A multi-period, multi-level risk coordination control method and system for a power system

Abstract

The present application relates to the technical field of power system, and more particularly to a kind of power system multi-period, multi-level risk coordination control method and system, using the method provided by the present application, mainly including the system operation risk quantification model considering source and load bilateral uncertainty based on the basic parameter;Get considering each level system operation risk constraint, time domain coupling operation constraint and conventional power system operation constraint, build risk coordination control scheduling optimization model;Set single-period multi-level coordinated operation and multi-period-multi-level coordinated operation two scenarios, based on the two interconnected power grids constructed for measurement analysis.By considering power supply shortage and power surplus, a risk quantification model is constructed to adapt to the operating characteristics of each level of power grid, laying a foundation for risk quantification research under the conditions of source and load bilateral uncertainty, facing the risk quantification of national-grid-provincial three-level collaborative balance mode.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a method and system for multi-cycle, multi-level risk coordination and control of power systems. Background Technology

[0002] Extreme weather events can usually be predicted in advance within a few days. Under the three-tiered coordinated dispatch model of the State Grid and provincial power systems in my country, the plan for inter-regional and inter-provincial power transmission lines is generally determined first based on the medium- and long-term electricity market. Then, day-ahead and intraday electricity markets, as well as emergency dispatch, are used to address the system balance challenges caused by uncertainties in power sources and loads. However, due to the complexity of coordination between different levels of the market and constraints such as power flow and key sections, resource coordination problems can easily arise under extreme operating scenarios, leading to a contradictory situation in provincial power grids where power supply gaps coexist with the abandonment of renewable energy.

[0003] To address the impact of uncertainties on power system balance from both the source and load sides, existing research often employs robust optimization and stochastic optimization methods. While robust optimization can account for extreme cases, it can impact the economic efficiency of system operation to some extent; stochastic optimization typically generates multiple scenarios based on the probability distribution of uncertainties. Many scholars also focus on dynamically adjusting dispatch strategies in response to changes in source and load over multiple cycles. However, current research primarily concentrates on coupling across multiple time scales, such as day-ahead and intraday, and fails to fully cover large-scale inter-regional and inter-provincial resource coordination. Therefore, it is urgent to quantify the operational risks of each level of the power grid based on the national-grid-provincial three-tiered coordinated dispatch framework and the actual operation of multi-level power markets, and incorporate these risks into the decision-making scope of dispatch operations at each level. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-cycle, multi-level risk coordination and control method and system for power systems to solve the above-mentioned problems in the prior art.

[0005] This invention is achieved through the following technical solution:

[0006] In a first aspect, the present invention provides a method for obtaining basic parameters of power grids at all levels, and constructing a system operation risk quantification model that takes into account uncertainties on both the source and load sides based on the basic parameters;

[0007] Obtain the operational risk constraints, time-domain coupled operational constraints, and conventional power system operational constraints of each system level, and construct a risk coordination control and scheduling optimization model;

[0008] Typical and extreme scenarios of source-load data are extracted using DBSCAN and K-means methods. Two scenarios are set up: single-cycle multi-level coordinated operation and multi-cycle multi-level coordinated operation. Calculations and analyses are performed based on the constructed interconnected power grids of multiple regions.

[0009] Secondly, the present invention also provides a multi-cycle, multi-level risk coordination and control system for power systems, used to execute the above-mentioned multi-cycle, multi-level risk coordination and control method for power systems, comprising:

[0010] Obtain the basic parameters of power grids at all levels, and construct a system operation risk quantification model that takes into account the uncertainties on both the source and load sides based on the basic parameters;

[0011] Obtain the operational risk constraints, time-domain coupled operational constraints, and conventional power system operational constraints of each system level, and construct a risk coordination control and scheduling optimization model;

[0012] Based on DBSCAN and K-means methods, typical and extreme scenarios of source-load data are extracted. Two scenarios are set up: single-cycle multi-level coordinated operation and multi-cycle multi-level coordinated operation. Calculation and analysis are performed based on the constructed interconnected power grids of multiple regions.

[0013] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0014] 1. The method provided by this invention mainly includes: constructing a system operation risk quantification model considering uncertainties on both the source and load sides based on the aforementioned basic parameters; obtaining system operation risk constraints, time-domain coupled operation constraints, and conventional power system operation constraints at each level, and constructing a risk coordination control and scheduling optimization model; setting two scenarios: single-cycle multi-level coordinated operation and multi-cycle multi-level coordinated operation, and performing calculation and analysis based on the constructed two-region interconnected power grid. By taking into account power supply deficits and power surpluses, a risk quantification model adapted to the operation characteristics of power grids at all levels is constructed, laying the foundation for risk quantification research on the national-grid-provincial three-level coordinated balance mode under the condition of uncertainties on both the source and load sides.

[0015] 2. This invention addresses the differentiated risk and cost requirements of system operation under different time scales and scheduling levels. It constructs a multi-cycle, multi-level probabilistic scheduling optimization model for power systems that takes into account both hierarchical risk constraints and time-coupled operational constraints. This model enhances the power system's ability to coordinate large-scale cross-regional and cross-provincial resources under extreme weather conditions, thereby strengthening the power system's supply guarantee capacity and renewable energy absorption capacity. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0018] Figure 2 This describes the process for generating extreme and typical scenario sets for the DBSCAN-K-means method of this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. The naming or numbering of steps in this application does not imply that the steps in the method flow must be executed in the chronological / logical order indicated by the naming or numbering. The execution order of named or numbered process steps can be changed according to the desired technical objective, as long as the same or similar technical effect is achieved.

[0021] Please refer to Figures 1-2 A multi-cycle, multi-level risk coordination and control method for power systems, comprising:

[0022] S101: Obtain the basic parameters of power grids at all levels, and construct a system operation risk quantification model that takes into account the uncertainties on both the source and load sides based on the basic parameters;

[0023] Considering the relative relationship between the equivalent load of each level of the power grid and the system's power generation capacity, and between the equivalent load and the system's ramp-up capability, a power system risk quantification model adapted to the characteristics of the entire region, regional region, and provincial power grid is constructed.

[0024] Among them, "full domain", "region", and "province" refer to the national power grid (national dispatch area), the grid-region power grid (grid dispatch area), and the provincial power grid (provincial dispatch area), respectively. The national-grid-province three-level collaborative balance model is designed to address the fact that the quantification of hierarchical differentiated risks has not yet been reflected in the national-grid-province three-level collaborative balance architecture. This invention adds multi-cycle coordination and hierarchical risk differentiation quantification methods on this architecture.

[0025] S102: Obtain the operational risk constraints, time-domain coupled operational constraints, and conventional power system operational constraints of each level of the system, and construct a risk coordination control and scheduling optimization model;

[0026] Taking into account the differences in power system risks and operating costs at D-3, day-ahead, and intraday levels, as well as the operating risk constraints, time-domain coupled operating constraints, and conventional power system operating constraints at each level, a multi-cycle, multi-level risk coordination control and scheduling optimization model is constructed.

[0027] S103: Based on DBSCAN and K-means methods, typical and extreme scenarios of source-load data are extracted, and two scenarios are set: single-cycle multi-level coordinated operation and multi-cycle multi-level coordinated operation. Calculation and analysis are performed based on the constructed interconnected power grids of multiple regions.

[0028] Based on DBSCAN and K-means methods, typical and extreme scenarios of source-load data are extracted. Two scenarios are set: single-cycle multi-level coordinated operation and multi-cycle multi-level coordinated operation. Calculation and analysis are carried out based on the constructed multi-regional interconnected power grid. The "region" in the multi-regional interconnected power grid has the same meaning as the "region" in the above text, referring to multiple grid domains, which include multiple provincial power grids.

[0029] Specifically, scenario extraction and scenario setting is a method for setting up simulation examples, which is a case analysis and verification of steps S101 and S102. The probability calculation of the scenario corresponds to the probability in the model, and is based on the probability of different scenarios occurring.

[0030] Multi-period-multi-level operation is the core model currently being built, which considers risk constraints and time-series coupling constraints for operation simulation. Single-period-multi-level operation removes risk constraints and time-series coupling and only performs operation simulation for a single intraday time series.

[0031] This invention breaks through the limitations of existing research that fails to consider the operational characteristics of power grids at all levels in constructing a power system risk quantification model. It takes into account the impact of extreme scenarios, analyzes the probability distribution characteristics of typical and extreme power system operation scenarios, and constructs a multi-period probabilistic coordination optimization model for the national-grid-provincial three-level coordination, taking into account the uncertainty of source and load. This achieves a dual extension of risk response strategies in terms of time dimension and hierarchical structure, effectively improving the cross-regional and cross-provincial resource coordination capabilities under extreme scenarios and resource coordination tensions, and to a certain extent reducing the power supply gap and the abandonment of new energy in the power system.

[0032] An exemplary embodiment of the present invention includes constructing a system operation risk quantification model that takes into account uncertainties on both the source and load sides, comprising:

[0033] Construct a global power grid risk quantification model, a regional power grid risk quantification model, and a provincial power grid risk quantification model.

[0034] Specifically, this invention takes into account the risks of insufficient or excessive power supply that may result from uncertainties on both the source and load sides. Starting from the operational characteristics of the power grid at the national, regional, and provincial levels, and considering the impact of internal and external risks, it constructs a risk quantification model adapted to different levels of power systems.

[0035]

[0036]

[0037]

[0038]

[0039] In the formula, for The risk of insufficient power supply across the entire region is constantly based on power generation capacity; for The risk of oversupply of electricity across the entire region is constantly based on power generation capacity; for Predict equivalent load across the entire domain at any given time; for The maximum adjustable power generation capacity of all units except wind and solar power across the entire region at any given time; for Real-time global system peak shaving rate; for Real-time, global load demand forecasting; for Real-time global wind power forecast output; for Real-time global photovoltaic power output forecast; for Minimum technical output of all units except wind and solar power across the entire region at all times.

[0040]

[0041]

[0042] In the formula: for The risk of insufficient power supply across the entire region is constantly based on ramp-up capabilities; for The risk of oversupply of electricity across the entire region is constantly based on ramp-up capabilities; for The uphill / downhill capability coefficient of all units except wind and solar power at any given time.

[0043] Specifically, based on the relative relationships between equivalent load and system generation capacity, and between equivalent load and system ramp-up capacity, the system operation risk is quantified, providing early warning for operation and dispatch from the perspective of global system risk, and guiding unit start-up and shutdown and inter-regional and inter-provincial interconnection plans. Simultaneously, considering the uncertainty of equivalent load, the system operation risk for the entire power grid is obtained.

[0044] The construction of a comprehensive power grid risk quantification model includes:

[0045]

[0046] In the formula, To mitigate system operational risks, For the equivalent load scenario set, For the scene, For about Scene The probability of occurrence, For the scene Below The risk of insufficient power supply across the entire region is constantly based on power generation capacity. For the scene Below The risk of insufficient power supply across the entire region is constantly based on the ability to climb hills. For the scene Below The risk of oversupply of electricity across the entire region is constantly based on power generation capacity. For the scene Below The risk of oversupply of electricity across the entire region is constantly assessed based on ramp-up capabilities. To determine the weight of supply security risk within system operational risk, The weight of risk in system operation risk is used to mitigate risk.

[0047] In one exemplary embodiment of the present invention, during the actual operation of a regional power grid, the main risk compared to the operation of the entire grid is the increase / decrease of inter-regional tie lines, which may cause changes in the equivalent load characteristics of the region due to the adjustment of boundary conditions.

[0048]

[0049] In the formula: for Predicted equivalent load for a given time zone; for Time-region load demand forecasting; for The transmission capacity of the inter-regional communication line at any given time is positive for output and negative for input. A collection of inter-regional connection lines related to the region; for Predicted wind power output for the region at any given time; for Predicted photovoltaic power output for the specified time period.

[0050] Further cluster analysis of the regional source-load data yielded an equivalent load scenario set, with the probability of each scenario occurring being... This leads to regional operational risks.

[0051] The regional power grid risk quantification model includes:

[0052]

[0053] In the formula, To mitigate regional operational risks, To obtain an equivalent load scenario set through further cluster analysis of regional source-load data, For about Scene The probability of occurrence, for The risk of insufficient regional power supply based on constant generation capacity; for The risk of insufficient regional power supply based on ramp-up capability at all times; for The risk of regional power oversupply based on constant generation capacity; for The risk of regional power oversupply is always based on ramp-up capabilities.

[0054] In one exemplary embodiment of the present invention, during the actual operation of a provincial power grid, compared with the operation of the entire network and regional operation, there are mainly situations involving the increase / condition of cross-regional and cross-provincial interconnection lines. The equivalent load of the province may change due to the influence of boundary condition adjustments.

[0055]

[0056] In the formula: for Predicted equivalent load for a given time zone; for Real-time provincial load demand forecasting; for The transmission capacity of inter-regional and inter-provincial communication lines is always related to the province; input is positive and output is negative. , These are collections of inter-regional and inter-provincial connecting lines related to the province. for Predicted wind power output in the province at any time; for Real-time forecast of provincial photovoltaic power output.

[0057] To account for the impact of provincial source-load data on boundary conditions, further cluster analysis was performed to obtain an equivalent load scenario set, with the probability of each scenario occurring being... Obtaining provincial operational risks .

[0058] The provincial power grid risk quantification model includes:

[0059]

[0060] In the formula, For provincial operational risks, To further cluster the provincial source load data and obtain an equivalent load scenario set, For about Scene The probability of occurrence, for The risk of insufficient provincial power supply based on power generation capacity; for The risk of insufficient provincial power supply based on ramp-up capabilities; for The risk of oversupply of provincial power based on constant generation capacity; for The risk of oversupply of provincial power is always based on the ability to scale up the load.

[0061] In one exemplary embodiment of the present invention, the construction of the risk coordination control scheduling optimization model includes constructing a D-3 coordination optimization model and constructing a D-1 coordination optimization model:

[0062] The D-3 coordinated optimization model includes a first objective function, a first power balance constraint, inter-regional and inter-provincial tie line constraints, key section constraints, and power supply risk constraints.

[0063] The D-1 coordinated optimization model includes a second objective function, a second power balance constraint, D-1 and D-3 coupling constraints, and a second power supply risk constraint.

[0064] Specifically, the first objective function includes:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] In the formula, Let the first objective function be... For system operating costs; Costs for cross-regional and cross-provincial data transmission lines within the system; For system operation risk costs; This refers to the number of runtime segments. for Periodic thermal power units Running cost function, for Periodic thermal power units Operating cost function; , These are thermal power units dispatched by the national and regional dispatch authorities on D-3. , of Operating status during the time period , These are thermal power units dispatched by the national and regional dispatch authorities on D-3. , of Operating status during the specified time period; 、 thermal power units , Startup costs; Day D-3 Periodic thermal power units contribute; , These refer to the number of thermal power units directly dispatched by the national dispatch center and those directly dispatched by the grid dispatch center, respectively. , , thermal power units Electricity generation cost coefficient; , These refer to the number of inter-regional and inter-provincial connecting lines, respectively. , These are the inter-regional connection lines for day D-3. Inter-provincial connection line exist Time-period transmission capacity; , They are respectively connecting lines , Transmission cost coefficient; This is the system operation risk cost coefficient.

[0071] First power balance constraint:

[0072]

[0073] In the formula: Day D-3 Periodic National Dispatch Direct-Regulation Hydropower Units contribute; , , , , D-3 days Time-of-use grid dispatching and direct dispatching thermal power units Hydropower units Pumped storage units Wind turbine Photovoltaic units contribute; The number of thermal power units directly controlled by the national dispatch center; , , , , These are the numbers of grid-dispatch and directly dispatchable thermal power units, hydropower units, pumped storage units, wind power units, and photovoltaic units, respectively. Day D-3 Forecast demand load for the entire network during the specified time period.

[0074] Inter-regional and inter-provincial connection line constraints:

[0075]

[0076]

[0077] In the formula, , These are inter-regional connection lines Upper and lower limits of transmission capacity; , Inter-provincial connection lines Upper and lower limits of transmission capacity For the inter-regional tie line transmission power on day D-3, The power transmitted via the inter-provincial communication line on day D-3.

[0078] Key section constraints:

[0079]

[0080] In the formula, A set of key cross-regional sections; , Cross-sections Upper and lower limits.

[0081] First system power supply risk constraints:

[0082] Phase D-3 avoids excessively strict power supply risk constraints to prevent uneconomical dispatching behaviors. From the perspective of ensuring power supply security, it requires that the risk of insufficient power supply not exceed zero. This constraint may lead to an unsolvable problem in the model if load growth exceeds the planning capacity, in which case a response needs to be made at the planning level.

[0083] .

[0084] In one exemplary embodiment of the present invention, the second objective function includes:

[0085]

[0086]

[0087]

[0088] In the formula, The second objective function is... For the region Operating costs; For the region Intra-provincial transmission costs; For the same region The number of related inter-regional connection lines; For regional power grid Operating risks and costs; This represents the risk cost coefficient for regional power grid operation. For the inter-regional connection line on D-1 day exist Time-period transmission capacity.

[0089] Second power balance constraint:

[0090] Unlike the balance constraints of the national coordinate system, its boundary needs to take into account the impact of inter-regional connection lines. The inter-regional connection line pre-plan is based on the D-3 plan as the initial boundary condition.

[0091]

[0092] In the formula: , , , , D-1 Time-of-use grid dispatching and direct dispatching thermal power units Hydropower units Pumped storage units Wind turbine Photovoltaic units contribute; , , , , Regional power grids Number of directly controlled thermal power units, hydropower units, pumped storage units, wind farms, and photovoltaic power stations; D-1 Time-period regional demand load forecast; D-1 Time period and region Related inter-regional interconnection line transmission capacity.

[0093] D-3—D-1 Coupling Constraints:

[0094] The inter-regional and inter-provincial tie-line plans and the State Grid's direct dispatch unit start-up and shutdown plans in the D-1 day dispatch model should be consistent with the tie-line plans and start-up and shutdown plans on D-3 day. The coupling of the tie-line plans is already reflected in the system balancing model. On D-1 day, adjustments to the inter-regional and inter-provincial tie-line plans are made through various day-ahead market mechanisms via trading. The start-up and shutdown plans for State Grid and State Grid's direct dispatch units on D-1 day must be consistent with those on D-3 day.

[0095]

[0096]

[0097]

[0098] In the formula, D-1 Inter-regional connection line The components of the current electricity market transactions; , On D-1 and D-3 respectively Operating status of thermal power units directly dispatched by the national dispatch center during the specified time period; , On D-1 and D-3 respectively The operating status of thermal power units directly dispatched by the national and grid dispatch centers during the specified time period. In order to cooperate with the region A collection of related inter-regional connection lines.

[0099] Second system power supply risk constraints:

[0100] In the D-1 phase, there is no need to consider overly strict power supply risk constraints to avoid some uneconomical dispatching behaviors. From the perspective of ensuring safety, it is sufficient to ensure that the risk of insufficient power supply is no greater than 0.

[0101] .

[0102] In one exemplary embodiment of the present invention, the extraction of typical and extreme scenarios from source payload data based on DBSCAN and K-means methods includes:

[0103] This patent takes into account the impact of extreme weather and uses a method that combines DBSACN and K-means to comprehensively extract typical and extreme scenarios, enabling probability analysis of various scenarios and providing a data foundation for multi-cycle and multi-level risk coordination and control calculations.

[0104] Obtain a standardized source load dataset, calculate the density threshold and neighborhood radius based on the K-distance curve, and extract noise points from the source load data based on the density threshold and neighborhood radius;

[0105] Clustering of noise point source load dataset based on K-means, and analysis of clustered data to identify noise point removal and probability distribution in extreme scenarios;

[0106] Clustering of regular source-load datasets based on K-means is performed to extract typical regular scenarios and their probability distribution.

[0107] Specifically, based on DBSCAN, noise points are extracted from the standard dataset of source payloads. The noise points are also source payload data, but they are relatively abnormal data. The number of cluster centers for these noise point datasets is determined, and extreme scenarios can be directly obtained by K-means clustering. The probability of each scenario is calculated based on the frequency of the label occurrence. Typical scenarios are similar. Based on the original dataset, the noise point data is removed, the number of cluster centers is determined, and then K-means clustering is performed.

[0108] A multi-cycle, multi-level risk coordination and control system for power systems, used in the aforementioned multi-cycle, multi-level risk coordination and control method for power systems, comprising:

[0109] Obtain the basic parameters of power grids at all levels, and construct a system operation risk quantification model that takes into account the uncertainties on both the source and load sides based on the basic parameters;

[0110] Obtain the operational risk constraints, time-domain coupled operational constraints, and conventional power system operational constraints of each system level, and construct a risk coordination control and scheduling optimization model;

[0111] Based on DBSCAN and K-means methods, typical and extreme scenarios of source-load data are extracted. Two scenarios are set up: single-cycle multi-level coordinated operation and multi-cycle multi-level coordinated operation. Calculation and analysis are performed based on the constructed interconnected power grids of multiple regions.

[0112] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0113] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This computer software product, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-period, multi-level risk coordinated control method for power systems, characterized in that, The method comprises the following steps: obtaining basic parameters of each level power grid, and constructing a system operation risk quantification model considering source-load bilateral uncertainty based on the basic parameters; obtaining system operation risk constraints, time domain coupling operation constraints and conventional power system operation constraints, and constructing a risk coordination control scheduling optimization model; extracting typical scenarios and extreme scenarios of source-load data based on DBSCAN and K-means methods, setting single-period multi-level coordinated operation and multi-period multi-level coordinated operation two scenarios, and performing calculation and analysis based on the constructed multiple regional interconnected power grids. The method comprises the following steps: constructing a global power grid risk quantification model, a regional power grid risk quantification model and a provincial power grid risk quantification model; The method comprises the following steps: In the formula, is a system operation risk, is an equivalent load scenario set, is a scenario, is a probability of occurrence of a scenario is a scenario a global power supply shortage risk based on power generation capacity at a moment in time under a scenario is a scenario a global power supply shortage risk based on ramping capacity at a moment in time under a scenario is a scenario a global power supply surplus risk based on power generation capacity at a moment in time under a scenario is a scenario a global power supply surplus risk based on ramping capacity at a moment in time under a scenario is a weight of a supply guarantee risk in a system operation risk, is a weight of a consumption risk in a system operation risk;​​​​​​ The method comprises the following steps: In the formula, To mitigate regional operational risks, To further cluster the regional source-load data and obtain an equivalent load scenario set, For about Scene The probability of occurrence, for The risk of insufficient regional power supply based on constant generation capacity; for The risk of insufficient regional power supply based on ramp-up capability at all times; for The risk of regional power oversupply based on constant generation capacity; for The risk of regional power oversupply is always based on ramp-up capabilities; The method comprises the following steps: In the formula, is the provincial operation risk, is the equivalent load scenario set obtained by further clustering analysis on the provincial source and load data, is the occurrence probability of the scenario is the provincial power supply shortage risk based on the generation capacity at is the provincial power supply shortage risk based on the ramping capacity at is the provincial power supply surplus risk based on the generation capacity at is the provincial power supply surplus risk based on the ramping capacity at ​​​​​​ 2. The multi-cycle, multi-level risk coordinated control method of power systems according to claim 1, characterized in that, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

3. The multi-cycle, multi-level risk coordinated control method of power systems according to claim 2, characterized in that, The method comprises the following steps: In the formula, is the first target function, is the system operation cost; is the system transmission cost of cross-region and cross-province tie lines; is the system operation risk cost; is the number of operation periods; is period thermal power unit operation cost function, is period thermal power unit operation cost function; , are respectively the D-3 day national dispatch and grid dispatch direct dispatch thermal power units , period operation state, , are respectively the D-3 day national dispatch and grid dispatch direct dispatch thermal power units , period operation state; , are respectively the thermal power unit , start-up cost; is the D-3 day period thermal power unit output; , are respectively the national dispatch direct dispatch and grid dispatch direct dispatch thermal power unit quantity; , , are respectively the thermal power unit power generation cost coefficient; , are respectively the cross-region and cross-province tie line quantity; , are respectively the D-3 day cross-region tie line , cross-province tie line transmission capacity in period; , are respectively the tie line , transmission cost coefficient; is the system operation risk cost coefficient.​​ 4. The multi-cycle, multi-level risk coordinated control method of power systems according to claim 3, characterized in that, The method comprises the following steps: wherein, is the second objective function, is the area operational cost; is the area inter-province tie-line transmission cost; is the same area related to the number of inter-area tie-lines; is the area power grid operational risk cost; is the area power grid operational risk cost coefficient, is the D-1 day inter-area tie-line transmission capacity in time period.

5. The multi-period, multi-level risk coordinated control method of power systems according to claim 4, characterized in that, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

6. 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