A power system real-time scheduling method, system, device and medium

Abstract

The application relates to the technical field of power system real-time scheduling, and discloses a power system real-time scheduling method, system, device and medium, the method comprising: predicting wind and light load uncertainty of a power system under different time scales; constructing a flexible resource model suitable for different response times; constructing a net load set based on the uncertainty; determining power system flexibility constraints; establishing and solving a scheduling model containing the constraints to realize real-time scheduling. The application improves the accuracy and reliability of real-time scheduling by considering uncertain factors and flexible resource responses in the power system. It constructs a flexible resource model and a net load uncertainty set suitable for different time scales to adapt to system dynamic changes and support stable operation. This method can also reduce operating costs and improve resource utilization efficiency, and has an important influence on the sustainable development of the power system.

Description

Technical Field

[0001] This application relates to the field of real-time dispatching technology for power systems, and in particular to a real-time dispatching method, system, equipment, and medium for power systems. Background Technology

[0002] The flexibility resources of a power system are widely distributed across the power generation, grid, load, and energy storage sides. Quantifying power system flexibility has always been a challenge in power system planning and operation simulation. Existing literature has explored the quantification of power system flexibility and the construction of constraints. For example, constraining the flexibility margin of the power system at different time periods based on the flexibility supply-demand balance mechanism improves the flexibility performance of traditional optimized operation and promotes the consumption of new energy. Probabilistic statistical methods are introduced to model the flexibility demand under a certain confidence level, and the load shedding size and energy curtailment size are added to the model constraints, achieving partial relaxation of flexibility constraints. Modeling flexibility supply-demand models under different confidence levels, a flexibility improvement evaluation index is proposed, and the flexibility of the power system after the participation of multiple flexibility resources is evaluated.

[0003] Robust optimization methods use uncertainty sets to describe the volatility of uncertain parameters and seek optimal decisions that satisfy all constraints within different uncertainty sets, thus reducing computational costs while maintaining a degree of conservatism. For example, a weakly robust planning method for power source expansion comprehensively considers wind power uncertainties and the flexibility retrofitting of thermal power units; based on Copula theory and combining scenario-based and interval-based methods to quantify flexibility requirements, a robust optimization scheduling model that can effectively control the flexibility and economy of power grid dispatching is proposed.

[0004] While current research on power system flexibility optimization has constrained the system's flexibility adjustment capability under different confidence levels, it still has the following shortcomings: Current research often constrains the system's flexibility on a single time scale, ignoring the response time of different flexibility resources to changes in net load, resulting in overly idealized constraints. Summary of the Invention

[0005] In view of the aforementioned existing problems, this application is hereby filed.

[0006] Therefore, this application provides a real-time dispatching method, system, equipment, and medium for power systems, which can solve the problems of slow response speed and low dispatching efficiency in traditional power system dispatching.

[0007] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0008] Firstly, this application provides a real-time power system dispatching method, including:

[0009] Based on historical data of the target power system, predict the uncertain set of wind and solar load at different time scales in the target scenario;

[0010] Based on the response time of different flexibility resources in the target power system, construct the first flexibility resource model under different time scales;

[0011] Based on the aforementioned uncertain set of wind and solar loads, construct the uncertain set of net loads for the target scenario;

[0012] Based on the first flexibility resource model and the net load uncertainty set, determine the power system flexibility constraints;

[0013] A scheduling model is established, and the flexibility constraints of the power system are used as the constraints of the scheduling model. The scheduling model is then solved to achieve real-time scheduling of the power system.

[0014] The scheduling model includes a day-ahead scheduling model, an intraday scheduling model, and a real-time update model.

[0015] The power system flexibility constraints in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include power system flexibility constraints.

[0016] As a preferred embodiment of the real-time dispatching method for power systems described in this application, the step of predicting the uncertain set of wind and solar loads at different time scales under the target scenario based on historical data of the target power system includes:

[0017] Based on the historical data of the target power system, the prediction error for each time period is obtained;

[0018] The target power system historical data includes historical time-series wind and solar load output data;

[0019] Based on the prediction errors for each time period, the expected value of the prediction error for wind, solar and load data is calculated, which serves as the uncertain set of wind, solar and load at different time scales in the target scenario.

[0020] As a preferred embodiment of the real-time power system dispatching method described in this application, the construction of the first flexibility resource model under different time scales includes:

[0021] The first flexibility resource model includes a first peak-shaving flexibility model and a second ramp-up flexibility model;

[0022] The different time scales include day-ahead time scale, intraday time scale, and real-time time scale.

[0023] This preferred solution can more comprehensively consider the flexibility requirements of the power system at different time scales, thereby improving the dispatch efficiency and stability of the power system.

[0024] Specifically, the first peak-shaving flexibility model can schedule power system load fluctuations over a larger time scale, ensuring supply and demand balance on day-ahead and intraday time scales; while the second ramp-up flexibility model can respond quickly to load changes on a smaller time scale, ensuring stable operation of the power system on a real-time time scale. This approach, which comprehensively considers different time scales and flexibility requirements, helps optimize power system dispatch strategies and improve the reliability and economy of the power system.

[0025] As a preferred embodiment of the real-time power system dispatching method described in this application, the step of constructing the net load uncertainty set under the target scenario based on the wind and solar load uncertainty set includes:

[0026] Based on the expected prediction error of the wind and solar load data in the aforementioned uncertain set of wind and solar load, a net load uncertainty set is established;

[0027] The uncertain set of wind, solar and load includes uncertain sets of wind power output, photovoltaic power output and load output.

[0028] As a preferred embodiment of the real-time power system dispatching method described in this application, the day-ahead dispatching model is used to formulate unit start-up and shutdown plans. Units not included in the start-up and shutdown plan are not started during the day, and only the unit output is adjusted. Based on this, the economically optimal unit combination scheme under the worst-case scenario of wind, solar and load forecast is obtained.

[0029] The constraints of the day-ahead dispatch model include system operation-related constraints, constraints related to various types of generating units, and power system flexibility constraints.

[0030] The system operation-related constraints include node power balance constraints, spinning reserve constraints, and line power flow equation constraints.

[0031] The constraints related to each type of unit include upper and lower limits of unit output, unit ramp-up constraints, and minimum start-up and shutdown time constraints.

[0032] As a preferred embodiment of the real-time power system dispatching method described in this application, the constraints of the intraday dispatching model include the same constraints related to various types of generating units as the day-ahead dispatching model, as well as system operation-related constraints, different power system flexibility constraints, compressed air energy storage-related constraints, and demand-side response-related constraints that are different from the day-ahead dispatching model.

[0033] The system operation-related constraints that differ from the day-ahead scheduling model include different spinning reserve constraints and different unit ramp-up constraints.

[0034] As a preferred embodiment of the real-time power system dispatching method described in this application, the constraints of the real-time update model include constraints related to various types of generating units that are the same as those of the day-ahead dispatching model, as well as system operation-related constraints, demand-side response-related constraints, and battery energy storage-related constraints that are different from those of the day-ahead dispatching model.

[0035] Secondly, this application provides a real-time dispatching system for a power system, comprising:

[0036] The interval prediction module is used to predict the uncertain set of wind and solar loads at different time scales under the target scenario based on historical data of the target power system.

[0037] The multi-timescale resource flexibility modeling module is used to construct the first flexibility resource model at different time scales based on the response time of different flexibility resources of the target power system.

[0038] The net load uncertainty set construction module is used to construct the net load uncertainty set under the target scenario based on the wind and solar load uncertainty set;

[0039] A flexibility constraint construction module is used to determine power system flexibility constraints based on the first flexibility resource model and the net load uncertainty set.

[0040] A multi-timescale operation simulation module is used to establish a scheduling model, take the power system flexibility constraints as the constraints of the scheduling model, and solve the scheduling model to realize real-time scheduling of the power system.

[0041] The scheduling model includes a day-ahead scheduling model, an intraday scheduling model, and a real-time update model.

[0042] The power system flexibility constraints in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include power system flexibility constraints.

[0043] Thirdly, this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0044] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0045] Compared with existing technologies, the beneficial effects of this application are as follows: This application proposes a real-time power system dispatching method. Based on historical data of the target power system, it predicts the uncertain sets of wind and solar loads at different time scales under the target scenario; based on the response times of different flexibility resources of the target power system, it constructs a first flexibility resource model at different time scales; based on the uncertain sets of wind and solar loads, it constructs a net load uncertainty set under the target scenario; based on the first flexibility resource model and the net load uncertainty set, it determines the power system flexibility constraints; it establishes a dispatching model, using the power system flexibility constraints as constraints of the dispatching model, and solves the dispatching model to achieve real-time power system dispatching. This application can comprehensively consider the uncertainties of wind and solar loads and the response characteristics of flexibility resources in the power system, improving the accuracy and reliability of real-time power system dispatching. Furthermore, by constructing flexibility resource models and net load uncertainty sets at different time scales, this application can better adapt to the dynamic changes of the power system, providing strong support for the stable operation of the power system. At the same time, the application of this method can also reduce the operating costs of the power system and improve the utilization efficiency of power resources, which is of great significance for promoting the sustainable development of the power system.

[0046] Specifically, when considering wind and solar load data in this application, deterministic prediction is not directly adopted. Instead, the error of deterministic prediction by the neural network is used to obtain interval prediction results through kernel density estimation algorithm and integral operation. At the same time, based on the definition of power system flexibility, the interval prediction results of wind and solar load are combined to generate an uncertain set of net load, and the power system flexibility at each time scale is considered according to its most severe net load demand.

[0047] This application incorporates a time scale distinction into the flexibility constraints, which on the one hand refines the distinction between different types of generating units, making calculations easier; on the other hand, based on the actual dispatching of the power system, the distinction of resources at different time scales is more practically significant.

[0048] This application, when considering unit flexibility constraints, embeds them into the existing day-ahead-intraday-real-time dispatch model using a robust constraint method, operating within both the day-ahead and intraday optimization modules. This approach saves computational resources while increasing the number of factors considered in the original system, preventing intraday flexibility deficits in the power system and helping dispatchers make judgments that balance grid flexibility and planning economics, thus possessing practical significance. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of a real-time power system dispatching method provided in one embodiment of this application.

[0051] Figure 2 This is a system architecture diagram of a real-time power system dispatching method provided in one embodiment of this application.

[0052] Figure 3 This is a schematic diagram of an interval prediction module for a real-time power system dispatching method provided in one embodiment of this application.

[0053] Figure 4 This is a schematic diagram illustrating the flexible resource time scale division of a real-time scheduling method for a power system provided in one embodiment of this application.

[0054] Figure 5 This is a schematic diagram of a multi-timescale scheduling model for a real-time scheduling method of a power system provided in one embodiment of this application.

[0055] Figure 6 This is a schematic diagram illustrating a calculation example of a real-time dispatching method for a power system provided in one embodiment of this application.

[0056] Figure 7 This is a schematic diagram of the net load uncertainty set of a real-time dispatching method for a power system provided in one embodiment of this application.

[0057] Figure 8 This is a schematic diagram illustrating the impact of hourly flexibility constraints on unit start-up and shutdown plans in a real-time power system dispatching method provided in one embodiment of this application.

[0058] Figure 9 This is a schematic diagram illustrating the intraday downscaling margin of a conventional scheme for a real-time power system dispatching method provided in one embodiment of this application.

[0059] Figure 10 This is a schematic diagram illustrating the conventional scheme of a real-time power system dispatching method for an embodiment of this application, showing the intraday increase in flexibility margin.

[0060] Figure 11 This is a schematic diagram illustrating the intraday operation results of a conventional scheme for a real-time power system dispatching method provided in one embodiment of this application.

[0061] Figure 12 This is a schematic diagram illustrating the intraday reduction in flexibility margin after adding minute-level flexibility constraints in a real-time power system dispatching method provided in one embodiment of this application.

[0062] Figure 13This is a schematic diagram illustrating the intraday upward adjustment of flexibility margin after adding minute-level flexibility constraints in a real-time power system dispatching method provided in one embodiment of this application.

[0063] Figure 14 This is a schematic diagram of the intraday operation results of a real-time power system dispatching method provided in one embodiment of this application after adding flexibility constraints.

[0064] Figure 15 This is an internal structure diagram of an electronic device for a real-time power system dispatching method provided in one embodiment of this application. Detailed Implementation

[0065] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort should fall within the scope of protection of this application.

[0066] Example 1, referring to Figure 1 , Figure 15 This is the first embodiment of the present application, which provides a real-time power system dispatching method, including:

[0067] Existing technologies have some problems. For example, traditional power system dispatching often only considers flexibility constraints under a single time scale, failing to fully reflect the differences in response time of different flexibility resources to changes in net load. This results in dispatching strategies being too idealistic and unable to effectively cope with uncertainties in actual operation.

[0068] This application provides a method that can effectively solve the problems mentioned above. The following will describe in detail how to implement the real-time dispatching method for the power system with reference to several embodiments.

[0069] Figure 1 A flowchart of a real-time power system dispatching method is shown, including:

[0070] S101, based on historical data of the target power system, predict the uncertain set of wind and solar loads at different time scales in the target scenario;

[0071] It should be noted that data acquisition is essential for power system dispatching. Historical data is used for prediction and analysis to improve the accuracy and efficiency of dispatching. This application designs a method to achieve real-time power system dispatching by predicting the uncertain sets of wind and solar loads at different time scales under the target scenario.

[0072] In this embodiment of the application, based on historical data of the target power system, the set of uncertain wind and solar loads at different time scales in the target scenario is predicted to include:

[0073] Based on historical data of the target power system, the prediction error for each time period is obtained;

[0074] Historical data for the target power system includes historical time-series wind and solar power output data;

[0075] Based on the prediction errors for each time period, the expected value of the prediction error of the wind-solar-load data is calculated, which serves as the uncertain set of wind-solar-load at different time scales in the target scenario.

[0076] In an optional embodiment, the prediction error for each time period can be obtained by setting a prediction model based on historical data of the target power system. This model is trained and learned on historical time-series wind and solar load output data using machine learning or deep learning algorithms, thereby enabling the prediction of wind and solar load output at different time scales in the target scenario.

[0077] In one optional embodiment, the model considers various influencing factors during training, such as weather changes, seasonal changes, and load fluctuations, to improve the accuracy and reliability of predictions. After obtaining the prediction results, the system compares the actual wind and solar load data with the predicted values ​​to calculate the prediction error for each time period.

[0078] It should be noted that these prediction errors will serve as the basis for subsequent calculations of the expected value of wind and solar load prediction errors, further constituting the uncertain set of wind and solar load at different time scales under the target scenario, providing strong support for real-time dispatch of the power system.

[0079] Specifically, the process begins by forecasting from historical data, followed by kernel density estimation of the forecast error to obtain an interval forecast for wind and solar load. The historical sample data consists of time-based wind and solar load data sequences; for example, a sequence of wind power, solar power, or load output in a given season may contain n samples x1, x2, ... x. n .

[0080] An LSTM neural network is used to predict the input wind and solar load data separately, and the predictions are compared with the actual values ​​to obtain the prediction errors for each time period. The mathematical model for neural network prediction is as follows:

[0081]

[0082] In the formula, x t Let h be the input to the LSTM network at time t, and h be the input to the LSTM network at time t. t The output function σ(·) of the LSTM network at time t is the sigmoid function; C t W represents the information value at time t.f W i W c and W o It is the coefficient matrix; h t For the output at time t, b f b i and b o These are the bias matrices.

[0083] Furthermore, the error probability density curve of wind and solar load output is obtained, and the interval prediction value of wind and solar load is derived from it. Kernel density estimation is a nonparametric estimation method. It estimates the probability density of the sample through a kernel function, obtaining the probability density function of the sample set, whose expression is:

[0084]

[0085] In the formula, N is the sample size, h is the bandwidth, Φ() is the kernel function, and x i Let be a sample point in the sample set.

[0086] Furthermore, based on the probability density curve of the error, the expected value of the prediction error can be obtained by integration.

[0087] It should be noted that predicting the uncertain sets of wind and solar loads at different time scales under the target scenario, based on historical data of the target power system, can improve the accuracy and flexibility of dispatching. In-depth analysis of historical data allows us to grasp the patterns of wind and solar load output changes, thus enabling more accurate prediction of the uncertain sets of wind and solar loads at different future time scales. This helps the dispatching system react more quickly to actual operation, optimize dispatching strategies, reduce the adverse effects of wind and solar load output fluctuations, and ensure the stable operation of the power system. Simultaneously, dispatching decisions based on the prediction results can better adapt to various complex scenarios, improving the overall operating efficiency and economic benefits of the power system.

[0088] S102, Based on the response time of different flexibility resources of the target power system, construct the first flexibility resource model under different time scales;

[0089] In an optional embodiment, the first flexibility resource model is used to model and classify various flexibility resources in the power system, so as to quickly call and configure the corresponding flexibility resources according to the needs of different time scales during real-time dispatching. These resources may include energy storage devices, adjustable loads, and fast-starting generator sets. By establishing the first flexibility resource model, the response speed, adjustment range, cost-effectiveness, and other characteristics of various resources can be accurately described, providing a scientific basis for subsequent dispatching decisions.

[0090] In an optional embodiment, the first flexibility resource model can be established through statistical analysis of historical data and machine learning algorithms. Specifically, for example, a classification or regression model can be trained using the response time and output of flexibility resources in historical data, which can predict the availability and output level of flexibility resources at different time scales based on the current state of the power system.

[0091] In an optional embodiment, the first flexibility resource model can also be built using an expert system approach. This approach, based on the knowledge and experience of domain experts, encodes the response characteristics and scheduling rules of flexibility resources into a rule base, and uses an inference engine to construct and apply the model.

[0092] In an optional embodiment, the first flexibility resource model may specifically include the type of flexibility resource, response time, output range, scheduling priority, and other aspects. These aspects can comprehensively reflect the characteristics and requirements of flexibility resources in real-time power system scheduling, providing strong support for scheduling decisions.

[0093] In this embodiment of the application, constructing a first flexibility resource model at different time scales includes:

[0094] The first flexibility resource model includes the first peak-shaving flexibility model and the second ramp-up flexibility model;

[0095] Different time scales include day-ahead time scale, intraday time scale, and real-time time scale.

[0096] Specifically, different flexibility resources can be divided into hourly, 15-minute, and 5-minute levels based on their response speed. Hourly flexibility resources include the start-up and shutdown of various types of units; minute-level flexibility resources can be divided into the output of coal-fired power units, gas-fired power units, pumped storage units, etc.; and second-level flexibility resources mainly consist of the output of various types of energy storage resources.

[0097] It should be noted that constructing first-flexibility resource models at different time scales, based on the response times of different flexibility resources in the target power system, can more accurately match the real-time demand of the power system with the supply of flexibility resources, thereby improving the efficiency and accuracy of dispatching. The constructed first-flexibility resource models fully consider the response characteristics of various resources, such as fast-responding gas turbines and slower-responding hydropower stations. This helps to rationally arrange the start-up and shutdown sequence and output of various resources during dispatching to cope with sudden load changes or fluctuations in renewable energy in the power system. Furthermore, by constructing models at different time scales, over-adjustment and resource waste during dispatching can be effectively avoided, improving the economy and stability of the entire power system.

[0098] S103, Based on the uncertain set of wind-solar-load, construct the uncertain set of net load under the target scenario;

[0099] In this embodiment of the application, constructing the net load uncertainty set under the target scenario based on the wind-solar load uncertainty set includes:

[0100] Based on the expected value of the prediction error of wind and solar load data in the uncertain set of wind and solar load, a net load uncertainty set is established;

[0101] The uncertain set of wind, solar and load includes the uncertain sets of wind power output, solar power output and load output.

[0102] Specifically, by analyzing the interval prediction results, the box-shaped uncertainty set of wind-solar load is obtained:

[0103]

[0104] In the formula, P t wind P t solar and P t Pd These represent the predicted wind power output, predicted solar power output, and predicted load size, respectively. and These represent the lower bounds of the uncertainty errors for wind power output, photovoltaic power output, and load size, respectively. and These represent the upper bounds of the uncertainty errors for wind power output, photovoltaic power output, and load size, respectively; U wind U solar and U Pd These represent uncertainties related to wind power output, solar power output, and load output, respectively. To embed flexibility constraints into the planning model, a net load uncertainty set considering wind and solar load demand is established:

[0105]

[0106] It should be noted that constructing the net load uncertainty set under the target scenario, based on the wind and solar load uncertainty set, can more accurately simulate the uncertainties of the power system in actual operation, thus providing a more reliable basis for dispatch decisions. By constructing the net load uncertainty set, the system can better cope with fluctuations in wind power, solar power output, and load size, ensuring the stable operation of the power system. Furthermore, this step also helps optimize dispatch strategies, improve the economy and efficiency of the power system, reduce operating costs, and provide strong support for the sustainable development of the power system.

[0107] S104. Determine the power system flexibility constraints based on the first flexibility resource model and the net load uncertainty set;

[0108] It should be noted that power system flexibility constraints play a crucial role in real-time dispatch systems. By accurately quantifying the flexibility requirements of the power system under different scenarios, the system can ensure the flexibility and adaptability of dispatch strategies.

[0109] Specifically, the introduction of power system flexibility constraints enables the dispatch system to maintain the power system's supply and demand balance even when faced with uncertainties in wind power, solar power output, and load size, preventing power outages or system instability caused by insufficient flexibility. This characteristic not only enhances the robustness of the power system but also provides a solid guarantee for its safe and reliable operation.

[0110] In this embodiment of the application, the flexibility constraints of the power system under multiple time scales can be expressed as:

[0111]

[0112] In the formula, and The upscaling and downscaling flexibility that available flexible resources can provide within the system at a time scale of τ; Given the uncertainty of wind and solar load forecasting with uncertainty α, this represents the fluctuation value of net load. The meaning of this formula is to search for the scenario with the largest flexibility deficit within the uncertainty concentration so that the flexibility supply meets the demand, thereby fulfilling the flexibility constraints in the operational simulation.

[0113] In an optional embodiment, the flexibility modeling for each type of resource (i.e., flexibility constraints of the power system across multiple time scales) is as follows:

[0114] Coal-fired power is the "ballast" for ensuring power security. The flexibility that traditional coal-fired power units can provide in terms of both upward and downward regulation is:

[0115]

[0116] In the formula, and These are the upper and lower rate limits for regulating the power output of coal-fired power plants, respectively; P g,max P g,min and P g,t These represent the maximum adjustable output (generally the rated capacity), minimum technical output, and output power at time t, respectively; τ is the time scale under study.

[0117] In one optional embodiment, a pumped-storage power station can generate electricity during peak load periods and store water during off-peak periods, possessing rapid start-up and shutdown capabilities and flexible switching between pumping and power generation modes, making it one of the important flexibility resources on the power supply side. Its uplink and downlink regulation flexibility capabilities are as follows:

[0118]

[0119] In the formula, P represents the power generation and pumping power of the pumped-storage unit during time period t, respectively. g,rated This refers to the rated power of the pumped storage power station; W g,max W g,min These represent the upper and lower limits of the reservoir's water storage capacity; W g,t η represents the amount of water stored in the upper reservoir at time t; g η h These are the conversion coefficients between pumping power and power generation and water flow rate, respectively.

[0120] In one alternative embodiment, the flexibility offered by the novel energy storage is related to the charge / discharge strategy and state of charge (air storage), and its up- and down-adjustment flexibility is as follows:

[0121]

[0122] In the formula, These represent the charging and discharging power of the new energy storage system at time t; These are the rated charging and discharging power of the new energy storage system; S max S min These represent the upper and lower limits of the new energy storage capacity; S t η represents the amount of electricity currently stored in the new energy storage device. c η d These refer to the charging and discharging efficiency of the new energy storage system.

[0123] It should be noted that determining power system flexibility constraints based on the first flexibility resource model and the net load uncertainty set can more accurately assess the flexibility requirements of the power system under different operating scenarios, thereby optimizing dispatch strategies. By considering the first flexibility resource model, the configuration and dispatch of various flexibility resources can be dynamically adjusted to cope with the uncertainty of net load. This helps reduce the power supply and demand imbalance caused by insufficient flexibility, and improves the stability and reliability of the power system. At the same time, the introduction of flexibility constraints can also promote the consumption of clean energy, reduce carbon emissions, and achieve the sustainable development of the power system.

[0124] S105. Establish a scheduling model, use the power system flexibility constraints as constraints of the scheduling model, and solve the scheduling model to realize real-time scheduling of the power system.

[0125] In this application embodiment, the scheduling model includes a day-ahead scheduling model, an intraday scheduling model, and a real-time update model;

[0126] The power system flexibility constraints in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include power system flexibility constraints.

[0127] In this embodiment of the application, the day-ahead scheduling model is used to formulate unit start-up and shutdown plans. Units outside the start-up and shutdown plan are not started during the day, and only the unit output is adjusted. Based on this, the economically optimal unit combination scheme under the worst-case scenario of wind, solar and load forecast is obtained.

[0128] The constraints of the current dispatch model include system operation-related constraints, constraints related to various types of generating units, and power system flexibility constraints.

[0129] System operation-related constraints include node power balance constraints, spinning reserve constraints, and line power flow equation constraints;

[0130] The constraints related to each type of unit include upper and lower limits of unit output, unit ramp-up constraints, and minimum start-up and shutdown time constraints.

[0131] In the embodiments of this application, the constraints of the intraday dispatch model include the same constraints related to various types of generating units as the day-ahead dispatch model, as well as system operation-related constraints, different power system flexibility constraints, compressed air energy storage-related constraints, and demand-side response-related constraints that are different from the day-ahead dispatch model.

[0132] The system operation-related constraints that differ from the daytime scheduling model include different spinning reserve constraints and different unit ramping constraints.

[0133] In the embodiments of this application, the constraints of the real-time update model include the same constraints related to various types of generating units as the day-ahead scheduling model, as well as system operation-related constraints, demand-side response-related constraints, and battery energy storage-related constraints that are different from the day-ahead scheduling model.

[0134] Specifically, in the day-ahead scheduling model, the wind and solar load forecast data is a short-term forecast for the next day, with a time scale of 1 hour. Since this is a short-term forecast, the uncertainty is significant. If the unit start-up and shutdown schedule is not properly arranged, forcibly starting the standby units would require a large amount of manpower and resources.

[0135] Therefore, the current optimized operation decision objective is: to formulate unit start-up and shutdown plans, avoid starting units outside the planned start-up and shutdown during the day, adjust unit output only, and ensure economic viability under the worst-case scenario of wind and solar load forecasts. The entire decision-making process can be described as a min-max optimization problem, with the objective function being:

[0136]

[0137] In the formula, C TP,t C PS,t C PF,t and C AS,tThese represent the costs of thermal power units and hydropower units, the penalty cost for insufficient flexibility, and the compensation cost for ancillary services; T is the scheduling time length, which is taken as 24 in the day-ahead optimization module; f(P) i,t Let be the operating cost of thermal power unit i at time t, where t∈[1,T]. For coal-fired power units, the cost is usually a quadratic function, while for gas-fired power units, it is usually a cubic function. These are the start-up and shutdown costs for thermal power unit i, respectively; Ω represents the start-up and shutdown states of thermal power unit i at time t; 火电 A collection of thermal power units; Ω 水电 A collection of hydroelectric power units; and These are the start-up and shutdown costs and unit power cost of hydropower unit a, respectively; These represent the start-up and stop states of hydropower unit a, respectively. These represent the pumping power and generating power of hydropower unit a at time t, respectively; c ne The cost of punitive measures for abandoning renewable energy sources; c represents the amount of renewable energy curtailed at time t in the system; ls To incur cost penalties for load shedding; This represents the load shedding at time t of the system. In this module, ancillary services mainly include the start-up, shutdown, and peak-shaving of thermal power units. It is the capacity compensation for peak shaving during start-up and shutdown, S a This refers to the full capacity of the peak-shaving generating units. The model decision variables are the start-up and shutdown status of thermal power units and hydropower units. In the objective function, the max(·) layer is used to find the worst-case scenario, and the min(·) layer finds the most economically optimal unit combination scheme based on this.

[0138] Its constraints include: system operation-related constraints such as node power balance constraints, spinning reserve constraints, and line power flow equations; unit-related constraints such as unit output upper and lower limits constraints, unit ramping constraints, and unit minimum start-up and shutdown time constraints; and hourly flexibility constraints of the power system generated in the previous module.

[0139] Specifically, in the intraday optimization model, the wind and solar load forecast data are ultra-short-term forecast data for the next 4 hours, with a time scale of 15 minutes. The decision objective of intraday optimization operation is: based on the unit start-up and shutdown plan obtained from the day-ahead optimization operation, the unit output plan can meet the flexibility constraints under the condition of maximum forecast error, and ensure the economic efficiency of the scheme.

[0140] Since the unit start-up and shutdown plan has already been provided in the previous optimization phase, the intraday optimization module does not consider start-up and shutdown related costs and constraints. The objective function of this module is:

[0141]

[0142] In the formula, C c,t C s,t C DR,a,t and C CAES,t These are the operating costs of thermal power, hydropower, intraday demand response compensation costs for incentivized heat loads, and operating costs of compressed air energy storage. c caes and c DR,a These represent the unit power cost of compressed air energy storage and intraday stimulated heat load demand response, respectively. These represent the charging and discharging power of compressed air energy storage, respectively; P t DR,a This represents the magnitude of the intraday stimulus-type heat load demand response at time t. The decision variables for this layer of the model are the output of coal-fired power units and the magnitude of the intraday stimulus-type heat load demand response. In this model, since the time scale is 15 minutes, T = 16, and t ∈ [1, T].

[0143] The constraints for thermal power units, hydropower units, and new energy units are consistent with those of the day-ahead optimization operation model. System operation-related constraints are similar, but due to different time scales, the system spinning reserve constraints and unit ramp-up constraints change. Specifically, the constraints for units providing spinning reserve include coal-fired units and intraday excitation-type heat load demand response. In addition, other constraints include: compressed air storage-related constraints, demand-side response-related constraints, and flexibility constraints at the 15-minute time scale generated in previous modules.

[0144] Specifically, the real-time optimization model primarily tracks flexible resources for ultra-short timescales not covered by the intraday optimization module, while also revising the output plan provided by the intraday optimization module. This module uses wind and solar load forecast data for the next 15 minutes and performs rolling optimization on a 5-minute timescale.

[0145] The objective of the real-time optimization model is to allocate power system generation resources on an ultra-short timescale to meet the supply-demand balance of the system at that timescale. The real-time optimization module primarily focuses on power balance and therefore does not consider power system flexibility constraints at this timescale. Furthermore, since the intraday optimization module already provides the intraday output plan for coal-fired power units and the intraday demand-side response plan for stimulus-type heat loads, their related costs and constraints are not considered in this model. Its objective function is:

[0146]

[0147] In the formula, C GAS,t and C DR,b,t These are the operating costs of the gas turbine unit and the intraday demand response compensation costs for incentivized electrical loads; c ess The unit power cost for energy storage in lithium-ion batteries; These represent the charging and discharging power of lithium-ion battery energy storage, respectively; c DR,b P t DR,b These represent the unit power cost of the intraday stimulus-type electrical load demand response and its magnitude at time t, respectively. In this module, the decision variables are the output of the gas turbine unit, the output of the hydropower unit, and the magnitude of the intraday stimulus-type electrical load demand response. Since the time scale is 5 minutes, T = 3, t ∈ [1, T].

[0148] The constraints related to gas turbines, hydropower units, compressed air energy storage, and new energy units are consistent with the day-ahead optimization scheduling model. System operation-related constraints are similar, but due to different time scales, the system spinning reserve constraints and unit ramp-up constraints change. Specifically, the constraints for units providing spinning reserve include gas turbines, hydropower units, and intraday demand response for stimuli-driven electrical loads. In addition, other constraints include: demand-side response-related constraints and battery energy storage-related constraints.

[0149] In an optional embodiment, by solving the scheduling model, real-time power system scheduling can be achieved, and a real-time power system scheduling scheme can be obtained.

[0150] In summary, this application proposes a real-time power system dispatching method. Based on historical data of the target power system, it predicts the uncertain sets of wind and solar loads at different time scales under the target scenario. Based on the response times of different flexibility resources in the target power system, it constructs a first flexibility resource model at different time scales. Based on the uncertain sets of wind and solar loads, it constructs a net load uncertainty set under the target scenario. Based on the first flexibility resource model and the net load uncertainty set, it determines the power system flexibility constraints. A dispatching model is established, using the power system flexibility constraints as constraints, and the dispatching model is solved to achieve real-time power system dispatching. This application comprehensively considers the uncertainties of wind and solar loads and the response characteristics of flexibility resources in the power system, improving the accuracy and reliability of real-time power system dispatching. Furthermore, by constructing flexibility resource models and net load uncertainty sets at different time scales, this application can better adapt to the dynamic changes of the power system, providing strong support for the stable operation of the power system. Simultaneously, the application of this method can reduce the operating costs of the power system and improve the utilization efficiency of power resources, which is of great significance for promoting the sustainable development of the power system.

[0151] Example 2, in a preferred embodiment, specifically, uses the HPR-38 node system to perform computational verification on the model proposed in this application, the topology of which is shown below. Figure 6 As shown in the figure. In the model, this application uses actual wind and solar load data of a certain region with a 10-day timescale of 5 minutes as input, which is then entered into the net load uncertainty set construction module for solution. Figure 7A schematic diagram of the net load uncertainty set is given.

[0152] Specifically, this application compares the traditional scheme, namely the day-to-day-intraday-real-time optimization scheduling model that does not consider flexibility constraints in the model, with the robust optimization model that considers multi-timescale flexibility constraints proposed in this application. Figure 8 The day-ahead optimization results of the two schemes and their differences are presented. It can be seen that the optimization model proposed in this paper increases the start-up and shutdown plans of some units to ensure intraday flexibility margin.

[0153] Based on this, Figures 9-14 The intraday and real-time optimization results of the traditional scheme and the intraday and real-time optimization results of the model proposed in this application are presented respectively. It can be seen that the day-ahead-intraday-real-time dispatching method with multi-timescale flexibility robust constraints for power systems proposed in this application, compared with the traditional unit combination optimization model, adds a net load uncertainty set and flexibility constraints. This improvement enables the model to more accurately schedule unit start-up and shutdown in the day-ahead phase and takes into account the dynamic characteristics of intraday net load fluctuations. The optimization results based on this model, by adding the start-up and shutdown scheduling of three coal-fired power units, and also including the extended operating time of gas-fired units and pumped-storage units, ensure that the power generation plan formulated in the day-ahead phase can effectively cope with intraday load changes. Meanwhile, in intraday operation simulation, the traditional method shows a flexibility gap of about 4 hours, accompanied by forced load shedding and energy curtailment. In contrast, the day-ahead-intraday-real-time dispatching method with multi-timescale flexibility robust constraints for power systems proposed in this application, through conservative unit start-up and shutdown plans and intraday flexibility constraints, can effectively allocate flexibility resources on the intraday time scale, ensuring that the system does not have a flexibility resource shortage.

[0154] Example 3: This example also provides a real-time power system dispatching system, such as... Figure 2 As shown, it includes:

[0155] The interval prediction module is used to predict the uncertain set of wind and solar loads at different time scales under the target scenario based on historical data of the target power system.

[0156] The multi-timescale resource flexibility modeling module is used to construct the first flexibility resource model at different time scales based on the response time of different flexibility resources of the target power system.

[0157] The Net Load Uncertainty Set Construction Module is used to construct the net load uncertainty set under the target scenario based on the wind and solar load uncertainty set.

[0158] The flexibility constraint construction module is used to determine the power system flexibility constraints based on the first flexibility resource model and the net load uncertainty set.

[0159] The multi-timescale operation simulation module is used to establish a scheduling model, using the power system flexibility constraints as constraints on the scheduling model, and solving the scheduling model to achieve real-time scheduling of the power system.

[0160] The scheduling models include day-ahead scheduling models, intraday scheduling models, and real-time update models;

[0161] The power system flexibility constraints in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include power system flexibility constraints.

[0162] Optionally, the interval prediction module includes an input module, a neural network prediction module, and a kernel density estimation interval prediction module;

[0163] The input module is used to acquire historical time-series wind and solar load data and input them into the system as historical data values.

[0164] The neural network prediction module is used to predict the historical data of the input module and obtain the prediction error for each time period.

[0165] The kernel density estimation interval prediction module calculates the target bandwidth of kernel density estimation by using the prediction error value of each time period and the method of minimizing integral mean square error, and obtains the expected value of the prediction error of future wind and solar load data, thus obtaining the interval prediction of future wind and solar load.

[0166] Optionally, the multi-timescale resource flexibility modeling module includes a flexible resource timescale partitioning module and a flexibility modeling module;

[0167] The time scale segmentation module is used to divide flexible resources into time scales such as hourly (day-ahead), 15-minute (intraday), and 5-minute (real-time) according to the adjustment capabilities of different flexible resources;

[0168] The flexibility modeling module is used to model the flexibility supply capacity of different flexibility resources, which are divided into peak shaving flexibility and ramp-up flexibility.

[0169] Optionally, the net load uncertainty set construction module includes a box uncertainty set construction module;

[0170] The box-type uncertainty set construction module is used to obtain the box-type uncertainty set of the net load by linearly combining the prediction results of wind and solar load intervals.

[0171] Optionally, the flexibility constraint building block is used for:

[0172] Based on the unit's flexibility adjustment capability at different time scales and the most severe scenario of future net load uncertainty concentration, the system's flexibility adjustment capability is constrained, resulting in multi-time scale flexibility robustness constraints for the power system.

[0173] Optionally, the multi-timescale operation simulation module includes a day-ahead operation simulation module, an intraday operation simulation module, and a real-time operation simulation module:

[0174] The day-ahead simulation module is used to optimize the day-ahead scheduling of the start-up and shutdown plans of various types of generating units in the power system based on the predicted wind and solar load values ​​on an hourly time scale.

[0175] The intraday operation simulation module is used to optimize the intraday scheduling of the power system's output plans for the corresponding time scale units based on the wind and solar load forecast values ​​at a 15-minute time scale.

[0176] The real-time simulation module is used to optimize and schedule the output plan of the power system's rapid response resources in real time based on the wind and solar load forecast values ​​on a 5-minute time scale.

[0177] Specifically, this system includes an interval prediction module, used to acquire historical wind and solar power output sample data. A schematic diagram of the interval prediction module is shown below. Figure 3 As shown.

[0178] Specifically, the net load uncertainty set construction module includes a box uncertainty set construction module, which obtains the box uncertainty set of wind and solar load by analyzing the interval prediction results;

[0179] To embed flexibility constraints into the planning model, a net load uncertainty set considering wind and solar load demand is established;

[0180] Specifically, this system includes a flexibility constraint construction module. It searches for scenarios with the largest flexibility deficit in the uncertainty set to ensure that the flexibility supply meets the demand, thereby fulfilling the flexibility constraints in the simulation.

[0181] Specifically, this system includes a multi-timescale flexibility resource flexibility modeling module, which comprises a flexibility resource timescale partitioning module and a flexibility modeling module. Specifically, different flexibility resources can be divided into hourly, 15-minute, and 5-minute timescales based on their response speed. A detailed partitioning diagram can be obtained by... Figure 4 As shown: Hourly flexibility resources include the start-up and shutdown of various types of generating units; minute-level flexibility resources can be divided into the output of coal-fired power units, gas-fired power units, pumped storage units, etc.; second-level flexibility resources mainly consist of the output of various types of energy storage resources.

[0182] Specifically, this system includes a multi-timescale operation simulation module, which comprises a day-ahead operation simulation module, an intraday operation simulation module, and a real-time operation simulation module, as illustrated in the diagram below. Figure 5As shown. Specifically, the day-ahead operation simulation module mainly makes decisions on hourly-level flexibility resources to optimize day-ahead start-up and shutdown plans; the intraday operation simulation module mainly makes decisions on 15-minute-level flexibility resources to optimize intraday output plans; and the real-time operation simulation module mainly makes decisions on 5-minute-level flexibility resources to ensure that the output plan meets the actual requirements of wind and solar loads.

[0183] Specifically, in the day-ahead optimization module, the wind and solar load forecast data is a short-term forecast for the next day, with a time scale of 1 hour. As this is short-term forecast data, it carries significant uncertainty. If the unit start-up and shutdown schedule is not properly arranged, forcibly starting standby units would require substantial manpower and resources. Therefore, the objective of day-ahead optimization operation decision-making is to: formulate unit start-up and shutdown plans, avoid starting units outside the planned schedule during the day, and only adjust unit output, while ensuring economic viability under the worst-case scenario of wind and solar load forecasts. The entire decision-making process can be described as a min-max optimization problem.

[0184] Its constraints include: system operation-related constraints such as node power balance constraints, spinning reserve constraints, and line power flow equations; unit-related constraints such as unit output upper and lower limits constraints, unit ramping constraints, and unit minimum start-up and shutdown time constraints; and hourly flexibility constraints of the power system generated in the previous module.

[0185] Specifically, in the intraday optimization module, the wind and solar load forecast data are ultra-short-term forecasts for the next 4 hours, with a time scale of 15 minutes. The decision-making objective of intraday optimization operation is to ensure that, based on the unit start-up and shutdown plans obtained from day-ahead optimization operation, the unit output plan can meet the flexibility constraints under the condition of maximum forecast error, while guaranteeing the economic efficiency of the scheme. Since the unit start-up and shutdown schemes have already been provided in the day-ahead optimization stage, the intraday optimization module does not consider start-up and shutdown-related costs and constraints.

[0186] The constraints for thermal power units, hydropower units, and new energy units are consistent with those of the day-ahead optimization operation model. System operation-related constraints are similar, but due to different time scales, the system spinning reserve constraints and unit ramp-up constraints change. Specifically, the constraints for units providing spinning reserve include coal-fired units and intraday excitation-type heat load demand response. In addition, other constraints include: compressed air storage-related constraints, demand-side response-related constraints, and flexibility constraints at the 15-minute time scale generated in previous modules.

[0187] Specifically, the real-time optimization module primarily tracks flexible resources for ultra-short timescales not covered by the intraday optimization module, while also revising the output plan provided by the intraday optimization module. This module uses wind and solar load forecast data for the next 15 minutes and performs rolling optimization on a 5-minute timescale. The goal of the real-time optimization module is to allocate power system generation resources on ultra-short timescales to meet the supply-demand balance of the system at that timescale. The real-time optimization module mainly focuses on power balance and therefore does not consider power system flexibility constraints at this timescale. Furthermore, since the intraday optimization module already provides the intraday output plan for coal-fired power units and the intraday demand-side response plan for stimulus-type heat loads, their related costs and constraints are not considered in this model.

[0188] The constraints related to gas turbines, hydropower units, compressed air energy storage, and new energy units are consistent with the day-ahead optimization scheduling model. System operation-related constraints are similar, but due to different time scales, the system spinning reserve constraints and unit ramp-up constraints change. Specifically, the constraints for units providing spinning reserve include gas turbines, hydropower units, and intraday demand response for stimuli-driven electrical loads. In addition, other constraints include: demand-side response-related constraints and battery energy storage-related constraints.

[0189] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0190] This embodiment also provides an electronic device, which can be a terminal, and its internal structure diagram can be as follows: Figure 15 As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a real-time power system dispatching method. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the device's casing, or an external keyboard, touchpad, or mouse.

[0191] This embodiment also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps:

[0192] Based on historical data of the target power system, predict the uncertain set of wind and solar load at different time scales in the target scenario;

[0193] Based on the response time of different flexibility resources in the target power system, construct the first flexibility resource model under different time scales;

[0194] Based on the uncertain set of wind-solar-load, construct the uncertain set of net load under the target scenario;

[0195] Based on the first flexibility resource model and the net load uncertainty set, determine the power system flexibility constraints;

[0196] A scheduling model is established, and the flexibility constraints of the power system are used as the constraints of the scheduling model. The scheduling model is then solved to achieve real-time scheduling of the power system.

[0197] The scheduling models include day-ahead scheduling models, intraday scheduling models, and real-time update models;

[0198] The power system flexibility constraints in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include power system flexibility constraints.

[0199] It should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application, and all such modifications and substitutions should be covered within the scope of the claims of this application.

[0200] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages.

[0201] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0202] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0203] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0204] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0205] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A real-time dispatching method for a power system, characterized in that, include: Based on historical data of the target power system, predict the uncertain set of wind and solar load at different time scales in the target scenario; Based on the response time of different flexibility resources in the target power system, construct the first flexibility resource model under different time scales; Based on the aforementioned uncertain set of wind and solar loads, construct the uncertain set of net loads for the target scenario; Based on the first flexibility resource model and the net load uncertainty set, determine the power system flexibility constraints; A scheduling model is established, and the flexibility constraints of the power system are used as the constraints of the scheduling model. The scheduling model is then solved to achieve real-time scheduling of the power system. The scheduling model includes a day-ahead scheduling model, an intraday scheduling model, and a real-time update model. The objective function of the current scheduling model is: In the formula, , , and These represent the costs of thermal power units and hydropower units, the penalty cost for insufficient flexibility, and the compensation cost for ancillary services; T is the scheduling time length, which is taken as 24 in the day-ahead scheduling model. In the objective function... Layers are used to find the worst-case scenario. Based on this, the layer finds the most economical unit combination scheme; The objective function of the intraday scheduling model is: In the formula, , , and These are the operating costs of thermal power, hydropower, intraday demand response compensation costs for stimulated heat loads, and compressed air energy storage, respectively. In this model, since the time scale is 15 minutes, T=16. ; The objective function for real-time model updates is: In the formula, and These are the operating costs of the gas turbine unit and the cost of intraday demand response compensation for stimulated electrical loads, respectively; since the time scale is 5 minutes, T=3. ; The power system flexibility constraints in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include power system flexibility constraints.

2. The real-time dispatching method for a power system as described in claim 1, characterized in that, The prediction of the uncertain set of wind and solar load at different time scales under the target scenario based on historical data of the target power system includes: Based on the historical data of the target power system, the prediction error for each time period is obtained; The target power system historical data includes historical time-series wind and solar load output data; Based on the prediction errors for each time period, the expected value of the prediction error for wind, solar and load data is calculated, which serves as the uncertain set of wind, solar and load at different time scales in the target scenario.

3. The real-time dispatching method for a power system as described in claim 2, characterized in that, The construction of the first flexible resource model under different time scales includes: The first flexibility resource model includes a first peak-shaving flexibility model and a second ramp-up flexibility model; The different time scales include day-ahead time scale, intraday time scale, and real-time time scale.

4. A real-time power system dispatching method as described in claim 3, characterized in that, The step of constructing the net load uncertainty set under the target scenario based on the wind-solar load uncertainty set includes: Based on the expected prediction error of the wind and solar load data in the aforementioned uncertain set of wind and solar load, a net load uncertainty set is established; The uncertain set of wind, solar and load includes uncertain sets of wind power output, photovoltaic power output and load output.

5. A real-time power system dispatching method as described in claim 4, characterized in that, The day-ahead scheduling model is used to formulate unit start-up and shutdown plans. Units not included in the start-up and shutdown plan will not be started during the day. Only the unit output will be adjusted. Based on this, the economically optimal unit combination scheme under the worst-case scenario of wind, solar and load forecast will be obtained. The constraints of the day-ahead dispatch model include system operation-related constraints, constraints related to various types of generating units, and power system flexibility constraints. The system operation-related constraints include node power balance constraints, spinning reserve constraints, and line power flow equation constraints. The constraints related to each type of unit include upper and lower limits of unit output, unit ramp-up constraints, and minimum start-up and shutdown time constraints.

6. A real-time power system dispatching method as described in claim 5, characterized in that, The constraints of the intraday dispatch model include the same constraints related to various types of generating units as those of the day-ahead dispatch model, as well as system operation-related constraints, different power system flexibility constraints, compressed air energy storage-related constraints, and demand-side response-related constraints that are different from those of the day-ahead dispatch model. The system operation-related constraints that differ from the day-ahead scheduling model include different spinning reserve constraints and different unit ramp-up constraints.

7. A real-time power system dispatching method as described in claim 6, characterized in that, The constraints of the real-time update model include the same constraints related to various types of generating units as the day-ahead scheduling model, as well as system operation-related constraints, demand-side response-related constraints, and battery energy storage-related constraints that are different from the day-ahead scheduling model.

8. A real-time dispatching system for a power system, employing the method described in any one of claims 1 to 7, characterized in that, include: The interval prediction module is used to predict the uncertain set of wind and solar loads at different time scales under the target scenario based on historical data of the target power system. The multi-timescale resource flexibility modeling module is used to construct the first flexibility resource model at different time scales based on the response time of different flexibility resources of the target power system. The net load uncertainty set construction module is used to construct the net load uncertainty set under the target scenario based on the wind and solar load uncertainty set; A flexibility constraint construction module is used to determine power system flexibility constraints based on the first flexibility resource model and the net load uncertainty set. A multi-timescale operation simulation module is used to establish a scheduling model, take the power system flexibility constraints as the constraints of the scheduling model, and solve the scheduling model to realize real-time scheduling of the power system. The scheduling model includes a day-ahead scheduling model, an intraday scheduling model, and a real-time update model. The power system flexibility constraints in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include power system flexibility constraints.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the real-time dispatching method for a power system according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the real-time dispatching method for a power system as described in any one of claims 1 to 7.

Images