A medium and long term power system partitioning and local power grid balance capability evaluation and early warning method and system

Abstract

The application discloses a kind of medium and long-term power system partition and local power grid balance ability evaluation and early warning method and system.Method includes: based on real-time power system model data, superimposed medium and long-term power system generation and consumption boundary data, equipment maintenance / put into operation / retirement information, generate the model and section data of medium and long-term power system that meet stable operation condition;Identify the power supply partition grid and local power grid and its internal power equipment information of each period;Using multi-period parallel algorithm, the balance of generation and consumption of partition grid and local power grid, positive and negative standby and power supply margin are calculated;Further evaluate the balance of medium and long-term power system and standby and power supply margin condition, provide medium and long-term partition and local power grid balance monitoring and early warning function.The application provides medium and long-term power grid balance ability analysis data, effectively supports the monitoring of future power grid balance ability operation characteristic change trend, improves power grid long-period stability analysis ability, improves future risk early warning ability.

Description

TECHNICAL FIELD

[0001] The present application 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 evaluation and early warning method and system. BACKGROUND

[0002] With the development of power grids, the scale of power grids is continuously expanding and new energy is being connected in large quantities, which increases the volatility of power grid load and new energy output. The expansion of the scale of power grids makes the operation of power grids more complex, requiring more advanced monitoring, control and dispatching systems. And large-scale power grids are more susceptible to disturbances, so more stringent stability analysis and control strategies are needed. At the same time, with the connection of new energy such as wind and solar energy, new type of power systems have undergone major changes in structure and balance theory, facing high uncertainty on both supply and demand sides and uncertainty of regulation resources. Due to the volatility and uncertainty of the output of new energy such as wind and solar energy, the instability of power systems is increased. Power system balance analysis is a key link to ensure the safe, stable and economic operation of power systems.

[0003] Power and energy balance analysis at different time scales is the basis for formulating power grid unit dispatching and external operation mode, arranging power supply grid planning scheme. In the prior art, power and energy balance analysis mostly focuses on short-term analysis, i.e. the operation of power systems within a few days or hours. Under the background of increasing proportion of new energy, power balance and energy balance need to be considered comprehensively, and source-grid-load-storage collaborative optimization dispatching, medium and long term (weekly, monthly) balance analysis plays an important role in power system planning and resource allocation. However, there is a lack of analysis of medium and long term power system balance at present, especially at the partition and local power grid level, which leads to great pressure on dispatching business in analysis and operation plan formulation.

[0004] Therefore, there is an urgent need for a medium and long term power system partition and local power grid balance capacity evaluation method to cope with these pressures. SUMMARY

[0005] The present application proposes a medium and long term power system partition and local power grid balance capacity evaluation and early warning method and system to overcome the shortcomings of the prior art, which realizes multi-dimensional dynamic prediction of medium and long term power grid balance indicators and early warning, can effectively support the monitoring of the change trend of the operation characteristics of the medium and long term power grid balance capacity, improve the long period stability analysis capability of the power grid and improve the future risk warning capability.

[0006] Technical scheme: In a first aspect, a medium and long term power system partition and local power grid balance capacity evaluation and early warning method comprises the following steps:

[0007] Obtaining a long-term power grid model, long-term power equipment maintenance, commissioning / retirement data, and long-term power system boundary condition data, the long-term power grid model refers to a data set of power grid equipment attributes and connection relationships at a specified time scale in the future, including equipment commissioning and retirement time attributes, the long-term power system boundary condition data includes long-term power system load forecasting, unit planning, tie line planning, centralized new energy forecasting, non-centralized and marketing photovoltaic forecasting, preprocessing the data, including non-centralized new energy merging processing, data missing time point assignment processing, non-integer point data integer point processing;

[0008] According to the preprocessed long-term power grid model and boundary condition data, considering power balance and unit reserve constraints, a long-term power system flow section that meets the stable operation condition is generated.

[0009] Based on the long-term power system flow section, combined with the topology relationship of the long-term power grid in each period, a multi-period parallel computing technology is used to dynamically identify the power supply partition and local power grid of the long-term power grid in each period, and the power supply and demand equipment information of the partition and local power grid is counted.

[0010] Based on the dynamic identification results of the long-term partition and local power grid and the power supply and demand equipment information, the power supply and demand balance information in each period of the partition and local power grid is calculated, while considering the power flow constraints, important section constraints and power grid balance constraints in the partition and local power grid, the multi-period parallel computing technology is used to calculate the power generation capacity, power receiving capacity, standby and power supply margin balance index of the partition and local power grid.

[0011] Based on the calculation results of the long-term partition and local power grid standby and balance index, the standby and power supply margin balance capacity of the long-term partition and local power grid is evaluated, and period and single-point early warning information is provided.

[0012] Further, based on the long-term power system flow section, combined with the topology relationship of the long-term power grid in each period, a multi-period parallel computing technology is used to dynamically identify the power supply partition and local power grid of the long-term power grid in each period, and the power supply and demand equipment information of the partition and local power grid is counted.

[0013] Based on the long-term power system flow section, considering the different architecture characteristics of the long-term power grid in different periods, the long-term is divided into multiple period sets, and the power grid model of multiple period sets is calculated in parallel, and the power system topology analysis is performed, wherein, for the power grid model of each period set, taking a specified transformer as the starting point, the downstream associated equipment is searched in depth to identify the dynamic partition, inter-partition tie line relationship and power supply and demand equipment information in the partition, and based on the relationship of multiple partitions divided by geography or electricity demand, the local power grid area is integrated into a local power grid, and finally the partition and local power grid division data under multiple period sets is obtained.

[0014] Further, taking the specified transformer as the starting point, the downstream associated devices are searched in depth to identify the dynamic partition, inter-partition interconnection relationship and intra-partition power supply and demand device information, including:

[0015] First, the root node, i.e. the starting point of the topology search, is marked, then the child nodes of the root node are obtained and added to the node queue, and then it is analyzed whether the nodes in the node queue are marked. If not, the child nodes of the node are obtained and added to the node queue at the same time, and the above process is looped until all nodes in the node queue are marked.

[0016] Further, based on the medium and long-term partition and local power grid dynamic identification results and power supply and demand device information, the power supply partition and local power grid within each period are calculated. The power supply and demand balance information considers the power flow constraints, important section constraints and power grid balance constraints in the partition and local power grid. The multi-period parallel technology is used to calculate the power generation capacity, power receiving capacity, standby and power supply margin balance indicators of the partition and local power grid, including:

[0017] Based on the medium and long-term power grid model, the partition power grid and local power grid to which the unit, load and main transformer power device belong are searched through the power grid topology relationship. Combining the unit plan, new energy plan, non-unified adjustment and marketing photovoltaic plan, tie line plan and bus load prediction data, the coal-fired, gas-fired, nuclear power, pumped storage, unified wind and light, non-unified wind and light, marketing photovoltaic power generation data and power receiving data in the partition power grid and local power grid are calculated. The single bus load growth rate is calculated using the single bus load prediction data, and then the load growth rate of the partition and local power grid is calculated. The load data of the medium and long-term partition and local power grid is calculated based on the real-time load of the partition and local power grid.

[0018] Considering the power flow constraints, important section constraints and power grid balance constraints of the partition and local power grid, the multi-period parallel calculation algorithm is used to calculate the power generation capacity, power receiving capacity, standby and power supply margin balance indicators of the medium and long-term partition power grid and local power grid.

[0019] Further, through the power grid topology relationship, the partition power grid and local power grid to which the unit, load and main transformer power device belong are searched. Combining the unit plan, new energy plan, non-unified adjustment and marketing photovoltaic plan, tie line plan and bus load prediction data, the coal-fired, gas-fired, nuclear power, pumped storage, unified wind and light, non-unified wind and light, marketing photovoltaic power generation data and power receiving data in the partition power grid and local power grid are calculated, including:

[0020] On the basis of the medium and long-term power grid model section, the output of the coal-fired and gas-fired units in the sending end power grid is increased by the percentage of the adjustable unit capacity, and the output of the coal-fired units in the receiving end power grid is reduced. The power flow is iteratively calculated, the power flow value of the boundary section is monitored, and when the section exceeds the limit, the unit adjustment output is halved and the calculation is repeated until the 99% of the section power flow value limit is reached. At this time, the sum of the power flow values of all the boundary monitoring sections is the internal maximum power receiving, 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 in 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 steps, and the output of the units outside the regional power grid is also increased. The power flow is calculated again, and when the section exceeds the limit, the adjustment step is halved and the calculation is repeated until the 99% of the section power flow value limit is reached. At this time, the sum of the power flow values of all the sections is the internal maximum power receiving, and the sum of the active values of the units in the receiving end power grid is the power generation capacity.

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

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

[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] Further, based on the calculation results of the medium and long-term partition and local power grid reserve and balance index, the balance capacity of the medium and long-term partition and local power grid reserve and power supply margin is evaluated, and time period and single point early warning information is provided, including:

[0027] Based on the calculation results of the medium and long-term partition and local power grid reserve and balance index, the medium and long-term partition and local power grid reserve and power supply margin are evaluated according to the specified threshold, and medium and long-term partition and local power grid reserve insufficient period early warning and extreme value time early warning, medium and long-term partition and local power grid power supply margin insufficient period early warning and extreme value time early warning, and balance generation and receiving data component detailed information of each early warning period are provided.

[0028] The second aspect is a medium and long-term power system partition and local power grid balance capacity evaluation and early warning system, which comprises:

[0029] A data acquisition and preprocessing unit is configured to acquire a medium-and-long-term power grid model, medium-and-long-term power equipment maintenance, commissioning and 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 at a specified time scale in the future, including equipment commissioning and decommissioning time attributes, and the medium-and-long-term power system boundary condition data includes medium-and-long-term power system load prediction, unit commitment, tie line plan, centralized new energy prediction, non-centralized and marketing photovoltaic prediction, and the data is preprocessed, including non-centralized new energy merging processing according to categories, data missing time point assignment processing, and non-integer point data integer point processing.

[0030] A power flow section generation unit is configured to generate a medium-and-long-term power system power flow section meeting stable operation conditions according to the preprocessed medium-and-long-term power grid model and boundary condition data, considering power balance and unit reserve constraints.

[0031] A partition and local power grid dynamic identification unit is configured to dynamically identify power supply partitions and local power grids of the medium-and-long-term power grid at each time period based on the medium-and-long-term power system power flow section, combined with the topology relationship of the medium-and-long-term power grid at each time period, and using multi-time period parallel computing technology, and to count power supply and demand equipment information of the partitions and local power grids.

[0032] A partition and local power grid balance capability evaluation unit is configured to calculate power supply and demand balance information in each time period in the power supply partitions and local power grids based on the dynamic identification results of the medium-and-long-term partitions and local power grids and the power supply and demand equipment information, considering power flow constraints, important section constraints and power grid balance constraints in the partitions and local power grids, and using multi-time period parallel computing technology, to calculate power generation capability, power supply capability, positive reserve and power supply margin balance indexes of the partitions and local power grids.

[0033] A partition and local power grid reserve and balance early warning unit is configured to evaluate the reserve and power supply margin balance capability of the medium-and-long-term partitions and local power grids based on the calculation results of the reserve and balance indexes of the medium-and-long-term partitions and local power grids, and to provide time period and single point early warning information.

[0034] In a third aspect, a computer 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 configured to be executed by the one or more processors, and the programs are executed by the processors to implement the steps of the method for medium-and-long-term power system partition and local power grid balance capability evaluation and early warning according to the first aspect of the present application.

[0035] In a fourth aspect, a computer storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method for medium-and-long-term power system partition and local power grid balance capability evaluation and early warning according to the first aspect of the present application.

[0036] Beneficial effects: The application provides a medium and long-term partition and local power grid balance capability evaluation and early warning method and system, provides a solution for comprehensively quantitatively analyzing the balance capability of the partition and local power grid from the whole network caliber in the medium and long-term time scale, and realizes multi-dimensional and all-around dynamic prediction and early warning of the medium and long-term power grid balance index. The application quantitatively senses and evaluates the balance capability index of the medium and long-term partition and local power grid, such as generation and reception balance capability, positive / negative standby, and power supply margin, from the multi-time period and multi-dimension, which can effectively support the monitoring of the operation characteristic change trend of the future power grid balance capability, improve the long-period stability analysis capability of the power grid, and improve the future risk early warning capability. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The application provides a medium and long-term power system partition and local power grid balance capability evaluation and early warning method.

[0038] Figure 2 The application provides a power system topology analysis algorithm flowchart.

[0039] Figure 3 The application provides an exemplary power system topology analysis node diagram.

[0040] Figure 4 The application provides an iterative algorithm flowchart for calculating the generation and reception capability of the partition and local power grid.

[0041] Figure 5 The application provides a system interface diagram of the medium and long-term power system local power grid balance capability evaluation and early warning.

[0042] Figure 6 The application provides a system interface diagram of the medium and long-term power system partition power grid balance capability evaluation and early warning. DETAILED DESCRIPTION

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

[0044] Reference Figure 1 The application provides a medium and long-term power system partition and local power grid balance capability evaluation and early warning method. The medium and long-term in the application refers to the time scale of week, ten days, month, year, and is different from the concept of intraday short-term or ultra-short-term time. The method comprises the following steps:

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

[0046] In power system operation and planning, the so-called "boundary condition data" refers to external parameters or input data that directly affect the state and behavior of the power system. These data usually define the boundaries or limitations of power system analysis and simulation. The relevant boundary data of medium and long-term power generation, power reception, and power consumption mainly includes:

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

[0048] Unit scheduling data: Unit scheduling data includes the predicted operation state and output level of generating units in the future period. These data are the key input for power system dispatching and operation, and determine the power supply capacity and reserve capacity of the system. As a boundary condition, unit scheduling data affects the operation cost and reliability of the system.

[0049] Interconnection line planning data: Interconnection line planning data involves the planned transmission capacity and operation state of transmission lines between different power grids. These data are crucial for interregional energy exchange of the power system, and are key factors to ensure the stability of system interconnection. As a boundary condition, interconnection line planning data limits the power flow between regions.

[0050] Forecasting of centralized wind and solar new energy: The forecasting data of wind and solar new energy provides the predicted value of wind and solar power generation in the future period. These data are crucial for power systems with high proportion of renewable energy, as they have volatility and uncertainty. As a boundary condition, these forecasting data affect the peak shaving demand and reserve capacity arrangement of the system.

[0051] Non-centralized and marketing photovoltaic forecasting data: Non-centralized photovoltaic forecasting data refers to the forecasting value of distributed photovoltaic power generation, which is usually predicted by local governments or enterprises. Marketing photovoltaic forecasting data is related to the market demand and potential income of photovoltaic power generation. These data as boundary conditions, because they affect the supply and demand balance and market operation of the power system.

[0052] Centralized small unit scheduling data: Small unit scheduling data involves the operation plan of small generating units (such as distributed generation, small hydropower stations, etc.). These data are crucial for the flexibility and responsiveness of the power system. As a boundary condition, small unit scheduling data affects the power supply diversity and peak shaving capacity of the system.

[0053] The invention takes 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 discussed here. For the obtained medium and long-term power system load prediction, unit planning data, tie line planning data, centralized wind and solar new energy prediction, non-centralized and marketing photovoltaic prediction data, and centralized small unit planning data, the boundary data need to be revalued in response to data problems such as single-point numerical anomalies or missing non-integral point data. At the same time, the obtained non-centralized new energy data is classified and merged, and is integrated into dispatching and marketing new energy data.

[0054] In the context of the invention, the model refers to a data set of power grid device attributes and connection relationships, and the medium and long-term power system real-time model refers to a data set of power grid device attributes and connection relationships that have been in real-time operation. The model itself does not have a future state (medium and long-term) concept, but only gives the model future state (medium and long-term) attributes for commissioning and decommissioning time. Therefore, the maintenance method of the medium and long-term power system model remains unchanged, and model management is performed in the model data platform of the control cloud. Only the commissioning and decommissioning time of each device needs to be correct.

[0055] The medium and long-term power system device maintenance, commissioning / decommissioning plan data is the medium and long-term time scale power system device maintenance plan and commissioning and decommissioning plan, which is obtained from the planning department. The pre-processing methods include missing data supplement and non-integral point data replaced by adjacent integral point data.

[0056] S2: Based on the pre-processed boundary data and the medium and long-term power system model, generate the medium and long-term power system flow section.

[0057] Based on the medium and long-term power system model data, superimpose the medium and long-term power system generation, power reception, and power consumption related boundary data, device maintenance, commissioning / decommissioning, and other power grid model change data, consider the power grid balance and unit reserve constraints, and generate a medium and long-term power system flow section data that meets the stable operation conditions. The specific method of power grid flow calculation is not described here.

[0058] S3: According to the medium and long-term power system model and section data, through dynamic partitioning automatic identification technology, dynamically partition each time period in the medium and long-term, and identify the partition power grid and local power grid and the regional power generation and power reception equipment information in each time period.

[0059] In order to grasp the characteristics of different structures of the power grid in different time periods, the application divides each time period of the medium and long term into dynamic partitions, first divides the medium and long term power grid into a plurality of time period set, then adopts a multi-time period parallel computing mode to simultaneously perform topology analysis and identification on the power grid model of the plurality of time period sets, and obtains the information of the power equipment belonging to the partitions and the local power grid of the medium and long term plurality of time period sets.

[0060] The multi-time period parallel computing mode first divides the medium and long term plurality of time period power grid into a plurality of sets according to a plurality of models, then divides each set according to days, and finally performs topology identification on the partitions and the local power grid for each time period of the day. For example, each time period of the month is divided into units of days, and parallel computing is performed in units of days, which greatly saves the calculation time scale of the whole month or even longer.

[0061] According to the embodiment of the application, the topology identification of the partitions and the local power grid is calculated by using a depth-first search method, a topology tree is constructed according to the selected network boundary section, expansion direction and expansion rule, and a node meeting the target state is searched, the downstream associated equipment is searched in depth, and thus the partition and local power grid model, the interconnection relationship between the partitions and the local power grid, and the equipment information in the partitions and the local power grid are obtained. The depth-first search method process is shown in Figure 2 As shown in the figure, first, the root node (topology search starting point) is marked, then the child nodes of the root node are obtained and added to the node queue, next, whether the node in the node queue is marked is analyzed, if not, the child nodes of the node are obtained and added to the node queue, and the above process is repeated until all the nodes in the node queue are marked. An example of topology analysis is shown in Figure 3 As shown in the figure, all the equipment on the power supply path from the 500kV voltage level main transformer is searched, and the power supply path search from high voltage to low voltage is an expansion search, and the set of all the equipment on the power supply path (including loads, generators, main transformers, etc.) is the equipment in the partition / local power grid of the 500kV voltage level main transformer.

[0062] S4: According to the partition and local power grid identification results of each time period of the power system and the information of the power supply and receiving equipment in the region, the balance of the power supply and receiving, the positive and negative reserve, and the power supply margin balance index of the partition and the local power grid are calculated.

[0063] The balance of power supply and receiving refers to the calculation of the data of power generation, power generation capacity, power receiving, power receiving capacity and power consumption, and the calculation of the positive and negative reserve and the power supply margin is a further index data based on these data. The power supply margin refers to the margin left after the maximum power generation capacity and the maximum power receiving capacity of the power grid region are added and then subtracted from the power consumption of the power grid region.

[0064] According to the embodiment of the present application, based on the information of the power equipment belonging partition and local power grid of the long-term multi-time period set, the generation and power receiving capacity of the long-term multi-time period partition and local power grid is calculated. For the load capacity, the load of the partition and local power grid is calculated by using the load growth rate algorithm, mainly using the single bus load prediction data to calculate the single load growth rate of the long-term multi-time period, and then calculating the long-term multi-time period load growth rate of the partition and local power grid, which is superimposed on the real-time load of the partition and local power grid to calculate the long-term multi-time period load data of the partition and local power grid.

[0065] Specifically, considering the flow constraint, important section constraint, power grid balance constraint and other constraint conditions in the partition power grid and local power grid, the generation capacity, power receiving capacity, positive and negative reserve and power supply margin and other balance indexes of the long-term multi-time period partition power grid and local power grid are calculated by using the multi-time period parallel calculation algorithm. Among them, the traditional generation capacity and power receiving capacity calculation is added by the rated limit value attribute of the power equipment itself, and cannot represent the real limit value in actual operation, therefore, the continuous flow simulation algorithm is used for calculation, and the Figure 4 On the basis of the long-term power grid model section, the output of the coal-fired and gas-fired units of the sending end power grid is increased by the percentage of adjustable unit capacity, and the output of the coal-fired units of the receiving end power grid is reduced, the flow is iteratively calculated, and the flow value of the boundary section is monitored. When the section exceeds the limit, it is returned to the last iteration when the flow does not exceed the limit, the unit adjustment output is halved, and the calculation is repeated, until the 99% of the section flow value limit is reached, at this time the sum of all boundary monitoring section flow values is the internal maximum power receiving, and the sum of the active values of the units in the receiving end power grid is the 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 steps, and the output of the units outside the regional power grid is also increased, and the flow is calculated again. When the section exceeds the limit, it is returned to the last iteration when the flow does not exceed the limit, the adjustment step is halved, and the calculation is repeated, until the 99% of the section flow value limit is reached, at this time the sum of all section flow values is the internal maximum power receiving, and the sum of the active values of the units in the receiving end power grid is the generation capacity.

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

[0067] The generation capacity of the partition and local power grid = the generation capacity of the coal-fired units in the partition and local power grid + the output of the gas-fired units + the output of the pumped storage units + the output of the nuclear power units + the output of the unified wind and light units + the output of the non-unified wind and light units + the output of the marketing distributed photovoltaic units;

[0068] The partition and local power grid coal-fired generating unit power generation capacity and power receiving capacity are calculated by the above continuous power flow iteration algorithm; in the above algorithm, the output of the coal-fired generating unit is modified in the process of iterative power flow calculation, and the power flow is iterated constantly. At the last time of calculation, the power generation of the generator unit in the region at this time is added to obtain the power generation capacity of the region, and the power flow of the monitored boundary section is added to obtain the power receiving capacity of the region.

[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 partition and local power grid load is calculated by a load growth rate algorithm. Based on the single-bus load forecast data and real-time data, the single-bus load forecast growth rate is calculated, the load growth rate of the regional power grid is calculated through the attribution relationship between the single-bus load and the regional power grid, and then the future load forecast of the regional power grid is calculated in combination with the real-time load data of the regional power grid.

[0072] Similarly, the multi-time period parallel computing technology is adopted, the partition and local power grid generation and reception power balance, positive and negative reserve and power supply margin balance indexes are calculated in each time period according to the power grid model divided by time periods, and are summarized into the multi-time period partition and local power grid balance index data.

[0073] S5: Based on the reserve and power supply margin of the long-term power system partition and local power grid, a long-term partition and local power grid balance monitoring and early warning function is provided.

[0074] Based on the reserve and power supply margin calculation results of the long-term multi-time period power system partition and local power grid, the reserve and power supply margin conditions of the long-term partition and local power grid are evaluated according to normal, alarm and emergency three states, and the state threshold values of different power grid levels can be set by themselves. For the insufficient reserve and insufficient power supply margin in the long-term time period, the advance warning of the time period and the single-point extreme moment is carried out, including the insufficient time period, the extreme time point, the partition and local power grid generation and reception power details curve of the insufficient time period, the partition and local power grid generation and reception power details data of the extreme time point, and the specific insufficient reasons can be directly known according to the details, such as the change of power receiving component, the change of power generation capacity component, the change of load component, the detailed information of equipment maintenance / operation / retirement, etc.

[0075] The long-term power system partition and local power grid balance capacity evaluation and early warning method provided by the application realizes the comprehensive quantitative analysis of the balance capacity of the partition and local power grid from the whole network in the long-term time scale, realizes the multi-dimensional and all-around dynamic prediction and early warning of the long-term power grid balance index.

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

[0077] A data acquisition and preprocessing unit is configured to acquire a medium and long-term power grid model, medium and long-term power equipment maintenance, commissioning / retirement data, and medium and long-term power system boundary condition data, wherein the medium and long-term power grid model refers to a data set of power grid equipment attributes and connection relationships at a specified time scale in the future, including equipment commissioning and retirement time attributes, and the medium and long-term power system boundary condition data includes medium and long-term power system load prediction, unit commitment, tie line plan, centralized new energy prediction, non-centralized and marketing photovoltaic prediction, and the data is preprocessed, including non-centralized new energy merging processing, data missing time point assignment processing, and non-integer point data integer point processing.

[0078] A power flow section generation unit is configured to generate a medium and long-term power system power flow section meeting stable operation conditions according to the preprocessed medium and long-term power grid model and boundary condition data, considering power balance and unit reserve constraints.

[0079] A partition and local power grid dynamic identification unit is configured to dynamically identify the power supply partition and local power grid of the medium and long-term power grid at each time period based on the medium and long-term power system power flow section, combined with the topology relationship of the medium and long-term power grid at each time period, using a multi-time period parallel computing technology, and to count the power supply and demand equipment information of the partition and local power grid.

[0080] A partition and local power grid balance capacity evaluation unit is configured to calculate the power supply and demand balance information in the power supply partition and local power grid at each time period based on the dynamic identification results of the medium and long-term partition and local power grid and the power supply and demand equipment information, considering the power flow constraints, important section constraints and power grid balance constraints in the partition and local power grid, and using a multi-time period parallel computing technology, to calculate the power generation capacity, power supply capacity, reserve and power supply margin balance indexes of the partition and local power grid.

[0081] A partition and local power grid reserve and balance early warning unit is configured to evaluate the reserve and power supply margin balance capacity of the medium and long-term partition and local power grid based on the calculation results of the medium and long-term partition and local power grid reserve and balance indexes, and to provide time period and single point early warning information.

[0082] In the embodiment of the application, the technology used in the method is verified and tested by taking the monthly partition and local power grid balance capacity evaluation and early warning of a certain power grid as an example, Figure 5 and Figure 6The interface display diagram is provided. The system can comprehensively quantitatively analyze the balancing ability of a subarea and a local power grid from a unified manner, dispatching, and multiple perspectives of the whole society, and realizes multi-dimensional and full dynamic prediction and early warning of monthly power grid balancing indexes. The balancing ability indexes of the subarea and the local power grid in a medium and long term, such as power generation and consumption balancing ability, positive / negative reserve, and power supply margin, are quantitatively perceived and evaluated in multiple time periods and multiple dimensions, so as to help users analyze the balancing situation of the subarea and the local power grid in a medium and long term in multiple time scales, and analyze the root cause of the problem in detail from various components, and effectively support perception of the operation characteristic change trend of the balancing ability of the power grid in a medium and long term, improve the long-period stability analysis ability of the power grid, and strengthen the risk early warning ability in the future.

[0083] The application further provides a computer device, which comprises one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs realize the steps of the method for evaluating and early warning the balancing ability of a subarea and a local power grid of a medium and long term power system when executed by the processors.

[0084] The application further provides a computer storage medium, which stores a computer program, and the computer program realizes the steps of the method for evaluating and early warning the balancing ability of a subarea and a local power grid of a medium and long term power system when executed by the processors.

[0085] Those skilled in the art should understand that the embodiments of the application can be provided as a method, device (system), computer device, or computer program product. Therefore, the application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can adopt a computer program product in the form of being 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 application is described with reference to a flowchart of a method according to an embodiment of the application. It should be understood that each flow in the flowchart and the combination of the flows in the flowchart can be realized 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 produce a device that realizes the function specified in the flow or the combination of the flows. Figure 1 The device that realizes the function specified in the flow or the combination of the flows.

[0087] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 specified in the flow or flows.

[0088] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 specified in the flow or flows.

Claims

1. A method for medium and long term power system partitioning and local grid balancing capability assessment and early warning, characterized in that, The method comprises the following steps: Obtain the medium and long term power grid model, medium and long term power equipment maintenance, commissioning / retirement data, and medium and long term power system boundary condition data, the medium and long term power grid model refers to the data set of the device attribute and connection relationship of the power grid in the future specified time scale, including the device commissioning, retirement time attribute, the medium and long term power system boundary condition data includes medium and long term power system load prediction, unit plan, tie line plan, unified new energy prediction, non-unified and marketing photovoltaic prediction, preprocessing the data, including non-unified new energy merging processing according to the type, data missing time point assignment processing, non-integer point data integer point processing; According to the preprocessed medium and long term power grid model and boundary condition data, considering the power balance and unit reserve constraint, the medium and long term power system flow section meeting the stable operation condition is generated; Based on the medium and long term power system flow section, combined with the topology relationship of the medium and long term power grid in each period, the multi-period parallel computing technology is adopted to dynamically identify the power supply partition and local power grid of the medium and long term power grid in each period, and the information of the power supply and demand equipment in the partition and local power grid is counted; Based on the dynamic identification results and the power supply and demand equipment information of the medium and long term partition and local power grid, the power supply and demand balance information in each period of the power supply partition and local power grid is calculated, considering the power flow constraint, important section constraint and power grid balance constraint in the partition and local power grid, the multi-period parallel computing technology is adopted to calculate the power generation capacity, power receiving capacity, standby and power supply margin balance index of the partition and local power grid; Based on the standby and balance index calculation results of the medium and long term partition and local power grid, the standby and power supply margin balance capacity of the medium and long term partition and local power grid is evaluated, and the period and single point early warning information is provided.

2. The method of claim 1, wherein, Based on the medium and long term power system flow section, combined with the topology relationship of the medium and long term power grid in each period, the multi-period parallel computing technology is adopted to dynamically identify the power supply partition and local power grid of the medium and long term power grid in each period, and the information of the power supply and demand equipment in the partition and local power grid is counted, including: Based on the medium and long term power system flow section, considering the different architecture characteristics of the medium and long term power grid in different periods, the medium and long term is divided into multiple period sets, and the power grid model of multiple period sets is calculated in parallel, and the power system topology analysis is carried out, wherein, for the power grid model of each period set, taking the specified transformer as the starting point, the downstream associated devices are searched in depth, the dynamic partition, inter-partition interconnection relationship and partition internal power supply and demand equipment information are identified, and based on multiple partitions, the local power grid area is divided according to the geographical division or power demand attribution relationship, and finally the partition and local power grid division data under multiple period sets are obtained.

3. The method of claim 2, wherein, Taking the specified transformer as the starting point, the downstream associated devices are searched in depth, the dynamic partition, inter-partition interconnection relationship and partition internal power supply and demand equipment information are identified, including: Firstly, the root node is marked, i.e. the starting point of the topology search, and then the child nodes of the root node are acquired and added to the node queue; next, it is analyzed whether the nodes in the node queue are marked, if not, the child nodes of the node are acquired and added to the node queue, and the above process is repeated until all the nodes in the node queue are marked.

4. The method of claim 1, wherein, The generation and consumption balance information in each period in the partition and local power grid is calculated based on the medium and long term partition and local power grid dynamic identification results and the power generation and consumption equipment information, while considering the power flow constraints, important section constraints and power grid balance constraints in the partition and local power grid, the generation capacity, consumption capacity, active reserve and power supply margin balance indexes of the partition and local power grid are calculated by using multi-period parallel technology, including: Based on the medium and long term power grid model, the partition power grid and local power grid to which the units, loads and main transformer power equipment belong are searched out through the power grid topology relationship, and the coal-fired, gas, nuclear power, pumped storage, unified wind and light, non-unified wind and light, marketing photovoltaic power generation data and consumption data in the partition power grid and local power grid are calculated by combining the unit plan, new energy plan, non-unified and marketing photovoltaic plan, tie line plan and bus load prediction data, the single load growth rate is calculated by using the single bus load prediction data, and then the load growth rate of the partition and local power grid is calculated, and the load data of the medium and long term partition and local power grid is calculated based on the real-time load of the partition and local power grid; The generation capacity, consumption capacity, active reserve and power supply margin balance indexes of the medium and long term partition power grid and local power grid are calculated by using multi-period parallel calculation algorithm considering the power flow constraints, important section constraints and power grid balance constraints of the partition and local power grid.

5. The method of claim 4, wherein, Through the power grid topology relationship, the partition power grid and local power grid to which the units, loads and main transformer power equipment belong are searched out, and the coal-fired, gas, nuclear power, pumped storage, unified wind and light, non-unified wind and light, marketing photovoltaic power generation data and consumption data in the partition power grid and local power grid are calculated by combining the unit plan, new energy plan, non-unified and marketing photovoltaic plan, tie line plan and bus load prediction data, including: On the basis of the section of the medium and long term power grid model, the output of the coal-fired and gas units in the sending end power grid is increased by the percentage of adjustable unit capacity, and the output of the coal-fired units in the receiving end power grid is reduced, the power flow is iteratively calculated, the boundary section power flow value is monitored, when the section power flow value exceeds the limit, the last iteration when the power flow does not exceed the limit is returned, the unit adjustment output is halved, and the calculation is repeated, until the section power flow value reaches 99% of the limit value, at this time the sum of all boundary monitoring section power flow values is the maximum consumption in the internal, and the sum of the active values of the units in the receiving end power grid is the generation capacity; If the sending end power grid or the receiving end power grid unit cannot be adjusted, and the monitored section does not exceed the limit, the load of the receiving end power grid is adjusted by simulation, the load in the regional power grid is increased by steps, the output of the unit outside the regional power grid is also increased, the power flow is calculated again, when the section exceeds the limit, the last iteration of the power flow without exceeding the limit is returned, the adjustment step is halved, and the calculation is repeated, until the power flow value of the section reaches 99%, at this time, the sum of all section power flow values is the internal maximum power receiving, and the sum of the active power values of the units in the receiving end power grid is the power generation capacity.

6. The method of claim 4, wherein, The power supply margin of the partition and local power grid = the power generation capacity of the partition and local power grid + the power receiving capacity of the partition and local power grid - the load of the partition and local power grid; The power generation capacity of the partition and local power grid = the power generation capacity of the coal-fired units in the partition and local power grid + the output of the gas units + the output of the pumped storage units + the output of the nuclear units + the output of the unified wind and solar units + the output of the non-unified wind and solar units + the output of the marketing distributed photovoltaic units; 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 of claim 1, wherein, Based on the calculation results of the medium and long term partition and local power grid reserve and balance index, the medium and long term partition and local power grid reserve and power supply margin balance capacity is evaluated, and time period and single point early warning information is provided, including: Based on the calculation results of the medium and long term partition and local power grid reserve and balance index, the medium and long term partition and local power grid reserve and power supply margin situation is evaluated according to the specified threshold, and the medium and long term partition and local power grid reserve insufficient time period early warning and extreme value time early warning, the medium and long term partition and local power grid power supply margin insufficient time period early warning and extreme value time early warning, and the balance of each early warning period are provided. Detailed information of data components.

8. A medium and long term power system partition and local power grid balance capability evaluation and early warning system, characterized in that, Including: A data acquisition and preprocessing unit is configured to acquire a medium and long term power grid model, medium and long term power equipment maintenance, commissioning / retirement 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 at a specified time scale in the future, including equipment commissioning and retirement time attributes. The medium and long term power system boundary condition data includes medium and long term power system load forecasting, unit planning, tie line planning, unified new energy forecasting, non-unified and marketing photovoltaic forecasting. The data is preprocessed, including non-unified new energy processing by type, data missing time point assignment processing, and non-integer point data integer point processing. A power flow section generation unit is configured to generate a medium and long term power system power flow section that meets the stable operation conditions based on the preprocessed medium and long term power grid model and boundary condition data, considering power balance and unit reserve constraints. A partition and local power grid dynamic identification unit is configured to dynamically identify the power supply partition and local power grid of the medium and long term power grid at each time period based on the medium and long term power system power flow section, combined with the topology relationship of the medium and long term power grid at each time period, using multi-time period parallel computing technology, and to statistic the partition and local power grid power supply and receiving equipment information. The partition and local power grid balance capability evaluation unit is configured to calculate the generation and consumption balance information in each time period in the power supply partition and local power grid based on the medium and long term partition and local power grid dynamic identification results and the generation and consumption equipment information, and to calculate the generation capacity, consumption capacity, active reserve and power supply margin balance indexes of the partition and local power grid by considering the power flow constraint, important section constraint and power grid balance constraint in the partition and local power grid and by using the multi-time period parallel calculation technology. The partition and local power grid reserve and balance early warning unit is configured to evaluate the medium and long term partition and local power grid reserve and power supply margin balance capability based on the medium and long term partition and local power grid reserve and balance index calculation results, and to provide time period and single point early warning information.

9. A computer device, comprising: The 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 configured to be executed by the one or more processors, and the programs, when executed by the processors, implement the steps of the medium and long term power system partition and local power grid balance capability evaluation and early warning method according to any one of claims 1-7.

10. A computer storage medium having stored thereon a computer program, characterized in that The computer program, when executed by the processor, implements the steps of the medium and long term power system partition and local power grid balance capability evaluation and early warning method according to any one of claims 1-7.

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