Day-ahead and intraday two-stage scheduling method and system for flexible ramping services

Abstract

The present invention belongs to the technical field of power system optimization and dispatching, and specifically relates to a day-ahead-day two-stage dispatching method and system for flexible ramping services, comprising: acquiring power load data; constructing flexible ramping standby demand and power load intraday scenario according to the acquired power load data; calculating the probability density function of power load prediction deviation based on the acquired power load data and a non-parametric kernel density estimation method; generating power load day-ahead scenario according to the obtained probability density function; constructing a day-ahead-day two-stage dispatching model according to the obtained flexible ramping standby demand, power load intraday scenario and power load day-ahead scenario; solving the constructed day-ahead-day two-stage dispatching model to obtain an optimal dispatching scheme considering the flexible ramping demand, and completing the optimized dispatching of power load.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization dispatching, and in particular relates to a day-ahead and day-intraday two-stage dispatching method and system for flexible ramping services. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The concept of flexible ramping products (FRPs) in the power market can ensure the intraday flexibility balance of the power system under the premise of considering the forecast errors and output fluctuations of renewable energy and load. Therefore, how to reasonably dispatch flexible resources on both the day-ahead and intraday time scales for flexible ramping services is an urgent problem to be solved.

[0004] According to the inventors, the flexible ramping service is different from the existing standby auxiliary services in the current power market. The flexible ramping service is mainly used to cope with the fluctuation of net load between different time periods, while the standby auxiliary service is mainly used to balance the forecast deviation of net load. Flexible ramping services can be provided by flexible resources, such as coal-fired units, cogeneration units, gas units, energy storage, microgrids, demand response, and wind turbines. However, simply incorporating these flexible resources into the power system cannot guarantee that they provide sufficient ramping capacity, resulting in load loss or wind and solar power abandonment.

[0005] Affected by the prediction errors and fluctuations of renewable energy and loads, the power grid needs to reserve ramping capacity in advance to ensure intraday power balance. How to consider multiple flexibility resources to collaboratively provide flexible ramping services, taking into account the changes in prediction accuracy at different time scales, and realizing the reasonable scheduling of flexibility resources in the two stages of day-ahead and intraday, and providing an economical and reliable flexible ramping service scheduling method for power system operation control, is of great significance to the stable operation of a high-proportion new energy system. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a two-stage day-ahead and day-intraday scheduling method and system for flexible ramping services, statistically calculates the probability density function of the net load forecast deviation, quantitatively estimates the flexible ramping demand of the power system, considers calling multiple resources at the same time to meet the flexible ramping demand of the system, and constructs a two-stage day-ahead and day-intraday mixed integer programming model with economic optimality as the objective function, so as to provide a scheduling scheme that takes into account both flexibility and economy for the operation of a high proportion of new energy systems.

[0007] According to some embodiments, a first solution of the present invention provides a two-stage scheduling method for day-ahead and day-intraday for flexible ramping services, using the following technical solutions:

[0008] A two-stage day-ahead and day-intraday scheduling method for flexible ramping services, comprising:

[0009] Obtain power load data;

[0010] Based on the acquired power load data, build flexible ramping reserve requirements and power load intraday scenarios;

[0011] Based on the acquired power load data and the nonparametric kernel density estimation method, the probability density function of the power load forecast deviation is calculated;

[0012] According to the obtained probability density function, the day-ahead scenario of power load is generated;

[0013] According to the obtained flexible ramp reserve demand, power load intraday scenario and power load day-ahead scenario, a day-ahead-intraday two-stage dispatching model is constructed;

[0014] The constructed two-stage scheduling model of day-ahead and day-intraday is solved to obtain the optimal scheduling plan considering the flexible ramping demand and complete the optimal scheduling of power load.

[0015] As a further technical limitation, a two-stage scheduling model of day-ahead and day-intraday is constructed with the minimum economic cost as the objective function, that is, the objective function is Among them, T, G, and E refer to the collection of all time periods, units, and battery energy storage, respectively. are the startup cost and shutdown cost of unit g, respectively, and u gt ,v gt They represent the start-up command and shutdown command of unit g at time t, respectively. g (g),F e (g) are the power generation cost functions of unit g and battery energy storage e, are the pre-dispatching outputs of unit g and energy storage e at time t, To provide the cost function for upward flexible ramping reserve and downward flexible ramping reserve, The upward flexible climbing reserve and downward flexible climbing reserve provided for unit g at time t, The upward flexible ramping reserve and downward flexible ramping reserve provided by the energy storage e at time t, and Z(p, r, y) is the cost of the daily operation stage.

[0016] Furthermore, the cost of the intraday operation phase includes the cost of load loss, the cost of wind curtailment and the cost of re-dispatching flexible resources, namely: Among them, λ ld ,λ lw are the load loss cost coefficient and wind abandonment penalty coefficient, σ s For the probability of each scenario occurring, They are the system load loss and wind power abandonment under scenario s at time t, respectively.

[0017] Furthermore, the constraints on the cost of the intraday operation stage include power balance constraints under uncertain scenarios in the intraday stage, flow constraints under uncertain scenarios in the intraday stage, the power generation of generator sets and energy storage in the intraday stage is limited by the planned power generation power and flexible ramping capacity in the day-ahead stage, and the constraints that load loss and wind abandonment do not exceed actual values.

[0018] As a further technical limitation, the flexible ramping reserve demand at time t depends on the power load at time t and time t+1, so the flexible ramping reserve demand is: in, and are the upward and downward ramping requirements of the power load, respectively, and d n,t is the load forecast value of n nodes at time t, d n,t+1 is the load forecast value of n nodes at time t+1, is the predicted power of wind turbine k at time t, w k,t+1 is the predicted power of wind turbine k at time t+1, σ t+1 is the forecast deviation parameter of the net load at time t+1 in the day-ahead phase.

[0019] As a further technical limitation, the acquired power load data are predicted values ​​and actual values ​​of power load power at multiple typical daily time scales, wherein the time scales include one hour before the day and fifteen minutes within the day.

[0020] According to some embodiments, the second solution of the present invention provides a day-ahead and day-intraday two-stage scheduling system for flexible ramping services, which adopts the following technical solutions:

[0021] A day-ahead and day-intraday two-stage scheduling system for flexible ramping services, comprising:

[0022] An acquisition module configured to acquire power load data;

[0023] A construction module is configured to construct flexible ramping reserve demand and intraday scenarios of power load according to the acquired power load data; calculate the probability density function of power load forecast deviation based on the acquired power load data and non-parametric kernel density estimation method; generate a day-ahead scenario of power load according to the obtained probability density function; and construct a day-ahead-intraday two-stage scheduling model according to the obtained flexible ramping reserve demand, intraday scenarios of power load and day-ahead scenarios of power load;

[0024] The scheduling module is configured to solve the constructed two-stage scheduling model of day-ahead and day-intraday, obtain the optimal scheduling plan considering the flexible ramping demand, and complete the optimal scheduling of power load.

[0025] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution:

[0026] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the day-ahead and day-intraday two-stage scheduling method for flexible ramping services as described in the first solution of the present invention.

[0027] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution:

[0028] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the day-ahead and day-intraday two-stage scheduling method for flexible ramping services as described in the first solution of the present invention are implemented.

[0029] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution:

[0030] A computer program product includes software codes, wherein the program in the software codes executes the steps in the day-ahead and day-intraday two-stage scheduling method for flexible ramping services as described in the first solution of the present invention.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] This embodiment obtains the probability density function of wind power and load forecast deviations based on the non-parametric kernel density estimation method, and then calculates the probability density function of the net load forecast deviation; uses a random sampling method to generate a forecast deviation sequence of wind power and load, and superimposes the forecast values ​​of wind power and load to generate multiple intraday scenarios; considers the volatility and uncertainty of the net load, and defines the calculation formula for flexible ramping demand; finally, establishes a random optimization scheduling model for electric energy and flexible ramping reserve in the two stages of day-ahead and intraday, and obtains the optimal day-ahead scheduling plan for flexible resources considering risks, realizes the reasonable scheduling of flexible resources in the two stages of day-ahead and intraday, provides an economical and reliable flexible ramping service scheduling method for power system operation control, and provides a scheduling plan that takes into account both flexibility and economy for the operation of a high proportion of new energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0034] Figure 1 This is a flow chart of the day-ahead and day-intraday two-stage scheduling method for flexible ramping services in the first embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of flexible climbing standby demand in the first embodiment of the present invention;

[0036] Figure 3 This is a schematic diagram of a rolling optimization framework in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0037] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0038] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0039] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0040] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0041] Embodiment 1

[0042] Embodiment 1 of the present invention introduces a day-ahead and day-intraday two-stage scheduling method for flexible ramping services.

[0043] This embodiment provides a two-stage scheduling method for day-ahead and day-intraday for flexible ramping services. Figure 1 As shown, the following steps are included:

[0044] Step S01: Obtain the predicted value and actual value of wind power generation power and load level in the day-ahead and day-intraday stages, and obtain the predicted value and actual value of net load;

[0045] Step S02: Based on the data obtained in step S01, a non-parametric kernel density estimation method is used to fit the probability density distribution of wind power and load forecast errors during the intra-day and day-ahead periods;

[0046] Step S03: taking the day-ahead forecast values ​​of wind power and load as a data set, and calculating the flexible ramp reserve demand at each moment in the day-ahead stage;

[0047] Step S04: Modeling the uncertainty of wind power and load, generating day-ahead scenarios of wind power and load, and simulating actual scheduling scenarios of the system;

[0048] Step S05: Establish a random optimization dispatch model of electric energy and flexible ramp reserve in two stages: the day-ahead stage determines the pre-dispatching plan of the unit in the day-ahead stage based on the predicted wind power and load level data, and the day-ahead stage reads the start-stop plan, output power and flexible ramp capacity of the unit in the day-ahead stage, and adjusts and compensates the day-ahead dispatch plan;

[0049] Step S06: Solve the random optimization scheduling model of the two-stage electric energy and flexible ramp reserve in the day-ahead and day-intraday periods to obtain the optimal day-ahead scheduling plan for the flexible resources.

[0050] As one or more implementation modes, in step S01, the predicted value and the actual value of the wind power generation power and the load level in the day-ahead and day-intraday stages are obtained, that is, the power load data information is obtained through the topological relationship, grid parameters, parameters of thermal power units and wind power units, energy storage battery parameters, load loss cost coefficient and wind abandonment cost coefficient.

[0051] As one or more implementation methods, in step S02, based on the data obtained in step S01, the probability density function of the wind power and load forecast deviation at time t on the day-ahead and intra-day time scales is estimated by a non-parametric kernel density estimation method. Where N is the sum of the number of time series data at a certain time scale; x i is sample i; K(g) is the kernel function; h is the window width.

[0052] The specific steps are:

[0053] Step S201: Take the day-ahead forecast value, intraday forecast value and actual value of wind power and load as typical samples, and calculate the forecast error of wind power and load at each time scale, that is, in, is the wind power forecast deviation, w k,t

[0054] , are the actual output and predicted output of the wind turbine, respectively. is the load forecast deviation, d n,t , are the actual value and predicted value of the load respectively.

[0055] Step S202: Based on the forecast error data obtained by statistics in step S201, a non-parametric kernel density estimation method is used to fit the probability density function of the wind power load forecast error. The window width h takes the time scale corresponding to the data, such as 1 hour for the day before and 15 minutes for the day. Common kernel functions in non-parametric kernel density estimation include Gaussian kernel function, Uniform kernel function, Triangle kernel function and Epannechnikov kernel function. Due to the excellent properties of the Gaussian kernel function that is continuous and differentiable and relatively easy to calculate, the Gaussian kernel function is used as the kernel function for non-parametric estimation, that is,

[0056] According to the basic principle of probability convolution operation, the probability density function of net load forecast deviation can be obtained by performing convolution operation on the probability density functions of load forecast deviation and wind power forecast deviation: in, represents the probability density function of the net load forecast deviation, They represent the probability density functions of load forecast deviation and wind power forecast deviation respectively, They are volume sum / volume difference operations respectively;

[0057] Step S203: Based on the probability density function fitted in step S202, 100 sets of forecast deviation sequences of wind power and load in the intra-day stage are extracted by random sampling. The forecast deviation sequence of wind power can be expressed as The load forecast deviation sequence can be expressed as

[0058] Step S204: Based on the probability density function of the net load forecast deviation of the day-ahead stage obtained in step S202, the value of the probability density function at time t with a confidence level of 95% is taken as the forecast deviation parameter σ of the net load at time t in the day-ahead stage t .

[0059] As one or more implementation modes, in step S03, the flexible ramp backup demand at time t depends on the power load at time t and time t+1, such as Figure 2 As shown in the figure, the flexible climbing reserve requirement is: in, and are the upward and downward ramping requirements of the power load, respectively, and d n,t is the load forecast value of n nodes at time t, d n,t+1 is the load forecast value of n nodes at time t+1, is the predicted power of wind turbine k at time t, w k,t+1 is the predicted power of wind turbine k at time t+1, σ t+1 is the forecast deviation parameter of the net load at time t+1 in the day-ahead phase.

[0060] As one or more implementation methods, in step S04, according to the power load scenario modeling, that is, using time series data Respectively represent the predicted values ​​of wind power and load level when the system is running. Each time series data contains the predicted data recorded once every hour on the day before. W Represents the number of wind turbines, N D represents the number of loads, and T is the total number of time series data.

[0061] Based on the fitted prediction deviation probability density function, random sampling is used to generate the prediction deviation of wind power and load. The prediction deviation is superimposed on the corresponding time series data to obtain the wind power and load scenarios. Each scenario consists of 24 wind power and load time series data once an hour on the day before.

[0062] As one or more implementation modes, in step S05, based on the flexible ramping demand model and intraday scenarios, a two-stage random optimization scheduling model of day-ahead and intraday electric energy and flexible ramping reserve is established, wherein the day-ahead stage determines the pre-scheduling plan of the unit intraday stage based on the predicted wind power output and load level data, and the intraday stage reads the start and stop plan, output power and flexible ramping capacity of the unit in the day-ahead stage, and adjusts and compensates the day-ahead scheduling plan under the generated multiple typical scenarios, and influences the decision of the day-ahead stage through coupling constraints and economic indicators.

[0063] In the day-ahead and day-intraday two-stage scheduling method for flexible ramping services, the objective function of the day-ahead and day-intraday two-stage stochastic optimization scheduling model for electric energy and flexible ramping reserve is composed of two parts: the pre-scheduling cost in the day-ahead stage and the cost considering uncertainty scenarios in the real-time stage, namely:

[0064] in, are the startup cost and shutdown cost of unit g, respectively, and u gt ,v gt They represent the start-up command and shutdown command of unit g at time t respectively. Fg(g) is the power generation cost function of unit g, which is generally a quadratic function. This embodiment performs piecewise linear processing on it to reduce the calculation complexity.

[0065] Day-ahead constraints include:

[0066] (1) Minimum start-up time constraints for units

[0067]

[0068] in, are the minimum start / stop time of unit g, x gt is a binary variable representing the switching state of unit g at time t.

[0069] (2) Minimum shutdown time constraints for units

[0070]

[0071] (3) Logical relationship between unit startup command and startup status

[0072]

[0073] in, x gt ,u gt ,v gt All are binary variables.

[0074] (4) Logical relationship between unit shutdown command and startup status

[0075]

[0076] (5) Power balance constraints

[0077]

[0078] in, are the planned power generation capacity of unit g at time t respectively.

[0079] (6) Power flow constraints based on power transfer factor calculation

[0080]

[0081] Among them, f nl is the power transfer factor of node n for line l, is the maximum transmission capacity of line l.

[0082] (7) Battery capacity constraints

[0083]

[0084] Among them, E et is the amount of energy stored e at time t, are the efficiency of energy storage charging / discharging, respectively.

[0085] (8) Battery energy storage discharge power constraints

[0086]

[0087] in, is the output power of the battery energy storage, They are respectively the upward / downward climbing reserve of the e-th energy storage at time t, are the maximum charging power and maximum discharging power of the battery, respectively, et +ζet ≤1,ψ et ,ζ et are the state variables of energy storage charging and discharging respectively.

[0088] (9) Battery energy storage power constraints

[0089]

[0090] (10) Constraints on the output power adjustment range of unit g

[0091]

[0092] Among them, P g , are the maximum / minimum power generation of unit g, They are the flexible climbing standby of unit g at time t respectively.

[0093] (11) Constrain the adjustment range of the unit's power generation to be within the flexible ramp reserve range

[0094]

[0095] (12) Constraints for flexible upward climbing reserve

[0096]

[0097] (13) Constraints for flexible downward climbing reserve

[0098]

[0099] in, are the upward climbing capacity per unit time of unit g at startup and during operation, respectively. are the downward climbing capacity per unit time during startup and operation, Δ d is the time interval of the day-ahead phase.

[0100] (14) Flexible ramping constraint (ensuring that the sum of all up / down ramping reserves provided by the unit is greater than the system's up / down flexible ramping requirements)

[0101]

[0102] The cost of the intraday operation phase includes the load loss cost, wind curtailment cost and flexibility resource re-dispatch cost, namely Among them, λ ld ,λ lw are the load loss cost coefficient and wind abandonment penalty coefficient, σ s For the probability of each scenario occurring, They are the system load loss and wind power abandonment under scenario s at time t, respectively.

[0103] Cost constraints during the intraday operation phase include:

[0104] (1) Power balance constraints under uncertain scenarios during the intraday period

[0105]

[0106] (2) Flow constraints under uncertain scenarios during the intraday period

[0107]

[0108] (3) The power generation of the generator set and energy storage in the intraday stage is limited by the planned power generation in the day-ahead stage and the flexible ramp capacity.

[0109]

[0110] (4) Constrain load loss and wind curtailment to not exceed actual values.

[0111]

[0112] Case Analysis

[0113] This embodiment verifies the superiority of the proposed optimization scheduling method through the IEEE-RTS-24 node system, specifically:

[0114] Obtain relevant data of the IEEE-RTS-24 node system, which contains 3 wind turbines with a total installed capacity of 450MW, 12 thermal power units with a total installed capacity of 3380MW, and a battery energy storage system with a total capacity of 200MWh. The maximum power of energy storage is 50MW, and the load peak is about 2600MW. The day-ahead and intraday forecast data and actual values ​​of wind power and load in the embodiment are derived from the GMLC dataset.

[0115] The wind power and load data are normalized and the abnormal data are eliminated. Then the day-ahead and day-intraday forecast deviations of wind power and load are calculated, and the probability density function of the day-ahead and day-intraday forecast deviations is fitted using a non-parametric kernel density estimation method based on a Gaussian kernel.

[0116] Based on the probability density function obtained by fitting, a random sampling method is used to generate 100 sets of wind power and load intraday forecast deviation sequences. Each set of sequences contains 24 data once a day, which are accumulated with the wind power and load intraday forecast values ​​of a certain day to obtain 100 sets of wind power and load intraday scenarios, and then the following can be obtained: Figure 3 Net load intraday scenario shown.

[0117] Take the forecast deviation parameter σ of the net load tis the value of the probability density function of the day-ahead forecast deviation with a confidence level of 95%. The flexible ramping demand in the day-ahead phase is calculated based on the definition of the flexible ramping demand.

[0118] The stochastic optimization scheduling model of the day-ahead and intraday electricity and flexible ramp reserve was input into the Matlab platform using Yamlip, and the commercial solver Gurobi was used to solve the model. The computer configuration is Intel Core I5-12500 series with 16GB memory.

[0119] In order to prove the effectiveness of the two-stage scheduling model (i.e., Model 1) constructed in this embodiment, we consider comparing it with two other models. Model 2 is a scheduling model that only includes the day-ahead stage, in which the prediction deviation parameter σ of the net load is t Take 1.95; Model 3 is a two-stage scheduling model with ramping reserve based on robust optimization. The three models are solved, and the total cost, ramping demand and average risk of each model scheduling scheme are shown in Table 1.

[0120] Table 1 Comparison of calculation results of three scheduling models

[0121]

[0122] From the analysis of Table 1, it can be seen that the flexible ramping requirements of the three models are similar. The cost of the model proposed in this patent is slightly higher than that of Model 2, but the abandoned wind volume and load loss are much smaller than those of Model 2. The risk of Model 3 based on the robust optimization method is slightly lower than that of Model 1, but the total scheduling cost is much higher than that of the model proposed in this embodiment. Therefore, Model 1 reduces the operating risk while increasing the scheduling cost, and has better economy and reliability.

[0123] This embodiment obtains the probability density function of wind power and load forecast deviations based on the non-parametric kernel density estimation method, and then calculates the probability density function of the net load forecast deviation; uses a random sampling method to generate a forecast deviation sequence of wind power and load, and superimposes the forecast values ​​of wind power and load to generate multiple intraday scenarios; considers the volatility and uncertainty of the net load, and defines the calculation formula for flexible ramping demand; finally, establishes a random optimization scheduling model for electric energy and flexible ramping reserve in the two stages of day-ahead and intraday, and obtains the optimal day-ahead scheduling plan for flexible resources considering risks, realizes the reasonable scheduling of flexible resources in the two stages of day-ahead and intraday, provides an economical and reliable flexible ramping service scheduling method for power system operation control, and provides a scheduling plan that takes into account both flexibility and economy for the operation of a high proportion of new energy systems.

[0124] Embodiment 2

[0125] The second embodiment of the present invention introduces a day-ahead and day-intraday two-stage scheduling system for flexible ramping services.

[0126] A day-ahead and day-intraday two-stage scheduling system for flexible ramping services, comprising:

[0127] An acquisition module configured to acquire power load data;

[0128] A construction module is configured to construct flexible ramping reserve demand and intraday scenarios of power load according to the acquired power load data; calculate the probability density function of power load forecast deviation based on the acquired power load data and non-parametric kernel density estimation method; generate a day-ahead scenario of power load according to the obtained probability density function; and construct a day-ahead-intraday two-stage scheduling model according to the obtained flexible ramping reserve demand, intraday scenarios of power load and day-ahead scenarios of power load;

[0129] The scheduling module is configured to solve the constructed two-stage scheduling model of day-ahead and day-intraday, obtain the optimal scheduling plan considering the flexible ramping demand, and complete the optimal scheduling of power load.

[0130] The detailed steps are the same as the day-ahead and day-intraday two-stage scheduling method for flexible ramping services provided in Example 1, and will not be repeated here.

[0131] Embodiment 3

[0132] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0133] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps in the day-ahead and day-intraday two-stage scheduling method for flexible ramping services as described in Embodiment 1 of the present invention.

[0134] The detailed steps are the same as the day-ahead and day-intraday two-stage scheduling method for flexible ramping services provided in Example 1, and will not be repeated here.

[0135] Embodiment 4

[0136] A fourth embodiment of the present invention provides an electronic device.

[0137] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, the steps in the day-ahead and day-intraday two-stage scheduling method for flexible ramping services as described in Embodiment 1 of the present invention are implemented.

[0138] The detailed steps are the same as the day-ahead and day-intraday two-stage scheduling method for flexible ramping services provided in Example 1, and will not be repeated here.

[0139] Embodiment 5

[0140] Embodiment 5 of the present invention provides a computer program product.

[0141] A computer program product includes software codes, wherein the program in the software codes executes the steps in the day-ahead and day-intraday two-stage scheduling method for flexible ramping services as described in the first embodiment of the present invention.

[0142] The detailed steps are the same as the day-ahead and day-intraday two-stage scheduling method for flexible ramping services provided in Example 1, and will not be repeated here.

[0143] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A two-stage scheduling method for day-ahead and day-intraday for flexible ramping services, characterized in that: include: Obtain power load data; Based on the acquired power load data, build flexible ramping reserve requirements and power load intraday scenarios; Based on the acquired power load data and the nonparametric kernel density estimation method, the probability density function of the power load forecast deviation is calculated; According to the obtained probability density function, the day-ahead scenario of power load is generated; According to the obtained flexible ramp reserve demand, power load intraday scenario and power load day-ahead scenario, a day-ahead-intraday two-stage dispatching model is constructed; The day-ahead and day-intraday two-stage scheduling model is constructed with the minimum economic cost as the objective function. The objective function is: Among them, T, G, and E refer to the collection of all time periods, units, and battery energy storage, respectively. are the startup cost and shutdown cost of unit g, respectively, and u gt ,v gt They represent the start-up command and shutdown command of unit g at time t, respectively. g (·),F e (·) are the power generation cost functions of unit g and battery energy storage e, respectively. are the pre-dispatching outputs of unit g and energy storage e at time t, To provide the cost function for upward flexible ramping reserve and downward flexible ramping reserve, The upward flexible climbing reserve and downward flexible climbing reserve provided for unit g at time t, The upward flexible ramp reserve and downward flexible ramp reserve provided by the energy storage e at time t, and Z(p, r, y) is the cost of the intraday operation stage; The intraday operation costs include load loss costs, wind curtailment costs and flexibility resource re-dispatch costs. Among them, λ ld ,λ lw are the load loss cost coefficient and wind abandonment penalty coefficient, σ s For the probability of each scenario occurring, They are the load loss and wind power abandonment of the system under scenario s at time t, respectively; Solve the constructed two-stage scheduling model of day-ahead and day-intraday, obtain the optimal scheduling scheme considering the flexible ramping demand, and complete the optimal scheduling of power load; The constraints of the cost in the intraday operation phase include power balance constraints under uncertain scenarios in the intraday phase, power flow constraints under uncertain scenarios in the intraday phase, power generation of generator sets and energy storage in the intraday phase is limited by the planned power generation and flexible ramp capacity in the day-ahead phase, and load loss and wind abandonment are constrained not to exceed actual values; The day-ahead constraints include the minimum start-up time constraint of the unit, the minimum shutdown time constraint of the unit, the logical relationship between the start-up command of the unit and the startup state, the logical relationship between the shutdown command of the unit and the startup state, the power balance constraint, the power flow constraint based on the power transfer factor calculation, the battery capacity constraint, the battery energy storage discharge power constraint, the battery energy storage power generation power constraint, the unit output power adjustment range constraint, the unit power generation adjustment range is constrained to be within the flexible ramping reserve, the upward flexible ramping reserve constraint, the downward flexible ramping reserve constraint, and the flexible climbing constraint; The flexible ramp reserve demand at time t depends on the power load at time t and time t+1, so the flexible ramp reserve demand is: in, and are the upward and downward ramping requirements of the power load, respectively, and d n,t is the load forecast value of n nodes at time t, d n,t+1 is the load forecast value of n nodes at time t+1, is the predicted power of wind turbine k at time t, w k,t+1 is the predicted power of wind turbine k at time t+1, σ t+1 is the forecast deviation parameter of the net load at time t+1 in the day-ahead phase.

2. A two-stage scheduling method for flexible ramping services, as claimed in claim 1, characterized in that: The acquired power load data are the predicted values ​​and actual values ​​of the power load power at multiple typical daily time scales, wherein the time scales include one hour before the day and fifteen minutes within the day.

3. A two-stage scheduling system for day-ahead and day-intraday for flexible ramping services, characterized in that: include: An acquisition module configured to acquire power load data; A construction module is configured to construct flexible ramping reserve demand and intraday scenarios of power load according to the acquired power load data; calculate the probability density function of power load forecast deviation based on the acquired power load data and non-parametric kernel density estimation method; generate a day-ahead scenario of power load according to the obtained probability density function; and construct a day-ahead-intraday two-stage scheduling model according to the obtained flexible ramping reserve demand, intraday scenarios of power load and day-ahead scenarios of power load; The day-ahead and day-intraday two-stage scheduling model is constructed with the minimum economic cost as the objective function. The objective function is: Among them, T, G, and E refer to the collection of all time periods, units, and battery energy storage, respectively. are the startup cost and shutdown cost of unit g, respectively, and u gt ,v gt They represent the start-up command and shutdown command of unit g at time t, respectively. g (·),F e (·) are the power generation cost functions of unit g and battery energy storage e, respectively. are the pre-dispatching outputs of unit g and energy storage e at time t, To provide the cost function for upward flexible ramping reserve and downward flexible ramping reserve, The upward flexible climbing reserve and downward flexible climbing reserve provided for unit g at time t, The upward flexible ramp reserve and downward flexible ramp reserve provided by the energy storage e at time t, and Z(p, r, y) is the cost of the intraday operation stage; The intraday operation costs include load loss costs, wind curtailment costs and flexibility resource re-dispatch costs. Among them, λ ld ,λ lw are the load loss cost coefficient and wind abandonment penalty coefficient, σ s For the probability of each scenario occurring, are the load loss and wind power abandonment of the system under scenario s at time t, respectively; the dispatch module is configured to solve the constructed two-stage dispatch model of day-ahead and day-intraday, obtain the optimal dispatch scheme considering the flexible ramping demand, and complete the optimal dispatch of power load; The constraints of the cost in the intraday operation phase include power balance constraints under uncertain scenarios in the intraday phase, power flow constraints under uncertain scenarios in the intraday phase, power generation of generator sets and energy storage in the intraday phase is limited by the planned power generation and flexible ramp capacity in the day-ahead phase, and load loss and wind abandonment are constrained not to exceed actual values; The day-ahead constraints include the minimum start-up time constraint of the unit, the minimum shutdown time constraint of the unit, the logical relationship between the start-up command of the unit and the startup state, the logical relationship between the shutdown command of the unit and the startup state, the power balance constraint, the power flow constraint based on the power transfer factor calculation, the battery capacity constraint, the battery energy storage discharge power constraint, the battery energy storage power generation power constraint, the unit output power adjustment range constraint, the unit power generation adjustment range is constrained to be within the flexible ramping reserve, the upward flexible ramping reserve constraint, the downward flexible ramping reserve constraint, and the flexible climbing constraint; The flexible ramp reserve demand at time t depends on the power load at time t and time t+1, so the flexible ramp reserve demand is: in, and are the upward and downward ramping requirements of the power load, respectively, and d n,t is the load forecast value of n nodes at time t, d n,t+1 is the load forecast value of n nodes at time t+1, is the predicted power of wind turbine k at time t, w k,t+1 is the predicted power of wind turbine k at time t+1, σ t+1 is the forecast deviation parameter of the net load at time t+1 in the day-ahead phase.

4. A two-stage scheduling system for flexible ramping services, as claimed in claim 3, characterized in that: The acquired power load data are the predicted values ​​and actual values ​​of the power load power at multiple typical daily time scales, wherein the time scales include one hour before the day and fifteen minutes within the day.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the day-ahead and day-intraday two-stage scheduling method for flexible ramping services as described in any one of claims 1-2 are implemented.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the day-ahead and day-intraday two-stage scheduling method for flexible ramping services as described in any one of claims 1-2 are implemented.

7. A computer program product comprising software code, characterized in that The program in the software code executes the steps of the day-ahead and day-intraday two-stage scheduling method for flexible ramping services as described in any one of claims 1-2.

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