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

Abstract

The invention discloses a power grid future operation trend generation method and system for main distribution network simulation regulation and control, and the method is characterized in that the method comprises the steps: 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 space-time range through power flow calculation, and constructing an initial section; the power generation plan data are analyzed, and the power proportion between the model objects is utilized to share the power generation plan; obtaining a bus load prediction curve in the load prediction data, matching the bus load prediction curve with a model object in the main distribution network topology model in real time, and adjusting the bus load prediction curve; simulating and deducing future operation trend data of the first power grid by using the new energy prediction data, and simulating and deducing future operation trend data of the second power grid by using the maintenance plan data; and superposing a preset scheduling 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 the pre-scheduling event.

Description

Technical Field

[0001] The present invention relates to a field, and more specifically, to a method and system for generating future operation trends of a power grid for simulation control of a main distribution network. Background Art

[0002] With the construction of new power systems and the deepening of market-oriented reforms, distributed renewable energy has shown a development pattern of "rapid development of installed capacity and increased proportion of low-voltage grid connection". Distributed renewable energy has experienced explosive growth in distribution networks, and the form and operation characteristics of distribution networks have undergone major changes. At present, the active and networked characteristics of distribution networks are becoming increasingly obvious, and the interaction between sources, networks, loads and storage is becoming more and more frequent, which brings new challenges to dispatching and operation.

[0003] The functions provided by the existing dispatching automation system are mainly real-time monitoring and dispatching. The dispatching method belongs to the real-time dispatching and post-dispatch analysis mode. There is no application for predicting and estimating future operating risks and their evolution trends. The dispatching personnel cannot perceive the development trend of distribution network risks in the future period, nor can they know the spatiotemporal distribution and evolution process of future operating risks. Due to the lack of prediction and pre-control means, it is difficult to cope with the high-intensity and complex control requirements of the active distribution network based on the experience of the dispatching personnel alone, which greatly restricts the potential of the active distribution network in risk resistance, local consumption of new energy, source-grid-load-storage complementarity, main-distribution coordination, and power supply guarantee.

[0004] The trend data construction technology that represents the future operation trend of the power grid is the data basis for the simulation and deduction of active distribution network regulation. Currently, there are few studies at home and abroad. Existing studies only focus on the research of a single initial section, without considering the future development trend of the power grid, and cannot support the simulation of the future long-term operation process of the power grid.

[0005] The existing trend data generation technology is similar to the initial section generation technology, which only generates an initial power grid operation section for the active distribution network control simulation application. It does not have information such as load following curves, distributed new energy following curves, thermal power generation plan curves, mode change event sequences, etc. that characterize the long-term operation process of the new power grid. It can only support the simulation of active distribution network control application on the power grid section at a certain point in time, and cannot support the simulation of the essential operating characteristics of the new power system such as random fluctuations in new energy output, peak and valley fluctuations in user load (such as electric vehicles), source-grid-load-storage interaction, and diverse operating modes.

[0006] In view of the above problems, there is an urgent need for a method and system for generating future operation trends of a power grid for simulation control of the main distribution network. Summary of the invention

[0007] In order to solve the deficiencies in the prior art, the present invention provides a method and system for generating future operation trends of a power grid for simulation control of a main distribution network, which constructs initial sections in sequence, implements power sharing, simulates new energy and maintenance plans after adjusting the load forecast of the transmission line, thereby generating future operation trend data of the power grid, and on this basis, implements simulation of pre-dispatching events and simulation and analysis of the power grid status.

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

[0009] The first aspect of the present invention relates to a method for generating future operation trends of a power grid for simulation and regulation of a main distribution network, the method comprising the following steps: collecting and extracting state data within a time and space range to be simulated, the state data at least including load forecast data, tie line plan data, power generation plan data, new energy forecast data and maintenance plan data obtained by using the actual operation process of the main distribution network; inputting the load forecast data and tie line plan data into a pre-constructed main distribution network topology model, obtaining state estimation data within the time and space range to be simulated by implementing a flow calculation, and constructing an initial section within the time and space range to be simulated using the state estimation data; parsing the power generation plan data, and using the model objects between the initial sections and within the initial sections to obtain state estimation data; The power ratio is calculated and the allocation of the power generation plan is implemented; the bus load forecast curve in the load forecast data is obtained and matched 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; the new energy power generation forecast curve is generated by using the new energy forecast data, and is input into the main distribution network topology model to simulate and deduce the future operation trend data of the first power grid, and the state of the model object in the main distribution network topology model is changed by using the maintenance plan data to simulate and deduce the future operation trend data of the second power grid; a pre-set dispatch 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-dispatching event.

[0010] Preferably, the status data at least includes load forecast data, interconnection line planning data, power generation planning data, new energy forecast data and maintenance planning data obtained by utilizing the actual operation process of the main distribution network, including: load forecast data includes main grid bus load forecast data, load forecast data at different voltage levels of the distribution network, and load forecast data of the terminal main transformer; interconnection line planning data includes injection power limit of each node on the interconnection line; power generation planning data includes power generation planning data of coal-fired units, power generation planning data of gas units, and power generation planning data of pumped storage units; new energy forecast data includes forecast data of centralized photovoltaic units and centralized wind turbine units; maintenance planning data includes maintenance equipment, decommissioning status, maintenance start time, and maintenance end time.

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

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

[0013] Preferably, the power generation plan data is parsed, and the power generation plan is allocated by using the power ratio between the initial sections and between the model objects within the initial sections, including: according to the time and space range to be simulated, the power generation plan data at different scheduling scales are spliced ​​to obtain the power generation curve to be simulated; the power generation curve is loaded into the main distribution network topology model, the constraint quantities of different equipment and lines in multiple initial sections are summed, and the power generation plan curve is allocated to multiple initial sections accordingly; within each initial section, the power generation plan curve is allocated to multiple equipment and lines according to the constraint relationship between different equipment and lines in the initial section.

[0014] Preferably, the bus load forecast curve in the load forecast data is obtained and matched 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, including: according to the time and space range to be simulated, the load forecast data at different scheduling scales are spliced ​​to obtain the system load forecast curve to be simulated and the bus prediction curve to be simulated; under the control of the initial 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 amount is proportionally allocated in other load models, and the allocated bus load forecast curve is verified based on the system load forecast bus.

[0015] Preferably, new energy prediction data is used to generate a new energy power generation prediction curve, and the curve is input into the main distribution network topology model to simulate and deduce the future operation trend data of the first power grid, including: splicing the new energy prediction data at different scheduling scales according to the time and space 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 the flow calculation to deduce the future operation trend data of the first power grid.

[0016] Preferably, the maintenance plan data is used to change the status of model objects in the main distribution network topology model to simulate and deduce the future operating trend data of the second power grid, including: generating an equipment power outage operation sequence and an equipment recommissioning operation sequence based on the maintenance plan data, changing the status of model objects in the main distribution network topology model according to the equipment power outage operation sequence and the equipment recommissioning operation sequence, and re-implementing the flow calculation to simulate and deduce the future operating trend data of the second power grid.

[0017] Preferably, a pre-set dispatching 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-dispatching events, including: the pre-set dispatching sequence is one or more of an anticipated fault sequence pre-set based on the main distribution network model, an anticipated dispatching operation sequence, a distributed power generation output regulation sequence, a load regulation and load control sequence, and an emergency dispatching disposal sequence.

[0018] The second aspect of the present invention relates to a system for generating future operation trends of a power grid for simulation and regulation of a main distribution network using the method of the first aspect of the present invention, the system comprising a data acquisition module, a section creation module, a power sharing module, a load regulation module, a simulation deduction module and a scheduling event module; wherein the data acquisition module is used to collect and extract state data within the time and space range to be simulated, the state data at least including load forecast data, interconnection line plan data, power generation plan data, new energy forecast data and maintenance plan data obtained by using the actual operation process of the main distribution network; the section creation module is used to input the load forecast data and the interconnection line plan data into a pre-constructed main distribution network topology model, obtain state estimation data within the time and space range to be simulated by implementing flow calculations, and construct an initial section within the time and space range to be simulated using the state estimation data; the power sharing module is used to analyze the power generation process, the load forecast data and the interconnection line plan data, the power generation plan data, the new energy forecast data and the maintenance plan data; the power generation module is used to analyze the power generation process, the power generation ... The system obtains the plan data and uses the power ratio between the model objects in the initial sections and within the initial sections to implement the allocation of the power generation plan; the load adjustment module is used to obtain the bus load forecast curve in the load forecast data and match it with the model objects 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; the simulation deduction module is used to generate the new energy power generation forecast curve using the new energy forecast 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, and use 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 superimpose a pre-set scheduling sequence 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-scheduling event.

[0019] The beneficial effect of the present invention is that, compared with the prior art, the method and system for generating the future operation trend of the power grid for simulation control of the main distribution network in the present invention constructs the initial section in sequence, implements power sharing, simulates new energy and maintenance plans after adjusting the load forecast of the transmission line, thereby generating the future operation trend data of the power grid, and on this basis, implements the simulation and analysis of the pre-dispatch event and the power grid state. The present invention effectively improves the potential of the active distribution network in risk resistance, local consumption of new energy, complementary source network load storage, main distribution coordination, and power supply guarantee through simulation and emulation, and improves the risk resistance ability of the active distribution network in multiple uncertain scenarios through active control, providing direction for power grid control decision-making.

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

[0021] 1. Forward-looking control simulation and deduction of active distribution networks, effectively using state estimation, planning, forecasting and other information to construct future operation trend data of the power grid, and simulate and pre-analyze the future operation status of the power grid based on trend data, to help dispatchers foresee future risks as early as possible, and improve the risk resistance of active distribution networks in multiple uncertain scenarios through active control, and assist control personnel in making decisions on the direction of power grid operation.

[0022] 2. Use information such as state estimation, power generation plan, maintenance plan, ultra-short-term power generation forecast of new energy, and ultra-short-term load forecast to build trend data that can simulate the future long-term operating characteristics of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of a flow chart of a method for generating future operation trends of a power grid for simulation control of a main distribution network according to the present invention;

[0024] Figure 2 The present invention is a schematic diagram of the module structure of a system for generating future operation trends of a power grid for simulation control of a main distribution network. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only embodiments of a part of the present invention, not all embodiments. Based on the spirit of the present invention, all other embodiments not recorded in the present invention obtained by ordinary technicians in this field according to the embodiments recorded in the present invention without making creative work should belong to the protection scope of the present invention.

[0026] Figure 1 The figure is a flow chart of a method for generating future operation trend of power grid for simulation control of main distribution network according to the present invention. Figure 1As shown, the first aspect of the present invention relates to a method for generating future operation trends of a power grid for simulation regulation of a main distribution network, and the method includes steps 1 to 6.

[0027] Step 1, collect and extract state data within the time and space range to be simulated, the state data at least including load forecast data, interconnection line plan data, power generation plan data, new energy forecast data and maintenance plan data obtained by utilizing the actual operation process of the main distribution network.

[0028] In order to extract and analyze trend data, the present invention first extracts state data within the time and space range to be simulated. The state data is data in a power grid operation state estimation file that matches the power grid primary model.

[0029] Preferably, the status data at least includes load forecast data, interconnection line planning data, power generation planning data, new energy forecast data and maintenance planning data obtained by utilizing the actual operation process of the main distribution network, including: load forecast data includes main grid bus load forecast data, load forecast data at different voltage levels of the distribution network, and load forecast data of the terminal main transformer; interconnection line planning data includes injection power limit of each node on the interconnection line; power generation planning data includes power generation planning data of coal-fired units, power generation planning data of gas units, and power generation planning data of pumped storage units; new energy forecast data includes forecast data of centralized photovoltaic units and centralized wind turbine units; maintenance planning data includes maintenance equipment, decommissioning status, maintenance start time, and maintenance end time.

[0030] Among them, the power generation plan curve may include the power generation plan data of gas units, coal-fired units, pumped storage units and other units dispatched by the centralized dispatching units and local dispatching units, including unit start and stop, 96-point plan curves on the day before, and 16-point plan curves on the day. The interconnection line plan curve may include multi-time scale plan data of inter-provincial and regional interconnection lines, such as specific node injection power, 96-point plan curves on the day before, and 16-point plan curves on the day. The new energy power generation forecast curve may include short-term and ultra-short-term forecast data of centralized photovoltaic and centralized wind turbines, including unit start and stop, short-term 96-point forecast curves, and ultra-short-term 16-point forecast curves. The load forecast curve mainly covers the load forecast data of the main grid busbar and the load forecast data of the 10 kV / 35 kV load on the low-voltage side or the terminal main transformer (substation), including the 96-point forecast curve on the day before and the 16-point forecast curve on the day. In addition, the main distribution network maintenance plan is used to obtain the monthly and weekly maintenance plan data of the main distribution network, including maintenance equipment, shutdown and re-commissioning status, start time and end time.

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

[0032] In order to analyze the future operation trend of the power grid, the present invention supports filtering the data content from the above data for specific time and space ranges. The spatial range parameter characterizes the spatial range of the power grid model, and is expressed by two sets of {voltage level 1, voltage level 2, ..., voltage level n} and {substation ID1, substation ID2, ..., substation IDn}. The power grid time range parameter is expressed by {starting time point (hours-minutes-seconds), ending time point (hours-minutes-seconds)} according to the future time length that can be covered. It is also mentioned below that the data content predicted using different time scales according to different scheduling methods and adjustment methods are all included in the above-mentioned power grid time range.

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

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

[0035] After collecting the corresponding status data, some of the data is input into the pre-built main distribution network topology model to implement simulation operations through the main distribution network topology model. The main distribution network model is pre-built based on the real equipment connection relationship, equipment status, limit and other information of the power grid.

[0036] Preferably, state estimation data within the time and space range to be simulated is obtained by implementing power flow calculations, and the initial section within the time and space range to be simulated is constructed using the state estimation data, including: loading load forecast data and interconnection line plan data into the main distribution network topology model to correspond to the model object; gradually adjusting the equipment state of the model object through power flow calculations until the power flow calculation verification is passed; and setting the initial section and initial section constraints including equipment and lines based on the power flow calculation results.

[0037] Based on the load forecast data, interconnection line planning data and other contents, load the primary equipment model of the main distribution network and the above-mentioned state estimation data, match the equipment model with the state estimation data, and modify and adjust the equipment state according to the state estimation data; perform topological analysis and main distribution coordinated flow calculation. If the flow calculation fails, the verification result is failed; if the flow calculation succeeds, the verification result is passed, and the initial section of the power grid is generated for subsequent processing and calculation.

[0038] Step 3: parse the power generation plan data, and use the power ratios between the initial sections and between the model objects within the initial sections to implement the allocation of the power generation plan.

[0039] Preferably, the power generation plan data is parsed, and the power generation plan is allocated by using the power ratio between the initial sections and between the model objects within the initial sections, including: according to the time and space range to be simulated, the power generation plan data at different scheduling scales are spliced ​​to obtain the power generation curve to be simulated; the power generation curve is loaded into the main distribution network topology model, the constraint quantities of different equipment and lines in multiple initial sections are summed, and the power generation plan curve is allocated to multiple initial sections accordingly; within each initial section, the power generation plan curve is allocated to multiple equipment and lines according to the constraint relationship between different equipment and lines in the initial section.

[0040] In the present invention, short-term, day-ahead, intraday, weekly and monthly power generation plans are spliced ​​according to the start time and duration of the pre-scheduling task to generate a power generation curve within the simulation period. Through the scheduling data on a variety of different time scales, a variety of scheduling data are spliced. The spliced ​​data content can contain potential information of a variety of different scheduling methods in a time period to be predicted, thereby implementing a comprehensive analysis and prediction of the pre-scheduling task.

[0041] By parsing the power generation plan, it is possible to form the plan object and its data within the space to be predicted, complete the matching of the plan object and the model object, and obtain the effective plan object and its data. In the case where the plan object contains multiple model objects, that is, the plan object is composed of multiple model objects, the power of the plan object is implemented according to the model object contained in the initial section. The power calculation of the model object or the initial section is implemented, and the power generation plan is allocated to multiple model objects according to the power ratio, and the multiple model objects are replaced with the corresponding plan objects. The plan objects here include unit plan objects and plant plan objects. Finally, the preprocessed power generation plan is generated through power allocation calculation, and the objects in the processed power generation plan are consistent with the objects in the model.

[0042] Step 4: Obtain the bus load prediction curve in 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.

[0043] Preferably, the bus load forecast curve in the load forecast data is obtained and matched 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, including: according to the time and space range to be simulated, the load forecast data at different scheduling scales are spliced ​​to obtain the system load forecast curve to be simulated and the bus forecast curve to be simulated; under the control of the initial section and the allocation result of the power generation plan, the bus forecast 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 amount is proportionally allocated in other load models, and the allocated bus load forecast curve is verified based on the system load forecast bus.

[0044] In the process of loading the load forecast data, according to the start time and duration of the pre-scheduling task, a similar method is used to splice the ultra-short-term, short-term, day-ahead, intraday, weekly, and monthly load forecasts to generate the load forecast curve within the simulation period. The system load forecast and bus load forecast are analyzed, and the bus load forecast object and the model object are matched to obtain a valid bus load forecast object and its data. The bus load forecast is corrected according to the system load forecast. If the power of the bus load forecast object is overloaded, the adjacent load models are extracted from the actual load model set, and the power is allocated according to the ratio between the adjacent load models. At the same time, the maximum current carrying capacity of the relevant lines and the transformer power are considered, and finally the pre-processed load forecast result is generated.

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

[0046] Preferably, new energy prediction data is used to generate a new energy power generation prediction curve, and the curve is input into the main distribution network topology model to simulate and deduce the future operation trend data of the first power grid, including: splicing the new energy prediction data at different scheduling scales according to the time and space 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 the flow calculation to deduce the future operation trend data of the first power grid.

[0047] According to the actual time and duration of the pre-scheduling task, the ultra-short-term, short-term, day-ahead, intraday, and weekly new energy power generation forecasts are spliced ​​to generate the new energy power generation forecast curve within the simulation period. For the new energy power generation forecast data, the new energy power generation forecast is analyzed to complete the matching of the new energy power generation forecast object and the model object to obtain effective new energy power generation objects and their forecast data.

[0048] After that, you can also load and parse the maintenance plan and power outage plan to complete the matching of maintenance objects and model objects; generate a maintenance operation sequence based on the current equipment status and maintenance start and end time, including equipment power outage operation sequence and equipment decommissioning operation sequence, and finally generate a pre-processed maintenance plan.

[0049] In the above process, the forecast data of renewable energy power generation in different states and different maintenance plans can be combined arbitrarily to analyze the future trend of the power grid in different situations. These renewable energy power generation forecasts and maintenance plans can be obtained based on the actual power grid fault analysis forecast demand and other information.

[0050] The present invention also supports obtaining future operation trend data of the first and second power grids in various states through prediction as a basis for implementing the next step of analysis, thereby analyzing the weak links and potential risks of the main distribution network in advance through the deduction and simulation process.

[0051] Step 6: superimpose a preset scheduling sequence 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 the pre-scheduling event.

[0052] After obtaining the future operation trend data of the power grid in the above steps, the simulation program can be used to superimpose user-set business data such as expected fault sequence, expected dispatch operation sequence, distributed power generation output regulation sequence, load regulation and load control sequence, emergency dispatch disposal sequence, etc. on the trend data according to business analysis needs, so as to simulate and analyze the future operation of the power grid.

[0053] Through the above-mentioned method, the present invention solves the problem of automatically generating the future long-term operation trend data of the power grid for the simulation and deduction of active distribution network control. The data includes the basic simulation calculation data obtained in the process of active distribution network control simulation and deduction, such as the initial power grid section, load following curve, distributed new energy following curve, thermal power generation plan adjustment, mode change event sequence and other information, which can be further used to simulate the future long-term operation change trend of the power grid, and according to the needs of business simulation analysis, fault events, control operations, load and power generation adjustment and other control operation events are superimposed on the trend data, so as to simulate and analyze the influence of control operations on the future long-term operation of the power grid.

[0054] The above method proposes a method for generating trend data that can represent the future operating trend of the power grid, which can support the simulation program to simulate the long-term operation process of the future power grid and has extremely high application value.

[0055] Figure 2 The module structure diagram of a system for generating future operation trend of power grid for simulation control of main distribution network in the present invention is shown in FIG. Figure 2As shown, the second aspect of the present invention relates to a system for generating future operation trends of a power grid for simulation and regulation of a main distribution network using the method in the first aspect of the present invention, the system comprising a data acquisition module, a section creation module, a power sharing module, a load regulation module, a simulation deduction module and a scheduling event module; wherein the data acquisition module is used to collect and extract state data within the time and space range to be simulated, the state data at least including load forecast data, interconnection line plan data, power generation plan data, new energy forecast data and maintenance plan data obtained by using the actual operation process of the main distribution network; the section creation module is used to input the load forecast data and the interconnection line plan data into a pre-constructed main distribution network topology model, obtain state estimation data within the time and space range to be simulated by implementing flow calculations, and construct an initial section within the time and space range to be simulated using the state estimation data; the power sharing module is used to analyze the generation and The power generation plan data is obtained by using the power ratio between the initial sections and between the model objects within the initial sections to implement the allocation of the power generation plan; the load adjustment module is used to obtain the bus load forecast curve in the load forecast data and match it with the model objects 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; the simulation deduction module is used to generate a new energy power generation forecast curve using the new energy forecast 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, and use 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 superimpose a pre-set scheduling sequence 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 the pre-scheduling event.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for generating future operation trend of power grid for simulation control of main distribution network, characterized in that: The method comprises the following steps: Collect and extract state data within the time and space range to be simulated, wherein the state data at least includes load forecast data, tie line plan data, power generation plan data, new energy forecast data and maintenance plan data obtained by using the actual operation process of the main distribution network; Input the load forecast data and the tie line plan data into a pre-built main distribution network topology model, obtain state estimation data within the time and space range to be simulated by performing power flow calculation, and construct an initial section within the time and space range to be simulated using the state estimation data; Parsing the power generation plan data, and using the power ratios between the initial sections and between the model objects within the initial sections to implement the allocation of the power generation plan; Obtaining a busbar load prediction curve in the load prediction data and matching it with a model object in the main distribution network topology model in real time, thereby adjusting the busbar load prediction curve according to the system load prediction curve; Generate a new energy power generation prediction curve using the 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, and use the maintenance plan data to change the state of the model object in the main distribution network topology model to simulate and deduce the future operation trend data of the second power grid; A preset scheduling sequence is superimposed on the first future operation trend data of the power grid or the second future operation trend data of the power grid to generate a future operation trend of the power grid based on the pre-scheduling event.

2. The method for generating future operation trend of power grid for simulation control of main distribution network according to claim 1 is characterized by: The state data at least includes load forecast data, tie line plan data, power generation plan data, new energy forecast data and maintenance plan data obtained by using the actual operation process of the main distribution network, including: The load forecast data includes the main grid bus load forecast data, the load forecast data at different voltage levels of the distribution network, and the load forecast data of the terminal main transformer; The tie line planning data includes injection power limits of each node on the tie line; The power generation plan data includes power generation plan data of coal-fired units, power generation plan data of gas-fired units, and power generation plan data of pumped storage units; The new energy forecast data includes forecast data of centralized photovoltaic units and centralized wind turbine units; The maintenance plan data includes maintenance equipment, decommissioning status, maintenance start time, and maintenance end time.

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

4. The method for generating future operation trend of power grid for simulation control of main distribution network according to claim 3 is characterized by: The step of obtaining state estimation data within the time and space range to be simulated by performing power flow calculation, and constructing an initial section within the time and space range to be simulated by using the state estimation data, comprises: Loading the load forecast data and the tie line plan data into the main distribution network topology model to correspond to the model object; Gradually adjusting the equipment status of the model object through power flow calculation until the power flow calculation verification passes; The initial sections and initial section constraints including equipment and lines are set based on the power flow calculation results.

5. A method for generating future operation trend of power grid for simulation control of main distribution network according to claim 4, characterized in that: The step of parsing the power generation plan data and using the power ratios between the initial sections and between the model objects within the initial sections to implement the allocation of the power generation plan includes: According to the time and space range to be simulated, the power generation plan data at different scheduling scales are spliced ​​to obtain the power generation curve to be simulated; The power generation curve is loaded into the main distribution network topology model, the constraint quantities of different equipment and lines in multiple initial sections are summed, and the power generation plan curve is allocated to multiple initial sections accordingly; Within each initial section, the power generation plan curve is allocated to multiple devices and lines according to the constraint relationships between different devices and lines in the initial section.

6. A method for generating future operation trend of power grid for simulation control of main distribution network according to claim 5, characterized in that: The obtaining of the bus load prediction curve in the load prediction data and matching it with the model object in the main distribution network topology model in real time, thereby adjusting the bus load prediction curve according to the system load prediction curve, includes: According to the time and space range to be simulated, the load forecast data at different dispatching scales are spliced ​​to obtain the system load forecast curve to be simulated and the bus forecast curve to be simulated; Under the control of the initial 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 amount is proportionally distributed in other load models, and the shared bus load prediction curve is verified based on the system load prediction bus.

7. A method for generating future operation trend of power grid for simulation control of main distribution network according to claim 6, characterized in that: The method of generating a new energy power generation prediction curve using the new energy prediction data and inputting the curve into the main distribution network topology model to simulate and deduce the future operation trend data of the first power grid includes: According to the time and space range to be simulated, the new energy forecast data at different scheduling scales are spliced ​​to obtain the new energy power generation forecast 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 operation trend data of the first power grid.

8. A method for generating future operation trend of power grid for simulation control of main distribution network according to claim 7, characterized in that: The method of using the maintenance plan data to change the state of the model object in the main distribution network topology model to simulate and deduce the future operation trend data of the second power grid includes: Generate a power outage operation sequence and a decommissioning operation sequence for the equipment based on the maintenance plan data, The states of the model objects in the main distribution network topology model are changed according to the equipment power outage operation sequence and the equipment recommissioning 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. A method for generating future operation trend of power grid for simulation control of main distribution network according to claim 8, characterized in that: The step of superimposing a preset scheduling sequence on the first future operation trend data of the power grid or the second future operation trend data of the power grid to generate a future operation trend of the power grid based on the pre-scheduling event includes: The pre-set dispatching sequence is one or more of an expected fault sequence, an expected dispatching operation sequence, a distributed power generation output regulation sequence, a load regulation and load control sequence, and an emergency dispatching sequence pre-set based on the main distribution network model.

10. A system for generating future operation trend of power grid for simulation control of main distribution network using the method described in any one of claims 1 to 9, characterized in that: The system includes a data acquisition module, a section creation module, a power sharing module, a load regulation module, a simulation module and a scheduling event module; wherein, The data acquisition module is used to collect and extract state data within the time and space range to be simulated, wherein the state data at least includes load forecast data, tie line plan data, power generation plan data, new energy forecast data and maintenance plan data obtained by using the actual operation process of the main distribution network; The section creation module is used to input the load forecast data and the tie line plan data into a pre-built main distribution network topology model, obtain the state estimation data within the time and space range to be simulated by performing power flow calculation, and construct the initial section within the time and space range to be simulated using the state estimation data; The power allocation module is used to analyze the power generation plan data and implement the allocation of the power generation plan by using the power ratio between the initial sections and between the model objects within the initial sections; The load adjustment module is used to obtain the bus load prediction curve in 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 deduction module is used to generate a new energy power generation prediction curve using the 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, and use the maintenance plan data to change the state of the model object 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 superimpose a preset scheduling sequence on the first power grid future operation trend data or the second power grid future operation trend data to generate a future power grid operation trend based on a pre-scheduling event.

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