system in the distribution automation system. It obtains the topology, operating parameters and real-time monitoring data of the real power grid from the master station system through a standard interface. It does not directly control the physical power grid, but constructs a software-based digital twin model that is highly consistent with the topology, parameters and data of the real power grid through a self-developed simulation algorithm platform. It integrates the new energy penetration rate optimization algorithm to solve the optimal access point, maximum capacity, safe penetration rate range, etc., and performs quantitative evaluation and verification of the generated access strategy. The optimized strategy after verification can be converted into standard control commands and fed back to the distribution automation master station system to provide a reference for the decision-making and control of the master station system.

[0027] (3) The cloud master station and property management platform: As the intermediate management layer, it is responsible for the two-way data interaction between the distribution network master station test system and the intelligent fusion terminal 11. On the one hand, it receives the new energy operation status data uploaded by the intelligent fusion terminal 11 of each new energy equipment through the MQTT protocol and forwards it to the distribution network master station test system. On the other hand, it transfers the optimization strategy and control instructions generated by the distribution network master station test system to the property management platform 13 through the cloud master station 12, and then the property management platform 13 sends them to the intelligent fusion terminal 11 of each new energy equipment through the MQTT protocol to realize the mapping and execution of virtual strategies to simulation equipment, thereby forming a closed-loop test system of "physical power grid - digital simulation - cloud management - master station control".

[0028] Combination Figure 2 As shown, the system includes: The simulation module is used to build a high-fidelity digital simulation environment for the distribution network that is consistent with the real distribution network topology. It calculates and simulates the operating data of each monitoring point in real time, and supports dynamic configuration of the access location, access capacity and equipment type of new energy to simulate different penetration rate scenarios. The algorithm and control module is used to calculate the optimal access point and penetration rate of new energy sources based on the real-time operating data output by the simulation module, combined with preset targets and system constraints, and to generate an adjustable new energy access strategy. The penetration rate includes a safe penetration rate range and a limit penetration rate value that makes the power distribution network simulation model tend to a stable critical state. The strategy evaluation module is used to quantitatively evaluate and simulate the new energy access strategy in the digital simulation environment of the distribution network. The verification includes verifying the stable operation capability of the distribution network system, the power supply efficiency of new energy and the adaptability of fault response under the access strategy, and verifying the operation control and decision-making capability of the distribution automation master station system under a high proportion of new energy access, so as to ensure that the new energy access does not interfere with the correctness of the original protection logic and fault location algorithm of the distribution network. The verification of operation control and decision-making capabilities includes the accuracy of master station state estimation, convergence of power flow calculation, adaptability of protection settings, voltage and reactive power control response logic, and correctness of feeder automation actions. The strategy evaluation module is also used to set up fault scenarios in the simulation environment to verify that when new energy sources exit according to the preset low-voltage ride-through and anti-islanding strategies during simulated faults, it does not cause the original distribution network relay protection to maloperate or fail to operate, or cause fault location algorithm errors. The interface module is used to realize standardized data interaction and protocol adaptation between the distribution network master station test system, the distribution automation master station system, the cloud management platform, and various new energy / load terminals. Specifically, the distribution automation master station system communicates with each distribution terminal through the 104 protocol, the cloud master station and the property management platform communicate with the intelligent fusion terminal through the MQTT protocol, and the intelligent fusion terminal communicates with the new energy system through the ModBus / 645 / 698 protocol. The interface module obtains the network structure, load distribution, power supply capacity, transformer location, load data, and line parameters of the real power grid data from the distribution automation master station system via the 104 protocol. This data is then provided to the simulation module for model initialization. The voltage, current, active power, and reactive power data of each distribution terminal node calculated by the simulation model are encapsulated into uploading messages and transmitted back to the distribution automation master station system. The control commands issued by the distribution automation master station system or cloud management platform are mapped to the power regulation parameters of the corresponding new energy equipment nodes in the simulation model. The access strategy generated by the algorithm and control module is transformed into control commands that are simulated and sent to the new energy equipment nodes in the simulation model via the distribution automation master station and cloud management platform. This completes the full-process closed-loop verification of the business control logic of the power automation master station system in the digital twin environment.

[0029] Furthermore, the simulation module specifically includes: The model library integration unit is used to integrate parameterizable distributed energy models such as wind power, photovoltaic, and energy storage, as well as traditional power grid component models of distribution terminals and various loads in the simulation environment to form a complete equipment model library. The model building unit is used to construct a full-level distribution network simulation model, including substations, transformers, distribution areas, feeders, buses and distribution terminals, based on the topology of the real distribution automation master station system and by calling the component models in the equipment model library. The data-driven initialization unit is used to obtain the actual network structure, load distribution, power supply capacity, transformer location, load data, and line parameters from the distribution automation master station system through a standard data interface. The obtained actual parameters are then assigned to the corresponding parameters in the full-level distribution network simulation model to initialize and calibrate the simulation model. For example, assuming that the area managed by a distribution network master station includes three transformer areas with current loads of 1MW, 0.8MW, and 1.2MW, the system obtains this actual data and initializes the access points and parameters of traditional loads and renewable energy loads. The scenario-flexible configuration unit is used to flexibly and dynamically adjust the access location, access capacity, and equipment type of new energy sources in the simulation environment after initialization and calibration. It can call the corresponding new energy equipment models from the equipment model library to access different nodes of the distribution network, and set short-circuit fault conditions in the distribution network topology to simulate the impact of different power generation penetration rates and fault scenarios on the operation of the distribution network, providing a basis for multi-scenario analysis. For example, by adjusting the output of new energy sources, it can simulate different penetration rate scenarios such as 30%, 50%, and 80%, and supports the simultaneous access of 1 to N new energy devices. The dynamic simulation and calculation unit is used to flexibly configure the initial model after the unit configuration based on the scenario. According to the physical laws of the power grid, it calculates and simulates the operating data of voltage, current, active power and reactive power of each monitoring point in real time, and outputs the calculated operating data to the algorithm and control module and the strategy evaluation module.

[0030] Furthermore, the algorithm and control module specifically includes: The access point algorithm optimization unit is used to determine the optimal location for new energy access within the distribution network and each distribution area by relying on the basic data of the distribution network and transformer area through built-in algorithm calculation, and output the access point optimization result. The selection of access points takes ensuring the stable operation of the distribution automation system as the core premise, while also taking into account key factors such as the load and power flow calculation of the distribution network system. The penetration rate algorithm optimization unit receives the optimal location result output by the access point algorithm optimization unit, combines it with real-time and basic data of distribution network system load and power flow calculation, as well as system operation indicators, preset targets and system constraints, calculates the current renewable energy penetration rate of the system through a built-in optimization algorithm, determines the maximum accessible capacity of each distribution area and the optimal penetration rate range and limit value for stable operation of the distribution network, outputs the penetration rate optimization result, and dynamically adjusts the virtual access renewable energy power in the distribution network digital simulation environment to simulate the system state under different penetration rate levels; for example, based on preset targets (such as minimum system network loss, minimum node voltage deviation) and constraints (such as node voltage limit, line current carrying capacity limit), iteratively calculates the optimal access point, maximum accessible capacity, optimal penetration rate range and limit value for each distribution area. The control strategy output unit is used to integrate the access point optimization results output by the access point algorithm optimization unit and the penetration rate optimization results output by the penetration rate algorithm optimization unit to generate a new energy access strategy for the distribution network. The new energy access strategy is then converted into control commands and injected into the simulation module to simulate the distribution automation master station system or cloud master station's issuance and execution process in a digital twin environment, thus completing the closed-loop pre-verification of the control logic.

[0031] Furthermore, the strategy evaluation module specifically includes: The operation status analysis unit is used to calculate and monitor the key operation indicators of the distribution network under the new energy access strategy based on the real-time operation data of the simulation module. The key operation indicators include, but are not limited to, node voltage deviation, line load rate, system network loss, short-circuit current level and power quality parameters. The distribution automation master station system function verification unit is used to simulate the impact of the implementation of the new energy access strategy on various functions of the distribution automation master station system, and to verify the state estimation accuracy, power flow calculation convergence, protection setting adaptability, voltage and reactive power control response logic and feeder automation action correctness of the distribution automation master station system under the corresponding operating scenario. The risk assessment and report generation unit is used to provide early warning and graded assessment of the risks of exceeding limits, oscillation, protection malfunction or failure to operate that may occur under the new energy access strategy, and generate a test evaluation report that includes strategy performance comparison, main station system compatibility analysis and improvement suggestions.

[0032] Furthermore, in the specific calculation process of the access point algorithm optimization unit, the system does not rely solely on electrical constraints as the sole criterion. During the optimization iteration, the unit uses the equivalent substitution rate of the electricity consumption of traditional power supply in each distribution area after the new energy source is connected as one of the comprehensive evaluation indicators. Under the premise of meeting safety constraints such as voltage and power flow, the system prioritizes access points with higher substitution rates under the same conditions, so as to maximize the local consumption efficiency of new energy power generation and reduce the risk of power backflow from the distribution network to the upper-level grid.

[0033] Meanwhile, when the strategy evaluation module simulates and verifies arbitrary access strategies, the system specifically constructs a fault test case library containing different types (single-phase grounding, two-phase short circuit, three-phase short circuit) and different locations (feeder start, end, branch line). During the transient simulation of injected faults, the system simulates the accessed new energy equipment actively exiting or switching to limited power operation according to the preset low voltage ride-through and anti-islanding protection strategies. It dynamically records and analyzes the voltage transient changes, short-circuit current attenuation characteristics, and high-frequency harmonic injection at each monitoring point at the moment of new energy exit. By comparing the above transient changes with the original relay protection settings of the distribution network and the characteristic quantity tolerance error band of the fault location algorithm item by item, it verifies that it does not trigger protection maloperation or failure to operate under any extreme conditions, thereby ensuring that the access and exit behavior of new energy does not interfere with the correctness of the original protection logic and fault location algorithm.

[0034] After successful verification, the control strategy output unit converts the strategy into control commands, sends them to the cloud master station via the interface module, and then the cloud master station forwards them to the property management platform via the MQTT protocol. The property management platform then issues the commands to the corresponding smart converged terminals via the ModBus / 645 / 698 protocol, changing the output power of the new energy equipment in the simulation model, thus completing a full "calculation-issuance-feedback" closed-loop verification. For example, under a certain access strategy, the verification shows that the node voltage deviation is <5%, the line load rate is <80%, and the protection logic is normal.

[0035] Furthermore, the system also includes: The solution application module is used to generate suggestions for expanding capacity or adjusting access points for existing distribution networks that have been connected to new energy sources, based on the new energy access strategy; and to generate planning suggestions for the scale and layout of new energy construction based on the predicted load and the new energy access strategy for newly built distribution networks, and output a planning scheme that includes the location, quantity and capacity of new energy access.

[0036] Example 2 Combination Figure 3 Following the content of Example 1, this example provides a distribution network master station testing method based on new energy penetration rate optimization control, including the following steps: S1: Obtain actual power grid data from the distribution automation master station system through the standard data interface, construct a distribution network digital simulation environment and distribution network simulation model that are consistent with the real distribution network topology, and initialize the model parameters in the distribution network digital simulation environment based on the obtained actual power grid data; S2: In the digital simulation environment of the power distribution network, the access location, number of access devices and penetration rate of new energy devices are dynamically adjusted to simulate various access scenarios and calculate the operation data of the power distribution network in real time. The penetration rate refers to the proportion of new energy power generation to the total load. The range of the penetration rate is adjusted from low to high until the grid stability critical condition preset in the simulation is reached to determine the limit penetration rate. S3: Based on the aforementioned operational data, combined with preset targets and system constraints, the optimal access point and penetration rate of new energy sources are calculated through optimization algorithms, and an adjustable new energy access strategy is generated. S4: Based on the new energy access strategy, perform simulation verification in the digital simulation environment of the distribution network to determine whether the new energy access strategy simultaneously meets the distribution network operation constraints, the main station system functional requirements, and the protection logic correctness requirements; if it meets the requirements, proceed to step S5; if it does not meet the requirements, return to step S3, adjust the preset target or system constraints based on the deviation information of this simulation verification, and recalculate the new energy access strategy until the verification is satisfied or the preset number of iterations is reached; The functional requirements of the master station system include verifying the accuracy of state estimation, convergence of power flow calculation, adaptability of protection settings, voltage and reactive power control response logic, and correctness of feeder automation actions of the distribution automation master station system under corresponding operating scenarios; the correctness requirements of the protection logic include simulating the exit of new energy sources according to preset low-voltage ride-through and anti-islanding strategies after setting fault scenarios in the simulation environment, and verifying that the original distribution network relay protection action sequence and fault section location results are not disturbed. S5: The new energy access strategy is converted into standard control commands, and the control commands are simulated to be issued to the new energy equipment nodes in the simulation model through the distribution automation master station system and cloud management platform. The grid data after the simulation model is adjusted is collected to complete the closed-loop verification of control command issuance and execution feedback.

[0037] When the new energy access strategy is converted into standard control commands, the simulation operation data is sent to the distribution automation master station system according to the 104 protocol through the interface module. The control commands issued by the distribution automation master station system and the cloud management platform to the new energy equipment nodes in the simulation model are mapped to the power setpoints of the new energy equipment nodes in the simulation model.

[0038] Furthermore, S1 specifically includes: The distribution automation master station system collects the network structure, load distribution, power supply capacity, transformer location, load data, line parameters, and operating data of voltage, current, active power, and reactive power of each distribution terminal node from the real power grid through a standard data interface. The distribution automation master station system collects the above data through the 104 protocol and simultaneously synchronizes the current output power and on-grid status of the new energy inverters in the cloud master station. The system employs file isolation to interoperate with the distribution automation master station system, importing collected data into the distribution network master station test system to construct a distribution network digital simulation environment consistent with the real power grid topology. The system then initializes and calibrates the parameters of substations, transformers, distribution areas, feeders, buses, and distribution terminals within this digital simulation environment. For example, assuming a distribution network master station manages an area containing three distribution areas with current loads of 1MW, 0.8MW, and 1.2MW respectively, the system acquires this real data and initializes the access points and parameters for traditional and renewable energy loads.

[0039] After the S1 model initialization and parameter calibration are completed, the system immediately establishes a bidirectional real-time communication link with the distribution automation master station system and the cloud management platform through the interface module. This communication link is not a one-time connection, but rather runs through all subsequent simulation, optimization, and verification steps, specifically supporting a complete closed loop for three data flow directions: In the uplink direction, real-time operating data such as voltage, current, active power, and reactive power of each distribution terminal node in the simulation model are encapsulated according to the standardized protocol (104 protocol) and uploaded to the distribution automation master station system for use by the master station in core functional modules such as state estimation and power flow calculation. In the downlink direction, control commands issued by the distribution automation master station or cloud master station are received, and after protocol parsing and data mapping, they are accurately converted into power adjustment parameters of the corresponding new energy equipment nodes in the simulation model. In the feedback direction, when the new energy equipment in the simulation model performs adjustment, the system automatically collects new grid operating status data and sends it back to the strategy evaluation module.

[0040] This closed-loop information system, encompassing "data acquisition, command issuance, and execution feedback," ensures deep integration between the distribution network master station testing system and the actual production control process, enabling the simulation verification environment to possess control response logic consistent with the real physical power grid.

[0041] Furthermore, in S2, The new energy equipment types include at least one of photovoltaic power generation, wind power generation and energy storage devices, and support the simulation of connecting multiple new energy equipment of the same type to different nodes of the distribution network; specifically, the connection location can be any node among the three transformer areas; the number of connected devices supports 1 to N new energy equipment to be connected at the same time, and also supports 1 to N terminals to be connected and tested at the same time. The adjustment range for renewable energy penetration rate ranges from low to high. The proportion of renewable energy power generation in the total distribution network load can be adjusted manually or automatically until the preset grid stability critical condition in the simulation is reached to determine the limit penetration rate. For example, by adjusting the output of renewable energy, different penetration rate scenarios such as 30%, 50%, and 80% can be simulated. The simulation module calculates and outputs electrical parameters such as power flow distribution, voltage deviation, and line loss in real time.

[0042] Specifically, the system provides a graphical human-computer interaction interface, allowing testers to manually and continuously change the output value of new energy sources by dragging sliders, or to set a fixed step size (such as 5% or 10% of the total load) for the system to automatically scan step by step. The system starts from a low penetration rate (such as 20%) and gradually increases it. Each adjustment triggers the dynamic simulation and calculation unit to resolve the power flow until the preset grid stability critical conditions (such as node voltage exceeding limits, line overload, or power flow non-convergence) are reached, so as to accurately determine the limit penetration rate under the current grid structure.

[0043] Furthermore, in step S3, the optimal access point and penetration rate of new energy sources are calculated using an optimization algorithm, specifically including: The generated new energy access strategy includes at least one of the following: the system's optimal new energy penetration rate, the optimal access location for newly added new energy units, the optimal access capacity and number of newly added new energy units, and the optimal access location and access capacity for new energy at newly built power stations. The preset objective is to use one or a combination of multiple objectives, such as minimizing system network loss, minimizing node voltage deviation, maximizing static voltage stability margin, or minimizing investment cost, as the preset objective. Under the system constraints of meeting node voltage limits, line current carrying capacity limits, and short-circuit current limits, the optimization algorithm iteratively calculates and finds the optimal parameter combination, which includes the optimal access point, maximum accessible capacity, penetration rate, and the optimal penetration rate range and limit value, to generate the new energy access strategy.

[0044] The maximum accessible capacity refers to the maximum power of new energy that can be accessed at a certain location; the safe penetration range refers to the range of new energy penetration rate during stable grid operation; the limit value refers to the critical penetration rate before grid collapse; and the optimal access point is determined to be the location with the best voltage quality and the lowest line loss, with the access capacity being a safe and economical scale for new energy access.

[0045] In this embodiment, the optimal access point and penetration rate of new energy are calculated through an optimization algorithm. A progressive algorithm framework of "Monte Carlo sampling—Newton-Raphson exact solution—multi-objective aggregation optimization" is adopted, specifically including: (1) Algorithm architecture This embodiment adopts a two-layer collaborative optimization mechanism that combines Monte Carlo simulation (MCS) and Newton-Raphson method (NR) to construct a two-layer closed loop of "probabilistic scenario-driven - accurate model evaluation": the outer MCS is responsible for generating a large number of new energy power output scenarios in the probability space, and the inner NR performs accurate power flow calculation for each scenario.

[0046] (2) Optimization Objective Determine the locations and capacity for new energy access, calculate the penetration rate of new energy (new energy capacity / total distribution network capacity), and find the optimal configuration parameters that balance rapid convergence and system stability.

[0047] (3) Main steps of the algorithm Step S3.1: Generate candidate strategies The optimization algorithm randomly generates a batch of new energy access strategies to be evaluated. Each strategy includes parameters such as access node location, new energy type, and access capacity.

[0048] Step S3.2: Monte Carlo Scene Generation For each candidate strategy, N operating scenarios are generated using Latin hypercube sampling technology based on the probability distribution of new energy output (photovoltaic output follows a Beta distribution, and wind power follows a Weibull distribution).

[0049] The value of N is determined according to the engineering accuracy requirements. Generally, the larger N is, the higher the estimation accuracy.

[0050] Step S3.3: Newton-Raphson power flow calculation For each Monte Carlo scenario, the Newton-Raphson method is used to perform accurate power flow calculations, specifically including: a. Input raw data: including network topology, line parameters, generator / load power, and node type; b. Form the nodal admittance matrix; c. Given initial values ​​for the state variables; d. Calculate the power imbalance; e. Form the Jacobian matrix; f. Solve the system of linear equations to obtain the state variable corrections; g. Update the state variables; h. Convergence determination: If the power imbalance is less than the set accuracy, stop; otherwise, return to step d and continue iterating. i. Calculate the output quantities such as branch power and network loss.

[0051] The Newton-Raphson method typically converges in 3 to 5 iterations. If NR fails to converge due to voltage collapse or other reasons, or if the calculated voltage / current exceeds the limit, the scenario is marked as "constraint not met".

[0052] Step S3.4: Define the probabilistic convergence index MCS schedules NR to solve all scenarios and records the number of iterations or whether convergence occurs each time. A key indicator is defined as probabilistic convergence, which is "the percentage of scenarios in which NR can converge within a specified number of iterations in all simulation scenarios".

[0053] The probabilistic convergence reflects the principle of "maximum static voltage stability margin": when the system operating point approaches the saddle-node bifurcation point of voltage collapse, the Jacobian matrix tends to be singular, leading to a sharp increase or even divergence in the number of NR iterations. Therefore, the essence of the rapid convergence of the NR method is that the grid operating point in the current scenario is far from the instability boundary, and the static voltage stability margin is sufficient. The closer the "probabilistic convergence" of a candidate access scheme is to 100%, the fewer the average number of iterations, proving that the scheme can still maintain a robust voltage stability range with a high probability even under drastic fluctuations in renewable energy output.

[0054] Step S3.5: Aggregate evaluation to form fitness After the MCS operation concludes, the results for all scenarios are statistically analyzed, and a comprehensive fitness value is calculated for the current candidate policy. This fitness function is a multi-objective weighted combination, expressed as: Fitness= w 1 E (Network loss)+ w 2 E (Voltage deviation) w 3 (Probabilistic convergence) Where E(network loss) is the expected value of the system network loss in MW; E(voltage deviation) is the expected value of the node voltage deviation; the probabilistic convergence is the percentage of scenarios in which the Newton-Raphson method converges within a specified number of iterations in all Monte Carlo simulation scenarios, reflecting the maximum static voltage stability margin. w 1. w 2. w 3 represents the weight coefficients for each objective, satisfying... w 1+ w 2+ w 3 = 1.

[0055] Step S3.6: Iterative Optimization The optimization algorithm employs a multi-objective particle swarm optimization algorithm or a genetic algorithm to compare the fitness of different strategies. Through operations such as selection, crossover, and mutation, it guides the search direction, repeatedly generates new strategies, and returns to step S3.2 for evaluation until the termination condition is met. The termination condition includes reaching the maximum number of iterations or the fitness improvement rate being less than a set threshold.

[0056] Step S3.7: Output the optimal parameter combination The final output is the optimal parameter combination: Optimal access point: The location of the power grid node that maximizes the overall objective; Maximum Access Capacity and Penetration Rate Safety Range: By gradually increasing the access capacity of candidate strategies, when it is found that the probability convergence of the scheme starts to decrease from 100% and falls below the preset safety threshold (such as 95%) at a certain capacity, this capacity is the maximum access capacity at that point; by combining the results of all nodes, the optimal penetration rate range at the system level can be obtained, and the penetration rate when the probability convergence is unacceptable is defined as the limit value.

[0057] Step S3.8: Handover Strategy Evaluation Module The final generated new energy access strategy will be sent to the strategy evaluation module for deterministic closed-loop verification, including protection logic adaptability, in the same high-fidelity environment.

[0058] (4) Parameter examples In this embodiment, the typical values ​​for each parameter are as follows: Monte Carlo sampling times: N = 500-1000 times The Newton-Raphson method typically requires 3 to 5 iterations to converge. Probabilistic convergence safety threshold: 95% Penetration rate adjustment step size: 5% Weighting coefficients: w 1. w 2. w 3. Designed according to actual engineering needs to meet the requirements. w 1+ w 2+ w 3 = 1.

[0059] (5) Calculation Example For example, the algorithm calculates that connecting 2MW of photovoltaic power to the Taiyuan area is the optimal solution, with a safe system penetration rate range of 50% to 80% and a maximum penetration rate of 85%. The strategy evaluation module verifies that under this solution, the node voltage deviation is <5%, the line load rate is <80%, and the protection logic is normal.

[0060] Furthermore, the simulation verification in S4 specifically includes: Calculate and monitor key operating indicators of the distribution network under the new energy access strategy to verify whether the operating constraints of the distribution network are met; The impact of the implementation of the new energy access strategy on various functions of the distribution automation master station system is simulated. The operation control and decision-making capabilities of the distribution automation master station system under a high proportion of new energy access are tested and verified to verify whether the functional requirements of the master station system are met. In the digital simulation environment of the power distribution network, a fault scenario is set up to verify that the withdrawal behavior of new energy sources does not affect the correctness of the original power distribution network protection logic and fault location algorithm when a fault is simulated, so as to verify whether the correctness requirements of the protection logic are met. After completing the above verification, the process also includes risk assessment and report generation steps: providing early warning and graded assessment of the risks of exceeding limits, oscillation, protection malfunction or failure to operate that may occur under the new energy access strategy, and generating a test evaluation report that includes strategy performance comparison, main station system compatibility analysis and improvement suggestions. The key operating indicators include node voltage deviation, line load rate, system network loss, short-circuit current level, and power quality parameters. The functional requirements of the master station system include verifying the accuracy of state estimation, the convergence of power flow calculation, the adaptability of protection settings, the voltage and reactive power control response logic, and the correctness of feeder automation actions.

[0061] Furthermore, the complete closed-loop verification in S5 specifically includes: The new energy access strategy is converted into control commands and sent to the cloud master station through the interface module. The cloud master station then forwards the commands to the property management platform via the MQTT protocol. The property management platform then sends the commands to the corresponding smart converged terminals via the ModBus / 645 / 698 protocol to change the output power of the new energy equipment in the simulation model and collect the grid data after the simulation model is adjusted, so as to complete the closed-loop verification of control command issuance and execution feedback.

[0062] In this step, the closed-loop integrity of the verification is reflected in the following: the issued command is transmitted through the complete path of "test system → cloud master station → property management platform → intelligent fusion terminal (simulation mapping)," triggering equipment power adjustment in the simulation model; after the adjustment is completed, the new operating data flows back to the test system and master station system along the original path for the strategy evaluation module to perform before-and-after comparison analysis. This two-way data link ensures that the verification conclusions of any strategy in the digital twin environment can be directly mapped to the control execution flow of the real physical system.

[0063] Furthermore, the renewable energy access strategy output based on the method also includes: The system automatically generates a test evaluation report, which includes strategy performance comparison, main site system compatibility analysis, and improvement suggestions. For existing distribution networks that have been connected to new energy sources, suggestions for expanding capacity or adjusting access points are generated based on the new energy access strategy. For newly built distribution networks, planning suggestions on the scale and layout of new energy construction are generated based on the predicted load and the new energy access strategy, and a planning scheme including the location, quantity and capacity of new energy access is output.

[0064] In summary, this method achieves full automation and intelligence across the entire process, from real-data-driven modeling, multi-scenario simulation, intelligent optimization decision-making, automatic strategy verification, to planning recommendation output. It not only provides an efficient and secure digital sandbox for the testing and evaluation of renewable energy integration, but its output optimization schemes and planning recommendations can also directly guide the planning, operation, and transformation of the power grid, improving the distribution network's capacity to support and absorb high proportions of renewable energy.

[0065] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A distribution network master station testing system based on optimized control of new energy penetration rate, characterized in that, include: The simulation module is used to build a high-fidelity digital simulation environment for the distribution network that is consistent with the real distribution network topology. It calculates and simulates the operating data of each monitoring point in real time, and supports dynamic configuration of the access location, access capacity and equipment type of new energy to simulate different penetration rate scenarios. The algorithm and control module is used to calculate the optimal access point and penetration rate of new energy sources based on the real-time operating data output by the simulation module, combined with preset targets and system constraints, and to generate an adjustable new energy access strategy. The strategy evaluation module is used to quantitatively evaluate and simulate the new energy access strategy in the digital simulation environment of the distribution network. The verification includes verifying the stable operation capability of the distribution network, the power supply efficiency of new energy and the adaptability of fault response under the access strategy, and verifying the operation control and decision-making capability of the distribution automation master station system under a high proportion of new energy access, so as to ensure that the new energy access does not interfere with the correctness of the original protection logic and fault location algorithm of the distribution network. The interface module is used to realize standardized data interaction and protocol adaptation between the distribution network master station test system, the distribution automation master station system, the cloud management platform, and various new energy / load terminals; The interface module obtains real power grid data from the distribution automation master station system and provides it to the simulation module for model initialization. It also converts the access strategy generated by the algorithm and control module into control commands and simulates them being sent to the new energy equipment nodes in the simulation model via the distribution automation master station system and cloud management platform. This completes the full-process closed-loop verification of the business control logic of the distribution automation master station system in the digital twin environment.

2. The distribution network master station testing system based on new energy penetration rate optimization control according to claim 1, characterized in that, The simulation module specifically includes: The model library integration unit is used to integrate parameterizable distributed energy models such as wind power, photovoltaic, and energy storage, as well as traditional power grid component models of distribution terminals and various loads in the simulation environment to form a complete equipment model library. The model building unit is used to construct a full-level distribution network simulation model, including substations, transformers, distribution areas, feeders, buses and distribution terminals, based on the topology of the real distribution automation master station system and by calling the component models in the equipment model library. The data-driven initialization unit is used to obtain the real network structure, load distribution, power supply capacity, transformer location, load data and line parameters from the distribution automation master station system through a standard data interface, and assign the obtained real parameters to the corresponding parameters in the full-level distribution network simulation model to initialize and calibrate the simulation model. The scenario flexible configuration unit is used to flexibly and dynamically adjust the access location, access capacity and equipment type of new energy in the simulation environment after initialization and calibration. It calls the corresponding new energy equipment models from the equipment model library to access different nodes of the distribution network, and sets short-circuit fault conditions in the distribution network topology to simulate the impact of different power generation penetration rates and fault scenarios on the operation of the distribution network, providing a basis for multi-scenario analysis. The dynamic simulation and calculation unit is used to flexibly configure the initial model after the unit configuration based on the scenario. According to the physical laws of the power grid, it calculates and simulates the operating data of voltage, current, active power and reactive power at each monitoring point in real time, and outputs the calculated operating data to the algorithm and control module and the strategy evaluation module.

3. The distribution network master station testing system based on optimized control of new energy penetration rate according to claim 1, characterized in that, The algorithm and control module specifically includes: The access point algorithm optimization unit is used to determine the optimal location for new energy access within the distribution network and each distribution area by relying on the basic data of the distribution network and transformer area through built-in algorithm calculation, and output the access point optimization result. The selection of access points takes ensuring the stable operation of the distribution automation system as the core premise, while also taking into account key factors such as the load and power flow calculation of the distribution network system. The penetration rate algorithm optimization unit is used to receive the optimal location result output by the access point algorithm optimization unit, combine the real-time and basic data of the distribution network system load and power flow calculation, as well as the system operation indicators, preset targets and system constraints, calculate the current new energy penetration rate of the system through the built-in optimization algorithm, determine the maximum accessible capacity of each transformer area and the optimal penetration rate range and limit value for stable operation of the distribution network, output the penetration rate optimization result, and dynamically adjust the virtual access new energy power in the distribution network digital simulation environment to simulate the system state under different penetration rate levels. The control strategy output unit is used to integrate the access point optimization results output by the access point algorithm optimization unit and the penetration rate optimization results output by the penetration rate algorithm optimization unit to generate a new energy access strategy for the distribution network. The new energy access strategy is then converted into control commands and injected into the simulation module to simulate the distribution automation master station system or cloud master station's issuance and execution process in a digital twin environment, thus completing the closed-loop pre-verification of the control logic.

4. The distribution network master station testing system based on optimized control of new energy penetration rate according to claim 1, characterized in that, The strategy evaluation module specifically includes: The operation status analysis unit is used to calculate and monitor the key operation indicators of the distribution network under the new energy access strategy based on the real-time operation data of the simulation module. The key operation indicators include, but are not limited to, node voltage deviation, line load rate, system network loss, short-circuit current level and power quality parameters. The distribution automation master station system function verification unit is used to simulate the impact of the implementation of the new energy access strategy on various functions of the distribution automation master station system, and to verify the state estimation accuracy, power flow calculation convergence, protection setting adaptability, voltage and reactive power control response logic and feeder automation action correctness of the distribution automation master station system under the corresponding operating scenario. The risk assessment and report generation unit is used to provide early warning and graded assessment of the risks of exceeding limits, oscillation, protection malfunction or failure to operate that may occur under the new energy access strategy, and generate a test evaluation report that includes strategy performance comparison, main station system compatibility analysis and improvement suggestions.

5. The distribution network master station testing system based on optimized control of new energy penetration rate according to claim 1, characterized in that, Also includes: The solution application module is used to generate suggestions for expanding capacity or adjusting access points for existing distribution networks that have been connected to new energy sources, based on the new energy access strategy. And for newly built distribution networks, it generates planning suggestions on the scale and layout of new energy construction based on predicted load and the new energy access strategy, and outputs a planning scheme including the location, quantity and capacity of new energy access.

6. A method for testing distribution network master stations based on optimized control of new energy penetration rate, characterized in that, Includes the following steps: S1: Obtain actual power grid data from the distribution automation master station system through the standard data interface, construct a distribution network digital simulation environment and distribution network simulation model that are consistent with the real distribution network topology, and initialize the model parameters in the distribution network digital simulation environment based on the obtained actual power grid data; S2: In the digital simulation environment of the power distribution network, the access location, number of access devices and penetration rate of new energy devices are dynamically adjusted to simulate various access scenarios and calculate the operation data of the power distribution network in real time. S3: Based on the aforementioned operational data, combined with preset targets and system constraints, the optimal access point and penetration rate of new energy sources are calculated through optimization algorithms, and an adjustable new energy access strategy is generated. S4: Based on the new energy access strategy, perform simulation verification in the digital simulation environment of the distribution network to determine whether the new energy access strategy simultaneously meets the distribution network operation constraints, the main station system functional requirements, and the protection logic correctness requirements; if it meets the requirements, proceed to step S5; if it does not meet the requirements, return to step S3, adjust the preset target or system constraints based on the deviation information of this simulation verification, and recalculate the new energy access strategy until the verification is satisfied or the preset number of iterations is reached; S5: The new energy access strategy is converted into standard control commands, and the control commands are simulated to be issued to the new energy equipment nodes in the simulation model through the distribution automation master station system and cloud management platform. The grid data after the simulation model is adjusted is collected to complete the closed-loop verification of control command issuance and execution feedback.

7. The distribution network master station testing method based on new energy penetration rate optimization control according to claim 6, characterized in that, S1 specifically includes: The system collects data from the distribution automation master station system via standard data interfaces, including the grid structure, load distribution, power supply capacity, transformer location, load data, line parameters, and operating data of voltage, current, active power, and reactive power of each distribution terminal node. The system uses file isolation to communicate with the distribution automation master station system, imports the collected data into the distribution network master station test system, constructs a distribution network digital simulation environment consistent with the real power grid topology, and initializes and calibrates the parameters of substations, transformers, distribution areas, feeders, buses and distribution terminals in the distribution network digital simulation environment.

8. The distribution network master station testing method based on new energy penetration rate optimization control according to claim 6, characterized in that, In S2, The new energy equipment types include at least one of photovoltaic power generation, wind power generation and energy storage devices, and support simulation of connecting multiple new energy equipment of the same type to different nodes of the power distribution network. The adjustment range of the new energy penetration rate is from low to high. The proportion of new energy power generation to the total load of the distribution network can be adjusted manually or automatically until the preset grid stability critical condition in the simulation is reached to determine the limit penetration rate.

9. The distribution network master station testing method based on new energy penetration rate optimization control according to claim 6, characterized in that, In step S3, the optimal access point and penetration rate of new energy sources are calculated using an optimization algorithm, specifically including: The generated new energy access strategy includes at least one of the following: the system's optimal new energy penetration rate, the optimal access location for newly added new energy units, the optimal access capacity and number of newly added new energy units, and the optimal access location and access capacity for new energy at newly built power stations. The preset objective is to use one or a combination of multiple objectives, such as minimizing system network loss, minimizing node voltage deviation, maximizing static voltage stability margin, or minimizing investment cost, as the preset objective. Under the system constraints of meeting node voltage limits, line current carrying capacity limits, and short-circuit current limits, the optimization algorithm iteratively calculates and finds the optimal parameter combination, which includes the optimal access point, maximum accessible capacity, penetration rate, and the optimal penetration rate range and limit value, to generate the new energy access strategy.

10. The distribution network master station testing method based on new energy penetration rate optimization control according to claim 6, characterized in that, The simulation verification in S4 specifically includes: Calculate and monitor key operating indicators of the distribution network under the new energy access strategy to verify whether the operating constraints of the distribution network are met; The impact of the implementation of the new energy access strategy on various functions of the distribution automation master station system is simulated. The operation control and decision-making capabilities of the distribution automation master station system under a high proportion of new energy access are tested and verified to verify whether the functional requirements of the master station system are met. In the digital simulation environment of the power distribution network, a fault scenario is set up to verify that the withdrawal behavior of new energy sources does not affect the correctness of the original power distribution network protection logic and fault location algorithm when a fault is simulated, so as to verify whether the correctness requirements of the protection logic are met. After completing the above verification, the process also includes risk assessment and report generation steps: providing early warning and graded assessment of the risks of exceeding limits, oscillation, protection malfunction or failure to operate that may occur under the new energy access strategy, and generating a test evaluation report that includes strategy performance comparison, main station system compatibility analysis and improvement suggestions. The key operating indicators include node voltage deviation, line load rate, system network loss, short-circuit current level, and power quality parameters. The functional requirements of the master station system include verifying the accuracy of state estimation, the convergence of power flow calculation, the adaptability of protection settings, the voltage and reactive power control response logic, and the correctness of feeder automation actions.

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