A method and system for dispatching power system with uncertainty under ice disaster weather

Abstract

The present invention relates to the field of preventive optimization scheduling, and discloses a power system scheduling method and system with uncertainty under ice disaster weather, including: obtaining forecast data of ice disaster weather, calculating the wind speed of the transmission line in the power system at each moment, and obtaining the wind speed information of the line at each moment; based on the wind speed information, calculating the ice thickness of the line at each moment, and constructing a comprehensive load model of the line; based on the comprehensive load model, calculating the failure rate of the transmission line at each moment under ice disaster weather, and establishing a line failure rate model under ice disaster weather; constructing a transmission line failure fuzzy set, a transmission line failure state support set, and a wind-solar-water prediction deviation uncertainty set under ice disaster weather; based on the line failure rate model and the uncertainty set, constructing a two-stage distributed blue-robust optimization scheduling model to obtain the optimal unit start-stop and line maintenance plan. Effectively reduce the operating cost of the power system under extreme weather conditions, and reduce the phenomenon of wind abandonment, solar abandonment, and water abandonment.

Description

Technical Field

[0001] The present invention relates to the field of preventive optimization scheduling, and in particular to a method and system for scheduling an uncertain power system under ice disaster weather. Background Art

[0002] There have been some important research advances in the field of preventive optimization scheduling in existing technologies. Some studies have proposed a safety-constrained unit combination scheduling model that reduces the system's over-limit risk level by taking into account the line outage model under extreme weather. Introducing preventive unit combination in multi-stage resilience improvement measures to improve the power grid's pre-disaster prevention capabilities against typhoons. Take preventive scheduling measures such as unit combination to utilize the potential wind energy brought by typhoons when they land to improve system resilience. Screen the expected fault set from the perspective of cascading failures caused by line outages, and establish a safety-constrained optimization scheduling model to obtain a more resilient scheduling plan. Use preventive unit scheduling in the power system source-grid-load resilience improvement model to reduce the amount of power grid load loss under typhoon disasters. Considering the uncertainty of predicting line failures under typhoons, a random unit combination preventive scheduling model is proposed in combination with machine learning. These studies explore the feasibility of preventive unit scheduling in improving resistance to extreme weather, creating favorable conditions for new power systems to cope with the uncertain impact of ice disasters.

[0003] Since the physical movement of ice disaster weather in the power grid will cause different damages to lines located in different locations, it is very important to reasonably consider the possible impact of ice disaster weather on new power systems in order to formulate preventive measures. Existing studies have constructed a line failure probability model by combining different physical factors in ice disasters in the grid assessment considering the impact of ice disasters.

[0004] Differentiated power grid planning was carried out based on the comprehensive line failure rate considering the probability of icing. Taking into account the influence of the spatiotemporal characteristics of ice and rain, power system recovery strategies under different fault scenarios were proposed. A set of transmission network failure scenarios under ice disaster weather was simulated and generated, and a load reduction model for the combined electric-thermal system for resilience improvement was constructed. Although these studies modeled the probability of line failure and its uncertainty under ice disaster weather, the description of line failures based on multiple scenarios ignored the changes in line failure rate under ice disaster weather with the spatiotemporal movement of ice disaster weather, and the resilience improvement measures taken were difficult to effectively prevent the spatiotemporal impact of ice disasters.

[0005] There are also studies that build a quantitative model for line icing risk under icy weather and propose a resilient scheduling strategy to deal with line failure uncertainty based on robust optimization. However, the robust optimization method that only models the consequences of line failures leads to an overly conservative model, resulting in a decrease in economic efficiency. Summary of the invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by the present invention is: how to improve the resilience and economy of the power system and reduce line failures and energy abandonment by optimizing scheduling during ice disasters.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for dispatching an uncertain power system under ice disaster weather, comprising:

[0009] Obtain forecast data for ice disasters, calculate the wind speed of the power transmission lines in the power system at each time, and obtain the wind speed information of the lines at each time;

[0010] Based on wind speed information, calculate the ice thickness of the line at each time and build a comprehensive load model of the line;

[0011] Based on the comprehensive load model, the failure rate of transmission lines at each time under ice disaster weather is calculated, and a line failure rate model under ice disaster weather is established;

[0012] Construct the fuzzy set of transmission line faults under ice disaster weather, the support set of transmission line fault states and the uncertain set of wind-solar-water forecast deviations;

[0013] Based on the line failure rate model and uncertainty set, a two-stage distributed robust optimization scheduling model is constructed to obtain the optimal unit start-up and shutdown and line maintenance plan.

[0014] As a preferred solution of the uncertain power system dispatching method under ice disaster weather described in the present invention, the predicted data of the ice disaster weather includes the ice disaster impact radius, ice disaster attenuation coefficient, ice disaster center position coordinates, wind speed data at each moment, freezing rain amount, air moisture content and temperature data.

[0015] As a preferred solution of the uncertain power system dispatching method under ice disaster weather of the present invention, wherein: the construction of the comprehensive load model of the line includes considering the combined effect of wind load and ice load to obtain the comprehensive load model borne by the transmission line under ice disaster weather;

[0016] The wind load includes calculating the wind load of the line under ice disaster weather based on the wind speed information at the location of each line, and the formula is expressed as:

[0017]

[0018] Among them, K represents the constant coefficient, S represents the span factor of the line, v represents the wind speed at the location of the line, and ω represents the ice thickness on the line. represents the wind load of the transmission line ij at time t, and D represents the conductor diameter;

[0019] The ice load includes calculating the ice load of the line under ice disaster weather based on the ice thickness and ice density of the line;

[0020]

[0021] in, represents the density of ice, D represents the diameter of the wire, represents the ice thickness of the transmission line ij at time t, represents the ice load of transmission line ij at time t;

[0022] The formula of the comprehensive load model is expressed as:

[0023]

[0024] in, represents the ice load of transmission line ij at time t, represents the wind load of transmission line ij at time t, represents the comprehensive load of transmission line ij at time t.

[0025] As a preferred solution of the uncertain power system dispatching method under ice disaster weather of the present invention, wherein: establishing a line failure rate model under ice disaster weather includes calculating the failure rate of the transmission line at each moment under ice disaster weather based on the comprehensive load model of the transmission line, and the formula is expressed as:

[0026]

[0027] Among them, p ij，t represents the failure probability of line ij at time t, A represents the comprehensive load borne by line ij at time t, ij represents the design load of line ij, B ij Represents the ultimate load of line ij, exp: represents the exponential function.

[0028] As a preferred solution of the uncertain power system dispatching method under ice disaster weather of the present invention, wherein: the transmission line fault fuzzy set under ice disaster weather includes: the failure rate of each transmission line at a certain moment corresponds to an ice disaster weather state at that moment, and the physical process of actual ice disaster weather has uncertainty on the influence of the failure rate of the transmission line at each moment;

[0029] Based on the prediction of the failure rate of transmission lines at different times under ice disaster weather, the transmission line fault fuzzy set is established, and the formula is expressed as follows:

[0030]

[0031] in, represents the transmission line fault fuzzy set, P represents the fault state probability distribution of each line, Λ represents the set of all possible fault state probability distributions, K represents the fault rate deviation factor, represents the failure rate of line ij at time t, represents the random variable 1-u ij，t The expected value under the probability distribution P; u ij，t represents the operating status of the transmission line ij at time t, where u ij，t =0 means the line is faulty, u ij，t =1 indicates that the line is operating normally; It means that it holds true for all times t;

[0032] The transmission line fault state support set includes the number of fault lines in the transmission network at different times, which is expressed as follows:

[0033]

[0034] Where U represents the fault state support set of the transmission line; u ij，t represents the operating status of line ij at time t, u ij，t =1 means that line ij operates normally at time t, u ij，t =0 means that line ij fails at time t; ∑ ij u ij，t represents the sum of the operating states of all lines ij at time t, N L represents the total number of transmission lines; K represents the maximum number of lines that are allowed to fail; T: represents the total duration of the entire scheduling period;

[0035] When a line fails at a certain moment during ice disaster weather, the line will remain in a faulty state in subsequent states;

[0036] The wind-solar-water forecast deviation uncertainty set includes that the output power of the renewable energy power plant is affected by its wind speed forecast value and light intensity forecast value respectively;

[0037] The output power of a hydropower station is determined by the predicted value of the inflow flow at its location;

[0038] The predicted values ​​of wind speed, light intensity and hydropower station output are highly random, and there is a certain deviation between their actual values ​​and predicted values;

[0039] Considering the uncertainty of wind farm and photovoltaic power station output forecast deviation and the uncertainty of hydropower station inflow forecast deviation, a box uncertainty set of renewable energy power generation forecast deviation is constructed, and the formula is expressed as follows:

[0040]

[0041] Among them, ε: represents the uncertainty set of wind, solar and water forecast deviations; represents the prediction deviation state of wind farm k at time t, where 1 indicates the existence of prediction deviation and 0 indicates no prediction deviation;

[0042] Indicates the prediction deviation state of PV power station m at time t, where 1 indicates the existence of prediction deviation and 0 indicates no prediction deviation;

[0043] represents the prediction deviation state of hydropower station n at time t, where 1 indicates the presence of prediction deviation and 0 indicates no prediction deviation;

[0044] Γ W represents the wind farm forecast deviation budget, Γ V represents the prediction deviation budget of the photovoltaic power station, Γ H Represents the forecast deviation budget of the hydropower station.

[0045] As a preferred solution of the uncertain power system dispatching method under ice disaster weather described in the present invention, the two-stage distributed robust optimization dispatching model includes: in the first stage, optimizing the start-stop decision of the unit under the worst conditions, taking the minimum start-stop cost of the thermal power unit as the goal, and considering the start-stop restriction of the unit and the line maintenance constraint;

[0046] The formula for minimizing the start-up and shutdown cost of thermal power units is as follows:

[0047]

[0048] Among them, min means minimizing the objective function, represents the startup cost of unit g, yg,t represents the startup state variable of unit g at time t, represents the shutdown cost of unit g, zg,t represents the shutdown state variable of unit g at time t, Represents other costs in the scheduling model.

[0049] As a preferred solution of the uncertain power system dispatching method under ice disaster weather described in the present invention, the two-stage distributed robust optimization dispatching model also includes, in the second stage, under the worst line fault and wind, solar and water forecast deviation conditions, optimizing the operation cost of the power system to form an optimal dispatching plan;

[0050] The formula of the objective function of the second stage is expressed as:

[0051]

[0052] in, represents the objective function of the optimal value of the system, P represents the probability measure, represents the set of all possible measures, represents the expectation operator, represents the benefit function of the optimal dispatch of the power system, t: represents the time index, g represents each generator set in the power system, C coal represents the unit cost coefficient of coal-fired power units, represents the coal consumption efficiency of coal-fired power unit g in scheme s, represents the output power of generator set g at time t and scheme s, C we represents the unit wind curtailment cost coefficient of wind power, represents the abandoned wind power of wind farm k at time t, C vc The unit cost coefficient of abandoned light in photovoltaic power stations, represents the abandoned light power of PV plant m at time t, represents the unit charging and discharging cost coefficient of the energy storage power station, represents the charging power of the energy storage station e at time t, represents the discharge power of the energy storage station e at time t, C ls represents the unit cost coefficient of load shedding, represents the load shedding b at time t, C hc represents the unit cost coefficient of abandoned water, represents the amount of water abandoned by hydropower station n at time t.

[0053] A power system dispatching system with uncertainty under ice disaster weather, wherein:

[0054] The data module obtains the forecast data of ice disaster weather, calculates the wind speed of the power transmission lines in the power system at each time, and obtains the wind speed information of the lines at each time;

[0055] The load module calculates the ice thickness of the line at each time based on wind speed information and constructs a comprehensive load model for the line;

[0056] The failure rate module calculates the failure rate of the transmission line at each time under ice disaster weather based on the comprehensive load model, and establishes a line failure rate model under ice disaster weather;

[0057] Uncertainty module, constructs the fuzzy set of transmission line faults under ice disaster weather, the support set of transmission line fault states, and the uncertainty set of wind-solar-water forecast deviations;

[0058] The scheduling module constructs a two-stage distributed robust optimization scheduling model based on the line failure rate model and uncertainty set to obtain the optimal unit start-up and shutdown and line maintenance plan.

[0059] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0060] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0061] Beneficial effects of the invention: The uncertain power system dispatching method under ice disaster weather provided by the invention introduces the influencing factors of ice disaster weather and combines it with the distributed robust optimization dispatching model, which can effectively reduce the operating cost of the power system under extreme weather conditions, reduce the phenomenon of wind, solar and water abandonment, and enhance the resilience and stability of the system, thereby ensuring the reliability of power supply and improving economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 An overall flow chart of a method for dispatching an uncertain power system under ice disaster weather provided by the first embodiment of the present invention;

[0064] Figure 2 An IEEE RTS-24 node system wiring diagram of an uncertain power system dispatching method under ice disaster weather provided by the fourth embodiment of the present invention;

[0065] Figure 3 A diagram of a unit optimization scheduling scheme for a power system scheduling method with uncertainty under ice disaster weather provided by the fourth embodiment of the present invention;

[0066] Figure 4 A fault line operation state diagram of a power system dispatching method with uncertainty under ice disaster weather provided by the fourth embodiment of the present invention;

[0067] Figure 5 A diagram of optimized scheduling results under different failure rate deviation factors of a power system scheduling method with uncertainty under ice disaster weather provided by the fourth embodiment of the present invention;

[0068] Figure 6 This is a diagram of optimized scheduling results under different maximum line repair quantities of a power system scheduling method with uncertainty under ice disaster weather provided by the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0070] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for dispatching an uncertain power system under ice disaster weather, comprising:

[0071] S1: Obtain forecast data of ice disaster weather, calculate the wind speed of the power transmission line in the power system at each moment, and obtain the wind speed information of the line at each moment.

[0072] The forecast data for ice disaster weather includes the ice disaster impact radius, ice disaster attenuation coefficient, ice disaster center location coordinates, wind speed data at each time, freezing rain amount, air moisture content and temperature data.

[0073] In order to describe the spatiotemporal impact of ice disaster weather on the transmission lines under the new power system, the failure rate of transmission lines at each time under ice disaster weather is modeled based on the Jones model. First, the wind speed at the location of the transmission line at each time affected by ice storms during the ice disaster is characterized, specifically:

[0074]

[0075] Where: ij,t is the wind speed at the location of the transmission line ij at time t; X i j and Y i j are the location coordinates of the transmission line ij; and is the coordinate of the center of the ice disaster at time t; ψ is the ice disaster attenuation coefficient; σ x and σ Y is the characteristic coefficient related to the disaster radius; R is the impact radius of the ice disaster.

[0076] Furthermore, by obtaining the forecast data of ice disaster weather, the wind speed at the location of the transmission line at each moment can be accurately calculated, and these data can be used to provide a basis for the subsequent line failure rate modeling. The impact of ice disaster weather on the power system depends not only on wind speed, but also on the combined effect of multiple meteorological factors such as freezing rain and air moisture content. Therefore, the comprehensiveness of the forecast data is crucial. Through the Jones model, the impact of ice disaster weather on the line can be dynamically adjusted according to the movement of the ice disaster center and its influence radius, so as to more accurately calculate the wind speed and failure rate of the line at each moment. This process ensures an accurate description of the spatiotemporal impact of the transmission line under ice disaster weather, and thus provides a reliable basis for subsequent line load, fault risk assessment and optimized scheduling. Through this method, the line risk under extreme weather conditions can be predicted in advance, the maintenance plan and emergency response measures can be optimized, and the reliability and safety of the power system can be improved.

[0077] S2: Based on the wind speed information, calculate the ice thickness of the line at each time and construct a comprehensive load model of the line.

[0078] After obtaining the wind speed at the location of the transmission line at each moment, the ice thickness accumulation model of the transmission line under ice disaster weather is constructed:

[0079]

[0080] Where: is the ice thickness of line ij at time t; and is the density of ice and water; q ι is the amount of freezing rain at time t;

[0081] W is the water content in the air, which can be obtained from W = 0.067 (q ι ) 0.864 Since the operation status of the transmission line in ice disaster weather is mainly affected by the wind speed and ice coverage of the ice rain storm, the wind load and ice load of each line are described based on the transmission line ice model, as shown below:

[0082]

[0083]

[0084] Where: and L i j,t I are the wind load and ice load of the transmission line ij at time t; K is a constant coefficient with a value of 6.964×10 -3 ; S is the span factor; D is the conductor diameter. Considering the combined effects of wind load and ice load, the comprehensive load borne by the transmission line in ice disaster weather is:

[0085]

[0086] Among them, L i j,t syn is the comprehensive load borne by the transmission line ij at time t.

[0087] Furthermore, the ice thickness of the line is calculated based on wind speed information, and a comprehensive load model of the line is constructed to accurately assess the risk of transmission lines in ice disaster weather. In ice disaster weather, the failure of transmission lines is mainly affected by freezing rain, wind speed and ice coverage. By establishing an ice thickness accumulation model, the ice accumulation on the line at each moment can be dynamically reflected, thus providing a basis for the evaluation of line load. This model combines factors such as the density of ice and water, the amount of freezing rain, and the water content of the air to calculate the ice thickness, and further combines wind load and ice load to construct a comprehensive load model. This helps to accurately assess the load condition of the line in ice disaster weather, identify possible failure risks, and improve the safety of the power system in severe weather. By considering the combined effects of wind and ice, the model provides a more detailed prediction for the safe operation of the line, and lays a scientific basis for formulating reasonable transmission line maintenance and emergency response measures.

[0088] S3: Based on the comprehensive load model, calculate the failure rate of the transmission line at each time under ice disaster weather, and establish a line failure rate model under ice disaster weather.

[0089] Based on the comprehensive load model of transmission lines, the failure rate of transmission lines at each moment in ice disaster weather is calculated:

[0090]

[0091] Among them, p ij,t represents the failure probability of line ij at time t, A represents the comprehensive load borne by line ij at time t, ij represents the design load of line ij, B ij represents the ultimate load of line ij, and exp: represents the exponential function.

[0092] Furthermore, by calculating the failure rate of transmission lines at various times during ice disaster weather based on a comprehensive load model, the reliability of the lines under extreme weather conditions can be accurately evaluated and predicted. During ice disaster weather, the loads (including ice, snow, wind, etc.) borne by the transmission lines will increase significantly, which may lead to line failures. By establishing a line failure rate model, the failure probability of the line under different time periods and load conditions can be accurately described, thereby providing a reliable basis for scheduling and operation decisions. This model helps to timely identify potential line failure risks, optimize line maintenance and emergency plans, and improve the safety and stability of the power system under severe weather. At the same time, the introduction of the comprehensive load model makes this failure rate calculation more accurate and dynamic, and better reflects the complexity of the impact of weather changes on the line in reality.

[0093] S4: Construct the fuzzy set of transmission line faults under ice disaster weather, the support set of transmission line fault states, and the uncertain set of wind-solar-water forecast deviations.

[0094] The fuzzy set of transmission line failures under ice disaster weather includes that the failure rate of each transmission line at a certain time corresponds to an ice disaster weather state at that time, and the physical process of actual ice disaster weather has uncertainty in the impact on the failure rate of transmission lines at each time.

[0095] Based on the prediction of the failure rate of transmission lines at different times under ice disaster weather, the transmission line fault fuzzy set is established, and the formula is expressed as follows:

[0096]

[0097] in, represents the transmission line fault fuzzy set, P represents the fault state probability distribution of each line, Λ represents the set of all possible fault state probability distributions, κ represents the fault rate deviation factor, represents the failure rate of line ij at time t, represents the random variable 1-u ij,t The expected value under the probability distribution P; u ij,t represents the operating status of the transmission line ij at time t, where u ij,t =0 means the line is faulty, u ij,t =1 indicates that the line is operating normally; It means that it holds true for all times t;

[0098] The transmission line fault state support set includes the number of fault lines in the transmission network at different times, which is expressed as follows:

[0099]

[0100] Where U represents the fault state support set of the transmission line; u ij,trepresents the operating status of line ij at time t, u ij,t =1 means that line ij operates normally at time t, u ij,t =0 means that line ij fails at time t; ∑ ij u ij,t represents the sum of the operating states of all lines ij at time t, N L represents the total number of transmission lines; K represents the maximum number of lines that are allowed to fail; T: represents the total duration of the entire scheduling period;

[0101] When a line fails at a certain moment during ice disaster weather, the line will remain in a faulty state in subsequent states;

[0102] The wind-solar-water forecast deviation uncertainty set includes that the output power of the renewable energy power plant is affected by its wind speed forecast value and light intensity forecast value respectively;

[0103] The output power of a hydropower station is determined by the predicted value of the inflow flow at its location;

[0104] The predicted values ​​of wind speed, light intensity and hydropower station output are highly random, and there is a certain deviation between their actual values ​​and predicted values;

[0105] Considering the uncertainty of wind farm and photovoltaic power station output forecast deviation and the uncertainty of hydropower station inflow forecast deviation, a box uncertainty set of renewable energy power generation forecast deviation is constructed, and the formula is expressed as follows:

[0106]

[0107] Among them, ε: represents the uncertainty set of wind, solar and water forecast deviations; represents the prediction deviation state of wind farm k at time t, where 1 indicates the existence of prediction deviation and 0 indicates no prediction deviation;

[0108] Indicates the prediction deviation state of PV power station m at time t, where 1 indicates the existence of prediction deviation and 0 indicates no prediction deviation;

[0109] represents the prediction deviation state of hydropower station n at time t, where 1 indicates the presence of prediction deviation and 0 indicates no prediction deviation;

[0110] Γ W represents the wind farm forecast deviation budget, Γ V represents the prediction deviation budget of the photovoltaic power station, Γ H It represents the forecast deviation budget of the hydropower station. The box uncertainty set is considered as the support set in the fuzzy set.

[0111] Furthermore, by constructing the fuzzy set of transmission line faults under ice disaster weather, the support set of transmission line fault states, and the uncertain set of wind-solar-water forecast deviation, the influence of uncertainty and random factors in the power system can be effectively dealt with. The fuzzy set of transmission line faults under ice disaster weather takes into account the impact of ice disaster weather on the line failure rate, and ensures the robustness of the transmission system under extreme weather conditions by modeling the failure rate deviation factor. The fault state support set avoids system overload by limiting the number of faulty lines, and ensures the controllability of line failures in sudden situations such as ice disasters. The uncertain set of wind-solar-water forecast deviation reflects the uncertainty of renewable energy generation by considering the forecast deviations of wind power, photovoltaic power, and hydropower, which helps to consider these volatility factors in scheduling decisions. By establishing these uncertain sets, the design provides strong support for system optimization scheduling, and can maintain the safety, reliability and economy of the system in the face of uncertain weather and forecast deviations.

[0112] S5: Based on the line failure rate model and uncertainty set, a two-stage distributed robust optimization scheduling model is constructed to obtain the optimal unit start-up and shutdown and line maintenance plan.

[0113] The two-stage distributed blue stick optimization scheduling model includes: in the first stage, the unit start-up and shutdown decisions are optimized under the worst conditions, with the goal of minimizing the start-up and shutdown costs of thermal power units, taking into account the unit start-up and shutdown restrictions and line maintenance constraints;

[0114] The formula for minimizing the start-up and shutdown cost of thermal power units is as follows:

[0115]

[0116] Among them, min means minimizing the objective function, represents the startup cost of unit g, y g,t represents the startup state variable of unit g at time t, represents the downtime cost of unit g, z g,t represents the shutdown state variable of unit g at time t, Represents other costs in the scheduling model.

[0117] The constraints are related constraints such as the start and stop of thermal power units and line maintenance scheduling restrictions, as follows:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] ∑ ij (ω ij,t -ω ij,t-1 )≤N rp t=2,…,T (17)

[0125]

[0126]

[0127]

[0128]

[0129] Among them, equations (11)-(12) are the minimum startup time constraint and minimum shutdown time constraint of thermal power units respectively; equations (13)-(14) represent the system up / down spinning reserve constraint; equations (15)-(16) constrain the logical relationship between the unit state variables; equation (17) limits the number of line maintenance at each moment to no more than the maximum value; equation (8) indicates that the line remains in operation after maintenance; equation 19) constrains the line to be in operation after t F Maintenance can only be carried out at the moment; Formula (20) constrains that only the faulty line can be repaired; Formula (21) constrains the unit state variables and line maintenance state variables to be binary variables. g,l is the operating state of the thermal power unit g at time t, a 0-1 variable, 1 represents the unit is running, and 0 represents not running; and Respectively represent the minimum start / stop time of thermal power units; and is the maximum and minimum output of thermal power unit g; δ is the system reserve rate; P b ,t D is the power load of node b at time t; ω ij,t is the 0-1 maintenance state variable of line ij at time ι, 1 represents line maintenance; N p Represents the maximum number of repairable items at a single moment; T i j F is the time when the fault of line ij begins.

[0130] In the second stage, under the worst line fault and wind, solar and water forecast deviation conditions, the operation cost of the power system is optimized and the optimal dispatching plan is formed;

[0131] The formula of the objective function of the second stage is expressed as:

[0132]

[0133] in, represents the objective function of the optimal value of the system, P represents the probability measure, represents the set of all possible measures, represents the expectation operator, represents the benefit function of the power system optimization dispatch, t: represents the time index, g represents each generator set in the power system, C coal represents the unit cost coefficient of coal-fired power units, represents the coal consumption efficiency of coal-fired power unit g in scheme s, represents the output power of generator set g at time t and scheme s, C we represents the unit wind curtailment cost coefficient of wind power, represents the abandoned wind power of wind farm k at time t, C vc The unit cost coefficient of abandoned light in photovoltaic power stations, represents the abandoned light power of PV plant m at time t, represents the unit charging and discharging cost coefficient of the energy storage power station, represents the charging power of the energy storage station e at time t, represents the discharge power of the energy storage station e at time t, C ls represents the unit cost coefficient of load shedding, represents the load shedding b at time t, C hc represents the unit cost coefficient of abandoned water, represents the amount of water abandoned by hydropower station n at time t.

[0134] In the above formula, obtaining Q(x,u,ε) inside the expected value is the min optimization problem, x is the decision variable vector of the first-stage model, including the unit operation status and line maintenance state variables, u represents the line fault state variable vector, s is the wind-solar-water forecast deviation state vector, and its specific objective function and constraints are as follows.

[0135] Among them, the first item in formula (23) is the linear segmented output cost of thermal power units, the second and third items are the costs of wind and solar power abandonment, the fourth item is the cost of energy storage charging and discharging, the fifth item is the cost of load shedding, and the sixth item is the cost of water abandonment. Since hydropower has a fast regulation speed and low cost, the operating cost of hydropower is not taken into account here. In the formula: C coal is the unit coal consumption cost of thermal power units; s is the linear segmentation of the thermal power coal consumption curve; is the linear segment slope of the coal consumption curve; is the output of thermal power unit g at time t in segment s; c wc is the unit wind curtailment cost; r k ,t wc is the abandoned wind power of wind farm k at time t; cwc is the unit abandoned light cost; is the abandoned light power of PV station m at time t; is the unit charging and discharging cost of energy storage e; P e ,t E,ch and P e ,t E,dis are the charging power and discharging power of the energy storage station e at time t respectively; C ls is the unit load shedding cost; P b ,t ls is the load shedding of node b at time ι in the transmission network of the new power system; C hc is the unit water abandonment cost; d n ,ι H is the water discharge of hydropower station n at time t.

[0136] The constraints are as follows:

[0137] Node power balance constraints

[0138]

[0139] Where: P k ,l w is the output of wind farm k at time t; P m ,l v is the output of photovoltaic power station m at time t; P n ,l H is the output of hydropower station n at time t; f i j,t is the power flow of line ij at time ι; Ω, Θ w , Θ v , Θ H , Φ are the sets of thermal power units, wind farms, photovoltaic power stations, hydropower stations and energy storage power stations respectively; the above symbols with subscript b in formula (24) all represent the sets connected to node b; Ψ re (b) and Ψ se (b) represents the set of lines with node b as the receiving and transmitting ends; Ξ represents the set of nodes in the transmission network.

[0140] 2) Line DC power flow constraints

[0141]

[0142] Where: ij is the susceptance of line ij; θ i,t and θ j,t are nodes i and j The voltage phase angle at time t; Ψ represents the line set. u ij,t +ω ij,t -u ij,t ωij,t Indicates the line status affected by faults and maintenance. When a line fails and is repaired, the line can maintain normal operation. If no maintenance is performed, its operating status is affected by the fault status.

[0143] 3) Line capacity constraints

[0144]

[0145] Where: f i j max and f i j min are the upper and lower limits of the transmission capacity of line ij respectively.

[0146] 4) Output constraints of thermal power units

[0147]

[0148] Where: and It is the upper and lower limits of thermal power unit g output.

[0149] 5) Thermal power unit climbing constraints

[0150]

[0151] Where: and is the up and down climbing rate of thermal power unit g.

[0152] 6) Wind farm operation constraints

[0153]

[0154] Where: and are the predicted output and deviation output of wind farm k at time t respectively; Represents the operating status of wind farm k at time t. When the wind farm is located within the radius of the ice disaster, its value is 0, and the wind farm exits the working state. Otherwise, its value is 1, and it maintains normal operation.

[0155] 7) PV power station operation constraints

[0156]

[0157] Where: and are the predicted output and deviation output of photovoltaic power station m at time t; g m ,t vRepresents the operating status of the photovoltaic power station m at time t. When the photovoltaic power station is located within the radius of the ice disaster, its value is 0, and the photovoltaic power station exits the working state. Otherwise, its value is 1, and it maintains normal operation.

[0158] 8) Hydropower station operation constraints

[0159]

[0160]

[0161] Among them, formula (34)-(35) is the output constraint of the hydropower station; formula (36)-(37) are the upper and lower limit constraints of the hydropower station's power generation flow and abandoned water flow respectively; formula (38) is the hydropower station's reservoir capacity constraint; formula (39)-(40) are the constraints on the change of the reservoir capacity state at each time and the initial time of the hydropower station; formula (41) is the initial and final balance constraint of the hydropower station's reservoir capacity. is the overall efficiency of the hydropower station n; is the power generation flow of hydropower station n at time t; H n is the water head of the hydropower station n; and is the upper and lower limits of the power generation flow of hydropower station n; d n ,t H is the water discharge of hydropower station n at time t; and V is the upper and lower limits of the abandoned water flow of hydropower station n; n,l is the storage capacity of hydropower station n at time t; and are the upper and lower limits of the storage capacity of hydropower station n respectively; is the predicted water flow of hydropower station n at time t; ΔI n ,t is the predicted water inflow deviation of hydropower station n at time t; is the initial storage capacity of hydropower station n; θ n ,ι н Represents the operating status of hydropower station n at time t. When the hydropower station is located within the radius of the ice disaster, its value is 0, and the hydropower station exits the working state. Otherwise, its value is 1, and it maintains normal operation.

[0162] 9) Energy storage power station operation constraints

[0163]

[0164] Among them, formula (42)-(43) are the charging and discharging power constraints of the energy storage power station; formula (44) is the capacity limit constraint of the energy storage power station; formula (45)(46) are the capacity state change constraints of the energy storage power station at each time and the initial time; formula (47) is the balance constraint of the energy storage power station capacity at the initial and final time. and E is the upper and lower limits of the energy storage station e power; e ,t is the capacity of the energy storage power station e at time t; and are the upper and lower limits of the capacity of the energy storage power station e; and is the charging and discharging efficiency of the energy storage station e; is the capacity of the energy storage station e at the initial moment.

[0165] 10) Load shedding constraints

[0166]

[0167] The optimal dispatch model of distributed Brunei units considering the uncertainty under ice disaster weather is a two-stage three-layer optimization problem. The expected value is eliminated by transforming the model, and the C&C&CG algorithm is used to solve the model.

[0168] Firstly, the matrix form of distributed robust optimization scheduling model is given.

[0169]

[0170]

[0171] Where: a and ι are constant vectors; K, Z, W, J, N and M are constant matrices; represents the known quantity of the decision vector; ν is the second-stage model pursuit variable. The matrix form of the fuzzy set is shown below.

[0172] A:={P∈Λ|E P (Gu)≤q} (51)

[0173] Where: q is a constant vector; G is a constant matrix.

[0174] Since the second-stage model includes the part of obtaining the expected value and cannot be solved directly, it is transformed to convert the objective function and constraints containing the expected value and related to the fuzzy set into the following optimization model.

[0175]

[0176] Where μ and λ are the dual variables and vectors of the first and second terms of constraint (53), respectively. Using the duality theory, the model (52)-(53) is transformed into the following optimization problem:

[0177] min(μ+q T λ)(54)

[0178]

[0179] The second constraint Q(x,u,ε) in equation (55) is a minimization problem, which contains the uncertain variable vectors u and ε. In order to meet the robustness requirements of the model, the second constraint of equation (55) is transformed into:

[0180] μ≥max u,ε [Q(x,u,ε)-λ T Gu] (56)

[0181] At this point, the two-stage optimization problem is transformed into the following form.

[0182] minc T x+(μ+q T λ)(57)

[0183]

[0184] The transformed model (Equations (57)-(58)) is still a two-stage three-layer optimization problem. According to the C&CG algorithm, it is divided into the main problem and sub-problems for cyclic solution.

[0185] The sub-problem is used to find the worst transmission line fault and the worst wind-solar-hydro forecast deviation. According to equations (57)-(58), the sub-problem model is given as follows:

[0186]

[0187] Where: ζ is the dual variable vector of constraint condition (60); is a known quantity. Based on the duality theory, the sub-problem model is transformed into:

[0188]

[0189] wxya T ζ≤h,ζ≥0 (62)

[0190] Since there are bilinear terms in the objective function (61) that are the product of the uncertain variable vector and the dual variable vector, the auxiliary variable method is used to linearize it. The sub-problem model after linearization is as follows:

[0191]

[0192]

[0193] Where: β, γ, and is an auxiliary variable introduced; Υ is a large positive number.

[0194] The main problem aims to optimize the optimal unit scheduling decision plan under the worst case identified by the sub-problems. Its model is as follows:

[0195] minc T x+q T λ+μ(65)

[0196]

[0197] Where: (o) is the search variable for the oth loop; is the known quantity of the uncertain vector, obtained by solving the sub-problem; o d is the current C&CG cycle number. 4.2.3.2.3 Solution process Based on the C&CG algorithm, the main and sub-problems are solved. The specific steps are as follows: Step 1: Initialization settings. Set the cycle upper bound UB = +∞ and the cycle lower bound LB = -∞, set the convergence gap φ, and give the initial value of the line fault vector and initial value of wind-light-water bias vector Set the number of loops o＝0 ; Step 2: Let o = o + 1, solve the main problem (Eq. (65)-(66)), and obtain the unit scheduling decision plan Dual variable value and Update the lower bound LB according to formula (67);

[0198]

[0199] Step 3: Solve the sub-problems (Eqs. (63)-(64)) to obtain the worst transmission line fault result Worst wind-solar-water forecast deviation results Dual variable value and auxiliary variable result value and Update the upper bound according to equations (68)-(69);

[0200]

[0201] Step 4: Determine whether equation (70) holds. If so, the C&CG algorithm converges and outputs the optimal unit scheduling decision plan; otherwise, return to step 2 and continue iterating.

[0202]

[0203] Furthermore, the core significance of the design of the two-stage robust optimization dispatching model is to deal with uncertain factors in the power system, such as wind, solar, and water forecast deviations and transmission line failures. By optimizing the start-up and shutdown decisions of the units in the first stage, the start-up and shutdown costs of the thermal power units are minimized, and the line maintenance is reasonably scheduled under constraints to improve the economy and stability of the system. In the second stage, under the worst weather and fault conditions, the operating cost of the system is optimized, and multiple objectives such as wind power, photovoltaic wind and light abandonment, energy storage charging and discharging are comprehensively considered to balance economic benefits and system reliability. The C&CG algorithm and duality theory are used to decompose large-scale optimization problems into main and sub-problems for cyclic solution, which improves the solution efficiency and accuracy. Overall, the model maximizes the economic benefits of the power system while ensuring the safe operation of the system through multi-objective robust optimization, and has important practical application value.

[0204] Embodiment 2 is an embodiment of the present invention, which provides an uncertain power system dispatching system under ice disaster weather, including:

[0205] The data module obtains the forecast data of ice disaster weather, calculates the wind speed of the transmission lines in the power system at each moment, and obtains the wind speed information of the lines at each moment.

[0206] The load module calculates the ice thickness of the line at each time based on wind speed information and constructs a comprehensive load model of the line.

[0207] The failure rate module calculates the failure rate of the transmission line at each moment under ice disaster weather based on the comprehensive load model, and establishes a line failure rate model under ice disaster weather.

[0208] The uncertainty module constructs the fuzzy set of transmission line faults under ice disaster weather, the support set of transmission line fault states, and the uncertainty set of wind-solar-water forecast deviations.

[0209] The scheduling module constructs a two-stage distributed robust optimization scheduling model based on the line failure rate model and uncertainty set to obtain the optimal unit start-up and shutdown and line maintenance plan.

[0210] Embodiment 3, an embodiment of the present invention, is different from the first two embodiments in that:

[0211] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0212] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0213] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0214] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0215] Example 4, this application uses the improved IEEE RTS-24 node system to verify the method proposed in this application. The total basic load of the system is 2850MW, and the original installed capacity of conventional units is 3405MW. The conventional units on nodes 2, 15, 21 and 22 are replaced by wind farms, photovoltaic power stations and hydropower stations. The wind farms and photovoltaic power stations are connected to nodes 2 and 22. The capacity of the wind and solar stations on node 2 is 200MW, and the capacity of the wind and solar stations on node 22 is 400MW. The hydropower station is connected to nodes 15 and 21, and the installed capacities are 200MW and 450MW respectively. In addition, nodes 2 and 22 are connected to energy storage power stations with a scale of 400MW / 800MWh. The wiring diagram of the modified IEEE RTS-24 node system is shown as follows. Figure 2 As shown in the figure, the operation cycle is 24h and the time interval is 1h. It is set that the predicted ice disaster weather starts to pass through the system at the third moment, the maximum number of lines to be repaired at each moment is 1, and the line can be repaired 1h after the failure. The load shedding penalty cost is 10000$ / MW, the wind and solar abandonment penalty cost is 750$ / MW, and the water abandonment penalty cost is 250$ / MW. This example uses YALMIP as a modeling tool on the MATLAB platform and uses the GUROBI solver for calculation.

[0216] In order to verify the effectiveness of the model and method of this application, the calculation results of the proposed distributed robust optimization dispatch model and the robust optimization dispatch model are compared. When K is 2, the upper and lower bounds of the algorithm converge after 25 iterations according to the C&CG algorithm, and the results of the unit dispatch operation are shown in Table 1. It can be seen that compared with the robust optimization method, the load shedding loss cost in the worst case obtained by the distributed robust optimization method proposed in this paper is smaller, which is reduced by 326.51k$, and the wind and light abandonment phenomena have been improved to a certain extent. Although the abandoned water flow has increased, in the preventive optimization dispatch with load supply as the main goal, the reduction of load shedding in ice disaster weather has a greater impact on the total cost. Therefore, the total cost of unit dispatch operation obtained by the distributed robust optimization method is smaller, which can be reduced by 433.18k$ compared with robust optimization. That is, the method proposed in this paper determines the worst line fault distribution by combining line fault probability information, improves the economy of preventive optimization dispatch of new power systems, and reduces the conservatism of traditional robust optimization. The results are shown in Table 1.

[0217] Table 1 Comparison of preventive optimization scheduling results

[0218]

[0219] Figure 3 and Figure 4 The specific unit optimization dispatching scheme under the proposed method and the line operation status under the most serious transmission line failure rate are given. Figure 3 It can be seen that some units on nodes 1, 7, 13 and 23 participate in start-up and shutdown. Among them, the unit on node 7 is frequently started and shut down during the scheduling cycle to improve the new energy consumption rate and reduce load losses. The remaining units remain on or off during the cycle. Figure 3 It shows that under the most serious line failure rate, lines 2-6 and 6-10 failed at the 4th moment, and then the maintenance team first repaired line 6-10, and then repaired line 2-6 at the next moment, until the two lines were repaired at the 7th moment.

[0220] The test analysis of the optimal dispatch results of the distributed blue-rod units under different line fault number budgets is carried out, and the specific results are shown in Table 2, where the energy abandonment cost is the sum of the wind abandonment cost, the solar abandonment cost and the water abandonment cost. It can be seen from the table that with the increase of the line fault number budget, the load shedding expectation value gradually increases, and the energy abandonment cost also shows an upward trend overall. When the line fault number budget is 6, due to the limitation of the maximum number of line repairs at a single moment, the line cannot be repaired in a short time, resulting in the inability to timely deliver power supply to the power users at the node, and the inability to timely absorb wind, solar and water, resulting in a load shedding cost of up to 209580.74k$ and an energy abandonment cost of 6852.65k$. The results are shown in Table 2.

[0221] Table 2 Optimization scheduling results under different line fault budgets

[0222]

[0223]

[0224] Table 3 shows the optimization scheduling results under different prediction deviation budgets. Taking the prediction deviation of the water flow of a hydropower station as an example, it can be seen that as the prediction deviation budget value Γ H As the forecast deviation budget increases, the hydropower generation expectation gradually decreases and the load shedding cost increases. This is because the increase in the hydropower forecast deviation budget reduces the power supply capacity of the hydropower station under ice disaster weather, resulting in a further increase in load shedding. Since the forecast deviation budget only affects the output of the hydropower station at the node and does not affect the transmission network under ice disaster weather, the impact of the water flow forecast deviation budget on the optimization scheduling result is much smaller than the impact of the change in the line fault number budget.

[0225] Table 3 Optimal dispatch results under different water flow forecast deviation budgets

[0226]

[0227] In order to analyze the influence of transmission line failure rate deviation and line maintenance capacity on preventive distributed robust optimization scheduling in the model, different transmission line failure rate deviation factors are first set to solve the unit scheduling results as follows: Figure 5 It can be seen that with the increase of the failure rate deviation factor, the load shedding cost and the total cost both show an upward trend. This is because the increase of the failure rate deviation factor expands the possible range of the failure rate of the transmission line under ice disaster weather. The transmission line with a higher failure rate makes the worst line failure situation under ice disaster weather worse, which in turn increases the load shedding cost and the total cost.

[0228] Then, the line failure budget is set to 4, and the maximum number of line repairs at different single moments is set. The optimal scheduling results are obtained as follows: Figure 6 As shown in the figure, the increase in the maximum number of line repairs at a single moment enables the faulty line to be repaired in time during ice disaster weather. After the repair, the line maintains normal operation and supplies power to each node load, thereby reducing the load shedding cost. When the maximum number of line repairs at a single moment is 2, as the maximum number of repairs increases, the wind-solar-water consumption is further increased by repairing the line, so the energy abandonment cost is reduced.

[0229] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for dispatching an uncertain power system under ice disaster weather, characterized in that: include: Obtain forecast data for ice disasters, calculate the wind speed of the power transmission lines in the power system at each time, and obtain the wind speed information of the lines at each time; Based on wind speed information, calculate the ice thickness of the line at each time and build a comprehensive load model of the line; Based on the comprehensive load model, the failure rate of transmission lines at each time under ice disaster weather is calculated, and a line failure rate model under ice disaster weather is established; Construct the fuzzy set of transmission line faults under ice disaster weather, the support set of transmission line fault states and the uncertain set of wind-solar-water forecast deviations; Based on the line failure rate model and uncertainty set, a two-stage distributed robust optimization scheduling model is constructed to obtain the optimal unit start-up and shutdown and line maintenance plan.

2. The uncertain power system dispatching method under ice disaster weather as claimed in claim 1, characterized in that: The forecast data of ice disaster weather includes ice disaster impact radius, ice disaster attenuation coefficient, ice disaster center position coordinates, wind speed data at each moment, freezing rain amount, air moisture content and temperature data.

3. The uncertain power system dispatching method under ice disaster weather as claimed in claim 2, characterized in that: The construction of the comprehensive load model of the line includes taking into account the combined effect of wind load and ice load to obtain a comprehensive load model that the transmission line bears under ice disaster weather; The wind load includes calculating the wind load of the line under ice disaster weather based on the wind speed information at the location of each line, and the formula is expressed as: Among them, K represents the constant coefficient, S represents the span factor of the line, v represents the wind speed at the location of the line, and ω represents the ice thickness on the line. represents the wind load of the transmission line ij at time t, and D represents the conductor diameter; The ice load includes calculating the ice load of the line under ice disaster weather based on the ice thickness and ice density of the line; in, represents the density of ice, D represents the diameter of the wire, represents the ice thickness of the transmission line ij at time t, represents the ice load of transmission line ij at time t; The formula of the comprehensive load model is expressed as: in, represents the ice load of transmission line ij at time t, represents the wind load of the transmission line ij at time t, represents the comprehensive load of transmission line ij at time t.

4. The uncertain power system dispatching method under ice disaster weather as claimed in claim 3 is characterized by: Establishing the line failure rate model under ice disaster weather includes calculating the failure rate of the transmission line at each moment under ice disaster weather based on the comprehensive load model of the transmission line. The formula is expressed as follows: Among them, p ij,t represents the failure probability of line ij at time t, A represents the comprehensive load borne by line ij at time t, ij represents the design load of line ij, B ij represents the ultimate load of line ij, and exp: represents the exponential function.

5. The uncertain power system dispatching method under ice disaster weather as claimed in claim 4, characterized in that: The fuzzy set of transmission line failures under ice disaster weather includes that the failure rate of each transmission line at a certain time corresponds to an ice disaster weather state at that time, and the physical process of actual ice disaster weather has uncertainty in the impact on the failure rate of the transmission line at each time; Based on the prediction of the failure rate of transmission lines at different times under ice disaster weather, the transmission line fault fuzzy set is established, and the formula is expressed as follows: in, represents the transmission line fault fuzzy set, P represents the fault state probability distribution of each line, Λ represents the set of all possible fault state probability distributions, κ represents the fault rate deviation factor, represents the failure rate of line ij at time t, represents the random variable 1-u ij,t The expected value under the probability distribution P; u ij,t represents the operating status of the transmission line ij at time t, where u ij,t =0 means the line is faulty, u ij,t =1 indicates that the line is operating normally; It means that it holds true for all times t; The transmission line fault state support set includes the number of fault lines in the transmission network at different times, which is expressed as follows: Where U represents the fault state support set of the transmission line; u ij,t represents the operating status of line ij at time t, u ij,t =1 means that line ij operates normally at time t, u ij,t =0 means that line ij fails at time t; ∑ ij u ij,t represents the sum of the operating states of all lines ij at time t, N L represents the total number of transmission lines; K represents the maximum number of lines that are allowed to fail; T: represents the total duration of the entire scheduling period; When a line fails at a certain moment during ice disaster weather, the line will remain in a faulty state in subsequent states; The wind-solar-water forecast deviation uncertainty set includes that the output power of the renewable energy power plant is affected by its wind speed forecast value and light intensity forecast value respectively; The output power of a hydropower station is determined by the predicted value of the inflow flow at its location; The predicted values ​​of wind speed, light intensity and hydropower station output are highly random, and there is a certain deviation between their actual values ​​and predicted values; Considering the uncertainty of wind farm and photovoltaic power station output forecast deviation and the uncertainty of hydropower station inflow forecast deviation, a box uncertainty set of renewable energy power generation forecast deviation is constructed, and the formula is expressed as follows: Among them, ε: represents the uncertainty set of wind, solar and water forecast deviations; represents the prediction deviation state of wind farm k at time t, where 1 indicates the existence of prediction deviation and 0 indicates no prediction deviation; Indicates the prediction deviation state of PV power station m at time t, where 1 indicates the existence of prediction deviation and 0 indicates no prediction deviation; represents the prediction deviation state of hydropower station n at time t, where 1 indicates the presence of prediction deviation and 0 indicates no prediction deviation; Γ W represents the wind farm forecast deviation budget, Γ V represents the prediction deviation budget of the photovoltaic power station, Γ H Represents the forecast deviation budget of the hydropower station.

6. The uncertain power system dispatching method under ice disaster weather as claimed in claim 5, characterized in that: The two-stage distributed robust optimization scheduling model includes: in the first stage, optimizing the start-up and shutdown decisions of the units under the worst conditions, taking the minimum start-up and shutdown costs of the thermal power units as the goal, and considering the start-up and shutdown restrictions of the units and the line maintenance constraints; The formula for minimizing the start-up and shutdown cost of thermal power units is as follows: Among them, min means minimizing the objective function, represents the startup cost of unit g, y g,t represents the startup state variable of unit g at time t, represents the downtime cost of unit g, z g,t represents the shutdown state variable of unit g at time t, Represents other costs in the scheduling model.

7. The uncertain power system dispatching method under ice disaster weather as claimed in claim 6, characterized in that: The two-stage distributed robust optimization dispatch model also includes, in the second stage, optimizing the operation cost of the power system and forming the optimal dispatch plan under the worst line fault and wind, solar and water forecast deviation conditions; The formula of the objective function of the second stage is expressed as: in, represents the objective function of the optimal value of the system, P represents the probability measure, represents the set of all possible measures, represents the expectation operator, represents the benefit function of the power system optimization dispatch, t: represents the time index, g represents each generator set in the power system, C coal represents the unit cost coefficient of coal-fired power units, represents the coal consumption efficiency of coal-fired power unit g in scheme s, represents the output power of generator set g at time t and scheme s, C we represents the unit wind curtailment cost coefficient of wind power, represents the abandoned wind power of wind farm k at time t, C vc The unit cost coefficient of abandoned light in photovoltaic power stations, represents the abandoned light power of PV plant m at time t, represents the unit charging and discharging cost coefficient of the energy storage power station, represents the charging power of the energy storage station e at time t, represents the discharge power of the energy storage station e at time t, C ls represents the unit cost coefficient of load shedding, represents the load shedding b at time t, C hc represents the unit cost coefficient of abandoned water, represents the amount of water abandoned by hydropower station n at time t.

8. An uncertain power system dispatching system under ice disaster weather, characterized by: The data module obtains the forecast data of ice disaster weather, calculates the wind speed of the power transmission lines in the power system at each time, and obtains the wind speed information of the lines at each time; The load module calculates the ice thickness of the line at each time based on wind speed information and constructs a comprehensive load model for the line; The failure rate module calculates the failure rate of the transmission line at each time under ice disaster weather based on the comprehensive load model, and establishes a line failure rate model under ice disaster weather; Uncertainty module, constructs the fuzzy set of transmission line faults under ice disaster weather, the support set of transmission line fault states, and the uncertainty set of wind-solar-water forecast deviations; The scheduling module constructs a two-stage distributed robust optimization scheduling model based on the line failure rate model and uncertainty set to obtain the optimal unit start-up and shutdown and line maintenance plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for dispatching an uncertain power system under ice disaster weather according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for dispatching an uncertain power system under ice disaster weather according to any one of claims 1 to 7 are implemented.

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