Electric power system robust optimization method and device considering meteorological uncertainty

Abstract

The embodiment of the invention provides a power system robust optimization method and device considering meteorological uncertainty. The method comprises the steps of obtaining historical wind and light output data, historical load power consumption data, historical meteorological data and day-ahead meteorological data of a power system; carrying out serialization processing on system elements of the power system to obtain a serialization processing result; based on the serialization processing result and the equivalent reliability index of the electric power equivalent system, determining the credible capacity of the electric power system; performing point prediction on the wind and light output and the load power to correspondingly obtain a wind and light output prediction result and a load prediction result; fitting the wind and light output prediction result and the load prediction result to correspondingly obtain an output prediction power interval and a load prediction power interval; constructing a day-ahead scheduling planning model; performing correction processing on the day-ahead scheduling planning model to obtain an intra-day scheduling optimization model; and solving the day-ahead scheduling planning model and the intra-day scheduling optimization model to obtain a power optimization strategy of the power system.

Description

Technical Field

[0001] The present invention relates to the field of power system optimization operation, and in particular to, but not limited to, a method and device for robust optimization of a power system taking into account meteorological uncertainty. Background Art

[0002] The increasing penetration of renewable energy in the power system, particularly wind and solar power, presents significant challenges to power system scheduling and operation, particularly due to its volatility and uncertainty. Simultaneously, power system planning and operation must ensure system integrity under all circumstances. This requires not only a focus on predictable, high-probability failures, but also vigilance against less-probable but potentially significant incidents. To effectively address these challenges, advanced optimization strategies are needed to ensure system reliability and cost-effectiveness.

[0003] Traditional power system dispatch strategies mostly rely on deterministic load and renewable energy output forecasts. However, these forecasts are often biased, especially under extreme meteorological conditions. Therefore, existing dispatch strategies may not be able to effectively address the uncertainties in actual operation, resulting in inefficient system operation and even power shortages or oversupply. To improve the robustness of power systems under uncertain conditions, researchers have proposed a variety of optimization strategies. For example, some studies have used stochastic optimization methods such as Monte Carlo simulation to generate possible wind and solar output scenarios and perform day-ahead and intraday dispatch based on these scenarios. While these methods can account for uncertainty, they are often computationally intensive and difficult to handle large-scale problems.

[0004] Furthermore, to reduce the conservatism of the optimization problem and improve the robustness of the system, some studies have proposed two-stage robust optimization methods. In the first stage, a day-ahead scheduling plan is formulated based on current forecast information; in the second stage, the intraday scheduling strategy is adjusted based on the latest forecast update. This approach can better cope with uncertainty, but efficiently solving the two-stage robust optimization problem remains a challenge. In terms of solution algorithms, the traditional Benders decomposition method is widely used for two-stage robust optimization problems, but it can suffer from slow convergence and low computational efficiency when dealing with large-scale problems. Summary of the Invention

[0005] The present invention provides a method and device for robust optimization of an electric power system taking into account meteorological uncertainty, so as to address the uncertainty inherent in existing meteorological forecasts and the difficulty in correctly evaluating the error of renewable energy output. Through interval prediction and two-stage robust optimization, the robustness and conservatism of the electric power system can be adjusted to better cope with extreme meteorological conditions.

[0006] The technical method of the embodiment of the present invention is implemented as follows:

[0007] In a first aspect, an embodiment of the present invention provides a method for robust optimization of a power system taking into account meteorological uncertainty, the method comprising:

[0008] Obtain historical wind and solar output data, historical load power consumption data, historical meteorological data, and day-ahead meteorological data of the power system;

[0009] Based on the historical wind and solar output data and the historical load power consumption data, serializing the system components of the power system to obtain a serialization processing result;

[0010] Determining the credible capacity of the power system based on the serialization processing result and the equivalent reliability index of the power equivalent system;

[0011] Based on the trusted capacity and the historical meteorological data, point forecasts are performed on the wind output, solar output, and load power, respectively, to obtain wind output forecast results, solar output forecast results, and load forecast results;

[0012] Using a conditional Copula function, the wind and solar output forecast results and the load forecast result are fitted respectively to obtain an output forecast power range and a load forecast power range;

[0013] Constructing a day-ahead scheduling planning model based on the constructed constraint conditions, the output forecast power range, the load forecast power range, and the day-ahead meteorological data;

[0014] Modifying the day-ahead scheduling model to obtain an intraday scheduling optimization model;

[0015] The day-ahead scheduling planning model and the intraday scheduling optimization model are solved to obtain a power optimization strategy for the power system.

[0016] In a second aspect, an embodiment of the present invention provides a device for robust optimization of a power system taking into account meteorological uncertainty, the device comprising:

[0017] An acquisition module is used to obtain historical wind and solar output data, historical load power consumption data, historical meteorological data, and day-ahead meteorological data of the power system;

[0018] a serialization module, configured to perform serialization processing on system components of the power system based on the historical wind and solar output data and the historical load power consumption data, and obtain a serialization processing result;

[0019] a determination module, configured to determine the credible capacity of the power system based on the serialization processing result and an equivalent reliability index of the power equivalent system;

[0020] a point prediction module, configured to perform point predictions on wind power, solar power output, and load power based on the trusted capacity and the historical meteorological data, and obtain wind power, solar power output prediction results, and load prediction results, respectively;

[0021] A fitting module is used to fit the wind and solar output forecast results and the load forecast results respectively using a conditional Copula function to obtain a corresponding output forecast power interval and a load forecast power interval;

[0022] A construction module, configured to construct a day-ahead scheduling planning model based on the constraints of the multiple units, the output forecast power range, the load forecast power range, and the day-ahead meteorological data;

[0023] A correction module, configured to correct the day-ahead scheduling model to obtain an intraday scheduling optimization model;

[0024] A solution module is used to solve the day-ahead scheduling planning model and the intraday scheduling optimization model to obtain the power optimization strategy of the power system.

[0025] In some embodiments, the system elements of the power system include: new energy units, energy storage equipment and load equipment; the serialization module is further used to perform serialization modeling on the new energy units to obtain serialization results of the new energy units; discretize based on the serialization results of the new energy units and the empirical probability distribution curve of historical data to obtain the available margin sequence of the new energy station; perform serialization modeling on the energy storage equipment to obtain the probability distribution of the equivalent power of the energy storage equipment; perform serialization modeling on the load equipment to obtain the load consumption margin sequence; determine the serialization results of the new energy units, the available margin sequence, the probability distribution and the load consumption margin sequence as the serialization processing results.

[0026] In some embodiments, the determination module is further used to perform random production simulation on the equivalent system to obtain the equivalent reliability index; compare the actual reliability index of the actual power system with the equivalent reliability index to determine the target reliability index; the difference between the target reliability index and the equivalent reliability index and the actual reliability index is within a preset error range; and the equivalent conventional unit capacity of the equivalent system corresponding to the target reliability index is determined as the credible capacity.

[0027] In some embodiments, the conditional Copula function is constructed in the following manner: based on the historical meteorological data and the historical wind and solar output data, meteorological condition characteristics and wind and solar output characteristics of adjacent time periods are constructed; based on the meteorological condition characteristics and the wind and solar output characteristics, multidimensional conditional variables are defined; the marginal empirical distribution function of wind power output and photovoltaic output is estimated; based on the correlation of the Gaussian Copula function, the Gaussian Copula function is determined as a Copula function; using the maximum likelihood estimation method, a first correlation coefficient of the Copula function is estimated; based on the first correlation coefficient, a second correlation coefficient of the conditional Copula function is estimated; based on the multidimensional conditional variable and the second correlation coefficient, the conditional Copula function is constructed.

[0028] In some embodiments, the day-ahead scheduling model is expressed as:

[0029]

[0030] Where T is the total number of dispatch periods; t is a period; G is the set of generators in the power system; is the power generation cost coefficient of generator set i in the power system; is the day-ahead output plan of power system generator set i in period t, is the starting state of power system generator set i in period t, =1 indicates the unit is started. is the startup cost of generator set i in the power system; is the daily investment cost of the energy storage device, The operation and maintenance cost of the energy storage device.

[0031] In some embodiments, the intraday scheduling optimization model is expressed as:

[0032]

[0033] Where t is a time period; is a set of uncertain parameters, including the actual output of wind power, photovoltaic power and load; G is the set of generator sets in the power system; is the possible value space of the uncertain parameter, determined by the power interval; is the power generation cost coefficient of unit i; is the daily output plan of power system generator set i in time period t.

[0034] In some embodiments, the constraints of the day-ahead scheduling model include:

[0035] Power balance constraints:

[0036]

[0037] in, is the day-ahead output plan of power system generator set i in period t, 、 、 They are the wind turbine output, photovoltaic unit output and load power consumption of the power system respectively; , , ; Any period of time;

[0038] Unit output constraints:

[0039]

[0040] in, is the startup status of unit i in period t, =1 means power on; 、 are the minimum and maximum output of unit i respectively; is the daily output plan of power system generator set i in time period t; for any generator set and any time;

[0041] Unit climbing constraints:

[0042]

[0043] in, 、 is the up and down climbing rate of unit i; is the day-ahead output plan of power system generator set i in time period t; is the day-ahead output plan of power system generator set i in time period t-1;

[0044] Energy storage constraints:

[0045]

[0046] in, , are the daily investment cost of the energy storage device and the operation and maintenance cost of the energy storage device respectively; is the daily cost conversion coefficient; is the annual operation and maintenance cost of the energy storage device; is the rated capacity of the i-th energy storage; is the daily power of the i-th energy storage; r is the discount rate; y is the service life of the energy storage system; is the number of energy storage units; is the unit capacity cost of the i-th energy storage, is the unit power cost of the i-th energy storage.

[0047] In some embodiments, the constraints of the intraday scheduling optimization model include:

[0048] Power balance constraints:

[0049]

[0050] in, is the daily output plan of power system generator set i in time period t; 、 、 They are the wind turbine output, photovoltaic unit output and load power consumption of the power system respectively; , , ; Any period of time;

[0051] Unit output constraints:

[0052]

[0053] in, is the startup status of unit i in period t, =1 means power on; 、 are the minimum and maximum output of unit i respectively; is the daily output plan of power system generator set i in time period t; for any generator set and any time;

[0054] Unit climbing constraints:

[0055]

[0056] in, 、 is the up and down climbing rate of unit i; is the daily output plan of power system generator set i in time period t; is the daily output plan of power system generator set i in time period t-1.

[0057] In some embodiments, the solution module is also used to determine the parameters of the day-ahead scheduling planning model and the intraday scheduling optimization model, and obtain an initial solution based on the parameter solution list and the main problem of the constraint generation algorithm; determine the sub-problem based on the initial solution, iteratively solve the sub-problem, and update the iterative solution result to the main problem until the preset optimization conditions are met to obtain the power optimization strategy.

[0058] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned robust optimization method for a power system considering meteorological uncertainty when executing the executable instructions stored in the memory.

[0059] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned robust optimization method for a power system considering meteorological uncertainty.

[0060] The embodiment of the present invention provides a method and device for robust optimization of an electric power system taking into account meteorological uncertainty, which obtains historical wind and solar output data, historical load power consumption data, historical meteorological data and day-ahead meteorological data of the electric power system; based on the historical wind and solar output data and historical load power consumption data, the system components of the electric power system are serialized to obtain serialization processing results; based on the serialization processing results and the equivalent reliability index of the electric power equivalent system, the reliable capacity of the electric power system is determined; based on the reliable capacity and historical meteorological data, point predictions are made on the wind, solar output and load power respectively, and wind and solar output prediction results and load prediction results are obtained accordingly; a conditional Copula function is used to fit the wind and solar output prediction results and load prediction results respectively, and output prediction power intervals and load prediction power intervals are obtained accordingly; a day-ahead scheduling planning model is constructed based on the constructed constraints, output prediction power intervals, load prediction power intervals and day-ahead meteorological data; the day-ahead scheduling planning model is corrected to obtain an intraday scheduling optimization model; the day-ahead scheduling planning model and the intraday scheduling optimization model are solved to obtain the power optimization strategy of the electric power system. In this way, on the one hand, the present invention combines the credible capacity assessment method with the conditional Copula function to perform point prediction and uncertainty modeling of wind power, photovoltaic power generation output and load power, captures the correlation between prediction errors, provides a more accurate power fluctuation range, and enhances the system's ability to cope with uncertainty; on the other hand, unlike the traditional single-stage robust optimization, the present invention proposes a two-stage robust optimization method that includes day-ahead scheduling planning and intraday scheduling strategy correction, so that the scheduling strategy can dynamically adapt to the update of meteorological information, reduce the conservatism of optimization, and improve the robustness of the system; thirdly, in response to the problem of integer variables in the robust optimization model, the present invention adopts the C&CG algorithm for solution, which improves the solution efficiency and accuracy, and solves the problems of slow convergence speed and low computational efficiency of traditional algorithms such as the Benders decomposition method when dealing with large-scale problems; fourthly, through the construction of power fluctuation range and the application of robust optimization strategy, it can effectively cope with the uncertainty of renewable energy output and load demand in extreme meteorological scenarios, and ensure the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 1 is a flow chart of a method for robust optimization of a power system taking into account meteorological uncertainty, provided by an embodiment of the present invention;

[0062] Figure 2 Schematic diagram of the process of constructing a conditional copula function provided by an embodiment of the present invention;

[0063] Figure 3 The following are interval prediction and point prediction curves for typical abnormal weather conditions in photovoltaic power plants provided by an embodiment of the present invention;

[0064] Figure 4 This is a two-stage scheduling flow chart provided by an embodiment of the present invention;

[0065] Figure 5 The embodiment of the present invention provides a daily load curve and a power generation plan diagram for each unit;

[0066] Figure 6 1 is a schematic diagram of the structure of a robust optimization device for a power system taking into account meteorological uncertainty provided by an embodiment of the present invention;

[0067] Figure 7 It is a schematic diagram of the composition structure of the power system robust optimization device considering meteorological uncertainty provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0069] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meaning as commonly understood by those skilled in the art to which the embodiments of the present invention pertain. The terms used in the embodiments of the present invention are for the purpose of describing the embodiments of the present invention only and are not intended to limit the present invention.

[0070] The following describes an exemplary application of a device for robust optimization of a power system that considers meteorological uncertainty according to an embodiment of the present invention. The device for robust optimization of a power system that considers meteorological uncertainty provided by an embodiment of the present invention can be implemented as either a terminal or a server. In one implementation, the device for robust optimization of a power system that considers meteorological uncertainty provided by an embodiment of the present invention can be implemented as various types of terminals, such as laptops, tablets, desktop computers, and mobile devices. In another implementation, the device for robust optimization of a power system that considers meteorological uncertainty provided by an embodiment of the present invention can also be implemented as a server. The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal and server can be connected directly or indirectly via wired or wireless communication, which is not limited in the embodiments of the present invention. The following describes an exemplary application of the device for robust optimization of a power system that considers meteorological uncertainty implemented as a server.

[0071] The embodiment of the present invention provides a method for robust optimization of power system considering meteorological uncertainty. Figure 1 , Figure 1 This is a flow chart of a robust optimization method for a power system considering meteorological uncertainty provided by an embodiment of the present invention, which is combined with Figure 1 The steps shown are explained.

[0072] Step S110 , obtaining historical wind and solar output data, historical load power consumption data, historical meteorological data, and day-ahead meteorological data of the power system.

[0073] In some embodiments, historical wind and solar output data refers to actual output power data of wind power generation and photovoltaic power generation over a period of time. This data can be used to analyze the patterns and characteristics of wind and solar power generation and provide a reference for power dispatch.

[0074] In some embodiments, historical load power consumption data refers to load demand data of the power system over a period of time in the past.

[0075] In some embodiments, historical meteorological data refers to meteorological information related to power system operation over a period of time in the past, such as wind speed, light intensity, temperature, etc. This data can be used to analyze the impact of meteorological conditions on power system operation.

[0076] In some embodiments, the day-ahead meteorological data refers to weather information predicted for the next day. In a power system, the day-ahead meteorological data can be used to formulate a day-ahead dispatch plan to cope with possible weather changes in the next day.

[0077] Step S120 : performing serialization processing on the system components of the power system based on the historical wind and solar power output data and the historical load power consumption data to obtain a serialization processing result.

[0078] In some embodiments, system elements refer to various equipment and devices used for power generation, transmission, transformation, distribution, and consumption in a power system.

[0079] In power systems, serialization processing can be used to convert historical wind and solar output data and load power consumption data into time series data for subsequent analysis and forecasting.

[0080] Step S130 : determining the credible capacity of the power system based on the serialization processing result and the equivalent reliability index of the power equivalent system.

[0081] In some embodiments, the equivalent reliability index is a quantitative indicator used to evaluate the reliability of the power equivalent system, which reflects the ability of the system to complete specified functions under specified conditions.

[0082] In some embodiments, the trusted capacity refers to the power capacity that can be reliably provided in the power system under given conditions.

[0083] Step S140 , based on the credible capacity and the historical meteorological data, point predictions are made for wind output, solar output and load power, respectively, to obtain wind output prediction results, solar output prediction results and load prediction results accordingly.

[0084] In some embodiments, a point forecast refers to a forecast of a single value of a variable (such as wind power, solar power output, or load power) at a certain moment in the future.

[0085] Step S150 , using a conditional Copula function, respectively fitting the wind and solar output forecast results and the load forecast results, and obtaining a corresponding output forecast power interval and a load forecast power interval.

[0086] In some embodiments, a conditional copula function is a tool for describing the joint distribution of multiple variables. In a power system, the conditional copula function can be used to fit the forecast results of wind power, solar power output, and load power to obtain their predicted power ranges.

[0087] Step S160: constructing a day-ahead scheduling model based on the constructed constraints, the output forecast power range, the load forecast power range, and the day-ahead meteorological data.

[0088] In some embodiments, constraints refer to various restrictions that need to be met when formulating a power dispatch plan, such as power balance constraints, unit output constraints, line transmission capacity constraints, etc.

[0089] In some embodiments, the output prediction power interval and the load prediction power interval refer to the possible value ranges of wind, solar output and load power in the future period, respectively.

[0090] Step S170: modify the day-ahead scheduling model to obtain an intraday scheduling optimization model.

[0091] In some embodiments, the day-ahead dispatch planning model refers to a power dispatch plan model developed based on day-ahead meteorological data and forecast results. The intraday dispatch optimization model amends and adjusts the day-ahead dispatch plan to address possible changes in actual conditions that may occur during the day.

[0092] Step S180 , solving the day-ahead scheduling planning model and the intraday scheduling optimization model to obtain a power optimization strategy for the power system.

[0093] The embodiments of the present invention provide a method, device and equipment for robust optimization of an electric power system taking into account meteorological uncertainty. The method first obtains historical wind and solar output data, historical load power consumption data, historical meteorological data and day-ahead meteorological data of the electric power system; based on the historical wind and solar output data and historical load power consumption data, the system components of the electric power system are serialized to obtain serialization processing results; based on the serialization processing results and the equivalent reliability index of the electric power equivalent system, the reliable capacity of the electric power system is determined; based on the reliable capacity and historical meteorological data, point predictions are made on the wind, solar output and load power respectively, and wind and solar output prediction results and load prediction results are obtained accordingly; a conditional Copula function is used to fit the wind and solar output prediction results and the load prediction results respectively, and output prediction power intervals and load prediction power intervals are obtained accordingly; a day-ahead scheduling planning model is constructed based on the constructed constraints, output prediction power intervals, load prediction power intervals and day-ahead meteorological data; the day-ahead scheduling planning model is corrected to obtain an intraday scheduling optimization model; the day-ahead scheduling planning model and the intraday scheduling optimization model are solved to obtain an electric power optimization strategy for the electric power system. In this way, on the one hand, the present invention combines the credible capacity assessment method with the conditional Copula function to perform point prediction and uncertainty modeling of wind power, photovoltaic power generation output and load power, captures the correlation between prediction errors, provides a more accurate power fluctuation range, and enhances the system's ability to cope with uncertainty; on the other hand, unlike the traditional single-stage robust optimization, the present invention proposes a two-stage robust optimization method that includes day-ahead scheduling planning and intraday scheduling strategy correction, so that the scheduling strategy can dynamically adapt to the update of meteorological information, reduce the conservatism of optimization, and improve the robustness of the system; thirdly, in response to the problem of integer variables in the robust optimization model, the present invention adopts the C&CG algorithm for solution, which improves the solution efficiency and accuracy, and solves the problems of slow convergence speed and low computational efficiency of traditional algorithms such as the Benders decomposition method when dealing with large-scale problems; fourthly, through the construction of power fluctuation range and the application of robust optimization strategy, it can effectively cope with the uncertainty of renewable energy output and load demand in extreme meteorological scenarios, and ensure the safe and stable operation of the power system.

[0094] In some embodiments, the system components of the power system include: new energy units, energy storage equipment, and load equipment; the above step S120 can be implemented by the following steps S121 to S125:

[0095] Step S121 , performing serialization modeling on the new energy generator set to obtain a serialization result of the new energy generator set.

[0096] Step S122: discretize based on the new energy unit sequence results and the historical data empirical probability distribution curve to obtain the available margin sequence of the new energy station.

[0097] Step S123 , performing serial modeling on the energy storage device to obtain a probability distribution of the equivalent power of the energy storage device.

[0098] Step S124: performing serial modeling on the load equipment to obtain a load consumption margin sequence.

[0099] Step S125 : determining the new energy unit sequence result, the available margin sequence, the probability distribution, and the load consumption margin sequence as the sequence processing result.

[0100] In some embodiments, the above step S130 can be implemented by the following steps S131 to S133:

[0101] Step S131 : performing random production simulation on the equivalent system to obtain the equivalent reliability index.

[0102] Step S132 : comparing the actual reliability index of the actual power system with the equivalent reliability index to determine a target reliability index.

[0103] Here, the differences between the target reliability index and the equivalent reliability index and the actual reliability index are within a preset error range.

[0104] Step S133: The target reliability index corresponds to the equivalent conventional unit capacity of the equivalent system, and is determined as the credible capacity.

[0105] In some embodiments, the conditional Copula function in step S150 is constructed in the following manner:

[0106] Step S151 : constructing meteorological condition characteristics and wind and solar output characteristics of adjacent time periods based on the historical meteorological data and the historical wind and solar output data.

[0107] Step S152: defining multidimensional condition variables based on the meteorological condition characteristics and the wind and solar output characteristics.

[0108] Step S153: Estimate the marginal empirical distribution functions of wind power output and photovoltaic power output.

[0109] Step S154: Based on the correlation of the Gaussian Copula function, determine the Gaussian Copula function as a Copula function.

[0110] Step S155: using the maximum likelihood estimation method to estimate the first correlation coefficient of the copula function.

[0111] Step S156: Estimate a second correlation coefficient of the conditional copula function based on the first correlation coefficient.

[0112] Step S157: construct the conditional Copula function based on the multidimensional conditional variable and the second correlation coefficient.

[0113] In some embodiments, the day-ahead scheduling model is expressed as:

[0114]

[0115] Where T is the total number of dispatch periods; t is a period; G is the set of generators in the power system; is the power generation cost coefficient of generator set i in the power system; is the day-ahead output plan of power system generator set i in period t, is the starting state of power system generator set i in period t, =1 indicates the unit is started. is the startup cost of generator set i in the power system; is the daily investment cost of the energy storage device, The operation and maintenance cost of the energy storage device.

[0116] In some embodiments, the intraday scheduling optimization model is expressed as:

[0117]

[0118] Where t is a time period; is a set of uncertain parameters, including the actual output of wind power, photovoltaic power and load; G is the set of generator sets in the power system; is the possible value space of the uncertain parameter, determined by the power interval; is the power generation cost coefficient of unit i; is the daily output plan of power system generator set i in time period t.

[0119] In some embodiments, the constraints of the day-ahead scheduling model include:

[0120] Power balance constraints:

[0121]

[0122] in, is the day-ahead output plan of power system generator set i in period t, 、 、 They are the wind turbine output, photovoltaic unit output and load power consumption of the power system respectively; , , ; Any period of time;

[0123] Unit output constraints:

[0124]

[0125] in, is the startup status of unit i in period t, =1 means power on; 、 are the minimum and maximum output of unit i respectively; is the daily output plan of power system generator set i in time period t; for any generator set and any time;

[0126] Unit climbing constraints:

[0127]

[0128] in, 、 is the up and down climbing rate of unit i; is the day-ahead output plan of power system generator set i in time period t; is the day-ahead output plan of power system generator set i in time period t-1;

[0129] Energy storage constraints:

[0130]

[0131] in, , are the daily investment cost of the energy storage device and the operation and maintenance cost of the energy storage device respectively; is the daily cost conversion coefficient; is the annual operation and maintenance cost of the energy storage device; is the rated capacity of the i-th energy storage; is the daily power of the i-th energy storage; r is the discount rate; y is the service life of the energy storage system; is the number of energy storage units; is the unit capacity cost of the i-th energy storage, is the unit power cost of the i-th energy storage.

[0132] In some embodiments, the constraints of the intraday scheduling optimization model include:

[0133] Power balance constraints:

[0134]

[0135] in, is the daily output plan of power system generator set i in time period t; 、 、 They are the wind turbine output, photovoltaic unit output and load power consumption of the power system respectively; , , ; Any period of time;

[0136] Unit output constraints:

[0137]

[0138] in, is the startup status of unit i in period t, =1 means power on; 、 are the minimum and maximum output of unit i respectively; is the daily output plan of power system generator set i in time period t; for any generator set and any time;

[0139] Unit climbing constraints:

[0140]

[0141] in, 、 is the up and down climbing rate of unit i; is the daily output plan of power system generator set i in time period t; is the daily output plan of power system generator set i in time period t-1.

[0142] In some embodiments, the above step S180 includes the following steps S181 to S182:

[0143] Step S181 , determining the parameters of the day-ahead scheduling planning model and the intraday scheduling optimization model, and solving the main problem of the column and constraint generation algorithm based on the parameters to obtain an initial solution.

[0144] Step S182: determining a sub-problem based on the initial solution, performing iterative solution processing on the sub-problem, and updating the iterative solution result to the main problem until the preset optimization condition is met, thereby obtaining the power optimization strategy.

[0145] The following describes an exemplary application of an embodiment of the present invention in a practical application scenario.

[0146] The present invention discloses a novel robust optimization strategy for power systems that considers the uncertainty of weather forecasts. This method aims to improve the robustness and reliability of power systems in the face of uncertain weather conditions by optimizing their dispatch strategies. First, a point forecast of wind and solar power output and load power is performed based on credible capacity assessment and historical data. Then, a conditional Copula function is used to fit the power range, providing a fluctuation range for wind and solar power output to effectively cope with extreme weather scenarios. Second, this method adopts a two-stage robust optimization. The first stage is a day-ahead dispatch plan based on the predicted wind and solar power output and load power. The second stage is an intraday dispatch strategy revised based on the day-ahead dispatch plan and weather information updates. Compared with traditional single-stage robust optimization, this method can reduce conservatism, improve system robustness, and better cope with extreme weather conditions. In addition, for the integer variables included in the robust optimization model, the present invention adopts the C&CG algorithm for solution. By continuously iteratively solving subproblems and feeding back the current worst-case scenario to the main problem, the main and subproblems eventually converge to the optimal strategy. Compared with traditional algorithms, the C&CG algorithm can reduce the number of iterations during the solution process while ensuring high accuracy of the solution results. The method of the present invention can effectively improve the robustness of the wind and solar power system in the face of uncertain meteorological conditions, thereby ensuring the stable operation of the power system.

[0147] An embodiment of the present invention provides another method for robust optimization of a power system taking into account meteorological uncertainty, the method comprising the following steps:

[0148] S1. Collect historical data (obtain historical meteorological data and historical power generation equipment data, the historical power generation equipment data including: historical power generation equipment output data and historical load power consumption data).

[0149] S11. Meteorological data: including wind speed v w , light intensity G, temperature T, time h and other meteorological factors affecting wind power and photovoltaic output; power generation equipment output data: historical wind power and photovoltaic output data; load power consumption data: historical load curve.

[0150] S2. Serialize and model the system components in the power system, and define the sequence operation as follows: generators and energy storage devices are resources; loads and energy storage devices are demands; the probability distribution of available power of resources is the available margin; the probability distribution of power consumption of demands is the consumption margin; and the system is in power balance, which means supply and demand balance.

[0151] S21. According to the generator set serialization modeling described in S2, the power generation of new energy units such as wind power and photovoltaic power generation is greatly affected by the weather, and their output may be between 0 and rated output. Define the discretization step size. , by step length Discretize it into a sequence S, where the i-th number of the sequence S is:

[0152]

[0153] in, The output power of the new energy unit is in the interval [(i-1) ,i ], F(x) is the probability density function of the output of the new energy power station; i is the i-th interval; (i-1) is the i-1th interval.

[0154] S22. After the new energy power generation units are serialized according to S2, the available margin sequence of the new energy stations can be obtained by discretizing them according to the empirical probability distribution curve of historical data.

[0155] S23. Based on the energy storage serialization modeling described in S2, considering the charge and discharge rate constraints, charge and discharge capacity constraints, and charge and discharge power constraints of the energy storage equipment, perform probability statistics on the historical energy storage output to obtain the probability distribution of the equivalent power of the energy storage equipment:

[0156]

[0157]

[0158] Where X represents the output power of the energy storage device, X>0 represents energy storage discharge, and X<0 represents energy storage charging; represents the probability distribution curve of energy storage equivalent generator, Represents the energy storage equivalent load probability distribution curve; when X>0 (discharge), represents the probability distribution of the energy storage system as a power generation device. When X<0 (charging), Represents the probability distribution of the energy storage system as a load.

[0159] S24. Based on the load serialization modeling described in S2, taking into account the changes in the load peak-to-valley difference, the upper and lower load limit constraints are obtained, and a sample analysis is performed on the 8760 hours of load historical data in the previous year to obtain the load consumption margin sequence.

[0160] S3. Perform random production simulations in various typical scenarios by calculating the supply and demand balance between sequences (referred to as the actual system). Calculate the expected electricity shortage value (EENS), a reliability indicator for the actual system. (EENS represents the amount of electricity required to achieve supply and demand balance in the system; the lower the value, the higher the system reliability.)

[0161] S31. Introduce equivalent systems based on the various typical scenarios described in S3.

[0162] S32. Based on the equivalent system described in S31, the photovoltaic units and wind turbine units are removed from the actual system and replaced with equivalent conventional units (conventional thermal power units). A random production simulation is performed to obtain the reliability index EENS' of the equivalent system.

[0163] S33. Based on the reliability index of the equivalent system described in S32, compare it with the reliability index of the actual system described in S3, and use the dichotomy method to adjust the equivalent conventional unit capacity of the equivalent system, so that the reliability index of the equivalent system is iteratively calculated until the difference between the reliability indexes of the two systems is within the error range.

[0164] S34. According to S33, the difference in the reliability index is within the error range, and the reliability of the two systems is the same. At this time, the total capacity of the equivalent conventional units of the equivalent system is the credible capacity of the actual system.

[0165] S35. Based on S31 and S32, introduce an equivalent system, remove the PV system or wind turbine system individually (but not both simultaneously), and replace it with an equivalent conventional system. Then, using the iterative method described in S33 and S34, the trusted capacity of the PV system or wind turbine system can be obtained. (Here, S34 and S35 distinguish between the trusted capacity of the entire actual system, i.e., the trusted capacity of the PV system or wind turbine system).

[0166] S4. Construct a conditional Copula function.

[0167] S41. Constructing meteorological condition characteristics , and wind power output characteristics in adjacent periods and photovoltaic output characteristics :

[0168]

[0169]

[0170]

[0171] Among them, v w , G, T and h are wind speed, light intensity, temperature and time respectively; is the credible capacity of the wind turbine at time t-1, is the credible capacity of the wind turbine at time t-2; is the credible capacity of the PV unit at time t-1; is the credible capacity of the PV system at time t-2.

[0172] S42. Define a multidimensional conditional variable Z based on the multiple features described in S41:

[0173] .

[0174] S43. Use empirical distribution function to estimate wind power output and photovoltaic output The marginal distribution of and .

[0175] Among them, the empirical distribution function is:

[0176]

[0177]

[0178] Where n is the number of samples, and is the indicator function, Any point in the wind power output range; is the k-th wind power output sample value; Any point in the photovoltaic output value range; is the kth photovoltaic output sample value; when The indicator function takes the value 1 when , otherwise it takes the value 0.

[0179] S44. Since the Gaussian Copula function can describe the correlation between continuous variables, the Gaussian Copula function is selected as the Copula function, and its expression is:

[0180]

[0181] in, is the two-dimensional standard normal distribution function, is the inverse function of the standard normal distribution, represents the cumulative distribution function value of the first random variable; represents the cumulative distribution function value of the second random variable; express and The correlation coefficient between two random variables.

[0182] S45. Use the MLE (Maximum Likelihood Estimation) method to estimate the correlation coefficient of the Copula function. , the parameter estimation formula is:

[0183]

[0184] in, is the Copula density function.

[0185] S46, estimating the correlation coefficient of the Copula function according to S45, and estimating the conditional Copula function Correlation coefficient:

[0186]

[0187] in, is the conditional Copula function under the multidimensional conditional variable Z.

[0188] S47: Construct a conditional Copula function based on the correlation coefficient of the multidimensional conditional variable Z described in S42 and the conditional Copula function described in S46. :

[0189] .

[0190] S5. Determine the power range.

[0191] S51. Determine the confidence interval: At the set confidence level In the case of the present invention Take 90% and calculate the forecast intervals for wind power and photovoltaic power:

[0192]

[0193] in, , , , ; is the inverse function of the conditional Copula function; is the significance level, which indicates the probability outside the prediction interval, that is, .

[0194] S52. Convert the upper and lower bounds of the probability space into actual wind power and photovoltaic output values, which are the predicted power ranges.

[0195] S6. Establish a two-stage robust optimization model.

[0196] S61. Phase 1: Day-ahead scheduling planning objective function:

[0197]

[0198] Where T is the total number of dispatch periods; t is a period; G is the set of generators in the power system; is the power generation cost coefficient of generator set i in the power system; is the day-ahead output plan of power system generator set i in period t, is the starting state of power system generator set i in period t, =1 indicates the unit is started. is the startup cost of generator set i in the power system; is the daily investment cost of the energy storage device, The operation and maintenance cost of the energy storage device.

[0199] Power balance constraints:

[0200]

[0201] in, is the day-ahead output plan of power system generator set i in period t, 、 、 They are the wind turbine output, photovoltaic unit output and load power consumption of the power system respectively; , , ; Any period of time;

[0202] Unit output constraints:

[0203]

[0204] in, is the startup status of unit i in period t, =1 means power on; 、 are the minimum and maximum output of unit i respectively; is the daily output plan of power system generator set i in time period t; for any generator set and any time;

[0205] Unit climbing constraints:

[0206]

[0207] in, 、 is the up and down climbing rate of unit i; is the day-ahead output plan of power system generator set i in time period t; is the day-ahead output plan of power system generator set i in time period t-1;

[0208] Energy storage constraints:

[0209]

[0210] in, , are the daily investment cost of the energy storage device and the operation and maintenance cost of the energy storage device respectively; is the daily cost conversion coefficient; is the annual operation and maintenance cost of the energy storage device; is the rated capacity of the i-th energy storage; is the daily power of the i-th energy storage; r is the discount rate; y is the service life of the energy storage system; is the number of energy storage units; is the unit capacity cost of the i-th energy storage, is the unit power cost of the i-th energy storage.

[0211] S62. Second stage: Intraday scheduling strategy modified objective function:

[0212]

[0213] Where t is a time period; is a set of uncertain parameters, including the actual output of wind power, photovoltaic power and load; G is the set of generator sets in the power system; is the possible value space of the uncertain parameter, determined by the power interval; is the power generation cost coefficient of unit i; is the daily output plan of power system generator set i in time period t.

[0214] Power balance constraints:

[0215]

[0216] in, is the daily output plan of power system generator set i in time period t; 、 、 They are the wind turbine output, photovoltaic unit output and load power consumption of the power system respectively; , , ; Any period of time;

[0217] Unit output constraints:

[0218]

[0219] in, is the startup status of unit i in period t, =1 means power on; 、 are the minimum and maximum output of unit i respectively; is the daily output plan of power system generator set i in time period t; for any generator set and any time;

[0220] Unit climbing constraints:

[0221]

[0222] in, 、 is the up and down climbing rate of unit i; is the daily output plan of power system generator set i in time period t; is the daily output plan of power system generator set i in time period t-1.

[0223] S7. Based on the two-stage robust optimization model established in S6, the C&CG (Column and Constraint Generation) algorithm is used to solve the problem, including the following steps:

[0224] S71. Solve the initial problem: Assume that the uncertain parameters take their average values, solve the main problem, and obtain the initial solution.

[0225] S72, iterative process:

[0226] 1) Subproblem solving: When the solution to the main problem is fixed, solve the subproblem and find the worst perturbation scenario , so that the objective function is minimized.

[0227] 2) Check the optimality condition: If the objective function value of the subproblem is less than a certain tolerance , it is considered that the optimal solution has been found and the iteration stops.

[0228] 3) Otherwise, the worst disturbance scenario Add to the main problem and update the constraints.

[0229] S73. Main problem update: Under the new constraints, the main problem is solved again to obtain an updated scheduling strategy.

[0230] S74, loop iteration: repeat steps S72 and S73 until the optimality condition is met.

[0231] Figure 2 This is the process for constructing a conditional copula function. First, meteorological conditions, wind power output characteristics, and photovoltaic power output characteristics are input as basic data. Multidimensional conditional variables are then defined, and empirical distribution functions are used to estimate the marginal distributions of wind power and photovoltaic output. Once the marginal distributions are obtained, a Gaussian copula function is used to describe the correlation between these continuous variables. The correlation coefficient is calculated, and finally, the conditional copula function is derived based on these steps.

[0232] Figure 3The figure shows the interval prediction and point prediction curves for typical abnormal weather conditions in photovoltaic power stations. The orange curve represents the photovoltaic output under normal spring weather conditions, while the blue curve represents the point prediction photovoltaic output under abnormal weather conditions. The blue fluctuation range represents the power prediction interval, which is obtained by fitting the point prediction output through a conditional Copula function.

[0233] Figure 4 The two-stage dispatching flow chart illustrates this process: The entire process begins with inputting the day-ahead dispatching strategy. The system then monitors short-term meteorological data and the operational status of the power source, grid, load, and storage in real time. Based on this information, status updates are performed, including updated wind and solar turbine and load power forecasts, the available capacity of thermal power units, and the charge state of energy storage devices. After the status update is complete, dispatching tasks are assigned to each type of unit, and the charging and discharging strategy for the energy storage devices is determined. The system then checks to see if power balance has been achieved. If not, it returns to re-update and adjust the system status. If balance is achieved, it outputs the intraday dispatching strategy and waits for the next round of dispatch instructions, completing a full dispatch cycle.

[0234] Figure 5 It is the daily load curve and the power generation plan of each unit; the four color blocks correspond to the output values ​​of thermal power, energy storage, wind power and photovoltaic power from bottom to top; the top curve is the load power value; based on power balance, when the power generation is insufficient to supply the load demand power, the shortfall is purchased from the grid.

[0235] Figure 6 Schematic diagram of the structure of a robust optimization device for a power system considering meteorological uncertainty provided by an embodiment of the present invention. Figure 6As shown, the robust optimization device 600 for a power system considering meteorological uncertainty includes: an acquisition module 601 for obtaining historical wind and solar output data, historical load power consumption data, historical meteorological data and day-ahead meteorological data of the power system; a serialization module 602 for serializing the system components of the power system based on the historical wind and solar output data and the historical load power consumption data to obtain a serialization processing result; a determination module 603 for determining the credible capacity of the power system based on the serialization processing result and the equivalent reliability index of the power equivalent system; a point prediction module 604 for performing point predictions on the wind, solar output and load power respectively based on the credible capacity and the historical meteorological data, and obtaining the corresponding to the wind and solar output forecast results and the load forecast results; a fitting module 605 is used to adopt a conditional Copula function to fit the wind and solar output forecast results and the load forecast results respectively, and obtain the output forecast power interval and the load forecast power interval accordingly; a construction module 606 is used to construct a day-ahead scheduling planning model based on the constraints of multiple units, the output forecast power interval, the load forecast power interval and the day-ahead meteorological data; a correction module 607 is used to correct the day-ahead scheduling planning model to obtain an intraday scheduling optimization model; a solution module 608 is used to solve the day-ahead scheduling planning model and the intraday scheduling optimization model to obtain the power optimization strategy of the power system.

[0236] It should be noted that the description of the device embodiment of the present invention is similar to the description of the above-mentioned method embodiment, and has similar beneficial effects as the same method embodiment, so it will not be repeated. For technical details not disclosed in the device embodiment, please refer to the description of the method embodiment of the present invention for understanding.

[0237] It should be noted that in the embodiments of the present invention, if the above-mentioned robust optimization method for the power system taking into account meteorological uncertainty is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a terminal to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk. In this way, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0238] Correspondingly, an embodiment of the present invention provides a robust optimization device for a power system taking into account meteorological uncertainty. Figure 7Schematic diagram of the composition structure of the power system robust optimization device considering meteorological uncertainty provided by an embodiment of the present invention, such as Figure 7 As shown, the robust optimization device for a power system considering meteorological uncertainty 700 includes at least: a processor 701 and a computer-readable storage medium 702 configured to store executable instructions. Processor 701 generally controls the overall operation of the robust optimization device for a power system considering meteorological uncertainty. Computer-readable storage medium 702 is configured to store instructions and applications executable by processor 701 and to cache data to be processed or processed by processor 701 and various modules in the robust optimization device for a power system considering meteorological uncertainty 700. This can be achieved using flash memory (FLASH) or random access memory (RAM).

[0239] An embodiment of the present invention provides a storage medium storing executable instructions, wherein the executable instructions are stored. When the executable instructions are executed by a processor, the processor will execute the method provided by the embodiment of the present invention, for example, Figure 1 The method shown.

[0240] In some embodiments, the storage medium can be a computer-readable storage medium, such as a ferroelectric random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); it can also be various devices including one or any combination of the above memories.

[0241] In some embodiments, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0242] By way of example, executable instructions may, but need not necessarily, correspond to a file in a file system, may be stored as part of a file storing other programs or data, such as one or more scripts in a Hypertext Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (e.g., files storing one or more modules, subroutines, or code portions). By way of example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located in one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0243] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of the present invention are included in the scope of protection of the present invention.

[0244] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0245] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, an element defined by the statement "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed.

[0246] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A robust optimization method for power systems considering meteorological uncertainty, characterized in that: The method comprises: Obtain the historical wind and solar output data, historical load power consumption data, historical meteorological data and day-ahead meteorological data of the power system; Based on the historical wind and solar output data and the historical load power consumption data, serializing the system components of the power system to obtain a serialization processing result; Determining the credible capacity of the power system based on the serialization processing result and the equivalent reliability index of the power equivalent system; Based on the credible capacity and the historical meteorological data, point predictions are made for wind, solar output and load power respectively, and wind, solar output prediction results and load prediction results are obtained accordingly; Using a conditional Copula function, the wind and solar output forecast results and the load forecast results are respectively fitted to obtain an output forecast power interval and a load forecast power interval; Based on the constructed constraint conditions, the output forecast power range, the load forecast power range and the day-ahead meteorological data, a day-ahead scheduling planning model is constructed; The day-ahead scheduling planning model is modified to obtain an intraday scheduling optimization model; The day-ahead scheduling planning model and the intraday scheduling optimization model are solved to obtain the power optimization strategy of the power system.

2. The method according to claim 1, characterized in that The system components of the power system include: new energy units, energy storage equipment and load equipment; The serialization processing is performed on the system components of the power system based on the historical wind and solar output data and the historical load power consumption data to obtain the serialization processing results, including: Performing serialization modeling on the new energy unit to obtain a serialization result of the new energy unit; Discretization is performed based on the sequencing results of the new energy units and the empirical probability distribution curve of historical data to obtain the available margin sequence of the new energy station; Performing serial modeling on the energy storage device to obtain a probability distribution of equivalent power of the energy storage device; Performing serial modeling on the load equipment to obtain a load consumption margin sequence; The new energy unit serialization result, the available margin sequence, the probability distribution and the load consumption margin sequence are determined as the serialization processing result.

3. The method according to claim 1, characterized in that The determining the credible capacity of the power system based on the serialization processing result and the equivalent reliability index of the power equivalent system includes: Performing random production simulation on the equivalent system to obtain the equivalent reliability index; Comparing the actual reliability index of the actual power system with the equivalent reliability index to determine a target reliability index; the difference between the target reliability index and the equivalent reliability index and the actual reliability index is within a preset error range; The target reliability index corresponds to the equivalent conventional unit capacity of the equivalent system, which is determined as the reliable capacity.

4. The method according to claim 1, characterized in that: The conditional Copula function is constructed in the following way: Based on the historical meteorological data and the historical wind and solar output data, construct meteorological condition characteristics and wind and solar output characteristics of adjacent time periods; Based on the meteorological condition characteristics and the wind and solar output characteristics, define a multidimensional condition variable; Estimate the marginal empirical distribution functions of wind power output and photovoltaic output; Based on the correlation of the Gaussian Copula function, determining the Gaussian Copula function as a Copula function; Using the maximum likelihood estimation method, estimating the first correlation coefficient of the Copula function; Based on the first correlation coefficient, estimating a second correlation coefficient of the conditional Copula function; The conditional Copula function is constructed based on the multidimensional conditional variable and the second correlation coefficient.

5. The method according to claim 1, characterized in that The day-ahead scheduling model is expressed as: ； Where T is the total number of dispatch periods; t is a period; G is the set of power system generators; is the power generation cost coefficient of power generation unit i in the power system; is the day-ahead output plan of power system generator set i in time period t, is the starting state of power system generator set i in period t, =1 indicates the unit is started. is the startup cost of power generation unit i in the power system; is the daily investment cost of the energy storage device, It is the operation and maintenance cost of the energy storage device.

6. The method according to claim 1, characterized in that The intraday scheduling optimization model is expressed as: ； Among them, t is a period of time; is a set of uncertain parameters, including the actual output of wind power, photovoltaic power and load; G is the set of power system generators; is the possible value space of the uncertain parameter, determined by the power range; is the power generation cost coefficient of unit i; is the daily output plan of power system generator set i in time period t.

7. The method according to claim 1, characterized in that The constraints of the day-ahead scheduling model include: Power balance constraints: ； in, is the day-ahead output plan of power system generator set i in time period t, , , They are the wind turbine output, photovoltaic unit output and load power consumption of the power system respectively; , , ; Any period of time; Unit output constraints: ； in, is the startup status of unit i in period t, =1 means power on; , are the minimum and maximum output of unit i respectively; is the daily output plan of power system generator set i in time period t; for any generator set and any time; Unit climbing constraints: ； in, , is the up and down climbing rate of unit i; is the day-ahead output plan of power system generator set i in time period t; is the day-ahead output plan of power system generator set i in time period t-1; Energy storage constraints: ； in, , They are the daily investment cost of the energy storage device and the operation and maintenance cost of the energy storage device; is the daily cost conversion factor; is the annual operation and maintenance cost of the energy storage device; is the rated capacity of the i-th energy storage; is the daily power of the i-th energy storage; r is the discount rate; y is the service life of the energy storage system; is the number of energy storage; is the unit capacity cost of the i-th energy storage, is the unit power cost of the i-th energy storage.

8. The method according to claim 1, characterized in that The constraints of the intraday scheduling optimization model include: Power balance constraints: ； in, is the daily output plan of power system generator set i in time period t; , , They are the wind turbine output, photovoltaic unit output and load power consumption of the power system respectively; , ; Any period of time; Unit output constraints: ； in, is the startup status of unit i in period t, =1 means power on; , are the minimum and maximum output of unit i respectively; is the daily output plan of power system generator set i in time period t; for any generator set and any time; Unit climbing constraints: ； in, , is the up and down climbing rate of unit i; is the daily output plan of power system generator set i in time period t; It is the daily output plan of power system generator set i in time period t-1.

9. The method according to claim 1, characterized in that: The step of solving the day-ahead scheduling planning model and the intraday scheduling optimization model to obtain the power optimization strategy of the power system includes: Determine the parameters of the day-ahead scheduling planning model and the intraday scheduling optimization model, and solve the main problem of the column and constraint generation algorithm based on the parameters to obtain an initial solution; Based on the initial solution, a sub-problem is determined, the sub-problem is iteratively solved, and the iterative solution result is updated to the main problem until a preset optimization condition is met, thereby obtaining the power optimization strategy.

10. A robust optimization device for a power system considering meteorological uncertainty, characterized in that: The device comprises: An acquisition module is used to obtain historical wind and solar output data, historical load power consumption data, historical meteorological data and day-ahead meteorological data of the power system; A serialization module, used for performing serialization processing on system components of the power system based on the historical wind and solar output data and the historical load power consumption data to obtain a serialization processing result; A determination module, configured to determine the credible capacity of the power system based on the serialization processing result and an equivalent reliability index of the power equivalent system; A point prediction module, used to perform point predictions on wind, solar output and load power respectively based on the trusted capacity and the historical meteorological data, and obtain wind, solar output prediction results and load prediction results accordingly; A fitting module, used to use a conditional Copula function to fit the wind and solar output forecast results and the load forecast results respectively, and obtain an output forecast power interval and a load forecast power interval accordingly; A construction module, used to construct a day-ahead scheduling planning model based on the constraints of multiple units, the output forecast power interval, the load forecast power interval and the day-ahead meteorological data; A correction module, used to correct the day-ahead scheduling model to obtain an intraday scheduling optimization model; A solution module is used to solve the day-ahead scheduling planning model and the intraday scheduling optimization model to obtain the power optimization strategy of the power system.

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