Power grid future operation trend generation method and system for main grid and distribution network simulation regulation and control

Abstract

A power grid future operation trend generation method and system for main distribution network simulation regulation, characterized in that the method comprises: inputting load prediction data and tie line plan data into a main distribution network topology model, obtaining state estimation data in a to-be-simulated time and space range through power flow operation, and constructing an initial section; analyzing power generation plan data, and implementing apportionment of the power generation plan by using power proportion between model objects; obtaining bus load prediction curves in the load prediction data and matching the bus load prediction curves with model objects in the main distribution network topology model in real time, and adjusting the bus load prediction curves; simulating and deducing first power grid future operation trend data by using new energy prediction data, and simulating and deducing second power grid future operation trend data by using maintenance plan data; and superimposing a pre-set dispatching sequence in the first power grid future operation trend data or the second power grid future operation trend data to generate a power grid future operation trend based on a pre-dispatching event.

Description

Technical Field

[0001] This invention relates to the field of power generation, and more specifically, to a method and system for generating future operating trends of power grids for simulation and control of main and distribution networks. Background Technology

[0002] With the construction of new power systems and the deepening of market-oriented reforms, distributed renewable energy is showing a development pattern of "rapid growth in installed capacity and an increase in the proportion of low-voltage grid connection." Distributed renewable energy in distribution networks has experienced explosive growth, leading to significant changes in the form and operating characteristics of distribution networks. Currently, the active and networked characteristics of distribution networks are becoming increasingly apparent, and the interaction between power sources, grids, loads, and storage is becoming more frequent, bringing new challenges to dispatching and operation.

[0003] The existing distribution automation system mainly provides real-time monitoring and dispatching functions. The dispatching methods are based on real-time dispatching and post-dispatch analysis. There are no applications that predict and estimate future operational risks and their evolution trends. Controllers cannot perceive the development trend of distribution network risks in future periods, nor can they know the spatiotemporal distribution and evolution process of future operational risks. Due to the lack of predictive and pre-control means, it is difficult to cope with the high-intensity and complex control requirements of active distribution networks based solely on the experience of controllers. This greatly restricts the potential of active distribution networks in risk mitigation, local consumption of new energy, complementarity between power generation, grid, load and storage, main distribution coordination, and power supply guarantee.

[0004] The technology for constructing trend data representing the future operation trend of the power grid is the data foundation for active distribution network control simulation and extrapolation, but there is currently limited research on this topic both domestically and internationally. Existing research only focuses on the study of a single initial section, without considering the future development trend of the power grid, and therefore cannot support the simulation of the power grid's long-term operation.

[0005] Existing trend data generation technologies are similar to initial profile generation technologies. These technologies only generate an initial grid operation profile for active distribution network control simulation applications. They lack information such as load following curves, distributed renewable energy following curves, thermal power generation plan curves, and mode change event sequences that characterize the long-term operation of new power grids. They can only support the simulation of active distribution network control applications on a grid profile at a certain point in time. They cannot support the simulation of the essential operating characteristics of new power systems, such as random fluctuations in renewable energy output, peak and valley fluctuations in user load (e.g., electric vehicles), interaction between power generation, grid, load, and storage, and diverse changes in operating modes.

[0006] To address the aforementioned issues, there is an urgent need for a method and system for generating future power grid operation trends for simulated control of the main and distribution networks. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method and system for generating future power grid operation trends for main and distribution network simulation and control. By sequentially constructing initial cross-sections and implementing power allocation, and simulating new energy and maintenance plans after adjusting the load forecast of transmission lines, future power grid operation trend data is generated. Based on this, simulation of pre-scheduled events and simulation and analysis of power grid status are performed.

[0008] The present invention adopts the following technical solution.

[0009] In a first aspect, this invention relates to a method for generating future operating trends of a power grid for simulation and control of a main distribution network. The method includes the following steps: collecting and extracting state data within the spatiotemporal range to be simulated; the state data includes at least load forecast data, tie-line planning data, generation planning data, renewable energy forecast data, and maintenance planning data obtained using the actual operation of the main distribution network; inputting the load forecast data and tie-line planning data into a pre-constructed main distribution network topology model; obtaining state estimation data within the spatiotemporal range to be simulated through power flow calculations; and constructing initial cross-sections within the spatiotemporal range to be simulated using the state estimation data; parsing the generation planning data; and utilizing the relationships between model objects within and between the initial cross-sections. Power ratio is used to allocate power generation plans; the bus load forecast curve in the load forecast data is obtained and matched in real time with the model objects in the main distribution network topology model, so as to adjust the bus load forecast curve according to the system load forecast curve; the new energy power generation forecast curve is generated using the new energy forecast data and input into the main distribution network topology model to simulate and deduce the future operation trend data of the first power grid; the state of the model objects in the main distribution network topology model is changed using the maintenance plan data to simulate and deduce the future operation trend data of the second power grid; a pre-set scheduling sequence is superimposed on the future operation trend data of the first power grid or the future operation trend data of the second power grid to generate the future operation trend of the power grid based on the pre-scheduled events.

[0010] Preferably, the status data includes at least load forecast data, tie-line planning data, power generation planning data, renewable energy forecast data, and maintenance planning data obtained from the actual operation of the main and distribution networks. Specifically: load forecast data includes main grid bus load forecast data, distribution network load forecast data at different voltage levels, and terminal main transformer load forecast data; tie-line planning data includes the injected power limits at each node on the tie-line; power generation planning data includes power generation planning data for coal-fired units, gas-fired units, and pumped storage units; renewable energy forecast data includes forecast data for centralized photovoltaic units and centralized wind turbine units; and maintenance planning data includes maintenance equipment, shutdown / reactivation status, maintenance start time, and maintenance end time.

[0011] Preferably, the load forecast data and tie-line plan data are input into the pre-built main distribution network topology model, including: the pre-built main distribution network topology model is obtained based on the primary power grid model file, the secondary power grid model file, and the power grid quota information file.

[0012] Preferably, the state estimation data within the spatiotemporal range to be simulated is obtained by performing power flow calculation, and the initial cross-section within the spatiotemporal range to be simulated is constructed using the state estimation data, including: loading load forecast data and tie line plan data into the main distribution network topology model to correspond to model objects; gradually adjusting the equipment state of the model objects through power flow calculation until the power flow calculation verification is passed; and setting the initial cross-section and initial cross-section constraints containing equipment and lines based on the power flow calculation results.

[0013] Preferably, the power generation plan data is parsed, and the power ratio between initial cross sections and between model objects within the initial cross sections is used to allocate the power generation plan. This includes: splicing power generation plan data at different scheduling scales according to the spatiotemporal range to be simulated to obtain the power generation curve to be simulated; loading the power generation curve into the main distribution network topology model, summing the constraints of different equipment and lines in multiple initial cross sections, and allocating the power generation plan curve to multiple initial cross sections accordingly; and within each initial cross section, allocating the power generation plan curve to multiple equipment and lines according to the constraint relationships of different equipment and lines in the initial cross section.

[0014] Preferably, the bus load forecast curve in the load forecast data is obtained and matched in real time with the model object in the main distribution network topology model, so as to adjust the bus load forecast curve according to the system load forecast curve. This includes: splicing load forecast data at different scheduling scales according to the spatiotemporal range to be simulated to obtain the system load forecast curve and the bus forecast curve to be simulated; loading the bus forecast curve to be simulated into the main distribution network topology model under the control of the initial section and the allocation result of the power generation plan; if the model object corresponding to any bus forecast curve is overloaded, the overload is allocated proportionally in other load models, and the allocated bus load forecast curve is verified based on the system load forecast bus.

[0015] Preferably, the method involves generating a new energy power generation prediction curve using new energy prediction data and inputting it into the main distribution network topology model to simulate and deduce the future operating trend data of the first power grid. This includes: splicing new energy prediction data at different scheduling scales according to the spatiotemporal range to be simulated to obtain the new energy power generation prediction curve of the system to be simulated; loading the new energy power generation prediction curve into the main distribution network topology model and re-implementing power flow calculation to deduce the future operating trend data of the first power grid.

[0016] Preferably, the state of the model objects in the main distribution network topology model is changed using maintenance plan data to simulate and deduce the future operation trend data of the second power grid. This includes: generating equipment outage operation sequences and equipment resumption operation sequences based on maintenance plan data; changing the state of the model objects in the main distribution network topology model according to the equipment outage operation sequences and equipment resumption operation sequences; and re-implementing power flow calculation to simulate and deduce the future operation trend data of the second power grid.

[0017] Preferably, a pre-set scheduling sequence is superimposed on the future operation trend data of the first power grid or the future operation trend data of the second power grid to generate a future operation trend of the power grid based on pre-scheduled events. The pre-set scheduling sequence includes one or more of the following: a pre-set fault sequence, a pre-set scheduling operation sequence, a distributed power generation output regulation sequence, a load regulation and load control sequence, and an emergency dispatching and handling sequence, based on the main distribution network model.

[0018] A second aspect of this invention relates to a power grid future operation trend generation system for main and distribution network simulation and control using the method of the first aspect of this invention. The system includes a data acquisition module, a cross-section creation module, a power allocation module, a load adjustment module, a simulation and deduction module, and a dispatch event module. The data acquisition module is used to collect and extract state data within the simulated spatiotemporal range. The state data includes at least load forecast data, tie-line plan data, generation plan data, renewable energy forecast data, and maintenance plan data obtained from the actual operation of the main and distribution network. The cross-section creation module is used to input the load forecast data and tie-line plan data into a pre-constructed main and distribution network topology model, obtain state estimation data within the simulated spatiotemporal range through power flow calculations, and construct an initial cross-section within the simulated spatiotemporal range using the state estimation data. The power allocation module is used to analyze generation... The system utilizes planned data and power ratios between and within initial cross-sections to allocate power generation plans. A load adjustment module obtains the bus load forecast curve from the load forecast data and matches it in real-time with model objects in the main distribution network topology model, thereby adjusting the bus load forecast curve based on the system load forecast curve. A simulation and deduction module generates a new energy power generation forecast curve using new energy forecast data and inputs it into the main distribution network topology model to simulate and deduce the future operating trend data of the first power grid. It also uses maintenance plan data to change the state of model objects in the main distribution network topology model to simulate and deduce the future operating trend data of the second power grid. A dispatching event module overlays pre-set dispatching sequences onto the future operating trend data of the first or second power grid to generate a future operating trend of the power grid based on pre-dispatch events.

[0019] The beneficial effects of this invention are as follows: Compared with the prior art, the method and system for generating future operating trends of the power grid for simulation and control of the main distribution network in this invention generate future operating trend data of the power grid by sequentially constructing initial sections, implementing power allocation, and simulating new energy and maintenance plans after adjusting the load forecast of transmission lines. Based on this, simulation of pre-scheduled events and simulation and analysis of the power grid status are performed. This invention effectively improves the potential of active distribution networks in risk mitigation, local consumption of new energy, complementarity between power generation, grid, load, and storage, coordination between main and distribution networks, and power supply guarantee through simulation. It enhances the risk mitigation capability of active distribution networks under multiple uncertain scenarios through proactive control, providing direction for power grid control decisions.

[0020] The beneficial effects of the present invention also include:

[0021] 1. Proactive control simulation of active distribution networks effectively utilizes information such as state estimation, planning, and forecasting to construct future operation trend data of the power grid. Based on the trend data, it simulates and analyzes the future operation status of the power grid, helping dispatchers to anticipate future risks as early as possible. Through proactive control, it enhances the risk resistance capability of active distribution networks under multiple uncertain scenarios and assists dispatchers in making decisions on the direction of power grid operation.

[0022] 2. By utilizing information such as state estimation, power generation plans, maintenance plans, ultra-short-term power generation forecasts for new energy sources, and ultra-short-term load forecasts, trend data that can simulate the long-term operating characteristics of the power grid can be constructed. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for generating future power grid operation trends for simulated control of main and distribution networks according to the present invention.

[0024] Figure 2 This is a schematic diagram of the module structure of a power grid future operation trend generation system for main and distribution network simulation control according to the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this invention are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments not described in this invention obtained by those skilled in the art based on the embodiments described in this invention without creative effort should fall within the protection scope of this invention.

[0026] Figure 1 This is a flowchart illustrating a method for generating future power grid operation trends for simulated control of main and distribution networks, according to the present invention. Figure 1As shown, the first aspect of the present invention relates to a method for generating future operating trends of a power grid for simulated control of a main distribution network, the method comprising steps 1 to 6.

[0027] Step 1: Collect and extract state data within the time and space range to be simulated. The state data shall include at least load forecast data, tie line plan data, power generation plan data, new energy forecast data, and maintenance plan data obtained from the actual operation of the main and distribution networks.

[0028] To extract and analyze trend data, this invention first extracts state data within the spatiotemporal range to be simulated. The state data is derived from a power grid operating state estimation file that matches the primary power grid model.

[0029] Preferably, the status data includes at least load forecast data, tie-line planning data, power generation planning data, renewable energy forecast data, and maintenance planning data obtained from the actual operation of the main and distribution networks. Specifically: load forecast data includes main grid bus load forecast data, distribution network load forecast data at different voltage levels, and terminal main transformer load forecast data; tie-line planning data includes the injected power limits at each node on the tie-line; power generation planning data includes power generation planning data for coal-fired units, gas-fired units, and pumped storage units; renewable energy forecast data includes forecast data for centralized photovoltaic units and centralized wind turbine units; and maintenance planning data includes maintenance equipment, shutdown / reactivation status, maintenance start time, and maintenance end time.

[0030] The power generation planning curves include power generation planning data for centrally dispatched and locally dispatched gas-fired, coal-fired, and pumped-storage units, including unit start-up and shutdown, 96-point day-ahead planning curves, and 16-point day-ahead planning curves. The tie-line planning curves include multi-timescale planning data for inter-provincial and regional tie-lines, such as specific node injection power, 96-point day-ahead planning curves, and 16-point day-ahead planning curves. The new energy power generation forecast curves include short-term and ultra-short-term forecast data for centralized photovoltaic and centralized wind turbine units, including unit start-up and shutdown, short-term 96-point forecast curves, and ultra-short-term 16-point forecast curves. The load forecast curves mainly cover main grid bus load forecast data and low-voltage side 10kV / 35kV load or terminal transformer (distribution area) load forecast data, including 96-point day-ahead forecast curves and 16-point day-ahead forecast curves. In addition, the main and distribution network maintenance plan is used to obtain monthly and weekly maintenance plan data for the main and distribution networks, including maintenance equipment, shutdown / reactivation status, start time, and end time.

[0031] The aforementioned data can be data collected during the actual operation of the power grid, or typical data and future prediction data obtained by analyzing and calculating historical power grid data.

[0032] To analyze future power grid operation trends, this invention supports filtering data content from the aforementioned data based on specific time and spatial ranges. The spatial range parameter characterizes the spatial range of the power grid model and is expressed using two sets: {voltage level 1, voltage level 2, ..., voltage level n} and {substation ID1, substation ID2, ..., substation IDn}. The power grid time range parameter is expressed using {start time point (hour-minute-second), end time point (hour-minute-second)} based on the length of future time it can cover. The following text also mentions that data content predicted using different time scales based on different scheduling and adjustment methods is included within the aforementioned power grid time range.

[0033] Step 2: Input the load forecast data and tie line plan data into the pre-built main distribution network topology model, obtain the state estimation data of the time and space range to be simulated by performing power flow calculation, and use the state estimation data to construct the initial cross section of the time and space range to be simulated.

[0034] Preferably, the load forecast data and tie-line plan data are input into the pre-built main distribution network topology model, including: the pre-built main distribution network topology model is obtained based on the primary power grid model file, the secondary power grid model file, and the power grid quota information file.

[0035] After collecting the relevant status data, some of the data is input into a pre-built main and distribution network topology model to perform simulation calculations. The main and distribution network model is pre-built based on the actual equipment connection relationships, equipment status, and limits of the power grid.

[0036] Preferably, state estimation data within the simulated spatiotemporal range is obtained by performing power flow calculations, and the initial cross-section within the simulated spatiotemporal range is constructed using the state estimation data. This includes: loading load forecast data and tie line planning data into the main distribution network topology model to correspond to model objects; gradually adjusting the equipment status of model objects through power flow calculations until the power flow calculation verification is passed; and setting initial cross-sections and initial cross-section constraints that include equipment and lines based on the power flow calculation results.

[0037] Starting from load forecast data, tie-line planning data, etc., the primary equipment model of the main distribution network and the aforementioned state estimation data are loaded. The equipment model and state estimation data are matched, and the equipment state is modified and adjusted according to the state estimation data. Topology analysis and main distribution coordinated power flow calculation are performed. If the power flow calculation fails, the verification result is "not passed"; if the power flow calculation is successful, the verification result is "passed", and the initial grid profile is generated for subsequent processing and calculation.

[0038] Step 3: Analyze the power generation plan data and allocate the power generation plan by utilizing the power ratios between the initial cross sections and between model objects within the initial cross sections.

[0039] Preferably, the power generation plan data is parsed, and the power ratio between initial cross sections and between model objects within the initial cross sections is used to allocate the power generation plan. This includes: splicing power generation plan data at different scheduling scales according to the spatiotemporal range to be simulated to obtain the power generation curve to be simulated; loading the power generation curve into the main distribution network topology model, summing the constraints of different equipment and lines in multiple initial cross sections, and allocating the power generation plan curve to multiple initial cross sections accordingly; and within each initial cross section, allocating the power generation plan curve to multiple equipment and lines according to the constraint relationships of different equipment and lines in the initial cross section.

[0040] In this invention, based on the start time and duration of the pre-scheduled task, short-term, day-ahead, intraday, weekly, and monthly power generation plans are spliced ​​together to generate a power generation curve within a simulated time period. By splicing together scheduling data at multiple time scales, the spliced ​​data can contain potential information about multiple different scheduling methods within a predicted time period, thereby enabling comprehensive analysis and prediction of the pre-scheduled task.

[0041] By analyzing the power generation plan, planned objects and their data within the predicted space can be generated. This allows for the matching of planned objects with model objects, resulting in valid planned objects and their data. When a planned object contains multiple model objects (i.e., the planned object is composed of multiple model objects), the power of the planned object is calculated according to the power of the model objects or the initial cross-section. The power generation plan is then allocated to multiple model objects based on the power ratio, and these model objects are replaced with their corresponding planned objects. These planned objects include unit planned objects and plant planned objects. Finally, a preprocessed power generation plan is generated through power allocation calculations, ensuring that the objects in the preprocessed plan are consistent with those in the model.

[0042] Step 4: Obtain the bus load forecast curve from the load forecast data and match it with the model object in the main distribution network topology model in real time, so as to adjust the bus load forecast curve according to the system load forecast curve.

[0043] Preferably, the bus load forecast curve in the load forecast data is obtained and matched in real time with the model object in the main distribution network topology model, so as to adjust the bus load forecast curve according to the system load forecast curve. This includes: splicing load forecast data at different scheduling scales according to the spatiotemporal range to be simulated to obtain the system load forecast curve and the bus forecast curve to be simulated; under the control of the allocation results of the initial section and the power generation plan, loading the bus forecast curve to be simulated into the main distribution network topology model; if the model object corresponding to any bus forecast curve is overloaded, the overload is allocated proportionally in other load models, and the allocated bus load forecast curve is verified based on the system load forecast bus.

[0044] During the loading of load forecast data, based on the start time and duration of the pre-scheduled task, ultra-short-term, short-term, day-ahead, intraday, weekly, and monthly load forecasts are spliced ​​in a similar manner to generate load forecast curves for the simulated period. System load forecasts and bus load forecasts are analyzed to match the bus load forecast objects with the model objects, obtaining valid bus load forecast objects and their data. The bus load forecast is corrected based on the system load forecast. If the bus load forecast object is overloaded, adjacent load models are extracted from the actual load model set, and power allocation is performed according to the ratio between adjacent load models, while also considering the maximum current carrying capacity of relevant lines and transformer power, ultimately generating preprocessed load forecast results.

[0045] Step 5: Generate a new energy power generation prediction curve using new energy prediction data and input it into the main distribution network topology model to simulate and deduce the future operation trend data of the first power grid. Use maintenance plan data to change the state of the model objects in the main distribution network topology model to simulate and deduce the future operation trend data of the second power grid.

[0046] Preferably, the method involves generating a new energy power generation prediction curve using new energy prediction data and inputting it into the main distribution network topology model to simulate and deduce the future operating trend data of the first power grid. This includes: splicing new energy prediction data at different scheduling scales according to the spatiotemporal range to be simulated to obtain the new energy power generation prediction curve of the system to be simulated; loading the new energy power generation prediction curve into the main distribution network topology model and re-implementing power flow calculation to deduce the future operating trend data of the first power grid.

[0047] Based on the actual time and duration of the pre-scheduled tasks, ultra-short-term, short-term, day-ahead, intraday, and weekly renewable energy power generation forecasts are spliced ​​together to generate renewable energy power generation forecast curves for the simulated period. For the renewable energy power generation forecast data, the forecasts are analyzed to match the forecast objects with the model objects, thereby obtaining valid renewable energy power generation objects and their forecast data.

[0048] After this, maintenance plans and power outage plans can be loaded and parsed to match maintenance objects with model objects; maintenance operation sequences, including equipment power outage operation sequences and equipment service resumption operation sequences, can be generated based on the current equipment status and maintenance start and end times, and finally, a pre-processed maintenance plan can be generated.

[0049] In the above process, renewable energy generation forecast data and different maintenance plans under different conditions can be arbitrarily combined to analyze the future trends of the power grid under different circumstances. These renewable energy generation forecasts and maintenance plans can be obtained based on information such as actual power grid fault analysis and forecasting needs.

[0050] This invention also supports using predictions to obtain future operating trend data of the first and second power grids under various different states as the basis for implementing the next step of analysis, thereby analyzing the weak links and potential risks of the main and distribution networks in advance through the simulation process.

[0051] Step 6: Overlay a pre-set scheduling sequence onto the first power grid future operation trend data or the second power grid future operation trend data to generate a power grid future operation trend based on pre-scheduled events.

[0052] After obtaining the future operation trend data of the power grid in the above steps, the simulation and simulation program can overlay user-set business data such as expected fault sequences, expected dispatch operation sequences, distributed power generation output control sequences, load regulation and load control sequences, and emergency dispatch and handling sequences into the trend data according to business analysis needs, so as to perform simulation calculation and analysis of the future operation of the power grid.

[0053] Through the above methods, this invention solves the problem of automatically generating long-term future operating trend data of the power grid for active distribution network control simulation. This includes basic simulation calculation data obtained during active distribution network control simulation, such as initial grid cross-sections, load following curves, distributed renewable energy following curves, thermal power generation plan adjustments, and sequence of mode change events. This data can be further used to simulate long-term future operating trends of the power grid. Based on the needs of business simulation analysis, fault events, control operations, load generation adjustments, and other control operation events can be superimposed on the trend data to simulate and analyze the impact of control operations on the long-term future operation of the power grid.

[0054] The above approach proposes a trend data generation method that can represent the future operation trend of the power grid. It can support simulation and extrapolation programs to simulate and extrapolate the long-term operation process of the power grid in the future, and has extremely high application value.

[0055] Figure 2 This is a schematic diagram of the module structure of a power grid future operation trend generation system for main and distribution network simulation control according to the present invention. Figure 2As shown, the second aspect of the present invention relates to a power grid future operation trend generation system for main and distribution network simulation and control using the method of the first aspect of the present invention. The system includes a data acquisition module, a cross-section creation module, a power allocation module, a load adjustment module, a simulation and deduction module, and a dispatch event module. The data acquisition module is used to collect and extract state data within the simulated spatiotemporal range. The state data includes at least load forecast data, tie-line plan data, generation plan data, new energy forecast data, and maintenance plan data obtained using the actual operation process of the main and distribution network. The cross-section creation module is used to input the load forecast data and tie-line plan data into a pre-constructed main and distribution network topology model, obtain state estimation data within the simulated spatiotemporal range through power flow calculation, and construct an initial cross-section within the simulated spatiotemporal range using the state estimation data. The power allocation module is used to analyze the power generation... The system utilizes power planning data and power ratios between and within initial cross-sections to allocate power generation plans. A load adjustment module obtains the bus load forecast curve from the load forecast data and matches it in real-time with model objects in the main distribution network topology model, thereby adjusting the bus load forecast curve based on the system load forecast curve. A simulation and deduction module generates a new energy power generation forecast curve using new energy forecast data and inputs it into the main distribution network topology model to simulate and deduce the future operating trend data of the first power grid. It also uses maintenance plan data to change the state of model objects in the main distribution network topology model to simulate and deduce the future operating trend data of the second power grid. A dispatching event module overlays pre-set dispatching sequences onto the future operating trend data of the first or second power grid to generate a future operating trend of the power grid based on pre-dispatch events.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for generating future operating trends of a power grid for simulated control of main and distribution networks, characterized in that, The method includes the following steps: Collect and extract state data within the time and space range to be simulated. The state data includes at least load forecast data, tie line plan data, power generation plan data, new energy forecast data, and maintenance plan data obtained from the actual operation of the main and distribution networks. The load forecast data and the tie line plan data are input into the pre-constructed main distribution network topology model. The state estimation data of the time and space range to be simulated is obtained by performing power flow calculation. The initial cross section of the time and space range to be simulated is constructed using the state estimation data. The power generation plan data is analyzed, and the power ratio between the initial cross sections and between model objects within the initial cross sections is used to allocate the power generation plan. Obtain the bus load forecast curve from the load forecast data and match it in real time with the model object in the main distribution network topology model, thereby adjusting the bus load forecast curve according to the system load forecast curve; New energy power generation prediction curves are generated using new energy forecast data and input into the main distribution network topology model to simulate and deduce the future operation trend data of the first power grid. The state of the model objects in the main distribution network topology model is changed using the maintenance plan data to simulate and deduce the future operation trend data of the second power grid. A pre-set scheduling sequence is superimposed on the first power grid future operation trend data or the second power grid future operation trend data to generate a power grid future operation trend based on pre-scheduled events.

2. The method for generating future power grid operation trends for main and distribution network simulation control according to claim 1, characterized in that: The status data includes at least load forecast data, tie-line plan data, power generation plan data, renewable energy forecast data, and maintenance plan data obtained from the actual operation of the main and distribution networks, including: The load forecast data includes main grid bus load forecast data, distribution network load forecast data at different voltage levels, and terminal main transformer load forecast data. The tie-line planning data includes the injection power limits for each node on the tie-line; The power generation plan data includes power generation plan data for coal-fired units, power generation plan data for gas-fired units, and power generation plan data for pumped storage units; The new energy forecast data includes forecast data for centralized photovoltaic units and centralized wind turbine units; The maintenance plan data includes the equipment to be maintained, its status as out of service or back in service, maintenance start time, and maintenance end time.

3. The method for generating future power grid operation trends for main and distribution network simulation control according to claim 2, characterized in that: The step of inputting the load forecast data and the tie-line plan data into the pre-constructed main distribution network topology model includes: The pre-built main and distribution network topology model is obtained based on the primary power grid model file, the secondary power grid model file, and the power grid quota information file.

4. The method for generating future power grid operation trends for main and distribution network simulation control according to claim 3, characterized in that: The step of obtaining state estimation data within the spatiotemporal range to be simulated through power flow calculation, and constructing an initial cross-section within the spatiotemporal range to be simulated using the state estimation data, includes: The load forecast data and the tie-line plan data are loaded into the main distribution network topology model to correspond to the model objects; The device status of the model object is gradually adjusted through power flow calculation until the power flow calculation verification is passed. Based on the power flow calculation results, set the initial cross-section and initial cross-section constraints, including equipment and lines.

5. The method for generating future power grid operation trends for main and distribution network simulation control according to claim 4, characterized in that: The process of parsing the power generation plan data and allocating the power generation plan using the power ratios between the initial cross-sections and between model objects within the initial cross-sections includes: Based on the spatiotemporal range to be simulated, the power generation plan data at different scheduling scales are spliced ​​together to obtain the power generation curve to be simulated. The power generation curve is loaded into the main distribution network topology model, the constraints of different equipment and lines in multiple initial sections are summed, and the power generation plan curve is distributed to multiple initial sections accordingly. Within each initial cross section, the power generation plan curve is distributed to multiple devices and lines based on the constraints of different devices and lines within the initial cross section.

6. The method for generating future power grid operation trends for main and distribution network simulation control according to claim 5, characterized in that: The process of obtaining the bus load forecast curve from the load forecast data and matching it in real time with the model object in the main distribution network topology model, thereby adjusting the bus load forecast curve according to the system load forecast curve, includes: Based on the spatiotemporal range to be simulated, load forecast data at different scheduling scales are spliced ​​together to obtain the system load forecast curve and the bus forecast curve to be simulated. Under the control of the initial cross-section and the allocation result of the power generation plan, the bus prediction curve to be simulated is loaded into the main distribution network topology model; If the model object corresponding to any bus prediction curve is overloaded, the overload is proportionally allocated to other load models, and the allocated bus load prediction curve is verified based on the system load prediction curve.

7. The method for generating future power grid operation trends for main and distribution network simulation control according to claim 6, characterized in that: The process of generating a new energy power generation forecast curve using new energy forecast data and inputting it into the main distribution network topology model to simulate and deduce the future operating trend data of the first power grid includes: Based on the spatiotemporal range to be simulated, the new energy prediction data at different scheduling scales are spliced ​​together to obtain the new energy power generation prediction curve of the system to be simulated. The new energy power generation prediction curve is loaded into the main distribution network topology model, and the power flow calculation is re-implemented to deduce the future operating trend data of the first power grid.

8. The method for generating future power grid operation trends for main and distribution network simulation control according to claim 7, characterized in that: The process of using the maintenance plan data to change the state of model objects in the main distribution network topology model in order to simulate and deduce the future operating trend data of the second power grid includes: Based on the maintenance plan data, generate equipment power outage operation sequences and equipment service resumption operation sequences. The state of the model objects in the main distribution network topology model is changed according to the equipment power outage operation sequence and the equipment service recovery operation sequence, and the power flow calculation is re-implemented to simulate and deduce the future operation trend data of the second power grid.

9. The method for generating future power grid operation trends for main and distribution network simulation control according to claim 8, characterized in that: The step of overlaying a pre-set scheduling sequence onto the first or second power grid future operation trend data to generate a power grid future operation trend based on pre-scheduled events includes: The pre-set scheduling sequence is one or more of the following based on the main and distribution network model: a pre-set fault sequence, a pre-set scheduling operation sequence, a distributed power generation output regulation sequence, a load regulation and load control sequence, and an emergency scheduling and handling sequence.

10. A power grid future operation trend generation system for main and distribution network simulation and control using the method described in any one of claims 1-9, characterized in that: The system includes a data acquisition module, a cross-section creation module, a power allocation module, a load adjustment module, a simulation and deduction module, and a scheduling event module; among which, The data acquisition module is used to collect and extract state data within the time and space range to be simulated. The state data includes at least load forecast data, tie line plan data, power generation plan data, new energy forecast data, and maintenance plan data obtained using the actual operation process of the main distribution network. The cross-section creation module is used to input the load forecast data and the tie line plan data into the pre-constructed main distribution network topology model, obtain the state estimation data of the time and space range to be simulated by performing power flow calculation, and construct the initial cross-section of the time and space range to be simulated using the state estimation data. The power allocation module is used to parse the power generation plan data and allocate the power generation plan by utilizing the power ratio between the initial cross sections and between the model objects within the initial cross sections. The load adjustment module is used to obtain the bus load prediction curve from the load prediction data and match it with the model object in the main distribution network topology model in real time, so as to adjust the bus load prediction curve according to the system load prediction curve. The simulation and deduction module is used to generate a new energy power generation prediction curve using new energy prediction data and input it into the main distribution network topology model to simulate and deduce the future operation trend data of the first power grid. It also uses the maintenance plan data to change the state of the model objects in the main distribution network topology model to simulate and deduce the future operation trend data of the second power grid. The scheduling event module is used to overlay a pre-set scheduling sequence onto the first power grid future operation trend data or the second power grid future operation trend data to generate a power grid future operation trend based on the pre-scheduled event.

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