A dynamic topology adaptive power flow optimization system with high proportion of new energy access
By constructing a dynamic topology adaptive power flow optimization system, the grid topology is updated in real time and combined with renewable energy forecast data to generate an adaptive optimization model and perform safety verification. This solves the dynamic balance problem of high-proportion renewable energy access to the grid, and improves the stability of the grid and the renewable energy absorption capacity.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NAT UNIV OF DEFENSE TECH
- Filing Date
- 2025-08-14
- Publication Date
- 2026-08-04
AI Technical Summary
Existing technologies are ill-equipped to address the dynamic balance challenges when a high proportion of renewable energy is integrated into the grid. Static topology models cannot respond to grid changes, the predictive data and the optimization control closed loop are disconnected, and there is no safety verification before the optimization strategy is executed, leading to grid oscillations and reduced reliability.
A dynamic topology adaptive power flow optimization system is constructed. By tracking the grid switch status in real time to update the topology structure, combining the power output prediction data of new energy sources, an adaptive optimization model is generated. The strategy execution results are verified by a security verification unit, and the parameters of equipment such as inverters are adjusted in a coordinated manner to achieve power flow optimization.
It has achieved dynamic balance of the power grid, improved the capacity for renewable energy absorption and grid stability, reduced network losses and voltage over-limit risks, reduced the frequency of manual intervention, and ensured the safe and efficient operation of the power grid.
Smart Images
Figure CN122512435A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power grid technology and relates to a dynamic topology adaptive power flow optimization system for high-proportion renewable energy access. Background Technology
[0002] The core challenge of integrating new energy sources into the power grid stems from the inherent volatility and intermittency of renewable energy, represented by wind and solar power. Unlike traditional thermal power, which is stable and controllable, their output varies drastically with wind speed and sunlight, posing a fundamental threat to the stable operation of the power grid. Therefore, the main focus of modern power grid technology development is on how to tame this volatility and ensure the safe, stable, and efficient operation of the power grid after large-scale integration of new energy sources.
[0003] As the proportion of new energy sources such as wind power and solar power in the power system gradually increases, the power grid faces unprecedented challenges in dynamic balance. The strong randomness of new energy output and the frequent changes in the power grid topology make traditional power flow optimization systems difficult to adapt to scenarios with a high proportion of new energy integration. Existing technologies generally adopt static topology modeling methods, relying on fixed network structures for optimization calculations.
[0004] In terms of the coordination of new energy forecasting and optimization control, current mainstream systems suffer from severe data fragmentation. Furthermore, existing systems lack pre-verification mechanisms for optimization strategies, leading to grid oscillations caused by some optimization commands failing to consider topological constraint conflicts, significantly reducing system reliability. This manifests primarily in three ways: firstly, static topology models cannot respond to dynamic changes in the grid, and manual modeling after switchover changes is inefficient; secondly, the closed loop between forecast data and optimization control is broken, preventing new energy fluctuations from driving dynamic strategy adjustments; and thirdly, there is no safety verification step before strategy execution, resulting in frequent equipment action conflicts.
[0005] Therefore, how to provide a dynamic topology adaptive power flow optimization system with a high proportion of new energy access is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0006] To address the problems existing in the aforementioned traditional methods, this invention proposes a dynamic topology adaptive power flow optimization system for high-proportion renewable energy integration. This system automatically updates the topology and identifies vulnerable areas by tracking real-time changes in grid switch states. It incorporates short-term renewable energy output forecast data into the optimization model and directly links the control execution unit to adjust inverter parameters, thus resolving power flow imbalances caused by renewable energy fluctuations. A new safety verification unit simulates the strategy execution results; upon failure, it automatically triggers re-optimization, avoiding delays caused by manual intervention.
[0007] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: On the one hand, a dynamic topology adaptive power flow optimization system with a high proportion of renewable energy access is provided. The system includes: a data acquisition unit, a dynamic topology analysis unit, a renewable energy prediction unit, an adaptive optimization unit, and a control execution unit. The data acquisition unit is used to collect the topology status information of the power grid, the power grid operating parameters, and the power output information of new energy power plants in real time. It sends the topology status information to the dynamic topology analysis unit and the power output information of new energy power plants to the new energy prediction unit.
[0008] The dynamic topology analysis unit is used to update the topology of the power grid based on the received topology status information, identify key nodes and vulnerable areas in the power grid, and send the updated topology information to the adaptive optimization unit.
[0009] The new energy forecasting unit is used to generate new energy output forecasting data based on the received new energy power plant output information and meteorological forecasting data, and then send the new energy output forecasting data to the adaptive optimization unit.
[0010] The adaptive optimization unit is used to establish an adaptive optimization model based on the received updated topology information and new energy output prediction data, generate a power flow optimization strategy, and send the power flow optimization strategy to the control execution unit.
[0011] The control execution unit is used to adjust the parameters of the control equipment in the power grid according to the received power flow optimization strategy, and to perform power flow optimization.
[0012] One of the above technical solutions has the following advantages and beneficial effects: The aforementioned dynamic topology adaptive power flow optimization system for high-proportion renewable energy access fundamentally solves the grid operation problems caused by high-proportion renewable energy access. This invention collects grid topology status and renewable energy output data in real time, dynamically analyzes network structure changes and accurately identifies vulnerable areas, and simultaneously integrates short-term renewable energy output forecast data. Based on real-time topology constraints and forecast results, a multi-objective optimization model is established to generate power flow control strategies. Finally, after verifying the feasibility of the strategies through a closed-loop security verification mechanism, the system coordinates and adjusts various control devices such as transformer tap changers, reactive power compensation equipment, and renewable energy inverters.
[0013] This system differs from traditional static optimization methods. Through a dynamic topology tracking mechanism, it ensures that the optimization strategy always aligns with the actual power grid structure, eliminating the risk of strategy failure due to switch changes. By deeply integrating predictive data into the optimization model, control commands can respond in advance to new energy fluctuations. Furthermore, a safety simulation pre-verification process eliminates potential equipment action conflicts. This achieves fully automated dynamic power flow balancing of the power grid without requiring complex algorithm models.
[0014] This system significantly improves the absorption capacity of new energy sources and the stability of the power grid, minimizing the frequency of manual intervention while greatly reducing network losses and the risk of voltage exceedances. This invention is highly adaptable and can be flexibly deployed as the power grid expands, providing safe and efficient operational assurance for power grids with a high proportion of new energy sources. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a block diagram illustrating the principle of a dynamic topology adaptive power flow optimization system with a high proportion of renewable energy access in one embodiment. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
[0019] It should be noted that, in this document, the reference to "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The presentation of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will understand that the embodiments described herein can be combined with other embodiments. The term "and / or" as used herein refers to any combination of one or more of the associated listed items, and all possible combinations, including such combinations.
[0020] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0021] In one embodiment, such as Figure 1 As shown, a dynamic topology adaptive power flow optimization system with a high proportion of renewable energy access is provided. The system includes: a data acquisition unit, a dynamic topology analysis unit, a renewable energy prediction unit, an adaptive optimization unit, and a control execution unit. The data acquisition unit is used to collect the topology status information of the power grid, the power grid operating parameters, and the power output information of new energy power plants in real time. It sends the topology status information to the dynamic topology analysis unit and the power output information of new energy power plants to the new energy prediction unit.
[0022] The dynamic topology analysis unit is used to update the topology of the power grid based on the received topology status information, identify key nodes and vulnerable areas in the power grid, and send the updated topology information to the adaptive optimization unit.
[0023] The new energy forecasting unit is used to generate new energy output forecasting data based on the received new energy power plant output information and meteorological forecasting data, and then send the new energy output forecasting data to the adaptive optimization unit.
[0024] The adaptive optimization unit is used to establish an adaptive optimization model based on the received updated topology information and new energy output prediction data, generate a power flow optimization strategy, and send the power flow optimization strategy to the control execution unit.
[0025] The adaptive optimization unit is used to construct an adaptive optimization model based on the updated topology and new energy prediction data. This adaptive optimization model is a dynamic simulation model (constructed based on system dynamics or surrogate modeling methods), and its parameters include boundary conditions such as energy conversion efficiency and carbon emission thresholds. Through an adaptive weight adjustment algorithm, resource priorities are dynamically allocated according to the renewable energy supply status, and iterative optimization is performed until convergence. Finally, a power flow optimization strategy (including power generation plan adjustment and energy storage charging / discharging strategy) is generated and sent to the control execution unit. The specific process is as follows: 1) Data acquisition and preprocessing; 2) Model parameter initialization; 3) Multi-objective fitness evaluation; 4) Gradient descent / genetic algorithm update of the solution set; 5) Output and execution of the optimization strategy.
[0026] The control execution unit is used to adjust the parameters of the control equipment in the power grid according to the received power flow optimization strategy, and to perform power flow optimization.
[0027] The aforementioned dynamic topology adaptive power flow optimization system for high-proportion renewable energy access solves the optimization lag problem when renewable energy fluctuations cause topology changes by updating the grid topology in real time through a dynamic topology analysis unit; it also integrates short-term output forecasts through a renewable energy prediction unit, enabling the optimization strategy to adapt to renewable energy fluctuations in advance and improving the grid's absorption capacity; and the control execution unit coordinates the adjustment of traditional equipment and renewable energy inverters to achieve dynamic power flow balance across the entire network and reduce network losses.
[0028] In one embodiment, the data acquisition unit includes a topology status acquisition subunit, used to acquire switch status information and line connection relationship information in the power grid.
[0029] The operating parameter acquisition subunit is used to collect voltage, current, and power parameters of each node in the power grid.
[0030] The new energy power output acquisition subunit is used to collect real-time power output data from new energy power plants.
[0031] Specifically, the topology status acquisition subunit, the operating parameter acquisition subunit, and the new energy output acquisition subunit work together to ensure that real-time data is synchronously transmitted to the dynamic topology analysis unit and the new energy prediction unit.
[0032] The topology status acquisition subunit, operating parameter acquisition subunit, and renewable energy output acquisition subunit work together to ensure real-time data is synchronously transmitted to the dynamic topology analysis unit and renewable energy prediction unit. The topology status acquisition subunit tracks switch status changes in real time, ensuring zero-delay topology updates; the operating parameter acquisition subunit synchronously acquires electrical parameters of all network nodes, providing a data foundation for vulnerable area identification; and the renewable energy output acquisition subunit accurately acquires wind and solar power output data, reducing prediction errors and forming high-precision optimized input.
[0033] In one embodiment, the dynamic topology analysis unit includes a topology update subunit, used to update the power grid topology in real time based on changes in switch status information.
[0034] The vulnerability analysis subunit is used to identify overloaded lines and voltage-over-limit nodes in the power grid based on the power grid topology and power grid operating parameters.
[0035] Specifically, the topology information output by the topology update subunit is directly associated with the vulnerability analysis subunit, ensuring that the identification of critical nodes and vulnerable areas is based on the latest topology state.
[0036] The topology update subunit completes topology reconstruction within 10 seconds after the switch changes position, solving the problem of response lag in traditional static models; the vulnerability analysis subunit locates overloaded lines and voltage over-limit nodes based on real-time parameters, enabling optimized resources to accurately focus on high-risk areas and improve equipment utilization.
[0037] The specific steps of the vulnerability analysis subunit include: (1) Real-time parameter acquisition: Acquire real-time data such as line power flow and node voltage, and generate a dynamic model of the power grid by combining the topology.
[0038] (2) Overload line location: Calculate the line vulnerability index and identify lines that contribute significantly to cascading failures.
[0039] (3) Identification of voltage over-limit nodes: The node voltage index trajectory is solved by the pure embedding method, and the over-limit risk is judged by combining the voltage safety domain boundary. The dynamic offset distance quantifies the impact of topology changes.
[0040] (4) Optimize resource allocation: Prioritize the adjustment of high-risk areas according to the index ranking, such as configuring low-voltage flexible DC voltage regulation devices or adjusting the interconnection line transfer scheme to improve equipment utilization.
[0041] In one embodiment, the new energy forecasting unit includes: a historical data storage subunit for storing historical data; the historical data includes historical new energy output data and historical meteorological data.
[0042] The predictive analysis subunit is used to generate new energy output prediction data based on historical data and real-time meteorological forecast data.
[0043] Specifically, the predictive analysis subunit dynamically corrects prediction results by associating them with data from the historical data storage subunit to adapt to the volatility of new energy sources.
[0044] The historical data storage subunit accumulates operational data from multiple scenarios to support the self-learning of the prediction model; the prediction and analysis subunit generates 15-30 minute high-precision power output forecasts by associating with meteorological data, enabling optimization strategies to avoid the risk of tidal current exceeding limits caused by a sudden drop in renewable energy.
[0045] In one embodiment, the adaptive optimization unit includes: a model building subunit for establishing a multi-objective optimization model based on the current power grid topology; the boundary conditions of the multi-objective optimization model include: minimum energy conversion efficiency and minimum carbon emission threshold.
[0046] The constraint adaptive sub-unit is used to dynamically adjust the constraints of the optimization model based on information about key nodes and vulnerable areas.
[0047] The optimization sub-unit is used to solve multi-objective optimization models and generate power flow optimization strategies.
[0048] Specifically, the constraint adaptive subunit is linked with the model building subunit (linkage means that after the constraint changes, the model building and optimization will also be adjusted with the topology change), ensuring that the optimized model is updated in real time with the topology change.
[0049] The constraint adaptive subunit and the model building subunit work together to ensure that the optimization model is updated in real time as the topology changes. The model building subunit establishes a multi-objective optimization model that balances minimizing network loss and voltage stability; the constraint adaptive subunit dynamically loads topology constraints to avoid conflicts between the optimization strategy and the real-time topology; and the optimization solution subunit generates executable strategies to improve optimization efficiency.
[0050] Multi-objective optimization models are constructed based on system dynamics or surrogate modeling methods. The boundary conditions of multi-objective optimization models include: minimum energy conversion efficiency and minimum carbon emission threshold.
[0051] Power flow optimization strategies include, but are not limited to: power generation plan adjustments and energy storage charging and discharging strategies.
[0052] In one embodiment, the constraint adaptive subunit is further configured to add load constraints (including thermal capacity constraints, power flow safety constraints, dynamic load rate constraints, etc.) to the optimization model based on the power grid overload line information identified by the vulnerability analysis subunit; and to add voltage constraints to the optimization model based on the voltage limit-crossing node information identified by the vulnerability analysis subunit.
[0053] Specifically, the load constraints and voltage constraints are directly associated with the optimization solution sub-unit, enabling the generated power flow optimization strategy to perform targeted optimization for the current power grid vulnerabilities.
[0054] Load constraints and voltage constraints are directly linked to the optimization solution sub-unit, enabling the generated power flow optimization strategy to be targeted at the current power grid vulnerabilities. Load constraints are added to identified overloaded lines to prevent the overload from expanding after optimization; voltage constraints are added to voltage limit-crossing nodes to effectively improve the voltage compliance rate; the targeted optimization mechanism reduces the risk of vulnerable areas after strategy execution.
[0055] In one embodiment, the control execution unit includes a transformer tap adjustment module for adjusting the transformer turns ratio to optimize voltage distribution.
[0056] The reactive power compensation control module is used to switch capacitor banks or adjust the output of the static var compensator.
[0057] The new energy inverter control module is used to adjust the power factor or output power of the new energy inverter.
[0058] Specifically, the modules work collaboratively based on the aforementioned power flow optimization strategy to achieve dynamic power flow balance across the entire network. The transformer tap changer module optimizes voltage distribution and reduces network losses; the renewable energy inverter control module adjusts the power factor to address voltage instability issues in renewable energy clusters.
[0059] In one embodiment, the system further includes a security verification unit; the security verification unit is used to receive the power flow optimization strategy generated by the adaptive optimization unit, simulate the power grid operating state of the power flow optimization strategy, and perform security verification based on the simulation results.
[0060] The security verification unit receives the power flow optimization strategy and verifies it according to the following steps: Data verification verifies the integrity of data such as power generation plans and topology in the strategy, matches historical sections, and generates future topology.
[0061] Power flow simulation is performed by calculating voltage and branch power by province based on the AC power flow model, simulating islanded operation under N-1 fault conditions.
[0062] Constraint verification determines whether branch power flow, voltage, and unit ramp-up exceed limits, and identifies valid cross-sections.
[0063] Adjust the feedback, optimize the output of local or cross-regional units through sensitivity, and output verification results or trigger strategy iteration.
[0064] When the safety check fails, a re-optimization instruction is sent to the adaptive optimization unit. The re-optimization instruction contains information on overloaded lines and voltage over-limit nodes from the simulation results, which is used by the adaptive optimization unit to correct the optimization model.
[0065] When the security check passes, an execution command is sent to the control execution unit.
[0066] Specifically, the safety verification unit can simulate the power grid operation state after the power flow optimization strategy is implemented, and determine whether there is line overload or voltage over-limit based on the simulation results; The re-optimization instructions include information on overloaded lines and voltage-limit-crossing nodes from the simulation results, which are used by the adaptive optimization unit to correct the optimization model.
[0067] Each module works collaboratively based on the aforementioned power flow optimization strategy to achieve dynamic power flow balance across the entire network. The transformer tap changer module optimizes voltage distribution and reduces network losses; the renewable energy inverter control module adjusts the power factor to address voltage instability issues in renewable energy clusters.
[0068] In a dynamic topology adaptive power flow optimization system with a high proportion of renewable energy access, the adaptive optimization unit and the control execution unit are respectively connected to a security verification unit, which is used to receive the power flow optimization strategy and perform security verification. When the verification fails, a re-optimization instruction is sent to the adaptive optimization unit; when the verification passes, an execution instruction is sent to the control execution unit. The safety verification unit simulates the policy execution effect to detect potential overload / limit exceeding issues in advance; the re-optimization instruction triggers model correction, forming a closed-loop control chain, avoiding optimization failures caused by delays in manual verification, and improving the policy safety pass rate.
[0069] The safety verification unit can simulate the power grid operation state after the power flow optimization strategy is executed, and determine whether there is line overload or voltage over-limit based on the simulation results; wherein, the re-optimization instruction includes information on overloaded lines and voltage over-limit nodes in the simulation results, which is used by the adaptive optimization unit to correct the optimization model.
[0070] This invention simulates the power grid operation state based on power flow calculation, accurately quantifies strategy risks, and drives model correction by carrying over-limit node information in the re-optimization command, realizing closed-loop optimization of "identification-correction-re-verification" and shortening the time required for strategy adjustment.
[0071] In one embodiment, the adaptive optimization unit further includes a human-machine interaction unit; the human-machine interaction unit is used to display the power grid operating status and optimization strategy; it is also used to receive strategy adjustment instructions input by operation and management personnel, and feed the adjustment instructions back to the adaptive optimization unit for model parameter correction.
[0072] Specifically, in a dynamic topology adaptive power flow optimization system with a high proportion of renewable energy access, the adaptive optimization unit is connected to a human-machine interaction unit. The human-machine interaction unit is used to display the power grid operating status and optimization strategy. The human-machine interaction unit receives strategy adjustment instructions input by operation and management personnel and feeds the adjustment instructions back to the adaptive optimization unit for model parameter correction.
[0073] The human-computer interaction unit of this invention can visualize the power grid status and optimization strategies, and supports the intervention and adjustment by operation and management personnel; the strategy adjustment instructions are fed back to the optimization unit in real time, realizing the integration of human experience and automatic optimization, and improving the decision accuracy in extreme scenarios.
[0074] The core principle of this application lies in constructing a system of "real-time perception - dynamic optimization - closed-loop verification". This system continuously acquires grid switch status, line parameters, and renewable energy output information through a data acquisition unit. Based on this, a dynamic topology analysis unit updates the grid topology in real time and identifies vulnerable areas such as overloaded lines and voltage-limited nodes. Simultaneously, a renewable energy forecasting unit integrates meteorological data to generate short-term output forecasts. An adaptive optimization unit uses the topology and forecast data as dual inputs to establish a multi-objective optimization model that considers minimizing network losses and voltage stability. It dynamically loads constraints based on vulnerable area information, generating a composite strategy that includes transformer tap adjustment, reactive power compensation switching, and renewable energy inverter power adjustment. A safety verification unit simulates the strategy execution effect through power flow calculations, intercepts potential conflict commands, and triggers model re-optimization. Finally, a control execution unit coordinates and regulates multiple devices, forming an adaptive balance from data perception to strategy execution.
[0075] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0076] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and all such modifications and improvements fall within the scope of protection of this application.
Claims
1. A dynamic topology adaptive power flow optimization system with high proportion of new energy access, characterized in that, The system includes: a data acquisition unit, a dynamic topology analysis unit, a new energy prediction unit, an adaptive optimization unit, and a control execution unit. The data acquisition unit is used to collect the topology status information of the power grid, the power grid operating parameters, and the power output information of the new energy power plants in real time, and send the topology status information to the dynamic topology analysis unit and the power output information of the new energy power plants to the new energy prediction unit. The dynamic topology analysis unit is used to update the topology of the power grid based on the received topology status information, identify key nodes and vulnerable areas in the power grid, and send the updated topology information to the adaptive optimization unit. The new energy forecasting unit is used to generate new energy output forecasting data based on the received new energy power station output information and meteorological forecasting data, and send the new energy output forecasting data to the adaptive optimization unit. The adaptive optimization unit is used to establish an adaptive optimization model, generate a power flow optimization strategy, and send the power flow optimization strategy to the control execution unit based on the received updated topology information and the new energy output prediction data. The control execution unit is used to adjust the parameters of the control equipment in the power grid according to the received power flow optimization strategy, and to perform power flow optimization.
2. The high ratio of new energy access dynamic topology adaptive power flow optimization system according to claim 1, characterized in that, The data acquisition unit includes: The topology status acquisition subunit is used to collect switch status information and line connection relationship information in the power grid; The operating parameter acquisition subunit is used to collect voltage, current, and power parameters of each node in the power grid; The new energy power output acquisition subunit is used to collect real-time power output data from new energy power plants. 3.The high ratio of new energy access dynamic topology adaptive power flow optimization system according to claim 1, characterized in that, The dynamic topology analysis unit includes: The topology update subunit is used to update the power grid topology in real time based on changes in switch status information. The vulnerability analysis subunit is used to identify overloaded lines and voltage-over-limit nodes in the power grid based on the power grid topology and power grid operating parameters.
4. The high renewable power penetration dynamic topology adaptive power flow optimization system of claim 1, wherein, The new energy prediction unit includes: The historical data storage subunit is used to store historical data, including historical renewable energy output data and historical meteorological data. The predictive analysis subunit is used to generate new energy output prediction data based on historical data and real-time meteorological forecast data.
5. The high renewable power penetration dynamic topology adaptive power flow optimization system of claim 1, wherein, The adaptive optimization unit includes: The model building sub-unit is used to establish a multi-objective optimization model based on the current power grid topology; the boundary conditions of the multi-objective optimization model include: the highest energy conversion efficiency and the lowest carbon emission threshold; The constraint adaptive sub-unit is used to dynamically adjust the constraints of the optimization model based on information about key nodes and vulnerable areas. The optimization sub-unit is used to solve multi-objective optimization models and generate power flow optimization strategies.
6. The high renewable power penetration dynamic topology adaptive power flow optimization system of claim 5, wherein, The constraint condition adaptive subunit is also used to add load constraints for the corresponding lines in the optimization model based on the power grid overload line information identified by the vulnerability analysis subunit; and to add voltage constraints for the corresponding nodes in the optimization model based on the voltage limit exceeding node information identified by the vulnerability analysis subunit. 7.The high renewable power penetration dynamic topology adaptive power flow optimization system of claim 1, wherein, The control execution unit includes: The transformer tap changer module is used to adjust the transformer turns ratio to optimize voltage distribution. The reactive power compensation control module is used to switch capacitor banks or adjust the output of the static var compensator. The new energy inverter control module is used to adjust the power factor or output power of the new energy inverter. 8.The high renewable power penetration dynamic topology adaptive power flow optimization system of claim 1, wherein, The system also includes a security verification unit; The security verification unit is used to receive the power flow optimization strategy generated by the adaptive optimization unit, simulate the power grid operation state of the power flow optimization strategy, and perform security verification based on the simulation results. When the safety verification fails, a re-optimization instruction is sent to the adaptive optimization unit; the re-optimization instruction contains information on overloaded lines and voltage over-limit nodes from the simulation results, which is used by the adaptive optimization unit to correct the optimization model. When the security check passes, an execution command is sent to the control execution unit. 9.The high renewable power penetration dynamic topology adaptive power flow optimization system of claim 1, wherein, The adaptive optimization unit also includes a human-computer interaction unit; The human-machine interaction unit is used to display the power grid operation status and optimization strategy; it is also used to receive strategy adjustment instructions input by operation and management personnel and feed the adjustment instructions back to the adaptive optimization unit for model parameter correction.