Simulation analysis method and device for distributed resource operation of provincial domain distribution network and medium

Abstract

The invention relates to a distributed resource operation simulation analysis method and device for a provincial domain distribution network and a medium, and the method comprises a main distribution integrated energy distribution simulation step, a multi-target multi-time-scale coordination control strategy simulation step, a multi-target multi-time-scale coordination control strategy simulation step and a multi-target multi-time-scale coordination control strategy simulation step. A provincial distributed resource planning simulation deduction and auxiliary decision-making step; and a provincial power distribution network resource configuration and adjustment step. According to the method, professional simulation of the power system of a'planning-operation-control 'full chain is covered, complexity and uncertainty caused by high-permeability distributed resources can be effectively handled, and safe, efficient and low-carbon operation of the power system is assisted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution system simulation analysis, in particular to a provincial distribution network distributed resource operation simulation analysis method, device and medium. BACKGROUND

[0002] With the advancement of new power system construction, the high proportion of wide-area large-scale distributed resources (such as distributed photovoltaic, energy storage, and flexible load) access poses a serious challenge to the power balance and stability characteristics of the main grid. Traditional power system simulation software focuses on independent modeling of the main grid or distribution network, lacks integrated simulation capability of main-distribution coordination, and cannot accurately reflect the aggregation characteristics of distributed resources and their impact on the main grid. The existing dispatching system usually simplifies the resources on the distribution network side using static equivalent models, resulting in insufficient simulation accuracy of dynamic processes, especially in scenarios of new energy output fluctuations and load mutations, leading to deviations in the analysis of main grid voltage and frequency stability. In addition, the temporal and spatial distribution of distributed resources within a province varies significantly, and existing planning tools rely heavily on historical data extrapolation, making it difficult to couple policy guidance with long-term changes in technical and economic performance, which restricts the scientific nature of resource optimization configuration. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a provincial distribution network distributed resource operation simulation analysis method, device and medium, which can help the safe, efficient and low-carbon operation of the power system.

[0004] The technical solution adopted by the present application to solve the technical problem is: providing a provincial distribution network distributed resource operation simulation analysis method, comprising the following steps:

[0005] Collecting various power system data, constructing a distributed resource aggregation model and a main-distribution grid model, and performing simulation calculation of the main grid and the distribution network;

[0006] Combining various data to perform day-ahead prediction of distributed resource output and load, solving the established optimization scheduling model to obtain a day-ahead scheduling plan, dynamically adjusting the day-ahead scheduling plan during the intra-day operation process according to real-time data and prediction results, and decomposing the dynamically adjusted distributed resource regulation strategy to each level of distribution network node and equipment to complete simulation calculation of the coordinated control strategy of multiple objectives and multiple time scales;

[0007] Collecting various data related to distributed resource planning, constructing a provincial distributed resource planning configuration model, simulating and deducing the system obtained by the provincial distributed resource planning configuration model, analyzing and evaluating the simulation results, and providing decision suggestions based on the results of analysis and evaluation;

[0008] Real-time acquisition of the operation data of the provincial distribution network, the output data of the distributed resources and the load data, and based on the operation data of the power grid, the output data of the distributed resources and the load data, the system is analyzed and evaluated, and the optimization adjustment strategy is automatically generated and executed according to the analysis and evaluation results, so as to complete the simulation calculation of the provincial distribution network resource configuration and adjustment.

[0009] When the simulation calculation of the main grid and the distribution network is performed, for the main grid part, the main grid model in the power system analysis software is used for modeling and simulation calculation to obtain the main grid simulation result; for the distribution network part, based on the distributed resource aggregation model, the access position, capacity and operation characteristics of the distributed resources are considered, and a distribution network model suitable for the characteristics of the distribution network is used for modeling and calculation to obtain the distribution network simulation result; the main grid simulation result and the distribution network simulation result are realized through the data interaction interface between the main grid model and the distribution network model Real-time sharing and collaborative optimization.

[0010] When the simulation calculation of the main grid and the distribution network is performed, the distributed resource aggregation model is simplified into an equivalent model capable of reflecting the overall dynamic characteristics of the distributed resources through dynamic multi-source equivalent technology.

[0011] When the day-ahead scheduling plan is dynamically adjusted according to the real-time data and the prediction results during the intraday operation process, a model predictive control method is used to dynamically adjust the day-ahead scheduling plan with the optimal system operation index in the future period as the target.

[0012] When the dynamically adjusted distributed resource adjustment strategy is decomposed to each level of distribution network node and equipment, a distributed collaborative optimization algorithm is used to convert the adjustment instruction of the upper level power grid into the specific control parameters of the lower level power grid and equipment under the premise of ensuring the operation safety of each level of power grid and the overall optimization target.

[0013] When the provincial distributed resource planning and configuration model is constructed, the sustainable development of the power system, the improvement of energy utilization efficiency and the safety of power supply are realized as the target, and the provincial distributed resource planning and configuration model is constructed with the power grid construction cost, the operation cost, the distributed power investment cost, the environmental cost, the carrying capacity of the power grid, the reliability requirement and the power quality standard as the constraint.

[0014] When the system planned by the provincial distributed resource planning and configuration model is simulated and deduced, the safety, stability and economy of the system planned by the provincial distributed resource planning and configuration model are simulated and evaluated by setting various operation scenarios, and the simulation evaluation results are displayed in a visual form by using big data analysis and visualization technology.

[0015] The grid-based operation data, distributed resource output data and load data are used to analyze and evaluate the system, specifically: when evaluating the operation data of the grid, the maximum capacity of the distributed resources that the distribution network can bear is evaluated by analyzing the topology of the grid, the equipment capacity and the operation state; when analyzing and evaluating the output data of the distributed resources, short-term and ultra-short-term output prediction is realized by using a deep learning method in combination with meteorological data and real-time operation data; when analyzing and evaluating the load data, different types of loads are predicted by using time series analysis and machine learning techniques.

[0016] The technical solution adopted by the present application to solve its technical problems is to provide an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the provincial distribution network distributed resource operation simulation analysis method described above.

[0017] The technical solution adopted by the present application to solve its technical problems is to provide a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the provincial distribution network distributed resource operation simulation analysis method described above.

[0018] Advantages

[0019] Compared with the prior art, the present application has the following advantages and positive effects due to the adoption of the above technical solutions:

[0020] The main and distribution integrated energy distribution simulation software can accurately simulate the influence of wide-area distributed resources access on the power balance and stability characteristics of the main grid, discover potential safety hazards in advance, provide scientific decision-making basis for dispatchers, effectively avoid problems such as abnormal power flow and voltage fluctuation of the grid caused by the access of distributed resources, and ensure the safe and stable operation of the grid.

[0021] The present application realizes precise scheduling and optimal adjustment of provincial distributed resources through multi-objective and multi-time scale coordinated control strategy simulation, ensures the stable operation of the main grid while ensuring the consumption of distribution network resources, and improves the ability of the power system to cope with various complex working conditions.

[0022] The provincial distributed resource planning simulation deduction and auxiliary decision-making of the present application fully considers factors such as long-period load level, distributed power output level and policy influence, provides scientific planning and configuration strategies and implementation paths for distributed resource investment and development, avoids blind investment and resource waste, and realizes optimal allocation of resources. The provincial distribution network resource configuration and adjustment platform realizes coordinated operation among distributed power sources, energy storage devices and loads through real-time calculation, analysis and optimal adjustment functions, improves energy utilization efficiency, and reduces system operation cost. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a flow chart of a provincial distribution network distributed resource operation simulation analysis method of the first embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application will be further described below in conjunction with specific examples. It should be understood that these examples are only used to illustrate the present application and not used to limit the scope of the present application. Furthermore, it should be understood that after reading the content taught by the present application, those skilled in the art can make various modifications or changes to the present application, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0025] The first embodiment of the present application relates to a provincial distribution network distributed resource operation simulation analysis method, as shown in Figure 1 includes the following steps:

[0026] Main distribution integration energy distribution simulation step: this step collects various power system data, constructs a distributed resource aggregation model and a main distribution network model, and performs simulation calculation of the main network and the distribution network.

[0027] In this step, statistical methods, data analysis and machine learning methods are comprehensively used to deeply mine the operation data of distributed photovoltaic, wind power, energy storage and other resources in the region. Considering factors such as geographical distribution, meteorological conditions and power consumption characteristics, a distributed resource aggregation model is established, which can analyze the output characteristics and aggregation rules of distributed resources under different time scales and working conditions. This step also establishes a main distribution network joint simulation model considering wide-area distributed resources connected to the grid under steady-state operation conditions. Distributed computing and parallel processing technology is used to establish the model, and the simulation calculation of the main network and the distribution network is reasonably divided and coordinated. Specifically, for the main network part, a mature main network model in the power system analysis software is used for modeling and simulation calculation, which focuses on the power flow distribution, voltage stability and other issues of the system, so that the main network simulation result can be obtained. For the distribution network part, based on the distributed resource aggregation model, the access location, capacity and operation characteristics of the distributed resource are considered, and a distribution network model suitable for the characteristics of the distribution network is used for modeling and calculation, such as a distribution network power flow calculation method based on branch current method, to obtain the distribution network simulation result. Among them, the main network model and the distribution network model realize real-time sharing and collaborative optimization of the main network simulation result and the distribution network simulation result through a data exchange interface, to ensure the simulation accuracy of the entire power system. In this embodiment, the distributed resource aggregation model is simplified to an equivalent model that can reflect the overall dynamic characteristics of the distributed resource through dynamic multi-source equivalent technology, to reduce the calculation amount and improve the simulation efficiency.

[0028] The step can be implemented by using a layered distributed software architecture, which includes a data layer, a model layer, a calculation layer and an application layer. The data layer is responsible for collecting and storing various power system data, including power grid topology, equipment parameters, distributed resource information, load data, etc.; the model layer implements the construction of distributed resource aggregation model, main distribution network model and various analysis models; the calculation layer performs simulation calculation tasks, including power flow calculation, stability analysis, etc.; the application layer provides a user interface to realize the visualization of simulation results, parameter setting, report generation and other functions. The software has a friendly user interface, which is convenient for dispatchers to operate and analyze, and can flexibly simulate according to different operation scenarios and requirements.

[0029] The simulation step of the multi-objective multi-time scale coordinated control strategy: This step combines various data to make day-ahead prediction of distributed resource output and load, and solves the established optimization scheduling model based on the prediction results to obtain a day-ahead scheduling plan. During the intraday operation process, the day-ahead scheduling plan is dynamically adjusted according to real-time data, and the dynamically adjusted distributed resource regulation strategy is decomposed to each level of distribution network node and equipment step by step to complete the simulation calculation of the multi-objective multi-time scale coordinated control strategy.

[0030] In this step, meteorological forecast data, historical load data and distributed power operation data are combined to perform day-ahead prediction of distributed resource output and load demand in the province using time series analysis, neural network and other prediction algorithms. An optimization scheduling model is established with the objectives of minimizing operation cost, maximizing new energy consumption, and ensuring safe and stable operation of the power grid, with power balance constraints, device operation constraints, and line transmission capacity as constraints. The optimization scheduling model is solved using mixed integer programming, dynamic programming and other optimization algorithms to obtain the optimal day-ahead scheduling plan, which determines the generation plan of various power sources and the charging and discharging strategy of energy storage devices. During the intraday operation process, the changes in distributed resource output and load are monitored in real time, and the day-ahead scheduling plan is updated based on the predicted data and actual operating conditions. This implementation can use model predictive control (MPC) and other methods to dynamically adjust the adjustment strategy, such as adjusting the output of distributed power sources, starting energy storage devices for charging and discharging, and implementing demand response, to ensure optimal operation of the system at all times during the intraday period. At the same time, considering the executability of the adjustment strategy and the inertia of the system, the adjustment amount is reasonably constrained and limited. Finally, the distributed resource adjustment strategy developed at the provincial level is decomposed to the city, county, and even specific distribution network nodes and devices. Specifically, a distributed collaborative optimization algorithm is used to convert the adjustment instructions from the upper-level grid into specific control parameters for the lower-level grid and devices, while ensuring the safety and overall optimization of the grid at all levels. For example, the power regulation target at the provincial level is decomposed to the distributed power sources and energy storage devices in each city, which further allocates the adjustment tasks to each distribution station and distributed resource aggregator based on their own grid conditions, achieving effective implementation and execution of the adjustment strategy.

[0031] This step can be implemented through software, which includes a prediction module, an optimization scheduling module, an intraday rolling update module, a strategy decomposition module, and a result display module. The prediction module implements the prediction function of distributed resource output and load; the optimization scheduling module solves the day-ahead optimization scheduling model; the intraday rolling update module dynamically adjusts the scheduling plan based on real-time data; the strategy decomposition module completes the step-by-step decomposition of the adjustment strategy; and the result display module displays the scheduling plan, adjustment strategy, and system operation indicators in an intuitive manner. This software has good scalability and compatibility, and can interact and work collaboratively with other power system software and platforms.

[0032] Provincial distributed resource planning simulation and decision support step: This step collects various types of data related to distributed resource planning, constructs a provincial distributed resource planning configuration model, simulates and deduces the system planned by the provincial distributed resource planning configuration model, analyzes and evaluates the simulation results, and provides decision recommendations based on the analysis and evaluation results.

[0033] In this step, economic development trend, industrial structure adjustment, energy policy changes and other factors are considered, and methods such as scenario analysis, grey prediction, and system dynamics are used to predict the load level in the province for a long period (e.g. 5 years, 10 years). For the output level of distributed power, combined with the distribution of new energy resources, the development trend of technology, and the policy subsidies, a distributed power development model is established to predict the installed capacity and output characteristics of different types of distributed power in the future long period. For example, according to the solar and wind resource assessment data of each city, as well as the government's planning target for new energy development, the growth trend of distributed photovoltaic and wind power installed capacity in the future is predicted, and the change rule of output affected by factors such as season and weather is analyzed. To achieve the goal of sustainable development of the power system, improve energy efficiency, and ensure the safety of power supply, the cost of power grid construction and operation, the investment cost of distributed power, environmental cost, the carrying capacity of the power grid, reliability requirements, and power quality standards are used as constraints to establish a provincial distributed resource planning and configuration model. A multi-objective optimization algorithm is used to solve the model to obtain the optimal distributed resource planning and configuration strategy, including the site selection and capacity determination of distributed power, and the configuration scheme of energy storage equipment. At the same time, combined with the policy environment and market mechanism, the implementation path of distributed resource development is formulated, and the development target, key task and safeguard measures of each stage are clarified. Through the simulation of different planning and configuration schemes and development implementation paths, the operation of the provincial power system is simulated, and various operating scenarios such as load mutation, new energy large output, and equipment failure are set to evaluate the safety, stability, and economy of the system. Using big data analysis and visualization technology, the simulation results are displayed in the form of intuitive charts and curves to decision makers, providing a scientific basis for distributed resource investment and development decisions. For example, through simulation, the power flow distribution and voltage fluctuation of the power grid under different distributed power installed capacity can be directly observed, as well as the impact on system operation cost and reliability, helping decision makers choose the optimal development scheme.

[0034] This step can be implemented using a service-oriented architecture (SOA) consisting of a data management module, a model building module, a simulation calculation module, a decision analysis module, and a user interface module. The data management module is responsible for collecting, storing, and managing various types of data related to distributed resource planning, including geographic information data, power grid data, energy data, and economic data. The model building module implements the construction of load prediction models, distributed power development models, and planning configuration models. The simulation calculation module performs simulation tasks. The decision analysis module analyzes and evaluates the simulation results and provides decision recommendations for decision makers. The user interface module provides a convenient operation interface for users to set parameters, compare schemes, and view results.

[0035] Provincial distribution network resource configuration and adjustment step: This step obtains the operation data of the provincial distribution network, the output data of the distributed resources and the load data in real time, and analyzes and evaluates the system based on the operation data of the power grid, the output data of the distributed resources and the load data, and automatically generates and executes the optimal adjustment strategy according to the analysis and evaluation results, so as to complete the simulation calculation of the provincial distribution network resource configuration and adjustment.

[0036] In this step, relying on the provincial company power grid resource business platform, enterprise-level real-time measurement center and other basic platforms, various data of the power system are integrated, including power grid topology structure, equipment parameters, operation state data, distributed resource information, load data and the like. Data mining and machine learning techniques are used to clean, preprocess and feature extract the data, and a data model resource pool for provincial distributed resource operation monitoring, energy analysis and optimal adjustment is established. For example, through analysis of a large amount of historical load data, a load characteristic model is established, which can accurately describe the load change law of different types of users; the operation data of the distributed power supply are processed to construct a distributed power supply output model, providing data support for subsequent analysis and control. When performing analysis and evaluation, for the output data of the distributed resources, high-precision short-term and ultra-short-term output prediction is realized by combining meteorological data and real-time operation data and using deep learning methods; for the load data, time series analysis and machine learning techniques are used to accurately predict different types of loads. For the operation data of the power grid, the maximum capacity of the distributed resources that the distribution network can carry is evaluated by analyzing the topology structure, equipment capacity, operation state and other factors of the power grid. The distributed resource cascade coordination control algorithm realizes the coordinated control among the distributed power supply, energy storage equipment and load according to the system operation state and optimization target, and optimizes the system operation performance. The distribution network reconfiguration algorithm optimizes the topology structure of the power grid by adjusting the switch state of the distribution network, with the goal of reducing network loss and improving power supply reliability.

[0037] This step can be realized by using a platform with functions of operation monitoring, energy analysis, optimal adjustment, and collaborative linkage. Among them, the operation monitoring function displays the operation state of the power grid, the output of the distributed resources and the load change in real time; the energy analysis function analyzes and evaluates the power quality, power balance and network loss of the system; the optimal adjustment function automatically generates and executes the optimal adjustment strategy according to the real-time calculation and analysis results; the collaborative linkage function realizes data sharing and collaborative work among systems through horizontal integration of the new generation of dispatching automation system, distribution automation system, marketing 2.0 system and external resource aggregator platform, so as to achieve the global optimization of distributed resource adjustment and control. The platform uses advanced communication technology and interface standards to ensure stable and reliable data interaction with other systems.

[0038] The application will be further described below through a specific embodiment.

[0039] This embodiment takes a certain eastern coastal province as a new energy high penetration area. In 2023, the distributed photovoltaic installed capacity reaches 22 million kilowatts, and the wind power reaches 8 million kilowatts. Due to insufficient consumption capacity, the annual curtailment rate reaches 7.2%, and the main grid voltage fluctuation exceeds the standard for more than 30 times on average per year. In order to solve these problems, the province has fully deployed the provincial distribution network resource configuration and regulation system.

[0040] At the software application level, through the main distribution integrated energy distribution simulation software, the 13 cities in the province are modeled, and the 220kV line flow reverse sending problem caused by the concentrated access of distributed power in the coastal area is accurately simulated. By adjusting the transformer tap in advance, the voltage qualified rate is increased to 99.8%. Through the multi-objective multi-time scale coordinated control software, more than 5000 distributed resource regulation instructions are generated daily. During the summer peak of 2023, through the daily rolling optimization, the storage charging and discharging strategy is matched with the real-time load, and the daily maximum consumption of new energy power is increased by 1.2 million kilowatt-hours, and the curtailment rate is reduced to 2.1%.

[0041] In terms of platform construction, the data model resource pool integrates 3.2 million user electricity data, 56,000 distribution line parameters and more than 2,000 meteorological monitoring point data to form a dynamic database updated at a minute level. Through the distribution network carrying capacity evaluation, the real-time calculation module finds that 37 10kV lines in some areas are at risk of overload, and immediately starts the network reconstruction algorithm, and adjusts the state of 12 tie switches to reduce the line load rate from 85% to below 60%. The horizontally integrated dispatching automation system is linked with 23 aggregator platforms. During the typhoon in 2023, 200,000 kilowatts of distributed load were cut off within 15 minutes to avoid the collapse of the main grid frequency.

[0042] Since the implementation of this embodiment, the new energy utilization rate in the province has increased by 5.1 percentage points, the main grid stable operation time has increased by 1200 hours, the average service life of the distribution line has been extended by 3 years, and the cumulative economic benefits have exceeded 4 billion yuan.

[0043] The second embodiment of the present application relates to an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the provincial distribution network distributed resource operation simulation analysis method of the first embodiment when executing the computer program.

[0044] The third embodiment of the present application relates to a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the provincial distribution network distributed resource operation simulation analysis method of the first embodiment.

[0045] It is readily apparent that this invention, through integrated main and distribution energy distribution simulation software, can accurately simulate the impact of wide-area distributed resource access on the power balance and stability characteristics of the main grid, identify potential safety hazards in advance, provide dispatchers with a scientific basis for decision-making, and effectively avoid problems such as abnormal power flow and voltage fluctuations caused by distributed resource access, thus ensuring the safe and stable operation of the power grid. Through multi-objective, multi-timescale coordinated control strategy simulation, it achieves precise scheduling and optimized regulation of provincial distributed resources, ensuring both the absorption of distribution network resources and the stable operation of the main grid, thereby improving the power system's ability to cope with various complex operating conditions.

[0046] This invention's provincial distributed resource planning simulation and decision support fully considers factors such as long-term load levels, distributed power generation output levels, and policy impacts, providing scientific planning and allocation strategies and implementation paths for distributed resource investment and development. This avoids blind investment and resource waste, achieving optimal resource allocation. The provincial distribution network resource allocation and regulation platform, through real-time calculation, analysis, and optimization regulation functions, realizes coordinated operation among distributed power sources, energy storage devices, and loads, improving energy utilization efficiency and reducing system operating costs.

[0047] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0048] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0049] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0050] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0051] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A simulation analysis method for the operation of distributed resources in a provincial power distribution network, characterized in that, Includes the following steps: Collect various power system data, construct distributed resource aggregation models and main distribution network models, and perform simulation calculations on the main network and distribution network; By combining various data to make day-ahead forecasts of distributed resource output and load, the established optimized scheduling model is solved to obtain the day-ahead scheduling plan. During the day-ahead operation, the day-ahead scheduling plan is dynamically adjusted based on real-time data and forecast results. The dynamically adjusted distributed resource regulation strategy is then decomposed to each level of distribution network node and equipment to complete the simulation calculation of multi-objective and multi-timescale coordinated control strategy. Collect various types of data related to distributed resource planning, construct a provincial distributed resource planning configuration model, simulate and extrapolate the system planned by the provincial distributed resource planning configuration model, analyze and evaluate the simulation results, and provide decision-making suggestions based on the analysis and evaluation results; The system acquires real-time power grid operation data, distributed resource output data, and load data of the provincial distribution network. Based on these data, it analyzes and evaluates the system and automatically generates and executes optimized regulation strategies to complete the simulation calculation of provincial distribution network resource allocation and regulation.

2. The provincial distribution network distributed resource operation simulation analysis method according to claim 1, characterized in that, When performing simulation calculations for the main grid and distribution network, for the main grid portion, the main grid model in the power system analysis software is used for modeling and simulation calculations to obtain the main grid simulation results; for the distribution network portion, based on the distributed resource aggregation model, considering the access location, capacity, and operating characteristics of distributed resources, a distribution network model suitable for the characteristics of the distribution network is adopted for modeling and calculations to obtain the distribution network simulation results; the main grid model and the distribution network model achieve real-time sharing and collaborative optimization of the main grid simulation results and the distribution network simulation results through a data interaction interface.

3. The provincial distribution network distributed resource operation simulation analysis method according to claim 2, characterized in that, When performing simulation calculations for the main network and distribution network, the distributed resource aggregation model is simplified into an equivalent model that can reflect the overall dynamic characteristics of distributed resources through dynamic multi-source equivalence technology.

4. The provincial distribution network distributed resource operation simulation analysis method according to claim 1, characterized in that, When dynamically adjusting the daily scheduling plan based on real-time data and forecast results during intraday operation, a model predictive control method is used to dynamically adjust the daily scheduling plan with the goal of optimizing the system operation indicators over a future period.

5. The provincial distribution network distributed resource operation simulation analysis method according to claim 1, characterized in that, When the dynamically adjusted distributed resource regulation strategy is decomposed to distribution network nodes and equipment at each level, a distributed collaborative optimization algorithm is adopted to transform the regulation instructions of the upper-level power grid into specific control parameters of the lower-level power grid and equipment, while ensuring the safe operation of the power grid at each level and the overall optimization goal.

6. The provincial distribution network distributed resource operation simulation analysis method according to claim 1, characterized in that, When constructing the provincial distributed resource planning and allocation model, the goal is to achieve sustainable development of the power system, improve energy utilization efficiency, and ensure power supply security. The provincial distributed resource planning and allocation model is constructed with constraints such as power grid construction cost, operating cost, distributed power source investment cost, environmental cost, power grid carrying capacity, reliability requirements, and power quality standards.

7. The provincial distribution network distributed resource operation simulation analysis method according to claim 1, characterized in that, When simulating the system planned by the provincial distributed resource planning and configuration model, various operating scenarios are set to simulate and evaluate the security, stability and economy of the system planned by the provincial distributed resource planning and configuration model, and the simulation evaluation results are displayed in a visual form using big data analysis and visualization technology.

8. The provincial distribution network distributed resource operation simulation analysis method according to claim 1, characterized in that, The system is analyzed and evaluated based on power grid operation data, distributed resource output data, and load data. Specifically, when evaluating power grid operation data, the maximum capacity of distributed resources that the distribution network can support is assessed by analyzing the power grid topology, equipment capacity, and operating status. When analyzing and evaluating distributed resource output data, deep learning methods are used to achieve short-term and ultra-short-term output forecasts by combining meteorological data and real-time operation data. When analyzing and evaluating load data, time series analysis and machine learning techniques are used to forecast different types of loads.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the provincial distribution network distributed resource operation simulation analysis method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the provincial distribution network distributed resource operation simulation analysis method as described in any one of claims 1-8.

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