Power system real-time scheduling method, system and device and medium

Abstract

The invention relates to the technical field of power system real-time scheduling, and discloses a power system real-time scheduling method, system and device and a medium, and the method comprises the steps: predicting the wind and light load uncertainty of a power system under different time scales; constructing a flexible resource model adapted to different response times; constructing a net load set based on uncertainty; determining a power system flexibility constraint; and establishing and solving a scheduling model containing the constraints so as to realize real-time scheduling. According to the method and the device, the accuracy and the reliability of real-time scheduling are improved by considering uncertain factors and flexible resource response in the power system. According to the method, a flexible resource model and a net load uncertainty set adaptive to different time scales are constructed so as to adapt to dynamic change of a system and support stable operation. The method can also reduce the operation cost and improve the resource utilization efficiency, and has an important influence on the sustainable development of a power system.

Description

Technical Field

[0001] The present application relates to the technical field of real-time dispatching of power systems, and particularly to a real-time dispatching method, system, device and medium for power systems. Background Art

[0002] Flexibility resources in power systems widely exist on each side of the power source, grid, load and energy storage. All along, the quantification of power system flexibility has been a difficult point in power system planning and operation simulation. Existing literature has discussed the quantification of power system flexibility and the construction method of constraints. For example, according to the flexibility supply-demand balance mechanism, the flexibility margin of each time period of the power system is constrained, which improves the flexibility performance of traditional optimal operation and promotes the consumption of new energy. By introducing probability statistics methods, the flexibility demand under a certain confidence level is modeled and added to the model constraints together with the magnitude of load shedding and energy curtailment, realizing partial relaxation of flexibility constraints. By modeling the flexibility supply-demand models under different confidence levels, an evaluation index for flexibility improvement is proposed to evaluate the flexibility of the power system after the participation of multiple flexibility resources.

[0003] The robust optimization method uses an uncertainty set to describe the volatility of uncertain parameters, and seeks the optimal decision that can satisfy all constraints within different certainty sets. It has a certain degree of conservatism while reducing the operation cost. For example, a weak robust planning method for power source expansion that comprehensively considers the uncertainty of wind power and the flexibility transformation of thermal power units; based on the Copula theory and combined with the scenario method and interval method to quantify the flexibility demand, a robust optimization dispatching model that can effectively control the flexibility and economy of power grid dispatching is proposed.

[0004] At present, although the research on the optimal operation of power system flexibility has constrained the flexibility regulation ability of the system under different confidence levels, the current research still has the following deficiencies: current research often constrains the flexibility of the system on a single time scale, ignoring the response time of different flexibility resources to the change of net load, resulting in overly idealized constraints. Summary of the Invention

[0005] In view of the above existing problems, the present application is proposed.

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

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] In the first aspect, the present application provides a real-time dispatching method for power systems, including:

[0009] Predict the uncertain sets of wind, light, and load under different time scales in the target scenario based on the historical data of the target power system;

[0010] Construct the first flexibility resource model under different time scales according to the response times of different flexibility resources in the target power system;

[0011] Construct the net load uncertainty set in the target scenario according to the uncertain sets of wind, light, and load;

[0012] Determine the flexibility constraints of the power system according to the first flexibility resource model and the net load uncertainty set;

[0013] Establish a scheduling model, use the flexibility constraints of the power system as the constraint conditions of the scheduling model, and solve the scheduling model 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 flexibility constraints of the power system in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include the flexibility constraints of the power system.

[0016] As a preferred solution of the power system real-time scheduling method described in this application, among them: the predicting the uncertain sets of wind, light, and load under different time scales in the target scenario according to the historical data of the target power system includes:

[0017] Obtain the prediction errors of each period according to the historical data of the target power system;

[0018] The historical data of the target power system includes historical time-series wind, light, and load output data;

[0019] Calculate the expected value of the prediction errors of the wind, light, and load data according to the prediction errors of each period, and use it as the uncertain sets of wind, light, and load under different time scales in the target scenario.

[0020] As a preferred solution of the power system real-time scheduling method described in this application, among them: the constructing 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 ramping flexibility model;

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

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

[0024] Specifically, the first peak - shaving flexibility model can schedule the load fluctuations of the power system on a relatively large time scale to ensure the balance between power supply and demand of the power system on the day - ahead and intraday time scales; while the second ramping flexibility model can quickly respond to the load changes of the power system on a relatively small time scale to ensure the stable operation of the power system on the real - time time scale. This method of comprehensively considering different time scales and flexibility requirements helps to optimize the scheduling strategy of the power system and improve the reliability and economy of the power system.

[0025] As a preferred solution of the real - time scheduling method of the power system described in this application, wherein: constructing the net - load uncertainty set under the target scenario according to the wind - solar - load uncertainty set includes:

[0026] Based on the expected value of the prediction error of the wind - solar - load data in the wind - solar - load uncertainty set, establish the net - load uncertainty set;

[0027] The wind - solar - load uncertainty set includes the uncertainty sets of wind power output, photovoltaic power output, and load output magnitude.

[0028] As a preferred solution of the real - time scheduling method of the power system described in this application, wherein: the day - ahead scheduling model is used to formulate the unit start - stop plan. During the intraday period, units outside the start - stop plan are not started, and only the output of the units is adjusted. On this basis, obtain the unit combination plan with the optimal economy under the worst - case scenario of wind - solar - load prediction;

[0029] The constraint conditions of the day - ahead scheduling model include system - operation - related constraints, various types of unit - related constraints, and power - system flexibility constraints;

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

[0031] The various types of unit - related constraints include unit output upper and lower limit constraints, unit ramping constraints, and unit minimum on - off time constraints.

[0032] As a preferred solution of the real - time scheduling method of the power system described in this application, wherein: the constraint conditions of the intraday scheduling model include the same various types of unit - related constraints as the day - ahead scheduling model, as well as system - operation - related constraints different from the day - ahead scheduling model, different power - system flexibility constraints, compressed - air energy - storage - related constraints, and demand - side response - related constraints;

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

[0034] As a preferred solution of the real-time scheduling method for the power system described in this application, wherein: the constraint conditions of the real-time update model include various types of unit-related constraints that are the same as those of 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 those of the day-ahead scheduling model.

[0035] In a second aspect, this application provides a real-time scheduling system for a power system, including:

[0036] An interval prediction module, configured to predict the uncertain sets of wind power, photovoltaic power, and load under different time scales in a target scenario according to the historical data of the target power system;

[0037] A multi-time scale resource flexibility modeling module, configured to construct a first flexibility resource model under different time scales according to the response times of different flexibility resources in the target power system;

[0038] A net load uncertainty set construction module, configured to construct a net load uncertainty set in a target scenario according to the uncertain sets of wind power, photovoltaic power, and load;

[0039] A flexibility constraint construction module, configured to determine the flexibility constraints of the power system according to the first flexibility resource model and the net load uncertainty set;

[0040] A multi-time scale operation simulation module, configured to establish a scheduling model, use the flexibility constraints of the power system as the constraint conditions of the scheduling model, and solve the scheduling model to implement the 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 flexibility constraints of the power system in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include the flexibility constraints of the power system.

[0043] In a third aspect, this application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0044] In a fourth aspect, this application provides a computer-readable storage medium, on which a computer program is stored, and the computer program implements the steps of the method described above when executed by a processor.

[0045] Compared with the prior art, the beneficial effects of the present application are as follows: The present application proposes a real-time scheduling method for a power system. According to the historical data of the target power system, the uncertain sets of wind power, photovoltaic power, and load under different time scales in the target scenario are predicted. According to the response times of different flexibility resources in the target power system, the first flexibility resource models under different time scales are constructed. According to the uncertain sets of wind power, photovoltaic power, and load, the uncertain set of net load in the target scenario is constructed. According to the first flexibility resource models and the uncertain set of net load, the flexibility constraints of the power system are determined. A scheduling model is established, and the flexibility constraints of the power system are used as the constraint conditions of the scheduling model, and the scheduling model is solved to achieve the real-time scheduling of the power system. The present application can comprehensively consider the uncertainties of wind power, photovoltaic power, and load in the power system and the response characteristics of flexibility resources, improving the accuracy and reliability of the real-time scheduling of the power system. In addition, by constructing the flexibility resource models and the uncertain set of net load under different time scales, the present application can better adapt to the dynamic changes of the power system and provide strong support for the stable operation of the power system. At the same time, the application of this method can also reduce the operating cost 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 the wind power, photovoltaic power, and load data of the model, the present application does not directly adopt deterministic prediction, but the interval prediction result obtained by the error of the neural network deterministic prediction through the kernel density estimation algorithm and integral operation. At the same time, according to the definition of the flexibility of the power system, the interval prediction results of wind power, photovoltaic power, and load are combined to generate the uncertain set of net load, and the flexibility of the power system under each time scale is considered according to its most severe net load demand.

[0047] The present application adds the distinction of time scales to the flexibility constraints. On the one hand, it refines and differentiates various types of units for convenient calculation. On the other hand, starting from the actual scheduling of the power system, the differentiation of resources under different time scales is more meaningful in practice.

[0048] When considering the flexibility constraints of the units, the present application embeds them into the original day-ahead - intra-day - real-time scheduling model in a robust constraint manner and functions in the day-ahead and intra-day optimization modules. It can save the operation scale while increasing the consideration factors of the original system, preventing the power system from having flexibility deficits during the intra-day period, and helping the dispatcher make judgments that take into account both the flexibility of the power grid and the economic efficiency of the plan, which has practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 The flowchart of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0051] Figure 2 The system structure diagram of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0052] Figure 3 The schematic diagram of the interval prediction module of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0053] Figure 4 The schematic diagram of the flexibility resource time scale division of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0054] Figure 5 The schematic diagram of the multi-time scale scheduling model of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0055] Figure 6 The schematic diagram of the case study of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0056] Figure 7 The schematic diagram of the net load uncertainty set of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0057] Figure 8 The schematic diagram of the impact of hourly flexibility constraints on the unit start-stop plan of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0058] Figure 9 The schematic diagram of the downward regulation margin within a day of the traditional scheme of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0059] Figure 10 The schematic diagram of the upward regulation flexibility margin within a day of the traditional scheme of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0060] Figure 11 The schematic diagram of the operating result within a day of the traditional scheme of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0061] Figure 12 The schematic diagram of the downward regulation flexibility margin within a day after adding minute-level flexibility constraints of a real-time scheduling method for a power system provided by an embodiment of the present application.

[0062] Figure 13Schematic diagram of the intraday upward flexibility margin after adding minute - level flexibility constraints for a real - time power system scheduling method provided by an embodiment of this application.

[0063] Figure 14 Schematic diagram of the intraday operation result after adding flexibility constraints for a real - time power system scheduling method provided by an embodiment of this application.

[0064] Figure 15 Internal structure diagram of an electronic device for a real - time power system scheduling method provided by an embodiment of this application. Detailed implementation manners

[0065] To make the above - mentioned objects, features, and advantages of this application more obvious and understandable, the following will describe the detailed implementation manners of this application in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of this application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this application.

[0066] Embodiment 1, referring to Figure 1 、 Figure 15 , which is the first embodiment of this application. This embodiment provides a real - time power system scheduling method, including:

[0067] In the existing related technologies, there are some problems. For example, traditional power system scheduling often only considers flexibility constraints under a single time scale, and fails to fully reflect the response time differences of different flexibility resources to the change of net load, resulting in an overly idealized scheduling strategy and being difficult to effectively cope with the uncertainties in actual operation.

[0068] This application provides a method that can effectively solve the above - mentioned problems. Next, multiple embodiments will be combined to elaborate in detail how to implement this real - time power system scheduling method;

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

[0070] S101, according to the historical data of the target power system, predict the uncertain sets of wind, light, and load under different time scales in the target scenario;

[0071] It should be noted that when performing power system scheduling, the acquisition of data is essential. Prediction and analysis are carried out through historical data to improve the accuracy and efficiency of scheduling. This application designs to finally realize real - time power system scheduling by predicting the uncertain sets of wind, light, and load under different time scales in the target scenario.

[0072] In the embodiments of the present application, according to the historical data of the target power system, predicting the uncertain sets of wind, light, and load at different time scales in the target scenario includes:

[0073] Obtaining the prediction errors for each time period according to the historical data of the target power system;

[0074] The historical data of the target power system includes historical time-series wind, light, and load output data;

[0075] Calculating the expected value of the prediction error of the wind, light, and load data according to the prediction errors of each time period, and using it as the uncertain sets of wind, light, and load at different time scales in the target scenario.

[0076] In an alternative embodiment, obtaining the prediction errors for each time period according to the historical data of the target power system can be achieved by setting a prediction model. This model is based on machine learning or deep learning algorithms and is trained and learned on the historical time-series wind, light, and load output data, so as to be able to predict the wind, light, and load output situations at different time scales in the target scenario.

[0077] In an alternative embodiment, various influencing factors such as weather changes, seasonal alternations, and load fluctuations are considered during the training process of the model to improve the accuracy and reliability of the prediction. After obtaining the prediction results, the system will compare the differences between the actual wind, light, and load output data and the predicted values, thereby calculating the prediction errors for each time period.

[0078] It should be noted that these prediction errors will serve as the basis for subsequent calculation of the expected value of the prediction error of the wind, light, and load data, further constituting the uncertain sets of wind, light, and load at different time scales in the target scenario, providing strong support for the real-time scheduling of the power system.

[0079] Specifically, first predict the historical data, and then perform kernel density estimation on the prediction errors to obtain the interval prediction of the wind, light, and load. Among them, the historical sample data is the wind, light, and load data sequence based on time, for example, the wind power, photovoltaic, or load output sequence in a certain season contains n samples x1, x2,... x n .

[0080] Use the LSTM neural network to separately predict the input wind, light, and load data, and compare it with the actual value to obtain the prediction errors for each time period. The mathematical model for neural network prediction is:

[0081]

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

[0083] Furthermore, the error probability density curve of the wind, light and load output is obtained, and the interval prediction values of the wind, light and load are obtained accordingly. Kernel density estimation is a non-parametric estimation. It estimates the probability density of the sample through a kernel function to obtain the probability density function of the sample set, and its expression is:

[0084]

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

[0086] Furthermore, according to the probability density curve of the error, the expected value of the prediction error can be obtained by integrating.

[0087] It should be noted that according to the historical data of the target power system, predicting the uncertain sets of wind, light and load at different time scales in the target scenario can improve the accuracy and flexibility of scheduling. By deeply analyzing the historical data, the law of the output change of wind, light and load can be grasped, so as to more accurately predict the uncertain sets of wind, light and load at different time scales in the future. This helps the scheduling system to make a faster response when facing actual operation, optimize the scheduling strategy, reduce the adverse effects brought by the output fluctuations of wind, light and load, and ensure the stable operation of the power system. At the same time, the scheduling decision based on the prediction results can also better adapt to various complex scenarios, improving the operation efficiency and economic benefits of the entire power system.

[0088] S102. Construct a first flexibility resource model at different time scales according to the response time of different flexibility resources in the target power system;

[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 requirements of different time scales during the real-time scheduling process. These resources may include energy storage devices, adjustable loads, fast-starting generator sets, etc. By establishing the first flexibility resource model, the characteristics such as the response speed, adjustment range, and cost-benefit of various resources can be accurately described, providing a scientific basis for subsequent scheduling decisions.

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

[0091] In an alternative embodiment, the first flexibility resource model can also be established through the method of an expert system. This method is based on the knowledge and experience of domain experts, encodes the response characteristics, scheduling rules, etc. of flexibility resources into a rule base, and realizes the construction and application of the model through an inference engine.

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

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

[0094] The first flexibility resource model includes a first peak shaving flexibility model and a second ramping flexibility model;

[0095] The different time scales include the day-ahead time scale, the intra-day time scale, and the real-time time scale.

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

[0097] It should be noted that constructing the first flexibility resource model at different time scales according to the response time 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 scheduling. In the constructed first flexibility resource model, the response characteristics of various resources are fully considered, such as gas turbines with fast response and hydropower stations with slow response, which helps to reasonably arrange the start-up and shutdown sequence and output size of various resources during the scheduling process to cope with sudden load changes or fluctuations in renewable energy in the power system. In addition, by constructing models at different time scales, it can also effectively avoid over-regulation and resource waste during the scheduling process, and improve the economy and stability of the entire power system.

[0098] S103. Construct the net load uncertainty set for the target scenario according to the wind-solar-load uncertainty set;

[0099] In the embodiment of the present application, constructing the net load uncertainty set for the target scenario according to the wind-solar-load uncertainty set includes:

[0100] Establish the net load uncertainty set based on the expected value of the prediction error of the wind-solar-load data in the wind-solar-load uncertainty set;

[0101] The wind-solar-load uncertainty set includes the uncertainty sets of wind power output, photovoltaic power output, and load output magnitude.

[0102] Specifically, by analyzing the interval prediction results, obtain the box-type uncertainty sets of wind-solar-load:

[0103]

[0104] In the formula, P t wind and P t solar and P t Pd respectively represent the predicted wind power output, predicted photovoltaic power output, and predicted load magnitude; and respectively represent the lower bounds of the uncertainty errors of wind power output, photovoltaic power output, and load magnitude; and respectively represent the upper bounds of the uncertainty errors of wind power output, photovoltaic power output, and load magnitude; U wind and U solar and U Pd respectively represent the uncertainty sets of wind power output, photovoltaic power output, and load output magnitude. To embed the flexibility constraints into the planning model, establish the net load uncertainty set considering the wind-solar-load demand:

[0105]

[0106] It should be noted that constructing the net load uncertainty set for the target scenario according to the wind-solar-load uncertainty set can more accurately simulate the uncertainty of the power system in actual operation, thus providing a more reliable basis for dispatching decisions. Through the construction of the net load uncertainty set, the system can better cope with the fluctuations of wind power, photovoltaic power output, and load magnitude, ensuring the stable operation of the power system. In addition, this step helps to optimize the dispatching strategy, improve the economy and efficiency of the power system, reduce the operation cost, and provide strong support for the sustainable development of the power system.

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

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

[0109] Specifically, the introduction of the flexibility constraints of the power system enables the scheduling system to maintain the power supply-demand balance of the power system in the face of the uncertainties of wind power, photovoltaic power output, and load size, and prevent 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 the safe and reliable operation of the power system.

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

[0111]

[0112] In the formula, and are the upward and downward flexibility that the available flexibility resources can provide at the τ time scale within the system; is the fluctuation value of the net load under the premise of the prediction uncertainty of wind, light, and load with an uncertainty of α. The meaning of this formula is to search for the scenario with the largest flexibility deficit in the uncertainty set so that the flexibility supply can meet the demand to complete the flexibility constraints in the operation simulation.

[0113] In an alternative embodiment, the flexibility modeling of various types of resources (i.e., the flexibility constraints of the power system at multiple time scales) is:

[0114] Coal-fired power is the "ballast stone" for power security. The upward and downward flexibility capabilities that traditional coal-fired power units can provide are:

[0115]

[0116] In the formula, and are the upper limits of the rates of the upward and downward regulation powers of coal-fired power respectively; P g,max 、P g,min and P g,t are the maximum adjustable output (generally the rated capacity), the minimum technical output, and the output power at time t of coal-fired power respectively; τ is the research time scale.

[0117] In an alternative embodiment, a pumped-storage power station can generate electricity during peak loads and store water during low loads. It has fast start-stop capabilities and flexible pumping and power generation mode conversion capabilities, and is one of the important flexibility resources on the power supply side. The upward and downward flexibility capabilities it can provide are:

[0118]

[0119] Wherein, are respectively the power generation power and pumping power of the pumped storage unit in the t period; P g,rated is the rated power of the pumped storage power station; W g,max and W g,min are respectively the upper and lower limits of the water storage capacity of the upper reservoir; W g,t is the water volume stored in the upper reservoir at time t; η g and η h are respectively the conversion coefficients of the pumping power and the power generation power to the water flow rate.

[0120] In an optional embodiment, the flexibility provided by the new energy storage is related to the charge-discharge strategy and the state of charge (air storage), and the up and down flexibility capabilities provided are:

[0121]

[0122] Wherein, are respectively the charge-discharge power of the new energy storage at time t; are respectively the rated charge-discharge power of the new energy storage; S max and S min are respectively the upper and lower limits of the electric energy of the new energy storage; S t is the electric energy currently stored in the new energy storage device; η c and η d are respectively the charge-discharge efficiencies of the new energy storage.

[0123] It should be noted that according to the first flexibility resource model and the net load uncertainty set, determining the flexibility constraint of the power system can more accurately evaluate the flexibility requirements of the power system under different operating scenarios, thereby optimizing the dispatching strategy. By considering the first flexibility resource model, the configuration and dispatching of various flexibility resources can be dynamically adjusted to cope with the uncertainty of the net load. This helps to reduce the power supply-demand imbalance problem caused by insufficient flexibility and improve 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 dispatching model, use the flexibility constraint of the power system as a constraint condition of the dispatching model, and solve the dispatching model to realize the real-time dispatching of the power system;

[0125] In the embodiment of the present application, the dispatching model includes a day-ahead dispatching model, an intra-day dispatching model, and a real-time update model;

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

[0127] In the embodiment of the present application, the day-ahead scheduling model is used to formulate the unit start-stop plan. During the day, units outside the start-stop plan are not started, and only the output of the units is adjusted. On this basis, the unit combination plan with the best economy under the worst scenario of wind-solar-load prediction is obtained.

[0128] The constraint conditions of the day-ahead scheduling model include system operation-related constraints, various types of unit-related constraints, and power system flexibility constraints.

[0129] The system operation-related constraints include node power balance constraints, spinning reserve constraints, and line power flow equation constraints.

[0130] The various types of unit-related constraints include upper and lower limits of unit output constraints, unit ramping constraints, and minimum start-stop time constraints of units.

[0131] In the embodiment of the present application, the constraint conditions of the intra-day scheduling model include the same various types of unit-related constraints as the day-ahead scheduling model, as well as system operation-related constraints different from the day-ahead scheduling model, different power system flexibility constraints, compressed air energy storage-related constraints, and demand response-related constraints.

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

[0133] In the embodiment of the present application, the constraint conditions of the real-time update model include the same various types of unit-related constraints as the day-ahead scheduling model, as well as system operation-related constraints different from the day-ahead scheduling model, demand response-related constraints, and battery energy storage-related constraints.

[0134] Specifically, in the day-ahead scheduling model, the wind-solar-load prediction data is short-term prediction data for the next 1 day, and the time scale is 1 h. The wind-solar-load data is short-term prediction data with great uncertainty. If the unit start-stop plan is not arranged reasonably, it will consume a large amount of manpower and material resources to forcibly start cold standby units.

[0135] Therefore, the day-ahead optimal operation decision objective is: to formulate the unit start-stop plan, not to start units outside the start-stop plan during the day, and only to adjust the output of the units. On this basis, ensure the economy under the worst case of wind-solar-load prediction. The entire decision-making process can be described as a min-max optimization problem, and its objective function is:

[0136]

[0137] In the formula, C TP,t 、C PS,t 、C PF,t and C AS,tThey are the costs of thermal power units and hydropower units, the penalty cost for insufficient flexibility, and the compensation cost for ancillary services respectively; T is the scheduling time length, and T takes 24 in the day-ahead optimization module; f(P i,t ) is the operating cost of thermal power unit i at time t, t ∈ [1, T]. For coal-fired power units, it is usually a quadratic function, and for gas-fired power units, it is usually a cubic function; They are the start-up and shut-down costs of thermal power unit i respectively; They are the start-up and shut-down states of thermal power unit i at time t respectively; Ω 火电 is the set of thermal power units; Ω 水电 is the set of hydropower units; and They are the start-up and shut-down costs and the unit power cost of hydropower unit a respectively; They are the start-up and shut-down states of hydropower unit a respectively; They are the pumping power and generating power of hydropower unit a at time t respectively; c ne is the penalty cost for new energy curtailment; is the new energy curtailment of the system at time t; c ls is the penalty cost for load shedding; is the load shedding of the system at time t; In this module, the ancillary services mainly include the start-stop peak regulation of thermal power units, is the capacity compensation for start-stop peak regulation, and S a is the full capacity of the start-stop peak regulation units. The model decision variables are the start-up and shut-down states 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 is used to find the economically optimal unit combination scheme on this basis.

[0138] Its constraint conditions include: system operation-related constraints such as node power balance constraint, spinning reserve constraint, and line power flow equation; unit output upper and lower limit constraints, unit ramp rate constraint, unit minimum on-off time constraint and other various types of unit-related constraints, as well as the hourly flexibility constraint of the power system generated in the previous module.

[0139] Specifically, in the intraday optimization model, the wind-solar-load prediction data is the ultra-short-term prediction data for the next 4h, and the time scale is 15min. The decision objective of the intraday optimization operation is: on the basis of the unit start-stop plan obtained from the day-ahead optimization operation, the unit output plan can meet the flexibility constraint under the condition of the largest prediction error and ensure the economy of the scheme.

[0140] Since the unit start-stop plan has been given in the day-ahead optimization stage, the intraday optimization module does not consider the start-stop 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 are respectively the thermal power operation cost, the hydropower operation cost, the compensation cost for the incentive-based thermal load demand response within the day, and the compressed air energy storage operation cost; c caes and c DR,a respectively represent the unit power costs of compressed air energy storage and the incentive-based thermal load demand response within the day; respectively represent the charging and discharging powers of compressed air energy storage; P t DR,a represents the magnitude of the incentive-based thermal load demand response at time t. The decision variables of this layer model are the output of the coal-fired power unit and the magnitude of the demand-side response of the incentive-based thermal load within the day. In this model, since the time scale is 15 min, T = 16, and t ∈ [1, T].

[0143] Among its constraint conditions, the relevant constraints of thermal power units, hydropower units, and new energy units are the same as those in the day-ahead optimal operation model. The relevant constraints of system operation are similar, but due to different time scales, the system spinning reserve constraint and the unit ramp rate constraint change, that is, the constraints of the units providing spinning reserve include coal-fired units and the incentive-based thermal load demand response within the day. In addition, other constraints also include: the relevant constraints of compressed air energy storage, the relevant constraints of demand-side response, and the flexibility constraints at the 15-minute time scale generated in the previous module.

[0144] Specifically, in the real-time optimization model, it mainly tracks the flexibility resources at the ultra-short time scale that are not covered in the intra-day optimization module, and at the same time corrects the output plan given by the intra-day optimization module. This module uses the predicted data of wind, light, and load in the next 15 min and conducts rolling optimization with a 5-min time scale.

[0145] The goal of the real-time optimization model is: to arrange the power generation resources of the power system at the ultra-short time scale to meet the supply-demand balance of the system at this time scale. The real-time optimization module mainly focuses on power balance, so it does not consider the flexibility constraints of the power system at this time scale. And since the output plan of the thermal power units within the day and the response plan of the demand-side response of the incentive-based thermal load within the day have been given in the intra-day optimization module, their relevant 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 are respectively the operation cost of gas-fired units and the compensation cost for the incentive-based electric load demand response within the day; c ess is the unit power cost of lithium-ion battery energy storage; are the charging and discharging powers of the lithium-ion battery energy storage, respectively; c DR,b and P t DR,b respectively represent the unit power cost of the in-day incentive-based electric load demand response and the magnitude at time t. 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 in-day incentive-based electric load demand-side response. Since the time scale is 5 min, T = 3 and t ∈ [1, T].

[0148] The relevant constraints of the gas turbine unit, hydropower unit, compressed air energy storage, and new energy unit are the same as those of the day-ahead optimal scheduling model. The relevant constraints for system operation are similar, but due to the different time scales, the system spinning reserve constraint and the unit ramp rate constraint change, that is, the unit constraints for providing spinning reserve include the gas turbine unit, the hydropower unit, and the in-day incentive-based electric load demand response. In addition, other constraints also include: the relevant constraints for demand-side response and the relevant constraints for battery energy storage.

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

[0150] In summary, the present application proposes a method for real-time scheduling of a power system. According to the historical data of the target power system, the uncertain sets of wind, light, and load under different time scales in the target scenario are predicted; according to the response times of different flexibility resources in the target power system, the first flexibility resource models under different time scales are constructed; according to the uncertain sets of wind, light, and load, the uncertain set of net load in the target scenario is constructed; according to the first flexibility resource models and the uncertain set of net load, the flexibility constraints of the power system are determined; a scheduling model is established, the flexibility constraints of the power system are used as the constraint conditions of the scheduling model, and the scheduling model is solved to achieve real-time scheduling of the power system. The present application can comprehensively consider the uncertainties of wind, light, and load in the power system and the response characteristics of flexibility resources, improving the accuracy and reliability of real-time scheduling of the power system. In addition, by constructing the flexibility resource models and the uncertain set of net load under different time scales, the present application can better adapt to the dynamic changes of the power system and provide strong support for the stable operation of the power system. At the same time, the application of this method can also reduce the operating cost 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] Embodiment 2. In a preferred embodiment, specifically, the present application uses the HPR-38 node system to verify the proposed model with a numerical example, and its topology diagram is as Figure 6 shown. In the model, the present application takes the actual wind, light, and load data of a certain area with a time scale of 5 minutes for 10 days as input and enters the module for constructing the uncertain set of net load for solution. Figure 7The schematic diagram of the net load uncertainty set is given.

[0152] Specifically, in this application, by comparing with the traditional scheme, that is, the day-ahead - intra-day - real-time optimization scheduling model without considering flexibility constraints in the model and the robust optimization model considering multi-time scale flexibility constraints proposed in this application. Figure 8 The day-ahead optimization results of the two schemes and their differences are given. It can be seen that the optimization model proposed in this paper adds the start-stop plans of some units to ensure the flexibility margin in the intra-day period.

[0153] On this basis, Figures 9 - 14 The intra-day and real-time optimization results of the traditional scheme and the intra-day and real-time optimization results given by the model proposed in this application are respectively given. It can be seen that a day-ahead - intra-day - real-time scheduling method for multi-time scale flexibility robust constraints of a power system proposed in this application, compared with the traditional unit commitment optimization model, adds a net load uncertainty set and flexibility constraints. This improvement enables the model to more accurately arrange the start-stop of units in the power system at the day-ahead stage and take into account the dynamic characteristics of the intra-day net load fluctuations. Based on the optimization results of this model, by increasing the start-stop scheduling of three coal-fired generating units, it also includes the extension of the operation time of gas-fired generating units and pumped-storage generating units, ensuring that the power generation plan formulated at the day-ahead stage can effectively respond to the intra-day load changes. At the same time, in the intra-day operation simulation, there is a flexibility gap of about 4 hours in the traditional method, accompanied by forced load shedding and energy curtailment phenomena. However, the day-ahead - intra-day - real-time scheduling method for multi-time scale flexibility robust constraints of a power system proposed in this application can effectively allocate flexibility resources on the intra-day time scale through conservative unit start-stop plans and intra-day flexibility constraints, ensuring that there is no shortage of flexibility resources in the system.

[0154] Embodiment 3, in this embodiment, a power system real-time scheduling system is also provided, as Figure 2 shown, including:

[0155] An interval prediction module, configured to predict the wind-solar-load uncertainty sets at different time scales under the target scenario according to the historical data of the target power system;

[0156] A multi-time scale resource flexibility modeling module, configured to construct a first flexibility resource model at different time scales according to the response times of different flexibility resources of the target power system;

[0157] A net load uncertainty set construction module, configured to construct a net load uncertainty set under the target scenario according to the wind-solar-load uncertainty sets;

[0158] A flexibility constraint construction module, configured to determine the power system flexibility constraints according to the first flexibility resource model and the net load uncertainty set;

[0159] The multi-time scale operation simulation module is used to establish a scheduling model, take the flexibility constraints of the power system as the constraints of the scheduling model, solve the scheduling model, and realize the real-time scheduling of the power system;

[0160] The scheduling model includes a day-ahead scheduling model, an intra-day scheduling model, and a real-time update model;

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

[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 obtain the historical time-series wind, light, and load output data and input it 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 errors for each time period;

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

[0166] Optionally, the multi-time scale resource flexibility modeling module includes a flexibility resource time scale division module and a flexibility modeling module;

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

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

[0169] Optionally, the net load uncertainty set construction module includes a box-type 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 interval prediction results of the wind, light, and load.

[0171] Optionally, the flexibility constraint construction module is used for:

[0172] Constraining the flexibility regulation capabilities of the system according to the flexibility regulation capabilities of the units at different time scales and the most severe scenarios in the future net load uncertainty set, so as to obtain the multi-time scale flexibility robust constraints of the power system.

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

[0174] The day-ahead operation simulation module is used to perform day-ahead optimal scheduling of the start-stop plans of various types of units in the power system according to the predicted values of wind, light, and load at the hourly time scale;

[0175] The intra-day operation simulation module is used to perform intra-day optimal scheduling of the output plans of units in the power system at the corresponding time scale according to the predicted values of wind, light, and load at the 15-minute time scale;

[0176] The real-time operation simulation module is used to perform real-time optimal scheduling of the output plans of fast-response resources in the power system according to the predicted values of wind, light, and load at the 5-minute time scale.

[0177] Specifically, the system includes an interval prediction module for obtaining historical wind and light output sample data. The schematic diagram of the interval prediction module is as Figure 3 shown.

[0178] Specifically, the net load uncertainty set construction module includes a box-type uncertainty set construction module. By analyzing the interval prediction results, a box-type uncertainty set of wind, light, and load is obtained;

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

[0180] Specifically, the system includes a flexibility constraint construction module. Search for the scenario with the largest flexibility deficit in the uncertainty set to make the flexibility supply meet the demand, so as to complete the flexibility constraints in the operation simulation.

[0181] Specifically, the system includes a multi-time scale flexibility resource flexibility modeling module, which includes a flexibility resource time scale division module and a flexibility modeling module. Specifically, different flexibility resources can be classified into hourly, 15-minute, and 5-minute levels according to the response speed. The specific division schematic diagram can be as Figure 4 shown: Hourly flexibility resources include the start-stop of various types of units; Minute-level flexibility resources can be divided into the output of coal-fired power units, the output of gas-fired power units, the output of pumped-storage units, etc.; Second-level flexibility resources are mainly the output of various types of energy storage resources.

[0182] Specifically, the system includes a multi-time scale operation simulation module, which includes a day-ahead operation simulation module, an intra-day operation simulation module, and a real-time operation simulation module. The schematic diagram is as Figure 5As shown in the figure. Specifically, the day-ahead operation simulation module mainly makes decisions on hourly flexibility resources and optimizes the day-ahead start-stop plan; the intra-day operation simulation module mainly makes decisions on 15-minute flexibility resources and optimizes the intra-day output plan; the real-time operation simulation module mainly makes decisions on 5-minute flexibility resources to make the output plan meet the actual values of wind, light, and load requirements.

[0183] Specifically, in the day-ahead optimization module, the wind, light, and load forecast data are short-term forecast data for the next day, with a time scale of 1h. The wind, light, and load data are short-term forecast data with high uncertainty. If the unit start-stop plan is not arranged reasonably, forcibly starting cold standby units will consume a large amount of manpower and material resources. Therefore, the decision-making goal of day-ahead optimized operation is: to formulate the unit start-stop plan, not to start units outside the start-stop plan within the day, but only to adjust the unit output, and on this basis, ensure the economy under the worst-case scenario of wind, light, and load forecasts. The entire decision-making process can be described as a min-max optimization problem.

[0184] Its constraint conditions include: system operation-related constraints such as node power balance constraints, spinning reserve constraints, and line power flow equations; various types of unit-related constraints such as unit output upper and lower limits constraints, unit ramp rate constraints, and unit minimum start-stop time constraints, as well as the hourly flexibility constraints of the power system generated in the previous module.

[0185] Specifically, in the intra-day optimization module, the wind, light, and load forecast data are ultra-short-term forecast data for the next 4h, with a time scale of 15min. The decision-making goal of intra-day optimized operation is: based on the unit start-stop plan obtained from day-ahead optimized operation, the unit output plan can meet the flexibility constraints under the maximum forecast error and ensure the economy of the plan. Since the unit start-stop plan has been given in the day-ahead optimization stage, the intra-day optimization module does not consider the costs and constraints related to start-stop.

[0186] Among its constraint conditions, the relevant constraints of thermal power units, hydropower units, and new energy units are the same as those of the day-ahead optimized operation model. The system operation-related constraints are similar, but due to different time scales, the system spinning reserve constraints and unit ramp rate constraints have changed, that is, the constraints for units providing spinning reserve include coal-fired units and intra-day incentive-based thermal load demand response. In addition, other constraints also include: relevant constraints of compressed air energy storage, relevant constraints of demand-side response, and flexibility constraints at a 15-minute time scale generated in the previous module.

[0187] Specifically, in the real-time optimization module, it mainly conducts flexible resource tracking for the ultra-short time scale not covered by the intra-day optimization module, and at the same time corrects the power output plan given by the intra-day optimization module. This module uses the predicted data of wind power, photovoltaic power, and load for the next 15 minutes and conducts rolling optimization on a 5-minute time scale. The goal of the real-time optimization module is to arrange the power generation resources of the power system on the ultra-short time scale to meet the supply-demand balance of the system at this time scale. The real-time optimization module mainly focuses on power balance, so it does not consider the flexibility constraints of the power system at this time scale. And since the power output plan of the intra-day coal-fired power units and the response plan of the intra-day incentive-based thermal load demand response have been given in the intra-day optimization module, their related costs and constraints are not considered in this model.

[0188] The relevant constraints of gas turbine units, hydroelectric units, compressed air energy storage, and new energy units are the same as those in the day-ahead optimal scheduling model. The relevant constraints for system operation are similar, but due to the different time scales, the system spinning reserve constraint and the unit ramp rate constraint change, that is, the unit constraints for providing spinning reserve include gas turbine units, hydroelectric units, and intra-day incentive-based electrical load demand response. In addition, other constraints also include: relevant constraints for demand response, relevant constraints for battery energy storage.

[0189] The above-mentioned various unit modules can be embedded in the processor of the electronic device in hardware form or be independent of it, or can be stored in the memory of the electronic device in software form, so as to facilitate the processor to call and execute the operations corresponding to each 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 Figure 15 shown. The electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes a real-time scheduling method for a power system. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, a touchpad, or a mouse, etc.

[0191] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0192] According to the historical data of the target power system, predict the uncertain sets of wind power, photovoltaics, and load under different time scales in the target scenario;

[0193] According to the response times of different flexibility resources in the target power system, construct the first flexibility resource models under different time scales;

[0194] According to the uncertain sets of wind power, photovoltaics, and load, construct the net load uncertainty set in the target scenario;

[0195] According to the first flexibility resource models and the net load uncertainty set, determine the flexibility constraints of the power system;

[0196] Establish a scheduling model, use the flexibility constraints of the power system as the constraint conditions of the scheduling model, and solve the scheduling model to achieve real-time scheduling of the power system;

[0197] The scheduling model includes a day-ahead scheduling model, an intraday scheduling model, and a real-time update model;

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

[0199] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not restrictive. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application, and they should all be covered by the scope of the claims of the present application.

[0200] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages.

[0201] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

[0202] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

[0203] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

[0204] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0205] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A real-time scheduling method for a power system, characterized in that, Including: Predict the uncertain sets of wind power, photovoltaic power, and load under different time scales in the target scenario based on the historical data of the target power system; Construct the first flexibility resource models under different time scales according to the response times of different flexibility resources in the target power system; Construct the net load uncertainty set in the target scenario according to the uncertain sets of wind power, photovoltaic power, and load; Determine the flexibility constraints of the power system according to the first flexibility resource models and the net load uncertainty set; Establish a scheduling model, use the flexibility constraints of the power system as the constraints of the scheduling model, and solve the scheduling model 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 flexibility constraints of the power system in the day-ahead scheduling model and the intraday scheduling model are different, and the real-time update model does not include the flexibility constraints of the power system.

2. The real-time scheduling method of a power system according to claim 1, characterized in that, The predicting the uncertain sets of wind power, photovoltaic power, and load under different time scales in the target scenario based on the historical data of the target power system includes: Obtain the prediction errors for each time period according to the historical data of the target power system; The historical data of the target power system includes historical time-series wind power, photovoltaic power, and load output data; Calculate the expected value of the prediction errors of the wind power, photovoltaic power, and load data as the uncertain sets of wind power, photovoltaic power, and load under different time scales in the target scenario according to the prediction errors for each time period.

3. A real-time scheduling method for a power system according to claim 2, characterized in that, The constructing the first flexibility resource models under different time scales includes: The first flexibility resource models include a first peak shaving flexibility model and a second ramping flexibility model; The different time scales include the day-ahead time scale, the intraday time scale, and the real-time time scale.

4. The real-time scheduling method of a power system according to claim 3, characterized in that The constructing the net load uncertainty set in the target scenario according to the uncertain sets of wind power, photovoltaic power, and load includes: Establish a net load uncertainty set for the expected values of the prediction errors of the wind power, photovoltaic power, and load data in the uncertain sets of wind power, photovoltaic power, and load; The uncertain sets of wind power, photovoltaic power, and load include the uncertainty sets of wind power output, photovoltaic power output, and load output magnitude.

5. A real-time scheduling method for a power system according to claim 4, characterized in that, The day-ahead scheduling model is used to formulate unit start-stop plans. During the intraday period, units outside the start-stop plans are not started, and only the unit outputs are adjusted. On this basis, obtain the economically optimal unit combination plan under the worst scenario of wind power, photovoltaic power, and load prediction; The constraint conditions of the day-ahead scheduling model include system operation-related constraints, constraints related to various types of units, and flexibility constraints of the power system; The system operation-related constraints include node power balance constraints, spinning reserve constraints, and line power flow equation constraints; The constraints related to various types of units include unit output upper and lower limits constraints, unit ramping constraints, and unit minimum on-off time constraints.

6. The real-time scheduling method for a power system according to claim 5, wherein The constraint conditions of the intraday scheduling model include the same constraints related to various types of units as the day-ahead scheduling model, as well as system operation-related constraints different from the day-ahead scheduling model, different flexibility constraints of the power system, compressed air energy storage-related constraints, and demand-side response-related constraints; The system operation-related constraints different from the day-ahead scheduling model include different spinning reserve constraints and different unit ramping constraints.

7. The real-time scheduling method for a power system according to claim 6, wherein The constraint conditions of the real-time update model include various types of unit-related constraints that are the same as those of 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 those of the day-ahead scheduling model.

8. A real-time scheduling system for a power system, which applies the method according to any one of claims 1 to 7, characterized in that Including: An interval prediction module for predicting the uncertain sets of wind, light, and load at different time scales in a target scenario based on the historical data of the target power system; A multi-time scale resource flexibility modeling module for constructing a first flexibility resource model at different time scales according to the response times of different flexibility resources in the target power system; A net load uncertainty set construction module for constructing a net load uncertainty set in a target scenario based on the uncertain sets of wind, light, and load; A flexibility constraint construction module for determining the power system flexibility constraints according to the first flexibility resource model and the net load uncertainty set; A multi-time scale operation simulation module for establishing a scheduling model, taking the power system flexibility constraints as the constraint conditions of the scheduling model, and solving the scheduling model to achieve real-time scheduling of the power system; The scheduling model includes a day-ahead scheduling model, an intra-day scheduling model, and a real-time update model; The power system flexibility constraints in the day-ahead scheduling model and the intra-day 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, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for real-time scheduling of 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 method for real-time scheduling of a power system according to any one of claims 1 to 7.

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