Power system unit combination method, system, equipment and medium

Abstract

The invention relates to the technical field of power system unit combination, and discloses a power system unit combination method, system and device and a medium, and the method comprises the steps: obtaining a plurality of key parameters of a power grid; constructing a fault probability model and a heat balance equation based on the parameters; the preprocessing model outputs to determine a fault scene and a power grid unit state; establishing a combined model containing an objective function and constraint conditions; and solving the model to obtain a unit commitment scheme. According to the method, the power system physical model and the multi-type power supply combination model under the ice disaster weather are developed, meanwhile, the line repair strategy during the ice disaster period is optimized, and the emergency response and capability of the power system in the ice disaster are shown. In addition, the invention also proposes to optimize the structure of the unit combination network under the ice disaster condition, so as to reduce the power transmission congestion, reduce the load loss and reduce the total cost.

Description

Technical Field

[0001] The present application relates to the technical field of power system unit combination, and in particular to a power system unit combination method, system, equipment and medium. Background Art

[0002] Traditional power system unit combinations typically formulate day-ahead power generation operation plans based on boundary conditions such as installed capacity and grid data, forecasted meteorological and environmental factors. However, this approach fails to meet the power system's resilience requirements in ice storm scenarios. Failures in power system components caused by ice storms can seriously threaten reliable power supply. Modeling the impact of ice storms on power system components is crucial, allowing for targeted unit operation plans and line maintenance programs to mitigate power load losses during ice storms. Numerous publications and patents have already considered the impact of ice storms in unit combinations.

[0003] Existing technologies consider the spatiotemporal impact of icy weather on the operational status of transmission lines and generate line repair and unit grouping plans tailored to icy fault scenarios. This significantly reduces load shedding while lowering unit operating costs, achieving a synergistic improvement in flexibility and economy. Others have proposed preventative resilience-enhancing operational measures that coordinate network structure optimization and unit grouping, enhancing the ability to maintain load during typhoon disasters. However, no research has been conducted on preventative unit grouping methods for icy weather, and the potential for applying dynamic line capacity expansion technology to resilience-enhancing operational measures has yet to be explored.

[0004] Traditional static line capacity constraints are difficult to adapt to the temporal and spatial variations of meteorological factors such as ice thickness and ambient temperature during ice disasters, resulting in insufficient improvement in line current carrying capacity and difficulty in achieving the optimal temporal and spatial matching of transmission capacity and power generation resource scheduling, reducing the network transmission margin under ice disaster weather and the ability to enhance the flexibility of power system operation.

[0005] In summary, the existing method of improving the flexibility of unit combination during ice disasters has not fully utilized the grid-side regulation measures to achieve flexible power transmission under component failure scenarios. It lacks multi-dimensional coordination between unit scheduling, grid structure adjustment and line repair, and it is difficult to effectively meet the demand for improving the flexibility of power system operation during extreme ice disasters. Summary of the Invention

[0006] In view of the above existing problems, this application is proposed.

[0007] Therefore, the present application provides a power system unit combination method, system, equipment and medium, which can solve the problems of high computational complexity and unsatisfactory optimization effect existing in traditional unit combination methods.

[0008] To solve the above technical problems, this application provides the following technical solutions:

[0009] In a first aspect, the present application provides a method for combining units in a power system, comprising:

[0010] Obtaining a first parameter and a second parameter of a target power grid;

[0011] The first parameter is the target power grid grid data, and the second parameter is the predicted meteorological data related to the target power grid;

[0012] Establishing a target power grid transmission line failure probability model and a heat balance equation based on the first parameter and the second parameter;

[0013] Performing a first preprocessing on the output of the target power grid transmission line fault probability model to obtain a target fault scenario and an operating state of a target power grid unit under the corresponding scenario;

[0014] Establishing a first combination model, wherein the first combination model includes a first objective function and a first constraint condition set;

[0015] The first set of constraints includes a number of constraints obtained through the heat balance equation;

[0016] The first combination model is solved to obtain a target power grid unit combination scheme.

[0017] As a preferred solution of the unit combination method described in the present application, wherein: the establishment of the target power grid transmission line failure probability model and the heat balance equation according to the first parameter and the second parameter includes:

[0018] Obtaining the change in the position of freezing rain during ice disaster weather according to the first parameter and the second parameter;

[0019] According to the change of freezing rain position during ice disaster, the wind speed of each transmission line at each time is characterized;

[0020] According to the wind speed that the transmission line can withstand after characterization, an ice thickness accumulation model for the transmission line under ice disaster weather is constructed.

[0021] As a preferred solution of the unit combination method described in the present application, wherein: the establishment of the target power grid transmission line failure probability model and the heat balance equation based on the first parameter and the second parameter further includes:

[0022] Obtaining ice load and wind load of the transmission line in ice disaster weather according to the first parameter and the second parameter;

[0023] The ice load and wind load are related to the ice thickness accumulation model;

[0024] Characterizing the comprehensive load borne by the transmission line at each moment according to the ice load and wind load of the transmission line in ice disaster weather;

[0025] According to the characterized comprehensive load, the transmission line failure rate at each moment under ice disaster weather is characterized, and the target power grid transmission line failure probability model is obtained.

[0026] As a preferred solution of the unit combination method described in the present application, wherein: the establishment of the target power grid transmission line failure probability model and the heat balance equation based on the first parameter and the second parameter further includes:

[0027] Obtaining convective heat loss, radiation heat, and solar heat according to the first parameter and the second parameter;

[0028] A heat balance equation is established based on the convective heat loss, radiation heat and solar heat.

[0029] As a preferred solution of the unit combination method described in this application, the several constraints obtained by the heat balance equation include:

[0030] Obtaining a dynamic capacity limit of the transmission line represented by current according to the heat balance equation;

[0031] Obtaining a dynamic capacity gain coefficient of the transmission line according to the dynamic capacity limit of the transmission line represented in the form of current;

[0032] Establish line security transmission constraints based on the dynamic capacity gain coefficient of the transmission line.

[0033] This optimal solution can more accurately reflect the actual transmission capacity of transmission lines under different environmental conditions, improving the safety and stability of power grid operations. Furthermore, by calculating the dynamic capacity gain coefficient of transmission lines, it can further optimize unit combination strategies, reduce grid operating costs, and improve energy efficiency.

[0034] As a preferred solution of the unit commitment method described in the present application, wherein: the first objective function is an arbitrary function with the total cost of day-ahead operation as the target;

[0035] The total day-ahead operating cost includes at least the following:

[0036] The operating costs of thermal power units, the costs of curtailing wind power, curtailing solar power, curtailing water power, the costs of energy storage charging and discharging, and the costs of load shedding.

[0037] As an optimal solution of the unit combination method described in the present application, the first constraint condition set also includes node power balance constraints, line DC flow constraints, line disconnection status constraints, line repair status constraints, minimum start-up and shutdown time constraints of thermal power units, start-up and shutdown status constraints of thermal power units, upper and lower limit constraints on thermal power unit output, thermal power unit climbing constraints, thermal power unit rotating standby constraints, wind power unit output constraints, photovoltaic unit output constraints, hydropower unit output constraints, energy storage power station operation constraints, load shedding constraints and elastic constraints.

[0038] In a second aspect, the present application provides a power system unit combination system, comprising:

[0039] A data acquisition module, configured to acquire a first parameter and a second parameter of a target power grid;

[0040] The first parameter is the target power grid grid data, and the second parameter is the predicted meteorological data related to the target power grid;

[0041] A model and equation building module, configured to build a target power grid transmission line failure probability model and a heat balance equation based on the first and second parameters;

[0042] a preprocessing module, configured to perform a first preprocessing on the output of the target power grid transmission line fault probability model to obtain a target fault scenario and an operating state of a target power grid unit under the corresponding scenario;

[0043] A combination model building module, configured to build a first combination model, wherein the first combination model includes a first objective function and a first constraint condition set;

[0044] The first set of constraints includes a number of constraints obtained through the heat balance equation;

[0045] A solution module is used to solve the first combination model to obtain a target power grid unit combination solution.

[0046] In a third aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when the computer program is executed by a processor.

[0048] Compared with the prior art, the beneficial effects of the present application are as follows: the present application proposes a method for combining units in a power system, obtaining a first parameter and a second parameter of a target power grid; establishing a target power grid transmission line failure probability model and a heat balance equation based on the first parameter and the second parameter; performing a first preprocessing on the output of the target power grid transmission line failure probability model to obtain a target failure scenario and the operating state of the target power grid units under the corresponding scenario; establishing a first combination model, the first combination model including a first objective function and a first set of constraints; solving the first combination model to obtain a target power grid unit combination scheme. The present application constructs a physical process model of ice disaster weather and a first combination model of multiple types of power units including wind, solar, water, fire and storage under ice disaster weather, and considers the optimization of line repair measures under ice disaster weather, reflecting the process and ability of the power system to respond to emergencies under ice disaster weather. The present application proposes to optimize the network structure in the unit combination under ice disaster weather, reduce load losses by alleviating transmission congestion, and reduce the total cost of the unit combination. This application proposes to consider the dynamic capacity expansion effect of the line in the unit combination under ice disaster weather, improve the transmission capacity of the transmission line under extreme conditions, enhance the operational flexibility of the power system unit combination and further reduce the total cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 A flow chart of a method for combining power system units provided in one embodiment of the present application.

[0051] Figure 2 A schematic diagram of the topology of an improved IEEE 39-node test system for a power system unit combination method provided in one embodiment of the present application.

[0052] Figure 3 Schematic diagrams of thermal power unit start-up and shutdown schemes for various scenarios of a power system unit combination method provided in one embodiment of the present application, (a) is scenario 1, (b) is scenario 2, (c) is scenario 3, and (d) is scenario 4.

[0053] Figure 4 Schematic diagrams of emergency repair plans for faulty transmission lines in various scenarios of a power system unit combination method provided in one embodiment of the present application, (a) is scenario 1, (b) is scenario 2, (c) is scenario 3, and (d) is scenario 4.

[0054] Figure 5A schematic diagram of the load rate of a transmission line equipped with a dynamic capacity expansion device in a power system unit combination method provided in one embodiment of the present application.

[0055] Figure 6 This is a diagram of the internal structure of an electronic device for a power system unit combination method provided in one embodiment of the present application. DETAILED DESCRIPTION

[0056] To make the above-mentioned purposes, features, and advantages of this application more clearly understood, the following detailed description of the specific embodiments of this application is given in conjunction with the accompanying drawings. It is obvious that the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of this application.

[0057] Example 1, reference Figures 1-6 , which is the first embodiment of the present application, provides a method for combining power system units, including:

[0058] There are some problems in the existing related technologies. For example, the traditional unit combination method has high computational complexity and unsatisfactory optimization effect when dealing with ice disaster weather.

[0059] This application provides a method that can effectively solve the above-mentioned problems. Next, we will describe in detail how to implement the power system unit combination method in combination with multiple embodiments.

[0060] Figure 1 A flow chart of a method for combining power system units is shown, including:

[0061] S101, obtaining a first parameter and a second parameter of a target power grid;

[0062] In the embodiment of the present application, the first parameter is the target power grid grid data, and the second parameter is the predicted meteorological data related to the target power grid.

[0063] In an optional embodiment, grid data can include real-time generator output data, actual line power flow data, actual transformer load data, and node voltage amplitude and phase angle data. This data can comprehensively reflect the operating status of the power system and provide accurate basic information for unit combination optimization. By analyzing and processing this data, the optimal unit combination solution can be further determined to achieve economical, safe, and reliable operation of the power system.

[0064] In an optional embodiment, forecasted meteorological data can be obtained from a variety of data sources, including but not limited to weather station observation data, satellite remote sensing data, and numerical weather forecast data. After preprocessing and integration, this data can provide more comprehensive and accurate meteorological information, providing a more reliable meteorological basis for power system unit configuration. By comprehensively analyzing this data, key meteorological factors such as future wind speed, temperature, and humidity can be predicted, thereby optimizing the unit's operating strategy and improving the stability and efficiency of the power system.

[0065] In an optional embodiment, the predicted meteorological data may include at least one of historical meteorological data, real-time meteorological data, and future meteorological data. Comprehensive analysis of this meteorological data can more accurately predict changing trends in power system load and renewable energy generation, thereby optimizing unit deployment strategies and improving the stability and economic efficiency of the power system.

[0066] In the embodiment of the present application, the improved IEEE 39-node standard calculation example is used as the test system to obtain grid time-series load data, wind-solar-hydro-thermal-storage power installed capacity data, network topology, line length, line static capacity and other grid data.

[0067] In the embodiment of the present application, the acquired data also includes technical parameters such as the upper and lower limits of thermal power output, the maximum and minimum start and stop times of thermal power, the thermal power ramp rate, the predicted output of wind and solar power stations, the predicted inflow of hydropower, the comprehensive efficiency of hydropower, the rated power of energy storage, the charging and discharging efficiency of energy storage, and economic parameters such as the unit operating costs of various types of power sources.

[0068] In an embodiment of the present application, the acquired data also includes the number of ice-melting repair teams and the completion time of ice-melting repair, and inputs meteorological data such as the movement speed of ice disaster freezing rain, the movement direction of ice disaster freezing rain, the maximum impact radius of freezing rain, the average amount of freezing rain, the maximum wind speed, the ice disaster attenuation coefficient, the radiation intensity, the air density and the ambient temperature predicted a few days ago.

[0069] It should be noted that obtaining the first and second parameters of the target power grid can improve the accuracy and efficiency of unit combination. By acquiring and analyzing the first parameters (such as load demand and power generation capacity) and the second parameters (such as forecast data for renewable energy sources such as wind and solar energy), a more comprehensive understanding of the current state and future trends of the power grid can be achieved, providing a more scientific basis for decision-making on unit combination. This helps optimize the allocation of power resources, reduce unnecessary energy waste, and improve the stability and economy of the power system.

[0070] S102, establishing a target power grid transmission line failure probability model and a heat balance equation based on the first parameter and the second parameter;

[0071] It should be noted that the transmission line failure probability model is used to assess the likelihood of transmission line failures under different meteorological conditions. This model comprehensively considers line aging, historical failure records, environmental factors (such as the impact of ice storms and freezing rain), and real-time meteorological data. Using a complex algorithm, it calculates the failure probability, providing decision-makers with critical information for transmission line maintenance priorities and emergency dispatch strategies. This facilitates rapid response in extreme weather conditions, reduces the risk of power outages caused by line failures, and ensures stable operation of the power system.

[0072] In an optional embodiment, a transmission line failure probability model can be trained using a machine learning algorithm based on historical failure data, environmental factors (such as temperature, humidity, wind speed, etc.), and multi-dimensional information such as line aging. This model can comprehensively consider various influencing factors and predict the probability of transmission line failure.

[0073] In an optional embodiment, the transmission line failure probability model can also incorporate Geographic Information System (GIS) data to comprehensively analyze factors such as the transmission line's location, topography, and surrounding buildings. Through spatial analysis and data mining techniques, the model can identify potential failure risk points, such as areas susceptible to wind damage, flooding, or geological disasters, further improving the accuracy and specificity of fault predictions.

[0074] It should be noted that the transmission line failure probability model established in the above steps cannot well reflect the actual transmission line failure situation. To overcome this shortcoming, this application designs a Jones model that characterizes the physical movement process of ice disaster weather, and then establishes a transmission line failure rate model under the influence of ice disaster weather.

[0075] In the embodiment of the present application, establishing a target power grid transmission line failure probability model and a heat balance equation according to the first parameter and the second parameter includes:

[0076] According to the first parameter and the second parameter, the position change of freezing rain in ice disaster weather is obtained;

[0077] According to the change of freezing rain position during ice disaster, the wind speed of each transmission line at each time is characterized;

[0078] According to the wind speed that the transmission line can withstand after characterization, an ice thickness accumulation model for the transmission line under ice disaster weather is constructed.

[0079] In the embodiment of the present application, establishing the target power grid transmission line failure probability model and the heat balance equation according to the first parameter and the second parameter further includes:

[0080] Obtaining ice load and wind load of the transmission line in ice disaster weather according to the first parameter and the second parameter;

[0081] Ice loads and wind loads are related to the ice thickness accumulation model;

[0082] The comprehensive load on the transmission line at each moment is characterized based on the ice load and wind load during ice disaster weather.

[0083] According to the characterized comprehensive load, the transmission line failure rate at each moment under ice disaster weather is characterized, and the target power grid transmission line failure probability model is obtained.

[0084] It should be noted that the heat balance equation describes the balance between the thermal effects of icing and wind on transmission lines and the heat dissipation during icy weather. This heat balance equation comprehensively considers the impact of icing and wind loads on transmission line temperature, as well as other factors such as ambient temperature and solar radiation. Through precise calculations, it can reflect the thermal state of transmission lines during icy weather in real time. The introduction of this equation enables the system to more accurately predict and assess the impact of icy weather on transmission lines, providing strong support for subsequent fault warning and emergency response.

[0085] In the embodiment of the present application, establishing the target power grid transmission line failure probability model and the heat balance equation according to the first parameter and the second parameter further includes:

[0086] Obtaining convective heat loss, radiation heat, and solar heat according to the first parameter and the second parameter;

[0087] The heat balance equation is established based on the convective heat loss, radiation heat and solar heat.

[0088] Specifically, the detailed steps of establishing the target power grid transmission line failure probability model and the heat balance equation according to the first parameter and the second parameter are as follows:

[0089] Based on the first and second parameters, the changing locations of freezing rain during ice disasters can be determined. Specifically, by analyzing the changing trends of the first and second parameters (such as precipitation and temperature), the accumulation and movement of freezing rain along transmission lines can be accurately captured. Furthermore, using this data, combined with Geographic Information System (GIS) technology, freezing rain distribution maps can be created, visually demonstrating the specific impact areas of freezing rain on transmission lines during ice disasters.

[0090] Furthermore, based on the changes in the location of freezing rain during ice disasters, the wind speed that each transmission line can withstand at each moment is characterized:

[0091]

[0092] Where V ij,t V is the wind speed that the transmission line ij can withstand at time t;max is the maximum wind speed; (x ij ,y ij ) is the center coordinate of the transmission line ij position; is the coordinate of the center of the ice disaster at time t; R max is the maximum impact radius of the ice disaster; is the attenuation coefficient; σ1 and σ2 are characteristic coefficients related to the ice disaster impact radius.

[0093] Furthermore, an ice thickness accumulation model for transmission lines under icy weather conditions is constructed based on the wind speed they withstand:

[0094]

[0095] Where, ψ ij,t is the ice thickness of transmission line ij at time t; ρ i and ρ w are the densities of ice and water respectively; Q t is the amount of freezing rain at time t; W t air is the water content in the air at time t.

[0096] Furthermore, considering the ice load and wind load on the transmission line during ice disaster weather, the comprehensive load borne by the transmission line at each moment is characterized:

[0097]

[0098] Where, and are the ice load and wind load of transmission line ij at time t; D is the diameter of the transmission line conductor; I N is a constant coefficient with a value of 6.964×10-3; I span is the span factor; is the comprehensive load of transmission line ij at time t.

[0099] Furthermore, based on the comprehensive load model of transmission lines, the failure rate of transmission lines at different times during ice disaster weather is characterized:

[0100]

[0101] Where, is the failure rate of transmission line ij at time t; a ij and b ij are the load warning value and design value of transmission line ij respectively.

[0102] Furthermore, according to the IEEE 738 standard, the steady-state heat balance equation of the transmission line is constructed by considering solar heat loss, convection heat loss and radiation heat loss.

[0103] J c +J r =I 2 R+J s (7)

[0104] Where, J c 、J r and J s are the convective heat loss, radiation heat and heat generated by sunlight respectively; I and R are the current and resistance of the transmission line.

[0105] Furthermore, the calculation method of convective heat loss, radiation heat and solar heat is as follows:

[0106]

[0107] J c =max(J c,1 ,J c,2 ) (10)

[0108]

[0109] J s =αQ ir DL ij (12)

[0110] Where, J c,1 and J c,2 are the convective heat loss at high and low wind speeds, respectively; J c is the convective heat loss of the larger value above; A wl is the angle between the transmission line and the wind direction; N Re is the Reynolds coefficient; T design The maximum temperature that the transmission line is allowed to withstand during operation; is the ambient temperature of the transmission line ij at time t; k f is the thermal conductivity of air; γ is the heat dissipation coefficient of the conductor; α is the solar absorption rate of the conductor; Q ir is the solar radiation intensity; L ij is the length of the transmission line ij.

[0111] Furthermore, after obtaining the above variables, the dynamic capacity limit of the transmission line represented by current can be calculated according to the following formula, and then the dynamic capacity gain coefficient of the transmission line can be obtained:

[0112]

[0113] Where, I DLR is the dynamic capacity limit of the transmission line; K DLRis the dynamic capacity gain coefficient of the transmission line; I SLR is the static capacity of the transmission line.

[0114] It should be noted that establishing a target power grid transmission line failure probability model and heat balance equation based on the first and second parameters allows for a more accurate assessment of transmission line failure risks under different operating conditions. By monitoring transmission line loads in real time and comparing them with preset load warning values, potential overload risks can be quickly identified, allowing timely adjustments to be made to avoid power outages caused by line failures. Furthermore, the introduction of the heat balance equation allows the system to more comprehensively account for the thermal effects of transmission lines, further improving the accuracy of fault prediction.

[0115] S103, performing a first preprocessing on the output of the target power grid transmission line fault probability model to obtain a target fault scenario and the target power grid unit operating status under the corresponding scenario;

[0116] In an optional embodiment, the first preprocessing is used to screen and sort the output results of the target power grid transmission line failure probability model to identify the transmission lines and failure scenarios that are most likely to cause power grid failure.

[0117] It should be noted that this process involves analyzing the output data of the fault probability model, identifying transmission lines with high failure probabilities and their corresponding failure modes, and then deriving the operating status of each unit in the target grid under these failure scenarios. By simulating and analyzing these failure scenarios, the impact of failures on grid operations can be predicted, providing key information for subsequent optimized scheduling and fault response.

[0118] In an embodiment of the present application, the first preprocessing step is to obtain the failure rate of each transmission line at each moment based on the transmission line failure rate model under the influence of ice disaster weather, sample the transmission line failure rate at each moment based on the hypercube Latin sampling method, generate transmission line failure scenarios under ice disaster weather and the working status of wind turbines, photovoltaic turbines, and hydropower turbines under the corresponding scenarios, and cluster the above scenarios using the k-medoids method to obtain typical failure scenarios of ice disaster weather.

[0119] Specifically, based on the transmission line failure rate under the influence of icy weather, the hypercube Latin sampling method was used to sample the transmission line fault status at each moment. This generated 1,000 transmission line failure scenarios under icy weather and the operating status of wind turbines, photovoltaic turbines, and hydropower units under the corresponding scenarios. The k-medoids method was then used to cluster these scenarios, resulting in four typical failure scenarios under icy weather.

[0120] It should be noted that performing a first preprocessing on the output of the target power grid transmission line failure probability model to obtain target failure scenarios and the operating states of the target power grid units under these scenarios can improve the accuracy and efficiency of the power system unit combination system. By performing a first preprocessing on the output of the target power grid transmission line failure probability model, the system can screen out key target failure scenarios and analyze the operating states of the target power grid units based on these scenarios. This helps the system more accurately evaluate the performance of different unit combinations under failure scenarios, thereby selecting the optimal unit combination solution. Furthermore, this process can reduce unnecessary calculations and analysis, thereby improving the system's operational efficiency.

[0121] S104, establishing a first combination model, where the first combination model includes a first objective function and a first constraint condition set;

[0122] In an optional embodiment, the first combination model is designed to optimize the unit deployment strategy for power systems during ice storms. This model comprehensively considers the probability of transmission line failures, the operating status of various types of units, and the overall needs of the power system. Through complex algorithms and logical judgment, it automatically selects the optimal unit deployment plan.

[0123] In an optional embodiment, the first combination model may utilize advanced mathematical optimization tools, such as linear programming, integer programming, mixed integer nonlinear programming, etc. These tools are capable of handling complex constraints and nonlinear relationships, ensuring that among multiple possible unit combination schemes, a scheme is found that not only meets the requirements for stable operation of the power system but also maximizes cost-effectiveness under specific weather conditions (such as ice disasters).

[0124] In an optional embodiment, the first combination model can also be trained and optimized using a deep learning-based algorithm to improve the accuracy and efficiency of unit combination. This deep learning algorithm can include a convolutional neural network, a recurrent neural network, or other suitable neural network structure. By learning and analyzing multi-dimensional data such as historical power load data, unit operating status data, and external environmental factors, it automatically adjusts the unit combination strategy and parameters to adapt to different power demands and system operating conditions.

[0125] It should be noted that this application considers many variables and parameters, and the use of deep learning for model design requires a large amount of computing resources. Therefore, this application does not consider the use of deep learning algorithms, but adopts mathematical programming algorithms for design.

[0126] In an embodiment of the present application, the first constraint condition set includes several constraint conditions obtained through a heat balance equation.

[0127] In the embodiment of the present application, several constraints obtained by the heat balance equation include:

[0128] Obtain the dynamic capacity limit of the transmission line in the form of current based on the heat balance equation;

[0129] According to the dynamic capacity limit of the transmission line represented by the current form, the dynamic capacity gain coefficient of the transmission line is obtained;

[0130] Establish line security transmission constraints based on the dynamic capacity gain coefficient of the transmission line.

[0131] In the embodiment of the present application, the first objective function is an arbitrary function with the total cost of the day-ahead operation as the target;

[0132] The total cost of day-ahead operation includes at least the following:

[0133] The operating costs of thermal power units, the costs of curtailing wind power, curtailing solar power, curtailing water power, the costs of energy storage charging and discharging, and the costs of load shedding.

[0134] In an embodiment of the present application, the first constraint condition set also includes node power balance constraints, line DC power flow constraints, line disconnection status constraints, line repair status constraints, minimum start-up and shutdown time constraints of thermal power units, start-up and shutdown status constraints of thermal power units, upper and lower limit constraints on thermal power unit output, thermal power unit climbing constraints, thermal power unit rotating standby constraints, wind turbine output constraints, photovoltaic unit output constraints, hydropower unit output constraints, energy storage power station operation constraints, load shedding constraints and elastic constraints.

[0135] Specifically, the objective function is as follows:

[0136]

[0137] The objective function of the elasticity improvement unit combination model is the total operating cost, which specifically includes the operating cost of thermal power units, wind power curtailment cost, solar power curtailment cost, hydropower curtailment cost, energy storage charging and discharging cost, and load shedding cost. and are the unit operating cost, startup cost and shutdown cost of thermal power unit g respectively; is the output of thermal power unit g at time t in scenario s; y g,t,s 、z g,t,s are the start-up state and shutdown state of thermal power unit g at time t in scenario s respectively; C cur,W 、C cur,V and C cur,H They are the unit wind curtailment cost, unit solar curtailment cost and unit water curtailment cost respectively; is the wind power abandoned by wind turbine w at time t in scenario s; is the abandoned power of PV unit v at time t in scenario s; C is the water abandonment of hydropower unit h at time t in scenario s;E The unit power cost of charging and discharging the energy storage station; and are respectively the charging power and discharging power of the energy storage station e at time t in scenario s; C LS is the unit load shedding cost; is the load shedding power of node b at time t in scenario s; ρ s is the occurrence probability of scenario s.

[0138] The constraints are as follows:

[0139] 1. Node power balance constraints

[0140]

[0141] Where, is the output of wind turbine w at time t in scenario s; is the output of PV unit v at time t in scenario s; is the output of hydropower unit h at time t in scenario s; is the power flowing through line ij at time t in scenario s; is the load power of node b at time t; Θ G 、Θ W 、Θ V and Θ H are the collections of thermal power units, wind power units, photovoltaic units and hydropower units respectively, Γ is the collection of transmission lines, Φ E is the set of energy storage power stations, the subscript b of the above set represents the corresponding set on node b; Ω is the node set.

[0142] 2. Line DC power flow constraints

[0143]

[0144] Where, is the susceptance value of the transmission line ij; θ ij,t,s is the phase angle difference of the transmission line ij at time t in scenario s; is the adjustable 0-1 disconnection state of the transmission line ij at time t in scenario s, which is used to optimize the network structure. When its value is 1, it indicates that the line is running, and when it is 0, it indicates that the line is disconnected.

[0145] 3. Line security transmission constraints

[0146]

[0147] Where, and are the upper and lower limits of the transmission capacity of transmission line ij respectively; is the dynamic capacity gain coefficient of the transmission line ij equipped with dynamic capacity expansion equipment.

[0148] 4. Line disconnection status constraints

[0149]

[0150] Where, ξ ij,t,s is the 0-1 fault state of the transmission line ij at time t in scenario s, which is obtained by sampling the failure rate of the transmission line under ice disaster weather. When its value is 1, it means the line is normal, and when its value is 0, it means the line is faulty; ω ij,t,s is the 0-1 emergency repair status of the transmission line ij at time t in scenario s. A value of 1 indicates that emergency repair is in progress. A value of 0 indicates that no emergency repair is in progress. This formula indicates that the transmission line can be freely disconnected only in the following two situations: 1) The line is not faulty, that is, ξ ij,t,s =1; 2) Line failure and emergency repair, that is, ξ ij,t,s =0 and ω ij,t,s =1.

[0151] 5. Line repair status constraints

[0152]

[0153] Among them, formula (20) represents the maximum line repair capacity of the maintenance team at a single moment, formula (21) represents the transmission line to maintain the repair completion state after the emergency repair, and formula (22) represents the transmission line after the fault t F Emergency repair can only be carried out at the moment; Formula (23) means that only the fault line can be repaired. In the above formula, Π is the maximum number of emergency repairs of the transmission line at a single moment; is the fault moment of the transmission line.

[0154] 6. Minimum start-up and shutdown time constraints for thermal power units

[0155]

[0156] In the formula, o g,t,s is the 0-1 operating state of the thermal power unit g at time t in scenario s, where 1 represents the unit operating and 0 represents the unit being in a non-operating state; and They represent the minimum start / stop time of thermal power unit g respectively.

[0157] 7. Constraints on the start and stop status of thermal power units

[0158]

[0159]

[0160] Where y g,t,sis the 0-1 startup status of thermal power unit g at time t in scenario s, where 1 represents the unit is on and 0 represents the unit is not on; g,t,s is the shutdown status of the thermal power unit at time t in scenario s (0-1), where 1 represents the unit shutdown and 0 represents the unit not shut down.

[0161] 8. Upper and lower limits on thermal power unit output

[0162]

[0163] Where, and are the upper and lower limits of the output of thermal power unit g respectively.

[0164] 9. Thermal power unit climbing constraints

[0165]

[0166] Where, is the upper limit of the climbing speed of thermal power unit g.

[0167] 10. Constraints on spinning standby of thermal power units

[0168]

[0169] Where δ is the system reserve rate.

[0170] 11. Wind turbine output constraints

[0171]

[0172] Where, is the predicted output of wind power installed capacity w at time t; is the 0-1 working state of wind turbine w at time t, where 1 represents normal operation and 0 represents shutdown due to ice disaster weather.

[0173] 12. Photovoltaic unit output constraints

[0174]

[0175] Where, is the predicted output of photovoltaic installed capacity v at time t; is the 0-1 working state of the photovoltaic unit v at time t, where 1 represents normal operation and 0 represents shutdown due to ice disaster weather.

[0176] 13. Output constraints of hydropower units

[0177]

[0178] Formula (35) is the output expression of the hydropower unit; Formula (36) is the output limit of the hydropower unit; Formulas (37)-(38) are the power generation flow and water abandonment flow limits of the hydropower unit; Formula (39) is the reservoir capacity limit of the hydropower unit; Formulas (40)-(41) are the reservoir capacity state expressions of the hydropower unit at each moment; Formula (42) is the reservoir capacity balance constraint of the hydropower unit at the beginning and end moments. is the power generation flow of hydropower unit h at time t in scenario s; is the comprehensive efficiency of the hydropower unit h; H h is the water head of hydropower unit h; and are the upper and lower limits of the output of hydropower unit h respectively; and are the upper and lower limits of the power generation flow of hydropower unit h respectively; is the water discharge of hydropower unit h at time t in scenario s; and are the upper and lower limits of the water discharge of hydropower unit h respectively; is the storage capacity of the reservoir of hydropower unit h at time t in scenario s; and are the upper and lower limits of the storage capacity of the hydropower unit h respectively; is the inflow flow of hydropower unit h at time t; is the initial storage capacity of hydropower unit h; is the 0-1 working state of the hydropower unit h at time t, where 1 represents normal operation and 0 represents shutdown due to ice disaster weather.

[0179] 14. Energy Storage Power Station Operation Constraints

[0180]

[0181]

[0182] Formulas (43) and (44) are the charging and discharging power limit constraints of the energy storage station; Formula (45) indicates that the energy storage station cannot charge and discharge simultaneously at a certain time; Formula (46) represents the capacity limit of the energy storage station; Formulas (47) and (48) represent the capacity state changes of the energy storage station at each time; Formula (49) is the capacity balance constraint of the energy storage station at the beginning and end of the time. is the rated power of the energy storage station e; and is the charging and discharging status of the energy storage station e at time t in scenario s, where 1 represents charging / discharging and 0 represents not charging / discharging; E e,t,s is the capacity of the energy storage station e at time t in scenario s; and are the upper and lower limits of the capacity of the energy storage station e; and are the charging efficiency and discharging efficiency of the energy storage station e, respectively; is the initial capacity of the energy storage station e; Δt is the unit operating time interval.

[0183] 15. Load shedding constraints

[0184]

[0185] 16. Elastic Constraints

[0186]

[0187] This formula indicates that the guaranteed power supply in each scenario should meet the elasticity requirements of power system operation. This is the minimum power supply guaranteed by the system during ice disasters.

[0188] It should be noted that establishing the first combined model, which includes the first objective function and the first set of constraints, comprehensively considers the economic and security aspects of the power system. The first objective function aims to minimize power generation costs and ensure the economic benefits of power supply; while the first set of constraints encompasses various operational constraints of the power system, such as unit output limits and grid transmission capacity limits, to ensure the stability and security of power supply. By establishing this combined model, optimal decisions can be made regarding the power system's unit mix, improving the overall operational efficiency of the power system.

[0189] S105 , solving the first combination model to obtain a target power grid unit combination plan.

[0190] In this embodiment, grid load data, installed power generation capacity, grid data, typical fault scenarios during ice storms, and the operating status of corresponding units are input into the model to solve a first unit combination model that takes into account network structure optimization and dynamic line capacity expansion. The model outputs start-up and shutdown plans and output plans for thermal power units, output plans for wind power, photovoltaic power, and hydropower, charging and discharging plans for energy storage power stations, emergency repair plans for transmission lines, network structure optimization plans, and load shedding results.

[0191] In summary, this application proposes a method for unit combination in a power system. The method comprises obtaining first and second parameters of a target power grid; establishing a target power grid transmission line failure probability model and a heat balance equation based on the first and second parameters; performing a first preprocessing on the output of the target power grid transmission line failure probability model to obtain a target failure scenario and the target power grid unit operating status under the corresponding scenario; establishing a first combination model comprising a first objective function and a first set of constraints; and solving the first combination model to obtain a target power grid unit combination scheme. This application constructs a physical process model for ice storm weather and a first combination model for multiple types of power generation units, including wind, solar, hydro, thermal, and storage, during ice storm weather. The model also considers the optimization of line repair measures during ice storm weather, reflecting the process and capability of the power system to respond to emergencies during ice storm weather. This application proposes optimizing the network structure in unit combination during ice storm weather, reducing load losses and overall unit combination costs by alleviating transmission congestion. This application proposes considering the dynamic capacity expansion of lines in unit combination during ice storm weather, improving the transmission capacity of transmission lines under extreme conditions, enhancing the operational flexibility of power system unit combination, and further reducing overall costs.

[0192] Example 2: In a preferred embodiment, the grid load data, power installed capacity, grid data, typical fault scenarios under ice disaster weather, and the working status of the corresponding units of the improved IEEE 39-node test system are input into the model, wherein the test system includes 46 transmission lines, 8 thermal power units, 2 wind farms, 2 photovoltaic power stations, 2 hydropower stations, and 2 energy storage power stations. The test system topology is as shown in the attached figure. Figure 2 As shown, the installed capacities of individual wind farms, photovoltaic power plants, and hydropower plants are 750MW, 800MW, and 500MW, respectively. The installed capacity of a single energy storage plant is 300MW / 600MWh. Seven transmission lines are equipped with dynamic capacity expansion equipment. The maximum temperature that the transmission lines can withstand is 70°C. The unit load shedding cost is $5000 / MW, the unit wind and solar curtailment costs are $750 / MW, and the unit hydro curtailment cost is $250 / MW. The unit combination operation cycle is 24 hours, with an operating time step of 1 hour. A unit combination model considering network structure optimization and dynamic line capacity expansion is solved. Output is the start-up and shutdown and output plans for the thermal power units, the output plans for wind power, photovoltaic power, and hydropower, the charging and discharging plans for the energy storage plant, the transmission line repair plan, the network structure optimization plan, and the load shedding results.

[0193] The start-up and shutdown plans for thermal power units and the repair plans for fault lines in each scenario are as shown in the attached Figure 3 (yellow - running, green - outage; horizontal axis - time, vertical axis - unit number, "G31" represents the thermal power unit located at node 31) and attached Figure 4(Blue - unrepaired, white - repaired; horizontal axis - time, vertical axis - line number; "10-11" represents the transmission line between nodes 10 and 11). The thermal power unit startup and shutdown plans show that in the four typical ice storm scenarios, units G32, G34, G36, and G38 were primarily started and shut down to cope with fluctuations in load, output from renewable energy units, and hydropower units. The other units remained operational to maintain load supply during the ice storm. The fault line repair plans show that scenario 1 had the least severe line fault, with six transmission lines faulted. All lines were repaired by the 17th time. Scenario 4 had the most severe line fault, with 17 lines faulted, and all lines were not repaired until the 23rd time. Based on the line repair sequence for the four scenarios, lines 28-29 and 29-38, as the primary channels for transmitting the power generated by thermal power unit G38, required priority repair.

[0194] Attachment Figure 5 The load rates of the lines equipped with dynamic capacity expansion equipment in Scenario 4 are shown. It can be seen that, except for some transmission lines that failed or were automatically disconnected at certain times, the transmission power of each line remained above a certain load level. Furthermore, due to the dynamic capacity expansion equipment, some transmission lines were able to increase their transmission capacity limits under the influence of environmental factors such as wind speed and temperature, enabling them to reach a load rate level exceeding 100%.

[0195] The comparison results of unit commitment with and without considering network structure optimization, and with and without considering line dynamic capacity expansion are shown in Table 1 and Table 2 respectively.

[0196] Table 1 Unit combination results under ice disaster weather with or without considering network structure optimization

[0197] Unit combination results No consideration of network structure optimization Consider network structure optimization Expected load shedding power (MWh) 232.55 229.40 Expected thermal power operation cost (k$) 2592.50 2592.50 Expected cost of wind curtailment (k$) 285.02 121.92 Expected cost of abandoned light (k$) 724.72 827.81 Expected cost of water abandonment (k$) 0 0 Expected energy storage charging and discharging cost (k$) 55.23 55.23 Total cost (k$) 4820.22 4744.46

[0198] Table 2 Unit combination results under ice disaster weather with or without considering dynamic capacity increase of line

[0199] Unit combination results Dynamic line capacity expansion is not considered Consider dynamic line capacity expansion Expected load shedding power (MWh) 243.17 229.40 Expected thermal power operation cost (k$) 2592.50 2592.50 Expected cost of wind curtailment (k$) 313.75 121.92 Expected cost of abandoned light (k$) 735.99 827.81 Expected cost of water abandonment (k$) 0 0 Expected energy storage charging and discharging cost (k$) 55.23 55.23 Total cost (k$) 4913.32 4744.46

[0200] Table 1 shows that considering network structure optimization can alleviate transmission congestion and increase wind power consumption by proactively disconnecting intact and repaired transmission lines. Although the expected curtailment cost of solar power increases by $103.09k in the solution with network structure optimization, the expected load shedding decreases by 3.15MW and the expected wind curtailment cost decreases by $163.1k, resulting in a total cost reduction of $75.76k. Table 2 shows that compared to the unit combination results without considering dynamic capacity expansion, the solution with dynamic capacity expansion can further reduce load shedding by 13.77MW. This is because the dynamic capacity expansion equipment increases the transmission margin of the transmission lines, allowing the lines equipped with dynamic capacity expansion equipment to transmit more power to the load in the event of a partial line failure. Furthermore, considering dynamic capacity expansion reduces the expected wind curtailment cost by $191.83k, and the total cost further reduces by $168.86k.

[0201] Therefore, when formulating unit combination plans during ice disasters, network structure optimization and line dynamic capacity expansion measures can be used to improve network transmission capacity in extreme situations, reduce load losses, and improve the economic efficiency of power system operation while enhancing flexibility.

[0202] Embodiment 3: This embodiment further provides a power system unit combination system, including:

[0203] A data acquisition module, configured to acquire a first parameter and a second parameter of a target power grid;

[0204] The first parameter is the target power grid data, and the second parameter is the predicted meteorological data related to the target power grid;

[0205] A model and equation building module, configured to build a target power grid transmission line failure probability model and a heat balance equation based on the first parameter and the second parameter;

[0206] a preprocessing module, configured to perform a first preprocessing on the output of the target power grid transmission line fault probability model to obtain a target fault scenario and an operating state of the target power grid unit under the corresponding scenario;

[0207] A combination model building module, configured to build a first combination model, the first combination model including a first objective function and a first set of constraints;

[0208] The first set of constraints includes several constraints obtained through the heat balance equation;

[0209] The solution module is used to solve the first combination model to obtain a target power grid unit combination plan.

[0210] The above-mentioned unit modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0211] This embodiment also provides an electronic device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for combining power system units is implemented. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad or mouse.

[0212] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

[0213] Obtaining a first parameter and a second parameter of a target power grid;

[0214] The first parameter is the target power grid data, and the second parameter is the predicted meteorological data related to the target power grid;

[0215] Establishing a target power grid transmission line failure probability model and a heat balance equation based on the first parameter and the second parameter;

[0216] Performing a first preprocessing on the output of the target power grid transmission line fault probability model to obtain a target fault scenario and an operating state of the target power grid unit under the corresponding scenario;

[0217] Establishing a first combination model, the first combination model includes a first objective function and a first constraint condition set;

[0218] The first set of constraints includes several constraints obtained through the heat balance equation;

[0219] The first combination model is solved to obtain a target power grid unit combination plan.

[0220] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application 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 application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, and all of these should be included in the scope of the claims of the present application.

[0221] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages.

[0222] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0223] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0224] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0225] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0226] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for combining units in a power system, characterized in that: include: Obtaining a first parameter and a second parameter of a target power grid; The first parameter is the target power grid grid data, and the second parameter is the predicted meteorological data related to the target power grid; Establishing a target power grid transmission line failure probability model and a heat balance equation based on the first parameter and the second parameter; Performing a first preprocessing on the output of the target power grid transmission line fault probability model to obtain a target fault scenario and an operating state of a target power grid unit under the corresponding scenario; Establishing a first combination model, wherein the first combination model includes a first objective function and a first constraint condition set; The first set of constraints includes a number of constraints obtained through the heat balance equation; The first combination model is solved to obtain a target power grid unit combination scheme.

2. A method for combining power system units according to claim 1, characterized in that: The establishing of a target power grid transmission line failure probability model and a heat balance equation according to the first parameter and the second parameter includes: Obtaining the change in the position of freezing rain during ice disaster weather according to the first parameter and the second parameter; According to the change of freezing rain position during ice disaster, the wind speed of each transmission line at each time is characterized; According to the wind speed that the transmission line can withstand after characterization, an ice thickness accumulation model for the transmission line under ice disaster weather is constructed.

3. The method for combining power system units according to claim 2, wherein: The establishing of the target power grid transmission line failure probability model and the heat balance equation according to the first parameter and the second parameter further includes: Obtaining ice load and wind load of the transmission line in ice disaster weather according to the first parameter and the second parameter; The ice load and wind load are related to the ice thickness accumulation model; Characterizing the comprehensive load borne by the transmission line at each moment according to the ice load and wind load of the transmission line in ice disaster weather; According to the characterized comprehensive load, the transmission line failure rate at each moment under ice disaster weather is characterized, and the target power grid transmission line failure probability model is obtained.

4. A method for combining power system units according to claim 3, characterized in that: The establishing of the target power grid transmission line failure probability model and the heat balance equation according to the first parameter and the second parameter further includes: Obtaining convective heat loss, radiation heat, and solar heat according to the first parameter and the second parameter; A heat balance equation is established based on the convective heat loss, radiation heat and solar heat.

5. A method for combining power system units according to claim 4, characterized in that: The constraints obtained from the heat balance equation include: Obtaining a dynamic capacity limit of the transmission line represented by current according to the heat balance equation; Obtaining a dynamic capacity gain coefficient of the transmission line according to the dynamic capacity limit of the transmission line represented in the form of current; Establish line security transmission constraints based on the dynamic capacity gain coefficient of the transmission line.

6. A method for combining power system units according to claim 5, characterized in that: The first objective function is an arbitrary function with the total cost of day-ahead operation as the target; The total day-ahead operating cost includes at least the following: The operating costs of thermal power units, the costs of curtailing wind power, curtailing solar power, curtailing water power, the costs of energy storage charging and discharging, and the costs of load shedding.

7. A method for combining power system units according to claim 6, characterized in that: The first constraint condition set also includes node power balance constraints, line DC power flow constraints, line disconnection status constraints, line repair status constraints, minimum start-up and shutdown time constraints of thermal power units, start-up and shutdown status constraints of thermal power units, upper and lower output limits of thermal power units, thermal power unit ramping constraints, thermal power unit spinning standby constraints, wind turbine output constraints, photovoltaic unit output constraints, hydropower unit output constraints, energy storage power station operation constraints, load shedding constraints and elasticity constraints.

8. A power system unit combination system, applying the method according to any one of claims 1 to 7, characterized in that: include: A data acquisition module, configured to acquire a first parameter and a second parameter of a target power grid; The first parameter is the target power grid grid data, and the second parameter is the predicted meteorological data related to the target power grid; A model and equation building module, configured to build a target power grid transmission line failure probability model and a heat balance equation based on the first and second parameters; a preprocessing module, configured to perform a first preprocessing on the output of the target power grid transmission line fault probability model to obtain a target fault scenario and an operating state of a target power grid unit under the corresponding scenario; A combination model building module, configured to build a first combination model, wherein the first combination model includes a first objective function and a first constraint condition set; The first set of constraints includes a number of constraints obtained through the heat balance equation; A solution module is used to solve the first combination model to obtain a target power grid unit combination solution.

9. An electronic 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 power system unit combination method 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 a power system unit combination method according to any one of claims 1 to 7 are implemented.

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