A method and device for robust optimization of power system considering meteorological uncertainty

Abstract

The embodiment of the present invention provides a method and device for robust optimization of an electric power system taking into account meteorological uncertainty, including: obtaining historical wind and solar output data, historical load power consumption data, historical meteorological data and day-ahead meteorological data of the electric power system; serializing system components of the electric power system to obtain serialization processing results; determining the reliable capacity of the electric power system based on the serialization processing results and equivalent reliability indicators of the electric power equivalent system; performing point predictions on wind, solar output and load power to obtain corresponding wind and solar output prediction results and load prediction results; fitting the wind and solar output prediction results and load prediction results to obtain corresponding output prediction power ranges and load prediction power ranges; constructing a day-ahead scheduling planning model; correcting the day-ahead scheduling planning model to obtain an intraday scheduling optimization model; solving the day-ahead scheduling planning model and the intraday scheduling optimization model to obtain an electric power optimization strategy for the electric power system.

Description

Technical Field

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

[0002] As the penetration rate of renewable energy in the power system continues to increase, especially the volatility and uncertainty of wind and solar energy, it has brought great challenges to the dispatch and operation of the power system. At the same time, the planning and operation of the power system needs to ensure that the integrity of the system can be maintained under any circumstances. This requires not only paying attention to predictable high-probability failures, but also being vigilant about those accidents with low probability but huge impact. In order to effectively respond to these challenges, advanced optimization strategies need to be adopted to ensure the reliability and economy of the system.

[0003] Traditional power system dispatch strategies mostly rely on deterministic load and renewable energy output forecasts, however, these forecasts often have deviations, especially under extreme meteorological conditions. Therefore, existing dispatch strategies may not be able to effectively cope with the uncertainty in actual operation, resulting in inefficient system operation, or even insufficient or excessive power supply. In order to improve the robustness of the power system under uncertainty conditions, researchers have proposed a variety of optimization strategies. For example, some studies use 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. Although these methods can take uncertainty into account, they are often computationally intensive and difficult to handle large-scale problems.

[0004] In addition, in order to reduce the conservatism of the optimization problem and improve the robustness of the system, some studies have proposed a two-stage robust optimization method. In the first stage, the day-ahead scheduling plan is formulated based on the current forecast information; in the second stage, the intraday scheduling strategy is adjusted according to the latest forecast update. This method can better cope with uncertainty, but how to efficiently solve the two-stage robust optimization problem is still a challenge. In terms of solution algorithms, the traditional Benders decomposition method is widely used in two-stage robust optimization problems, but it may encounter problems of 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 solve the uncertainty existing in existing meteorological forecasts. It is difficult to correctly evaluate the error of the output of renewable energy. 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 the 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 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;

[0012] 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;

[0013] 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;

[0014] The day-ahead scheduling planning model is modified to obtain an intraday scheduling optimization model;

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

[0016] In a second aspect, an embodiment of the present invention provides a robust optimization device for 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, 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;

[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, 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;

[0021] 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;

[0022] 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;

[0023] A correction module, used 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 also 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; the serialization results of the new energy units, the available margin sequence, the probability distribution and the load consumption margin sequence are determined as the serialization processing results.

[0026] In some embodiments, the determination module is also 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 light output data, meteorological condition characteristics and wind and light output characteristics of adjacent time periods are constructed; based on the meteorological condition characteristics and the wind and light output characteristics, multidimensional conditional variables are defined; the marginal empirical distribution functions of wind power output and photovoltaic output are estimated; based on the correlation of the Gaussian Copula function, the Gaussian Copula function is determined as a Copula function; the first correlation coefficient of the Copula function is estimated by the maximum likelihood estimation method; based on the first correlation coefficient, the second correlation coefficient of the conditional Copula function is estimated; based on the multidimensional conditional variables 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 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.

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

[0032]

[0033] 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.

[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 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;

[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, , 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.

[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; It 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 to solve the main problem of the constraint generation algorithm based on the parameter solution column to obtain an initial solution; to determine the sub-problem based on the initial solution, to iteratively solve the sub-problem, and to 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 taking into account 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 taking into account meteorological uncertainties.

[0060] The robust optimization method and device of the power system considering meteorological uncertainty provided by the embodiment of the present invention 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 historical load power consumption data, the system components of the power system are serialized to obtain the serialization processing results; based on the serialization processing results and the equivalent reliability index of the power equivalent system, the reliable capacity of the power system is determined; based on the reliable capacity and historical meteorological data, the wind, solar output and load power are point predicted respectively, and the wind, solar output prediction results and load prediction results are obtained correspondingly; the conditional Copula function is used to fit the wind, solar output prediction results and load prediction results respectively, and the output prediction power interval and the load prediction power interval are obtained correspondingly; based on the constructed constraints, the output prediction power interval, the load prediction power interval and the day-ahead meteorological data, a day-ahead scheduling planning model is constructed; the day-ahead scheduling planning model is corrected to obtain the 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. 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 on 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 including 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, 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 It is a flowchart 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 It is a schematic diagram of a process for constructing a conditional copula function provided by an embodiment of the present invention;

[0063] Figure 3 It is an interval prediction and point prediction curve diagram of typical abnormal weather in a photovoltaic power station provided by an embodiment of the present invention;

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

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

[0066] Figure 6 It is a schematic diagram of the composition 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 a robust optimization device for a power system taking into account meteorological uncertainty provided by an embodiment of the present invention. DETAILED DESCRIPTION

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

[0069] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but 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 those 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 only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0070] The following describes an exemplary application of a robust optimization device for a power system considering meteorological uncertainty according to an embodiment of the present invention. The robust optimization device for a power system considering meteorological uncertainty provided by an embodiment of the present invention can be implemented as a terminal or a server. In one implementation, the robust optimization device for a power system considering meteorological uncertainty provided by an embodiment of the present invention can be implemented as various types of terminals such as laptops, tablet computers, desktop computers, and mobile devices; in another implementation, the robust optimization device for a power system considering meteorological uncertainty provided by an embodiment of the present invention can also be implemented as a server, wherein the server can be an independent physical server, or a server cluster or distributed system composed 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 distribution networks (CDN, Content Delivery Network), and big data and artificial intelligence platforms. The terminal and the server can be directly or indirectly connected by wired or wireless communication, which is not limited in the embodiment of the present invention. The following describes an exemplary application of a robust optimization device for a power system considering meteorological uncertainty when it is implemented as a server.

[0071] The embodiment of the present invention provides a method for robust optimization of a power system taking into account meteorological uncertainty. Figure 1 , Figure 1 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 in the past period of time. These data can be used to analyze the laws and characteristics of wind and solar power generation and provide reference for power dispatching.

[0074] In some embodiments, the historical load power consumption data refers to the 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 the operation of the power system in the past period of time, such as wind speed, light intensity, temperature, etc. These data can be used to analyze the impact of meteorological conditions on the operation of the power system.

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

[0077] Step S120: Based on the historical wind and solar output data and the historical load power consumption data, serialization processing is performed on the system components of the power system 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 use in the power system.

[0079] In the power system, 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 prediction.

[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 index used to evaluate the reliability of the power equivalent system, which reflects the ability of the system to complete the specified function 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, solar output and load power respectively, and corresponding wind, solar output prediction results and load prediction results are obtained.

[0084] In some embodiments, point prediction refers to a prediction 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 fit the wind and solar output forecast results and the load forecast results to obtain output forecast power intervals and load forecast power intervals.

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

[0087] Step S160, constructing a day-ahead scheduling planning model based on the constructed constraint conditions, the output forecast power interval, the load forecast power interval 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.

[0090] Step S170, modifying the day-ahead scheduling model to obtain an intra-day scheduling optimization model.

[0091] In some embodiments, the day-ahead dispatch planning model refers to a power dispatch planning 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 cope with possible changes in actual conditions during the day.

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

[0093] The robust optimization method, device and equipment of the power system considering meteorological uncertainty provided by the embodiment of the present invention first 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 historical load power consumption data, the system components of the power system are serialized to obtain the serialization processing results; based on the serialization processing results and the equivalent reliability index of the power equivalent system, the reliable capacity of the power system is determined; based on the reliable capacity and historical meteorological data, the wind, solar output and load power are point predicted respectively, and the wind, solar output prediction results and load prediction results are obtained correspondingly; the conditional Copula function is used to fit the wind, solar output prediction results and load prediction results respectively, and the output prediction power interval and the load prediction power interval are obtained correspondingly; based on the constructed constraints, the output prediction power interval, the load prediction power interval and the day-ahead meteorological data, a day-ahead scheduling planning model is constructed; the day-ahead scheduling planning model is corrected to obtain the 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. 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 on 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 including 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, 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 unit to obtain a serialization result of the new energy unit.

[0096] Step S122, discretization is performed based on the sequence results of the new energy units 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 serialization result, the available margin sequence, the probability distribution and the load consumption margin sequence as the serialization 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: compare 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 respectively within a preset error range.

[0104] Step S133: determining the equivalent conventional unit capacity of the equivalent system corresponding to the target reliability index as the reliable capacity.

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

[0106] Step S151, 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.

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

[0108] Step S153, estimating 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: estimating a second correlation coefficient of the conditional Copula function based on the first correlation coefficient.

[0112] Step S157, constructing 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 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.

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

[0117]

[0118] 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.

[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 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;

[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, , 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.

[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; It 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 sequence 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 a 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 considering the uncertainty of meteorological forecasts. The method aims to improve the robustness and reliability of power systems in the face of uncertain meteorological conditions by optimizing the dispatching strategy of the power system. First, the wind and solar output and load power are predicted based on credible capacity assessment and historical data, and then the power interval is obtained by fitting the conditional Copula function, providing a fluctuation interval for the wind and solar output to effectively deal with extreme meteorological scenarios. Secondly, the method adopts two-stage robust optimization, the first stage is the day-ahead dispatch planning proposed based on the predicted wind and solar output value and load power, and the second stage is the intraday dispatching strategy revised based on the day-ahead dispatch planning and meteorological information update. Compared with the traditional single-stage robust optimization, it can reduce conservatism, improve the robustness of the system, and better deal with extreme meteorological conditions. In addition, for the integer variables contained in the robust optimization model, the present invention adopts the C&CG algorithm for solution, and continuously iterates to solve the sub-problems, and feeds back the current worst scenario to the main problem. Finally, the main and sub-problems converge consistently to obtain the optimal strategy. Compared with the traditional algorithm, the C&CG algorithm can reduce the number of iterations in the solution process, while ensuring the high precision of the solution results. Through the method of the present invention, the robustness of the wind-solar power system in the face of uncertain meteorological conditions can be effectively improved, 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 elements 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 demand is the consumption margin; the system is in power balance, that is, 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 generating sets 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, the probability statistics of the energy storage historical output are performed 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 (discharging), 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, the load peak-to-valley difference is taken into consideration to obtain the load upper and lower limit constraints, and a sample analysis is performed on the load historical data of 8760 hours in the previous year to obtain the load consumption margin sequence.

[0160] S3. In various typical scenarios, through the supply and demand balance calculation between sequences (called the actual system), random production simulation is performed to calculate the expected power shortage value EENS of the actual system reliability indicator (EENS represents the amount of electricity required for the system power to achieve supply and demand balance, that is, 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. According to 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), and 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 binary 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 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. According to S31 and S32, an equivalent system is introduced, and the credible capacity of the PV unit or wind turbine unit can be obtained by removing the PV unit or wind turbine unit alone (not removing PV and wind power at the same time) and replacing the equivalent conventional unit, and then the iterative method described in S33 and S34. (Here, S34 and S35 distinguish the credible capacity of the entire actual system, that is, the credible capacity of the PV unit or wind turbine unit).

[0166] S4. Construct a conditional Copula function.

[0167] S41. Constructing meteorological condition characteristics , and the 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 unit at time t-2.

[0172] S42. According to the multiple features described in S41, define a multidimensional conditional variable Z:

[0173] .

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

[0175] 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 kth 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, estimating the conditional Copula function Correlation coefficient:

[0186]

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

[0188] S47: construct a conditional Copula function according to 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 interval 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 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.

[0199] Power balance constraints:

[0200]

[0201] 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;

[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, , 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.

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

[0212]

[0213] 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.

[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; It 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. Solving 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) Sub-problem solving: When the solution to the main problem is fixed, solve the sub-problem 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, updating 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 construction process of the conditional Copula function. First, the meteorological conditions, wind power output characteristics, and photovoltaic output characteristics are input as basic data. Then, multidimensional conditional variables are defined, and the empirical distribution function is used to estimate the marginal distribution of wind power output and photovoltaic output. After obtaining the marginal distribution, the Gaussian Copula function is used to describe the correlation between these continuous variables, and the correlation coefficient is obtained by calculation. Finally, the conditional Copula function is obtained based on the above steps.

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

[0233] Figure 4 It is a two-stage dispatching flow chart, specifically: the whole process starts with the input of the day-ahead dispatching strategy, and then the system will monitor the short-term meteorological data and the operation of the source, grid, load and storage in real time. Based on this information, the status is updated, including updating the wind and solar units and load power forecast values, the available capacity of the thermal power units, and the charge status of the energy storage equipment. After the status update is completed, the dispatching tasks are assigned to each type of unit, and the charging and discharging strategy of the energy storage equipment is determined. The system will then check whether the power balance state is reached. If it is not balanced, it will return to re-update and adjust the system status; if it is balanced, it will output the intraday dispatching strategy, and finally wait for the next round of dispatching instructions to complete a complete dispatching 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 will be purchased from the power grid.

[0235] Figure 6 is a schematic diagram of the composition structure of a robust optimization device for a power system considering meteorological uncertainty provided by an embodiment of the present invention, such as Figure 6As shown, the robust optimization device 600 for the power system considering meteorological uncertainty includes: an acquisition module 601, which is used to 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; a serialization module 602, which is used to serialize 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; a determination module 603, which is used to determine the reliable capacity of the power system based on the serialization processing results and the equivalent reliability index of the power equivalent system; a point prediction module 604, which is used to perform point predictions on the wind, solar output and load power respectively based on the reliable capacity and the historical meteorological data, and the corresponding to the wind, solar output forecast results and the load forecast results; a fitting module 605, used to adopt a conditional Copula function to fit the wind, 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, 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, used to correct the day-ahead scheduling planning model to obtain an intraday scheduling optimization model; a solution module 608, 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 of the embodiment of the present invention is similar to the description of the above 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 embodiment of the device, please refer to the description of the method embodiment of the present invention for understanding.

[0237] It should be noted that, in the embodiment of the present invention, if the above-mentioned robust optimization method of the power system considering meteorological uncertainty is implemented in the form of a software function 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 can be essentially or partly reflected in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a terminal to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), disk or optical disk and other media that can store program code. In this way, the embodiment of the present invention is 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 7is a schematic diagram of the composition structure of a 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 at least includes: a processor 701 and a computer-readable storage medium 702 configured to store executable instructions, wherein the processor 701 generally controls the overall operation of the robust optimization device for a power system considering meteorological uncertainty. The computer-readable storage medium 702 is configured to store instructions and applications executable by the processor 701, and can also cache data to be processed or processed by each module in the processor 701 and the robust optimization device for a power system considering meteorological uncertainty 700, which can be implemented by flash memory (FLASH) or random access memory (RAM, Random Access Memory).

[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 be caused to 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, for example, 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 disk, or a compact disk 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] As an example, executable instructions may, but need not 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 Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions). As an example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0243] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement and improvement made within the spirit and scope of the present invention are included in the protection scope of the present invention.

[0244] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that 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 number 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 only for description 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 including a series of elements includes not only those elements, but also includes 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 one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In 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. There may be other division methods in actual implementation, 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 is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope 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.

Images