Medium and long term power system partition and local power grid balance capability evaluation and early warning method and system

Abstract

The invention discloses a medium and long term power system partition and local power grid balance capability evaluation and early warning method and system. The method comprises the following steps: based on real-time power system model data, superposing boundary data such as power generation and application of a medium-and-long-term power system and equipment maintenance / commissioning / retreating information to generate a medium-and-long-term power system model and section data meeting stable operation conditions; identifying power grid power supply subarea power grids and local power grids and internal power equipment information of the power grids in each time period; a multi-period parallel algorithm is adopted to calculate power generation and receiving balance, positive and negative standby and power supply margins of the subarea power grid and the local power grid; and then the medium-and-long-term power system balance condition and the standby and power supply margin condition are evaluated, and medium-and-long-term partitioned and local power grid balance monitoring and early warning functions are provided. Medium and long term power grid balance capability analysis data is provided, monitoring of future power grid balance capability operation characteristic change trend is powerfully supported, power grid long-period stability analysis capability is improved, and future risk early warning capability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medium- and long-term power system balance analysis, and in particular to a medium- and long-term power system partition and local power grid balance capacity assessment and early warning method and system. Background Art

[0002] With the development of the power grid, the scale of the power grid continues to expand and new energy is connected on a large scale, and the volatility of the power grid load and new energy output has increased. The expansion of the scale of the power grid makes the operation of the power grid more complicated, requiring more advanced monitoring, control and dispatching systems. In addition, large-scale power grids are more susceptible to disturbances, so more stringent stability analysis and control strategies are required. At the same time, with the access of new energy such as wind and solar energy, the new power system has undergone major changes in structural form and balance theory, facing high uncertainty on both the supply and demand sides and uncertainty in regulation resources. Since the output of new energy such as wind and solar energy is volatile and uncertain, this increases the instability of the power system. Power system balance analysis is a key link in ensuring the safe, stable and economical operation of the power system.

[0003] The analysis of power and electricity balance at different time scales is the basis for formulating the dispatching and transmission operation mode of power grid units and arranging the power grid planning scheme. In the existing technology, the analysis of power and electricity balance mostly focuses on short-term analysis, that is, the operation of the power system within a few days or hours. In the context of the increasing proportion of new energy, it is necessary to comprehensively consider the power balance and electricity balance, as well as the coordinated optimization of source, grid, load and storage scheduling. The medium- and long-term (weekly and monthly) balance analysis plays an important role in the planning and resource allocation of the power system. However, the current analysis of the medium- and long-term power system balance is insufficient, especially at the sub-regional and local power grid levels, resulting in tremendous pressure on the dispatching business in terms of analysis and operation plan formulation.

[0004] Therefore, a medium- and long-term power system partitioning and local grid balancing capacity assessment method is urgently needed to cope with these pressures. Summary of the invention

[0005] Purpose of the invention: In view of the shortcomings of the prior art, the present invention proposes a medium- and long-term power system zoning and local power grid balancing capacity assessment and early warning method and system, which can realize multi-dimensional dynamic prediction and early warning of medium- and long-term power grid balancing indicators, and can effectively support the monitoring of changing trends of operating characteristics of future medium- and long-term power grid balancing capacity, enhance the long-term stability analysis capability of the power grid, and improve future risk warning capabilities.

[0006] Technical solution: In the first aspect, a medium- and long-term power system partition and local power grid balancing capacity assessment and early warning method includes the following steps:

[0007] Obtain medium- and long-term power grid models, medium- and long-term power equipment maintenance, commissioning / decommissioning data, and medium- and long-term power system boundary condition data. The medium- and long-term power grid model refers to a data set of power grid equipment attributes and connection relationships on a specified time scale in the future, including equipment commissioning and decommissioning time attributes. The medium- and long-term power system boundary condition data includes medium- and long-term power system load forecasts, unit plans, interconnection line plans, unified dispatching new energy forecasts, non-unified dispatching and marketing photovoltaic forecasts, and pre-process the data, including merging non-unified dispatching new energy by category, assigning values ​​to missing data time points, and converting non-integer data to integer points;

[0008] Based on the preprocessed medium- and long-term power grid model and boundary condition data, the medium- and long-term power system flow section that meets stable operation conditions is generated by considering power balance and unit reserve constraints;

[0009] Based on the medium- and long-term power system flow section, combined with the topological relationship of each period of the medium- and long-term power grid, multi-period parallel computing technology is used to dynamically identify the power supply divisions and local power grids in each period of the medium- and long-term power grid, and to count the power generation, reception and consumption equipment information of the divisions and local power grids;

[0010] Based on the mid- and long-term dynamic identification results of sub-regions and local power grids and the information of power generation, reception and consumption equipment, the power generation, reception and consumption balance information in the power supply sub-regions and local power grids in each period is calculated. At the same time, the power flow constraints, important section constraints and power grid balance constraints in the sub-regions and local power grids are considered, and multi-period parallel computing technology is used to calculate the power generation capacity, power receiving capacity, positive and standby and power supply margin balance indicators of the sub-regions and local power grids;

[0011] Based on the calculation results of medium- and long-term regional and local power grid reserve and balance indicators, the medium- and long-term regional and local power grid reserve and power supply margin balance capabilities are evaluated, and time period and single-point early warning information is provided.

[0012] Furthermore, based on the medium- and long-term power system flow section, combined with the topological relationship of each period of the medium- and long-term power grid, multi-period parallel computing technology is used to dynamically identify the power supply partitions and local power grids in each period of the medium- and long-term power grid, and statistically calculate the power generation, reception and consumption equipment information of the partitions and local power grids, including:

[0013] Based on the medium- and long-term power system flow section, considering the different architectural characteristics of the medium- and long-term power grid in different time periods, the medium- and long-term is divided into multiple time period sets, and the power grid models of multiple time period sets are calculated in parallel to perform power system topology analysis. Among them, for the power grid model of each time period set, starting from the specified transformer, a deep search of the topology traversal of the downstream associated equipment is performed to identify dynamic partitions, communication relationships between partitions, and information on power generation, reception, and consumption equipment within the partition. Based on the relationship between multiple partitions divided by geography or belonging to local power grid areas according to power demand, they are integrated into local power grids. Finally, the partition and local power grid division data under multiple time period sets are obtained.

[0014] Furthermore, starting from the specified transformer, the topology is deeply searched to traverse the downstream related devices, and the dynamic partitions, the communication relationship between partitions and the information of the power generation and reception equipment in the partitions are identified, including:

[0015] First, mark the root node, which is the starting point of the topological search. Then obtain the child nodes of the root node and add the child nodes to the node queue. Next, analyze whether the nodes in the node queue are marked. If not, obtain the child nodes of the node in the same way and add them to the node queue. Repeat the above process until all nodes in the node queue are marked.

[0016] Furthermore, based on the mid- and long-term dynamic identification results of the sub-areas and local power grids and the information of power generation, reception and consumption equipment, the power generation, reception and consumption balance information in the power supply sub-areas and local power grids in each time period is calculated, and the power flow constraints, important section constraints and power grid balance constraints in the sub-areas and local power grids are considered at the same time, and the multi-time period parallel technology is used to calculate the power generation capacity, power receiving capacity, positive standby and power supply margin balance indicators of the sub-areas and local power grids, including:

[0017] Based on the medium- and long-term power grid model, through the power grid topology relationship, search out the sub-district power grid and local power grid to which the units, loads, and main transformer power equipment belong. Combined with the unit plan, new energy plan, non-centrally dispatched and marketing photovoltaic plan, tie line plan, and bus load forecast data, calculate the coal-fired, gas-fired, nuclear power, pumped storage, centrally dispatched wind and solar, non-centrally dispatched wind and solar, marketing photovoltaic power generation data and power receiving data in the sub-district power grid and local power grid. Use the single bus load forecast data to calculate the single load growth rate, and then calculate the load growth rate of the sub-district and local power grid. Calculate the medium- and long-term sub-district and local power grid load data based on the real-time load of the sub-district and local power grid;

[0018] Taking into account the power flow constraints, important section constraints, and power grid balance constraints of the sub-regional and local power grids, a multi-period parallel computing algorithm is used to calculate the balance indicators such as the generating capacity, power receiving capacity, positive and standby, and power supply margin of the medium- and long-term sub-regional and local power grids.

[0019] Furthermore, through the topological relationship of the power grid, the sub-district power grid and local power grid to which the units, loads, and main transformer power equipment belong are searched, and combined with the unit plan, new energy plan, non-centralized and marketing photovoltaic plan, tie line plan, and bus load forecast data, the coal-fired, gas-fired, nuclear power, pumped storage, centrally-regulated wind and solar, non-centrally-regulated wind and solar, and marketing photovoltaic power generation data and power receiving data in the sub-district power grid and local power grid are calculated, including:

[0020] On the basis of the medium- and long-term power grid model section, increase the output of coal-fired and gas-fired units in the sending-end power grid according to the percentage of adjustable unit capacity, reduce the output of coal-fired units in the receiving-end power grid, iteratively calculate the power flow, monitor the power flow value of the boundary section, and when the section exceeds the limit, return to the last iteration where the power flow does not exceed the limit, adjust the unit output by half, and continue to repeat the calculation until it reaches 99% of the section power flow limit. At this time, the sum of all the boundary monitoring section power flow values ​​is the internal maximum power reception, and the sum of the active values ​​of the units in the receiving-end power grid is the power generation capacity;

[0021] If the units of the sending-end power grid or the receiving-end power grid cannot be adjusted and the monitored section does not exceed the limit, the load in the regional power grid is increased by step size through simulating the adjustment of the load of the receiving-end power grid, and the output of the units outside the regional power grid is increased at the same time, and the power flow is calculated again. When the section exceeds the limit, it falls back to the last iteration where the power flow does not exceed the limit, the adjustment step size is halved, and the calculation is repeated until the section power flow value reaches 99%. At this time, the sum of all section power flow values ​​is the maximum internal power reception, and the sum of the active values ​​of the units in the receiving-end power grid is the power generation capacity.

[0022] Furthermore, the power supply margin of the sub-regional and local power grid = the power generation capacity of the sub-regional and local power grid + the power receiving capacity of the sub-regional and local power grid - the load of the sub-regional and local power grid;

[0023] The power generation capacity of the regional and local power grids = the power generation capacity of coal-fired units in the regional and local power grids + the output of gas-fired units + the output of pumped storage units + the output of nuclear power units + the output of wind and solar power from unified regulation + the output of wind and solar power from non-unified regulation + the output of distributed photovoltaic power;

[0024] Positive reserve = maximum power generation capacity + power receiving capacity - load forecast;

[0025] Negative reserve = load forecast - minimum power generation capacity - power receiving capacity.

[0026] Furthermore, based on the calculation results of the mid- and long-term regional and local power grid reserve and balance indicators, the mid- and long-term regional and local power grid reserve and power supply margin balance capabilities are evaluated, and time period and single point warning information is provided, including:

[0027] Based on the calculation results of the medium- and long-term regional and local power grid reserve and balance indicators, the medium- and long-term regional and local power grid reserve and power supply margin are evaluated according to the specified thresholds, and early warnings and extreme value moment warnings for insufficient medium- and long-term regional and local power grid reserve periods, early warning machines and extreme value moment warnings for insufficient power supply margin periods of medium- and long-term regional and local power grids are provided, as well as detailed information on the balanced sending and receiving data components in each warning period.

[0028] Second, a medium- and long-term power system partition and local power grid balancing capacity assessment and early warning system, including:

[0029] A data acquisition and preprocessing unit is used to acquire medium- and long-term power grid models, medium- and long-term power equipment maintenance, commissioning / decommissioning data, and medium- and long-term power system boundary condition data. The medium- and long-term power grid model refers to a data set of power grid equipment attributes and connection relationships on a future specified time scale, including equipment commissioning and decommissioning time attributes. The medium- and long-term power system boundary condition data includes medium- and long-term power system load forecasts, unit plans, interconnection line plans, unified dispatching new energy forecasts, non-unified dispatching and marketing photovoltaic forecasts, and preprocesses the data, including merging non-unified dispatching new energy by category, assigning values ​​to missing data time points, and converting non-integer data to integer points.

[0030] A power flow section generation unit is used to generate a medium- and long-term power system power flow section that meets stable operation conditions based on the preprocessed medium- and long-term power grid model and boundary condition data, taking into account power balance and unit reserve constraints;

[0031] The dynamic identification unit of the sub-area and local power grid is used to dynamically identify the power supply sub-area and local power grid in each period of the medium- and long-term power grid based on the medium- and long-term power system flow section, combined with the topological relationship of each period of the medium- and long-term power grid, using multi-period parallel computing technology, and statistically calculate the power generation, reception and consumption equipment information of the sub-area and local power grid;

[0032] The balancing capacity assessment unit of the sub-area and local power grid is used to calculate the power generation, reception and consumption balance information in the power supply sub-area and local power grid in each period based on the mid- and long-term dynamic identification results of the sub-area and local power grid and the information of power generation, reception and consumption equipment. At the same time, the power flow constraints, important section constraints and power grid balance constraints in the sub-area and local power grid are considered, and the multi-period parallel computing technology is used to calculate the power generation capacity, power receiving capacity, positive standby and power supply margin balance indicators of the sub-area and local power grid;

[0033] The zone and local power grid backup and balance warning unit is used to evaluate the medium- and long-term zone and local power grid backup and power supply margin balance capabilities based on the calculation results of the medium- and long-term zone and local power grid backup and balance indicators, and provide time period and single-point warning information.

[0034] In a third aspect, a computer device comprises one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of a medium- and long-term power system zoning and local power grid balancing capacity assessment and early warning method as described in the first aspect of the present invention are implemented.

[0035] In a fourth aspect, a computer storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of a medium- and long-term power system partitioning and local power grid balancing capacity assessment and early warning method as described in the first aspect of the present invention.

[0036] Beneficial effects: The present invention provides a method and system for evaluating and warning the balancing capacity of medium- and long-term sub-regions and local power grids, which provides a solution for comprehensively and quantitatively analyzing the balancing capacity of sub-regions and local power grids from the perspective of the entire network in the medium- and long-term time scale, and realizes multi-dimensional and comprehensive dynamic prediction and early warning of medium- and long-term power grid balancing indicators. The present invention quantitatively perceives and evaluates the balancing capacity indicators such as the generation and reception balancing capacity, positive / negative standby, and power supply margin of medium- and long-term sub-regions and local power grids from multiple dimensions in the medium- and long-term, multiple time periods, and can effectively support the monitoring of the changing trends of the operating characteristics of the future power grid balancing capacity, improve the long-term stability analysis capability of the power grid, and improve the future risk warning capability. It is of great significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is an overall flow chart of the medium- and long-term power system partitioning and local power grid balancing capacity assessment and early warning method provided by the present invention.

[0038] Figure 2 This is a flow chart of the power system topology analysis algorithm provided by the present invention.

[0039] Figure 3 An exemplary power system topology analysis node diagram provided by the present invention.

[0040] Figure 4 A flow chart of an iterative algorithm for calculating the power generation and receiving capabilities of a partitioned and local power grid provided by the present invention.

[0041] Figure 5 This is a system interface diagram for the medium- and long-term power system local power grid balancing capability assessment and early warning provided by the present invention.

[0042] Figure 6 This is a system interface diagram for mid- to long-term power system partitioned grid balancing capability assessment and early warning provided by the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings.

[0044] Reference Figure 1 The present invention provides a medium- and long-term power system partition and local power grid balance capacity assessment and early warning method. The medium- and long-term in the present invention refers to time scales such as weeks, ten days, months, and years, which are different from the short-term or ultra-short-term concepts within a day. The method includes the following steps:

[0045] S1: Obtain the boundary data related to power generation, power reception and power consumption of the medium- and long-term power system, the medium- and long-term power system model, the medium- and long-term power system equipment maintenance, commissioning / decommissioning plan data, and perform data preprocessing.

[0046] In power system operation and planning, the so-called "boundary condition data" refers to those external parameters or input data that have a direct impact on the state and behavior of the power system. These data usually define the boundaries or constraints of power system analysis and simulation. The boundary data related to power generation, power reception, and power consumption in the medium and long term power system mainly include:

[0047] Load forecast data: Load forecast data is the estimated value of power demand in the future. It is the basis for power system operation and planning, and determines the power and energy that the system needs to provide. Load forecast serves as a boundary condition because it limits the power supply capacity requirements of the power system.

[0048] Unit planning data: Unit planning data includes the expected operating status and output level of the generator set in the future. These data are key inputs for power system scheduling and operation, and determine the system's power supply capacity and backup capacity. As a boundary condition, unit planning data affects the system's operating cost and reliability.

[0049] Tie-line planning data: Tie-line planning data involves the scheduled transmission capacity and operating status of transmission lines between different power grids. This data is crucial for inter-regional energy exchange in the power system and is a key factor in ensuring the stability of system interconnection. As a boundary condition, tie-line planning data restricts the flow of power between regions.

[0050] Wind and solar energy forecast: The forecast data of wind and solar energy provide the estimated value of wind and solar power generation in the future. These data are crucial for power systems with a high proportion of renewable energy because they are volatile and uncertain. As boundary conditions, these forecast data affect the peak demand and reserve capacity arrangement of the system.

[0051] Non-centrally coordinated and marketed PV forecast data: Non-centrally coordinated PV forecast data refers to the forecast value of distributed PV power generation, which is usually predicted by local governments or enterprises. Marketing PV forecast data is related to the market demand and potential benefits of PV power generation. These data serve as boundary conditions because they affect the supply and demand balance and market-oriented operation of the power system.

[0052] Small unit planning data: Small unit planning data involves the operation plan of small generating units (such as distributed generation, small hydropower stations, etc.). This data is crucial for the flexibility and responsiveness of the power system. As a boundary condition, small unit planning data affects the power supply diversity and peak load regulation capability of the system.

[0053] The present invention uses boundary condition data as the input of the method. The required boundary condition data can be obtained through a data collection system or by signing a contract with a data provider. This is not the focus of the invention and will not be described in detail here. For the boundary data such as the medium- and long-term power system load forecast, unit planning data, interconnection line planning data, unified dispatching wind and solar new energy forecast, non-unified dispatching and marketing photovoltaic forecast data, unified dispatching small unit planning data, etc., it is necessary to re-assign the data problems, such as single-point numerical abnormalities or missing data at non-integer time points. At the same time, the acquired non-unified dispatching new energy data is merged by category and integrated into dispatching-caliber new energy data and marketing-caliber new energy data.

[0054] In the context of the present invention, the model refers to a data set of the attributes and connection relationships of power grid equipment. The real-time model of the medium- and long-term power system refers to a data set of the attributes and connection relationships of power grid equipment that is already running in real time. The model itself does not have the concept of future state (medium- and long-term), but only gives the model the attributes of the future state (medium- and long-term) for the commissioning and retirement time. Therefore, the maintenance method of the medium- and long-term power system model remains unchanged. Model governance is carried out in the control cloud model data platform, but it is necessary to pay attention to whether the commissioning and retirement time of each device is correct.

[0055] The medium- and long-term power system equipment maintenance, commissioning / decommissioning plan data refers to the medium- and long-term time scale of power system equipment maintenance plan and commissioning / decommissioning plan, which is obtained from the planning office. The preprocessing methods include supplementing missing data and replacing non-integer data with adjacent integer data.

[0056] S2: Generate medium- and long-term power system power flow sections based on the preprocessed boundary data and medium- and long-term power system model.

[0057] Based on the medium- and long-term power and system model data, superimposed with medium- and long-term power system generation, power reception, and power consumption related boundary data, equipment maintenance, commissioning / decommissioning and other power grid model change data, and considering constraints such as power grid balance and unit standby, medium- and long-term power system flow section data that meet stable operation conditions are generated. The specific method of power grid flow calculation will not be repeated here.

[0058] S3: Based on the medium- and long-term power system model and cross-sectional data, dynamic partition automatic identification technology is used to dynamically divide each medium- and long-term period into zones, and identify the zoned power grids and local power grids of the power system in each period, as well as the information on power generation, reception and consumption equipment in the region.

[0059] In order to grasp the characteristics of different structures of medium- and long-term power grids in different time periods, the present invention dynamically partitions each medium- and long-term period. First, the medium- and long-term power grid is divided into multiple time period sets. Then, for the power grid model of the multiple time period sets, a multi-time period parallel calculation method is adopted, and topological analysis and identification are performed at the same time to obtain the information of the power equipment belonging to the partition and the local power grid of the medium- and long-term multi-time period sets.

[0060] The multi-time period parallel computing method first divides the medium- and long-term multi-time period power grid into several sets according to multiple models, then divides each set by day, and finally performs topological identification on the partitions and local power grids for each day's time period. For example, the monthly time periods are divided into daily units, and parallel computing is performed on a daily basis, which greatly saves the computing time scale of a whole month or even longer.

[0061] According to the implementation mode of the present invention, the topology identification of the partition and local power grid is calculated by using the depth-first search method. The topology tree is constructed according to the selected network boundary sections, expansion directions and expansion rules, and the nodes that meet the target state are found. The deep search topology traverses the downstream related devices, thereby obtaining the partition and local power grid model, the connection relationship between the partition and local power grid, and the device information in the partition and local power grid. The depth-first search method process is as follows: Figure 2 As shown in the figure, first mark the root node (starting point of topology search), then get the child nodes of the root node and add the child nodes to the node queue. Next, analyze whether the nodes in the node queue are marked. If not, get the child nodes of the node in the same way and add them to the node queue. Repeat the above process until all nodes in the node queue are marked. An example of topology analysis is shown in Figure 3 As shown, through the grid topology, all devices on the power supply path are searched from the 500kV voltage level main transformer downward. The power supply path search from high voltage to low voltage is an expansive search. The collection of all devices on the power supply path (including loads, generators, main transformers, etc.) are all devices in the partition / local power grid where the 500k voltage level main transformer is located.

[0062] S4: Based on the identification results of the power system’s zoning and local power grids in each time period and the information of power generation, reception and consumption equipment in the area, calculate the power generation, reception and consumption balance, positive and negative standby and power supply margin balance indicators of the zoning and local power grids.

[0063] The balance of power generation, reception and consumption refers to the calculation of power generation, power generation capacity, power reception, power reception capacity and power consumption data. The calculation of positive and negative backup and power supply margin is a further indicator data based on these data. The power supply margin refers to the margin left after adding the maximum power generation capacity and the maximum power reception capacity of the power grid area and subtracting the power consumption of the power grid area.

[0064] According to the implementation mode of the present invention, based on the information of the power equipment belonging to the partition and local power grid in the medium- and long-term multi-time period set, the power generation and receiving capacity of the partition and local power grid in the medium- and long-term multi-time period is calculated. In terms of load capacity, the load growth rate algorithm is used to calculate the load of the partition and local power grid, mainly using a single bus load forecast data to calculate the medium- and long-term multi-time period single load growth rate, and then calculate the medium- and long-term multi-time period load growth rate of the partition and local power grid, superimpose on the real-time load of the partition and local power grid, and calculate the medium- and long-term multi-time period load data of the partition and local power grid.

[0065] Specifically, considering the constraints such as power flow constraints, important section constraints, and power balance constraints in the sub-grid and local grids, a multi-period parallel calculation algorithm is used to calculate the balance indicators such as the power generation capacity, power receiving capacity, positive and negative backup, and power supply margin of the mid- and long-term multi-period sub-grid and local grids. Among them, the traditional calculation of power generation capacity and power receiving capacity is to add the rated limit attributes of the power equipment itself, which cannot represent the actual limit value during actual operation. Therefore, the present invention adopts a continuous power flow simulation algorithm for calculation, referring to Figure 4 On the basis of the medium- and long-term power grid model section, increase the output of coal-fired and gas-fired units in the sending-end power grid according to the percentage of adjustable unit capacity, reduce the output of coal-fired units in the receiving-end power grid, iterate the calculation of the flow, and monitor the flow value of the boundary section. When the section exceeds the limit, return to the last iteration where the flow does not exceed the limit, adjust the unit output by half, and continue to repeat the calculation until it reaches 99% of the section flow value limit. At this time, the sum of all boundary monitoring section flow values ​​is the internal maximum power reception, and the sum of the active values ​​of the units in the receiving-end power grid is the power generation capacity. If the units of the sending-end power grid or the receiving-end power grid cannot be adjusted and the monitored section does not exceed the limit, the load in the regional power grid is increased by step size through simulating the adjustment of the load of the receiving-end power grid, and the output of the units outside the regional power grid is increased at the same time, and the power flow is calculated again. When the section exceeds the limit, it falls back to the last iteration where the power flow does not exceed the limit, the adjustment step size is halved, and the calculation is repeated until the section power flow value reaches 99%. At this time, the sum of all section power flow values ​​is the maximum internal power reception, and the sum of the active values ​​of the units in the receiving-end power grid is the power generation capacity.

[0066] The power supply margin of the sub-area and local power grid = the power generation capacity of the sub-area and local power grid + the power receiving capacity of the sub-area and local power grid - the load of the sub-area and local power grid;

[0067] The power generation capacity of the regional and local power grids = the power generation capacity of coal-fired units in the regional and local power grids + the output of gas-fired units + the output of pumped storage units + the output of nuclear power units + the output of wind and solar power from unified regulation + the output of wind and solar power from non-unified regulation + the output of distributed photovoltaic power;

[0068] The calculation of the power generation capacity and power receiving capacity of coal-fired units in the sub-area and local power grid is calculated by the above-mentioned continuous power flow iteration algorithm; in the above-mentioned algorithm, in the process of iterative power flow calculation, the output of the coal-fired units is modified, and the power flow is continuously iterated. At the end of the calculation, the power generation of the generators in the area is added up to be the power generation capacity of the area, and the power flow of the monitored boundary sections is added up to be the power receiving capacity of the area.

[0069] Positive reserve = maximum power generation capacity + power receiving capacity - load forecast;

[0070] Negative reserve = load forecast - minimum power generation capacity - power receiving capacity;

[0071] The load of the sub-area and local power grid is calculated by the load growth rate algorithm. The predicted growth rate of the single bus load is calculated based on the single bus load forecast data and real-time data. The load growth rate of the regional power grid is calculated through the relationship between the single bus load and the regional power grid. Combined with the real-time load data of the regional power grid, the future load forecast of the regional power grid is calculated.

[0072] Similarly, multi-time period parallel computing technology is used. According to the power grid model divided by time periods, the power generation and consumption balance, positive and negative standby and power supply margin balance indicators of the sub-region and local power grid are calculated in each time period, and summarized into medium- and long-term multi-time period sub-region and local power grid balance indicator data.

[0073] S5: Based on the mid- to long-term power system partitions and local power grid backup and power supply margin conditions, provide mid- to long-term partition and local power grid balance monitoring and early warning functions.

[0074] Based on the calculation results of the reserve and power supply margin of the medium- and long-term multi-period power system partitions and local power grids, the reserve and power supply margin of the medium- and long-term partitions and local power grids are evaluated according to the three states of normal, alarm, and emergency. The state threshold values ​​of different power grid levels can be set by themselves. For insufficient reserve and power supply margin in the medium- and long-term time period, advance warnings are carried out for time periods and single-point extreme moments, including insufficient time periods, extreme time points, partitions of insufficient time periods and detailed curves of power generation, reception and consumption of local power grids, partitions of extreme time points and detailed data of power generation, reception and consumption of local power grids. According to the details, the specific reasons for the shortage can be directly known, such as changes in the power receiving component, changes in the power generation capacity component, changes in the load component, detailed information on equipment maintenance / commissioning / decommissioning, etc.

[0075] The medium- and long-term power system partition and local power grid balancing capacity assessment and early warning method provided by the present invention realizes a comprehensive quantitative analysis of the balancing capacity of partitions and local power grids from the perspective of the entire network on a medium- and long-term time scale, and realizes multi-dimensional and comprehensive dynamic prediction and early warning of medium- and long-term power grid balancing indicators.

[0076] Based on the same technical concept as the method embodiment, the present invention also provides a medium- and long-term power system partition and local power grid balance capacity assessment and early warning system, including:

[0077] A data acquisition and preprocessing unit is used to acquire medium- and long-term power grid models, medium- and long-term power equipment maintenance, commissioning / decommissioning data, and medium- and long-term power system boundary condition data. The medium- and long-term power grid model refers to a data set of power grid equipment attributes and connection relationships on a future specified time scale, including equipment commissioning and decommissioning time attributes. The medium- and long-term power system boundary condition data includes medium- and long-term power system load forecasts, unit plans, interconnection line plans, unified dispatching new energy forecasts, non-unified dispatching and marketing photovoltaic forecasts, and preprocesses the data, including merging non-unified dispatching new energy by category, assigning values ​​to missing data time points, and converting non-integer data to integer points.

[0078] A power flow section generation unit is used to generate a medium- and long-term power system power flow section that meets stable operation conditions based on the preprocessed medium- and long-term power grid model and boundary condition data, taking into account power balance and unit reserve constraints;

[0079] The dynamic identification unit of the sub-area and local power grid is used to dynamically identify the power supply sub-area and local power grid in each period of the medium- and long-term power grid based on the medium- and long-term power system flow section, combined with the topological relationship of each period of the medium- and long-term power grid, using multi-period parallel computing technology, and statistically calculate the power generation, reception and consumption equipment information of the sub-area and local power grid;

[0080] The balancing capacity assessment unit of the sub-area and local power grid is used to calculate the power generation, reception and consumption balance information in the power supply sub-area and local power grid in each period based on the mid- and long-term dynamic identification results of the sub-area and local power grid and the information of power generation, reception and consumption equipment. At the same time, the power flow constraints, important section constraints and power grid balance constraints in the sub-area and local power grid are considered, and the multi-period parallel computing technology is used to calculate the power generation capacity, power receiving capacity, positive standby and power supply margin balance indicators of the sub-area and local power grid;

[0081] The partition and local power grid backup and balance warning unit is used to evaluate the medium- and long-term partition and local power grid backup and power supply margin balance capabilities based on the calculation results of the medium- and long-term partition and local power grid backup and balance indicators, and provide time period and single point warning information. The functions of each unit can be specifically implemented according to the method in the above method embodiment, and its specific implementation process can refer to the relevant description in the above embodiment, which will not be repeated here.

[0082] In the embodiment of the present invention, the monthly zoning and local grid balance capacity assessment and early warning of a certain local grid are taken as an example to verify and test the technology used in the method. Figure 5 and Figure 6The interface is displayed in Figure 2. The system can comprehensively and quantitatively analyze the balancing capacity of sub-regions and local power grids from the perspectives of unified dispatching, dispatching, and the whole society, and realize multi-dimensional and comprehensive dynamic prediction and early warning of monthly power grid balancing indicators. From the perspective of monthly multi-period and multi-dimensional quantitative perception and evaluation of the balancing capacity indicators such as power generation and reception, positive / negative standby, and power supply margin of medium- and long-term sub-regions and local power grids, it helps users analyze the balance of sub-regions and local power grids in the medium- and long-term multi-time scales. At the same time, it can analyze the root causes of the problems in detail from all aspects, effectively supporting the perception of the changing trend of the operating characteristics of the future medium- and long-term power grid balancing capacity, improving the long-term stability analysis capability of the power grid, and strengthening the future risk warning capability.

[0083] The present invention also provides a computer device, comprising one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the medium- and long-term power system partitioning and local power grid balancing capacity assessment and early warning method are implemented.

[0084] The present invention also provides a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the medium- and long-term power system partitioning and local power grid balancing capacity assessment and early warning method are implemented.

[0085] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices (systems), computer equipment or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0086] The present invention is described with reference to a flowchart of a method according to an embodiment of the present invention. It should be understood that each process in the flowchart and a combination of processes in the flowchart can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A device that specifies functions in a process or multiple processes.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A function specified in a process or multiple processes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

Claims

1. A medium- and long-term power system partition and local power grid balancing capacity assessment and early warning method, characterized in that: The method comprises the following steps: Obtain medium- and long-term power grid models, medium- and long-term power equipment maintenance, commissioning / decommissioning data, and medium- and long-term power system boundary condition data. The medium- and long-term power grid model refers to a data set of power grid equipment attributes and connection relationships on a specified time scale in the future, including equipment commissioning and decommissioning time attributes. The medium- and long-term power system boundary condition data includes medium- and long-term power system load forecasts, unit plans, interconnection line plans, unified dispatching new energy forecasts, non-unified dispatching and marketing photovoltaic forecasts, and pre-process the data, including merging non-unified dispatching new energy by category, assigning values ​​to missing data time points, and converting non-integer data to integer points; Based on the preprocessed medium- and long-term power grid model and boundary condition data, the medium- and long-term power system flow section that meets stable operation conditions is generated by considering power balance and unit reserve constraints; Based on the medium- and long-term power system flow section, combined with the topological relationship of each period of the medium- and long-term power grid, multi-period parallel computing technology is used to dynamically identify the power supply divisions and local power grids in each period of the medium- and long-term power grid, and to count the power generation, reception and consumption equipment information of the divisions and local power grids; Based on the mid- and long-term dynamic identification results of sub-regions and local power grids and the information of power generation, reception and consumption equipment, the power generation, reception and consumption balance information in the power supply sub-regions and local power grids in each period is calculated. At the same time, the power flow constraints, important section constraints and power grid balance constraints in the sub-regions and local power grids are considered, and multi-period parallel computing technology is used to calculate the power generation capacity, power receiving capacity, positive and standby and power supply margin balance indicators of the sub-regions and local power grids; Based on the calculation results of medium- and long-term regional and local power grid reserve and balance indicators, the medium- and long-term regional and local power grid reserve and power supply margin balance capabilities are evaluated, and time period and single-point early warning information is provided.

2. The method according to claim 1, characterized in that: Based on the medium- and long-term power system flow section, combined with the topological relationship of each period of the medium- and long-term power grid, multi-period parallel computing technology is used to dynamically identify the power supply partitions and local power grids in each period of the medium- and long-term power grid, and statistically calculate the power generation, reception and consumption equipment information of the partitions and local power grids, including: Based on the medium- and long-term power system flow section, considering the different architectural characteristics of the medium- and long-term power grid in different time periods, the medium- and long-term is divided into multiple time period sets, and the power grid models of multiple time period sets are calculated in parallel to perform power system topology analysis. Among them, for the power grid model of each time period set, starting from the specified transformer, a deep search of the topology traversal of the downstream associated equipment is performed to identify dynamic partitions, communication relationships between partitions, and information on power generation, reception, and consumption equipment within the partition. Based on the relationship between multiple partitions divided by geography or belonging to local power grid areas according to power demand, they are integrated into local power grids. Finally, the partition and local power grid division data under multiple time period sets are obtained.

3. The method according to claim 2, characterized in that Starting from the specified transformer, the system deeply searches the topology to traverse the downstream related devices, identifies the dynamic partitions, the connection relationship between partitions, and the information of the power generation and consumption equipment in the partition, including: First, mark the root node, which is the starting point of the topological search. Then obtain the child nodes of the root node and add the child nodes to the node queue. Next, analyze whether the nodes in the node queue are marked. If not, obtain the child nodes of the node in the same way and add them to the node queue. Repeat the above process until all nodes in the node queue are marked.

4. The method according to claim 1, characterized in that: Based on the mid- and long-term dynamic identification results of the sub-areas and local power grids and the information of power generation, reception and consumption equipment, the power generation, reception and consumption balance information in the power supply sub-areas and local power grids in each time period is calculated, and the power flow constraints, important section constraints and power grid balance constraints in the sub-areas and local power grids are considered at the same time. The multi-time period parallel technology is used to calculate the power generation capacity, power receiving capacity, positive standby and power supply margin balance indicators of the sub-areas and local power grids, including: Based on the medium- and long-term power grid model, through the power grid topology relationship, search out the sub-district power grid and local power grid to which the units, loads, and main transformer power equipment belong. Combined with the unit plan, new energy plan, non-centrally dispatched and marketing photovoltaic plan, tie line plan, and bus load forecast data, calculate the coal-fired, gas-fired, nuclear power, pumped storage, centrally dispatched wind and solar, non-centrally dispatched wind and solar, marketing photovoltaic power generation data and power receiving data in the sub-district power grid and local power grid. Use the single bus load forecast data to calculate the single load growth rate, and then calculate the load growth rate of the sub-district and local power grid. Calculate the medium- and long-term sub-district and local power grid load data based on the real-time load of the sub-district and local power grid; Taking into account the power flow constraints, important section constraints, and power grid balance constraints of the sub-regional and local power grids, a multi-period parallel computing algorithm is used to calculate the balance indicators such as the generating capacity, power receiving capacity, positive and standby, and power supply margin of the medium- and long-term sub-regional and local power grids.

5. The method according to claim 4, characterized in that Through the topological relationship of the power grid, search out the sub-district power grid and local power grid to which the units, loads, and main transformer power equipment belong. Combined with the unit plan, new energy plan, non-centralized and marketing photovoltaic plan, tie line plan, and bus load forecast data, calculate the coal-fired, gas-fired, nuclear power, pumped storage, centrally-regulated wind and solar, non-centrally-regulated wind and solar, marketing photovoltaic power generation data and power receiving data in the sub-district power grid and local power grid, including: On the basis of the medium- and long-term power grid model section, increase the output of coal-fired and gas-fired units in the sending-end power grid according to the percentage of adjustable unit capacity, reduce the output of coal-fired units in the receiving-end power grid, iteratively calculate the power flow, monitor the power flow value of the boundary section, and when the section exceeds the limit, return to the last iteration where the power flow does not exceed the limit, adjust the unit output by half, and continue to repeat the calculation until it reaches 99% of the section power flow limit. At this time, the sum of all the boundary monitoring section power flow values ​​is the internal maximum power reception, and the sum of the active values ​​of the units in the receiving-end power grid is the power generation capacity; If the units of the sending-end power grid or the receiving-end power grid cannot be adjusted and the monitored section does not exceed the limit, the load in the regional power grid is increased by step size through simulating the adjustment of the load of the receiving-end power grid, and the output of the units outside the regional power grid is increased at the same time, and the power flow is calculated again. When the section exceeds the limit, it falls back to the last iteration where the power flow does not exceed the limit, the adjustment step size is halved, and the calculation is repeated until the section power flow value reaches 99%. At this time, the sum of all section power flow values ​​is the maximum internal power reception, and the sum of the active values ​​of the units in the receiving-end power grid is the power generation capacity.

6. The method according to claim 4, characterized in that The power supply margin of the sub-area and local power grid = the power generation capacity of the sub-area and local power grid + the power receiving capacity of the sub-area and local power grid - the load of the sub-area and local power grid; The power generation capacity of the regional and local power grids = the power generation capacity of coal-fired units in the regional and local power grids + the output of gas-fired units + the output of pumped storage units + the output of nuclear power units + the output of wind and solar power from unified regulation + the output of wind and solar power from non-unified regulation + the output of distributed photovoltaic power; Positive reserve = maximum power generation capacity + power receiving capacity - load forecast; Negative reserve = load forecast - minimum power generation capacity - power receiving capacity.

7. The method according to claim 1, characterized in that The calculation results of the medium- and long-term regional and local power grid reserve and balance indicators are used to evaluate the medium- and long-term regional and local power grid reserve and power supply margin balance capabilities, and provide time period and single point warning information, including: Based on the calculation results of the medium- and long-term regional and local power grid reserve and balance indicators, the medium- and long-term regional and local power grid reserve and power supply margin are evaluated according to the specified thresholds, and early warnings and extreme value moment warnings for insufficient medium- and long-term regional and local power grid reserve periods, early warning machines and extreme value moment warnings for insufficient power supply margin periods of medium- and long-term regional and local power grids are provided, as well as detailed information on the balanced sending and receiving data components in each warning period.

8. A medium- and long-term power system partition and local power grid balance capacity assessment and early warning system, characterized in that: include: A data acquisition and preprocessing unit is used to acquire medium- and long-term power grid models, medium- and long-term power equipment maintenance, commissioning / decommissioning data, and medium- and long-term power system boundary condition data. The medium- and long-term power grid model refers to a data set of power grid equipment attributes and connection relationships on a future specified time scale, including equipment commissioning and decommissioning time attributes. The medium- and long-term power system boundary condition data includes medium- and long-term power system load forecasts, unit plans, interconnection line plans, unified dispatching new energy forecasts, non-unified dispatching and marketing photovoltaic forecasts, and preprocesses the data, including merging non-unified dispatching new energy by category, assigning values ​​to missing data time points, and converting non-integer data to integer points. A power flow section generation unit is used to generate a medium- and long-term power system power flow section that meets stable operation conditions based on the preprocessed medium- and long-term power grid model and boundary condition data, taking into account power balance and unit reserve constraints; The dynamic identification unit of the sub-area and local power grid is used to dynamically identify the power supply sub-area and local power grid in each period of the medium- and long-term power grid based on the medium- and long-term power system flow section, combined with the topological relationship of each period of the medium- and long-term power grid, using multi-period parallel computing technology, and statistically calculate the power generation, reception and consumption equipment information of the sub-area and local power grid; The balancing capacity assessment unit of the sub-area and local power grid is used to calculate the power generation, reception and consumption balance information in the power supply sub-area and local power grid in each period based on the mid- and long-term dynamic identification results of the sub-area and local power grid and the information of power generation, reception and consumption equipment. At the same time, the power flow constraints, important section constraints and power grid balance constraints in the sub-area and local power grid are considered, and the multi-period parallel computing technology is used to calculate the power generation capacity, power receiving capacity, positive standby and power supply margin balance indicators of the sub-area and local power grid; The zone and local power grid backup and balance warning unit is used to evaluate the medium- and long-term zone and local power grid backup and power supply margin balance capabilities based on the calculation results of the medium- and long-term zone and local power grid backup and balance indicators, and provide time period and single-point warning information.

9. A computer device, characterized in that: The device includes one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the medium- and long-term power system partitioning and local power grid balancing capacity assessment and early warning method as described in any one of claims 1-7 are implemented.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the medium- and long-term power system partitioning and local power grid balancing capacity assessment and early warning method as described in any one of claims 1-7 are implemented.

Images