A method, device and storage medium for configuring standby capacity for new energy grid connection

Abstract

The present invention discloses a method, device and storage medium for configuring reserve capacity for new energy grid connection. The method includes: calculating a first expected value of system power shortage due to the uncertainty of unit component failures based on a probability model of forced outage scenarios caused by failures of thermal power and gas turbine unit components; performing Monte Carlo sampling simulation on a density function model to obtain the day-ahead net load prediction deviation; obtaining a second expected value of system power shortage according to the probability distribution of the day-ahead net load prediction deviation; calculating a third expected value of system power shortage considering the uncertainty of unit component failures and the uncertainty of net load prediction; establishing an objective function model and calculating the day-ahead reserve capacity configuration scale according to the objective function model; introducing the real-time net load prediction deviation into the confidence interval and calculating the real-time reserve capacity configuration scale. The present invention can effectively improve the accuracy of reserve capacity configuration for new energy grid connection.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power, and in particular to a method, device and storage medium for configuring reserve capacity for new energy grid connection. Background Art

[0002] The development of new energy plays an important role in ensuring power supply and energy transformation. At the same time, after large-scale new energy grid connection, its characteristics such as large prediction difficulty and strong volatility have brought great impacts on the operation of the power system. Therefore, high-proportion new energy grid connection has brought severe challenges to the operation and dispatching of traditional power systems based on load prediction, and new requirements have been put forward for the peak shaving capacity and reserve capacity of the system. Due to the slow start-up of thermal power units and the constraints of minimum technical output and ramp rate, if the configured reserve capacity is too small, it will not be able to follow the rapid random fluctuations of wind power, resulting in wind and light abandonment and load loss risks; if the configured reserve capacity is too large, it will reduce the economic efficiency of system operation, and the existing reserve capacity configuration methods cannot accurately configure the reserve capacity for new energy grid connection. Summary of the Invention

[0003] The present invention provides a method, device and storage medium for configuring reserve capacity for new energy grid connection to solve the technical problem that the existing reserve capacity configuration methods cannot accurately configure the reserve capacity.

[0004] An embodiment of the present invention provides a method for configuring reserve capacity for new energy grid connection, including:

[0005] Calculating a first system expected power shortage value of the uncertainty of unit component failures based on a probability model of forced outage scenarios caused by failures of thermal power and gas turbine unit components;

[0006] Ignoring the first system expected power shortage value, performing Monte Carlo sampling simulation on the density function model to obtain the day-ahead net load prediction deviation;

[0007] Obtaining a second system expected power shortage value of the uncertainty of the day-ahead net load prediction according to the probability distribution of the day-ahead net load prediction deviation;

[0008] Calculating a third system expected power shortage value considering the uncertainty of unit component failures and the uncertainty of net load prediction based on the first system expected power shortage value and the second system expected power shortage value;

[0009] Calculating the minimum outage cost according to the third system expected power shortage value, and establishing an objective function model based on the minimum outage cost, fuel cost and unit output fluctuation, and calculating the day-ahead reserve capacity configuration scale according to the objective function model;

[0010] Set the confidence interval of the real-time net load prediction deviation, introduce the real-time net load prediction deviation into the confidence interval, and calculate the configuration scale of the real-time reserve capacity.

[0011] Further, the calculation of the first system power shortage expectation value of the unit component fault uncertainty based on the probability model of the forced outage scenario caused by the faults of thermal power units and gas turbine units includes:

[0012] Assume that the operating states of thermal power units and gas turbine units are discrete distributions and follow the (0,1) distribution;

[0013] Construct a probability model for the forced outage scenario caused by the faults of thermal power units and gas turbine units;

[0014] Based on the probability model, calculate the first system power shortage expectation value of the unit component fault uncertainty.

[0015] Further, the density function model includes the probability density function of load, the probability density function of wind speed, and the probability density function of light intensity. Ignoring the first system power shortage expectation value, performing Monte Carlo sampling simulation on the density function model to obtain the day-ahead net load prediction deviation includes:

[0016] Ignoring the first system power shortage expectation value, performing Monte Carlo random sampling simulation according to the density function model to obtain the predicted values for each sampling time, and calculating the day-ahead load prediction deviation according to the predicted values. The predicted values include the load prediction value, the wind speed prediction value, and the light intensity prediction value.

[0017] Further, obtaining the second system power shortage expectation value of the day-ahead net load prediction uncertainty according to the probability distribution of the day-ahead net load prediction deviation includes:

[0018] Statistical sampling results, drawing the probability distribution curve of the net load prediction deviation according to the kernel smoothing density, and sorting the values of the probability distribution curve from small to large and dividing them into several equally spaced arrays;

[0019] Drawing the probability density histogram of each equally spaced array. The abscissa of the probability density histogram corresponds to the day-ahead net load prediction deviation values for different sampling times, and using the day-ahead net load prediction deviation values as the unit outage capacity corresponding to the prediction uncertainty;

[0020] Ignoring the influence of unit component fault uncertainty on the power shortage expectation value, calculating the second system power shortage expectation value considering the net load prediction uncertainty.

[0021] Further, the calculation of the day-ahead reserve capacity configuration scale according to the objective function model includes:

[0022] Set constraint conditions based on system power balance, the output of thermal power and gas turbine units, the ramping of thermal power and gas turbine units, and the output of wind power and photovoltaic units;

[0023] According to the constraint conditions and the objective function model, use the particle swarm optimization algorithm to simulate and obtain the output of thermal power and gas turbine units, and calculate the day-ahead reserve capacity configuration scale according to the output of thermal power and gas turbine units.

[0024] Furthermore, set the confidence interval of the real-time net load prediction deviation, introduce the real-time net load prediction deviation into the confidence interval, and calculate the real-time reserve capacity configuration scale, including:

[0025] Simulate the real-time situation of the operation day, replace the test set with the real-time true value of the operation day, obtain the improved output of thermal power and gas turbine units, and calculate the real-time net load prediction deviation according to the output of thermal power and gas turbine units;

[0026] Set the confidence interval of the real-time net load prediction deviation, introduce the real-time net load prediction deviation into the confidence interval, and force the thermal power and gas turbine units to increase or decrease their output within the confidence interval to cope with the real-time net load prediction deviation;

[0027] Calculate the real-time reserve capacity configuration scale according to the amount of output increased or decreased by the thermal power and gas turbine units to cope with the real-time load prediction deviation, the rated capacity of the thermal power unit, the rated capacity of the gas turbine unit, and the output of the thermal power and gas turbine units.

[0028] An embodiment of the present invention provides a reserve capacity configuration device for new energy grid connection, including:

[0029] The first system power shortage expectation calculation module is used to calculate the first system power shortage expectation of the uncertainty of unit component failures based on the probability model of forced outage scenarios caused by component failures of thermal power and gas turbine units;

[0030] The day-ahead net load prediction deviation acquisition module is used to ignore the first system power shortage expectation and perform Monte Carlo sampling simulation on the density function model to obtain the day-ahead net load prediction deviation;

[0031] The second system power shortage expectation calculation module is used to obtain the second system power shortage expectation of day-ahead net load prediction uncertainty according to the probability distribution of the day-ahead net load prediction deviation;

[0032] The third system power shortage expectation calculation module is used to calculate the third system power shortage expectation considering the uncertainty of unit component failures and the uncertainty of net load prediction according to the first system power shortage expectation and the second system power shortage expectation;

[0033] A day-ahead reserve capacity configuration scale calculation module, which is used to calculate the minimum power outage cost according to the expected value of power shortage of the third system, establish an objective function model according to the minimum power outage cost, fuel cost and unit output fluctuation, and calculate the day-ahead reserve capacity configuration scale according to the objective function model;

[0034] A day-ahead reserve capacity configuration scale calculation module, which is used to set a confidence interval for the deviation of real-time net load prediction, introduce the deviation of real-time net load prediction into the confidence interval, and calculate the real-time reserve capacity configuration scale.

[0035] An embodiment of the present invention provides a computer storage medium, and the computer-readable storage medium includes a stored computer program; wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the reserve capacity configuration method for new energy grid connection as described above.

[0036] Based on the probability model of the forced outage scenario caused by the failure of thermal power and gas turbine unit components, the embodiment of the present invention calculates the expected value of power shortage of the first system for the uncertainty of unit component failure, obtains the expected value of power shortage of the second system for the uncertainty of day-ahead net load prediction according to the probability distribution of the day-ahead net load prediction deviation, calculates the expected value of power shortage of the third system considering the uncertainty of unit component failure and the uncertainty of net load prediction according to the expected value of power shortage of the first system and the expected value of power shortage of the second system, calculates the minimum power outage cost according to the expected value of power shortage of the third system, establishes an objective function model according to the minimum power outage cost, fuel cost and unit output fluctuation, calculates the day-ahead reserve capacity configuration scale according to the objective function model, and by setting a confidence interval for the deviation of real-time net load prediction, introducing the deviation of real-time net load prediction into the confidence interval, calculates the real-time reserve capacity configuration scale, comprehensively considers the influence of the expected value of power shortage of the system considering the uncertainty of unit component failure and the uncertainty of net load prediction on capacity configuration, calculates the day-ahead reserve capacity configuration scale by establishing an objective function model, and by introducing the deviation of real-time net load prediction into the confidence interval, calculates the real-time reserve capacity configuration scale, can accurately calculate the day-ahead reserve capacity configuration scale and the real-time reserve capacity configuration scale, so as to effectively improve the reserve capacity configuration effect of new energy grid connection. Description of the Drawings

[0037] Figure 1 is a schematic flow chart of the reserve capacity configuration method for new energy grid connection provided by the embodiment of the present invention;

[0038] Figure 2 is a probability density distribution diagram of the predicted net load deviation provided by the embodiment of the present invention;

[0039] Figure 3It is a schematic diagram of the output power of each type of unit at 24 moments in the daily plan provided by the embodiment of the present invention;

[0040] Figure 4 It is a schematic diagram of the output power of each type of unit at 24 moments in the real-time correction provided by the embodiment of the present invention;

[0041] Figure 5 It is a schematic structural diagram of a spare capacity configuration device for new energy grid connection provided by the embodiment of the present invention. Specific embodiments

[0042] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0043] In the description of the present application, it should be understood that the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0044] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal communication of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0045] Please refer to Figures 1-4 , an embodiment of the present invention provides a Figure 1 As shown in the spare capacity configuration method for new energy grid connection, including:

[0046] S1. Calculate the first system power shortage expectation value of the uncertainty of unit component failures based on the probability model of the forced outage scenarios caused by the failures of thermal power and gas turbine unit components;

[0047] In the embodiment of the present invention, from the perspective of accident reserve, the first system power shortage expectation value of the uncertainty of unit forced outages is selected based on the constructed probability model of the forced outage scenarios caused by the failures of thermal power and gas turbine unit components.

[0048] S2. Ignore the expected value of the power shortage in the first system, and perform Monte Carlo sampling simulation on the density function model to obtain the day-ahead net load prediction deviation.

[0049] In the embodiment of the present invention, ignoring the expected value of the power shortage in the first system, from the perspective of load reserve, Monte Carlo sampling simulation is performed according to the density function models of load, wind speed, and light intensity to obtain the day-ahead net load prediction deviation. Among them, the density function models of load, wind speed, and light intensity are constructed according to the normal distribution, Weibull distribution, and beta distribution respectively. The embodiment of the present invention uses Monte Carlo random sampling simulation to obtain the predicted values of load, wind speed, and light intensity for each sampling time, and further calculates the predicted values of load, wind power and photovoltaic power output and their deviations, so as to obtain the day-ahead net load prediction value and the day-ahead net load prediction deviation.

[0050] S3. Obtain the expected value of the power shortage in the second system of the day-ahead net load prediction uncertainty according to the probability distribution of the day-ahead net load prediction deviation.

[0051] S4. Calculate the expected value of the power shortage in the third system considering the uncertainty of unit component failures and the uncertainty of net load prediction according to the expected value of the power shortage in the first system and the expected value of the power shortage in the second system.

[0052] S5. Calculate the minimum outage cost according to the expected value of the power shortage in the third system, establish an objective function model based on the minimum outage cost, fuel cost, and unit output fluctuation, and calculate the day-ahead reserve capacity configuration scale according to the objective function model.

[0053] In the embodiment of the present invention, an objective function model is established based on the minimum outage cost, fuel cost, and unit output fluctuation, and with the system power balance, the output of thermal-gas-photovoltaic units, and the ramping of thermal-gas units as constraints, the particle swarm optimization algorithm is used to simulate the output of thermal-gas units, so as to obtain the day-ahead reserve capacity configuration scale.

[0054] S6. Set the confidence interval of the real-time net load prediction deviation, introduce the real-time net load prediction deviation into the confidence interval, and calculate the real-time reserve capacity configuration scale.

[0055] In the embodiment of the present invention, from the perspective of new energy energy storage, set the confidence interval of the real-time net load prediction deviation and determine the energy storage capacity to be configured, calculate the output of thermal-gas-storage units in real time, so as to obtain the real-time reserve capacity configuration scale.

[0056] In one embodiment, calculating the expected value of the power shortage in the first system of the unit component failure uncertainty based on the probability model of the forced outage scenario caused by the failures of thermal power units and gas turbine units includes:

[0057] Assume that the operating states of thermal power units and gas power units are discretely distributed and follow a (0,1) distribution;

[0058] Construct a probability model for the forced outage scenarios caused by component failures of thermal power units and gas power units;

[0059] Based on the probability model, calculate the first-system expected value of power deficiency due to the uncertainty of component failures of the units.

[0060] It should be noted that the probability of two or more units failing simultaneously and the probability of component failures in nuclear power units are relatively low. In the embodiments of the present invention, only the scenarios where at most one unit component failure (Component Failure, CF) causes forced outage are considered. Assume that the operating states of thermal power units and gas power units are discretely distributed and follow a (0,1) distribution, that is, the units have only two states, namely forced outage and generating power according to the power generation plan. In the embodiments of the present invention, the cumulative forced outage rate P of thermal power and gas power unit k at time t k,t and the cumulative probability of the forced outage scenario caused by the component failure of only one unit k at time t are as follows:

[0061]

[0062]

[0063] In the formula, k is the out-of-service unit, k = 1, 2,..., N; T MTTR,k is the mean time to repair (Mean Time To Repair) of thermal power and gas power unit k from the occurrence of the failure to the repair; T MTTF,k is the mean time to failure (Mean Time To Failure) of thermal power and gas power unit k before the failure; T RBF,k,t is the elapsed time before failure (Elapsed Time Before Failure) of thermal power and gas power unit k at time t; P r,t is the cumulative forced outage rate of the normally operating unit r at time t.

[0064] The planned correction amount W for the outage of thermal power and gas power unit components at time t CF,t is:

[0065]

[0066] In the formula, X CF,k,t is the outage capacity of the kth thermal power and gas power unit due to component failure at time t.

[0067] Ignoring the impact of the uncertainty of the net load (Payload, PLD) prediction on the expected value of power deficiency, the day-ahead expected value of power deficiency EENS for the forced outage of a single unit k at time tCF,k,t is:

[0068] EENS CF,k,t = [X CF,k,t - (M t - W PLD,B1,t )]P CF,k,t

[0069] M t = W T,t + W N,t + W G,t

[0070] W PLD,B1,t = W L,B,t - W W,B,t - W PV,B,t

[0071] In the formula, when X CF,k,t ≤ M t - W L,2,t , EENS CF,k,t is 0. M t is the total output of the thermal-nuclear-gas power generation units in the system at time t; W PLD,B1,t is the predicted value of the system's day-ahead net load at time t; W T,t , W N,t , W G,t are the planned output capacities of the thermal-nuclear-gas power generation units in the t period respectively; W L,B,t is the predicted value of the load in the t period; W W,B,t , W PV,B,t are the predicted output capacities of wind and light in the t period respectively.

[0072] Ignoring the influence of the uncertainty of the net load prediction on the expected value of power shortage, and solving the expected value of power shortage EENS of the system at the day-ahead time t for the uncertainties of the failures of the thermal power and gas turbine units CF,t is:

[0073]

[0074] In one embodiment, the density function model includes the probability density function of the load, the probability density function of the wind speed, and the probability density function of the light intensity. Ignoring the first system's expected value of power shortage, Monte Carlo sampling simulation is performed on the density function model to obtain the day-ahead net load prediction deviation, including:

[0075] Ignoring the first system's expected value of power shortage, Monte Carlo random sampling simulation is performed according to the density function model to obtain the predicted values for each sampling time, and the day-ahead load prediction deviation is calculated based on the predicted values. The predicted values include the load prediction value, the wind speed prediction value, and the light intensity prediction value.

[0076] It should be noted that the prediction deviations of load, wind speed, and light intensity decibels follow the normal distribution, Weibull distribution, and beta distribution, specifically:

[0077] The load prediction deviation follows the normal distribution, and the probability density function of the load is:

[0078]

[0079] In the formula, W L,t is the load at time t; μ L,t , σ L,t are the two parameters of the normal distribution, representing the mean and standard deviation respectively, and the determination of their parameters is obtained by fitting two sets of actual load data.

[0080] The wind power prediction deviation follows the Weibull distribution, and the probability density function of the wind speed is:

[0081]

[0082] In the formula, v W,t is the wind speed at time t; λ t , k t are the two parameters of the Weibull distribution at time t, and the determination of their parameters is obtained by fitting two sets of actual wind speed data.

[0083] After obtaining the probability density function of the wind speed, it is necessary to substitute it into the wind turbine output constraint for calculation:

[0084]

[0085] In the formula, W W,t is the output of the wind turbine at time t; v ci is the cut-in wind speed; v co is the cut-out wind speed; v r is the rated wind speed of the wind turbine; W r is the rated output power of the wind turbine.

[0086] Please refer to Table 1 for the data of predicted net load, prediction deviation, and deviation correction amount on a certain winter day.

[0087] Table 1 Data of Predicted Net Load, Prediction Deviation, and Deviation Correction Amount on a Certain Winter Day

[0088]

[0089]

[0090] In the embodiment of the present invention, the photovoltaic prediction deviation follows the beta distribution, and the probability density function of the light intensity:

[0091]

[0092] where r PV,t is the actual sunlight intensity of the photovoltaic power generation unit at time t; r max is the maximum sunlight intensity of the photovoltaic power generation unit; Γ() is the Gamma function; α t , β t are the two parameters of the beta distribution at time t, and the determination of the parameters is obtained by fitting two sets of actual sunlight intensity data.

[0093] After obtaining the probability density function of the sunlight intensity, substitute it into the calculation formula of the photovoltaic power station output:

[0094] W PV,t = r PV,t ηS

[0095] where W PV,t is the output of the photovoltaic power station at time t; η is the energy conversion efficiency; S is the total area of the photovoltaic panels in the photovoltaic power station.

[0096] Let the total number of Monte Carlo sampling be J. Use the probability density function models of the normal distribution, Weibull distribution, and beta distribution to perform J times of Monte Carlo random sampling simulation. After obtaining the predicted values of the load and wind-solar power output, the total predicted deviation (Total Deviation, TD) ε TD,B1,j,t is:

[0097] ε TD,B1,j,t = d A1,t - d B,j,t = (W L,A1,t - W W,A1,t - W PV,A1,t ) - (W L,B,j,t - W W,B,j,t - W PV,B,j,t )

[0098] where d A1,t is the difference between the actual load of the training set at time t minus the wind-solar power output, that is, the actual net load W PLD,A1,t ; d B,j,t is the difference between the predicted value of the load at time t minus the predicted value of the wind-solar power output in the jth sampling, that is, the predicted value of the day-ahead net load W PLD,B1,j,t ; W L,A1,t , W W,A1,t , W PV,A1,t are the actual load value, actual wind power output value, and actual photovoltaic power output value of the training set at time t, respectively; W L,B,j,t , W W,B,j,t , W PV,B,j,t are the predicted load value, wind power output predicted value, and photovoltaic power output predicted value at time t in the jth sampling, respectively.

[0099] Further, from the perspective of load reserve, the embodiment of the present invention analyzes, obtains the deviation correction amount of the uncertainty of the day-ahead net load prediction according to the probability density distribution of the day-ahead net load prediction deviation as shown in Table 1, and calculates the expected value of power shortage of the uncertainty of the day-ahead net load prediction, specifically including:

[0100] Total deviation ε TD,B1,j,t That is, the day-ahead net load prediction deviation ε at the t-th moment of the j-th sampling PLD,B1,j,t , follows a normal distribution. When it is greater than 0, it means that the actual value is more, and a positive predicted net load correction amount needs to be added; when it is less than 0, it means that the actual value is less, and a negative predicted net load correction amount needs to be added. The embodiment of the present invention statistically analyzes the sampling results of a total of J times, and uses the kernel smoothing density estimation to draw the probability distribution curve of the net load prediction deviation at the t-th moment. Sort ε PLD,B1,j,t from small to large and divide it into h equally spaced arrays, draw the probability density histogram of each equally spaced array. The abscissa of the probability density histogram corresponds to the day-ahead net load prediction deviation value ε PLD,B1,j,t of different sampling times. This value can be used as a unit outage capacity X FU,j,t caused by the forecast uncertainty (FU) of load and wind-solar power output in the forced outage model of thermal power and gas turbine units FU,j,t . The area of the histogram corresponding to this outage capacity X FU,j,t is the probability P FU,j,t occurring within this range. When the number of equally spaced arrays divided is small, the probability density histogram will be higher than the probability distribution curve, resulting in a higher fitting probability of the prediction deviation; otherwise, it will result in a lower fitting probability of the prediction deviation; until the probability density histogram corresponding to the equally spaced arrays divided fits the probability distribution curve, the fitting probability of the prediction deviation is meaningful. The product of this unit outage capacity X FU,j,t and the probability P FU,j,t occurring within this range is the planned correction amount W

[0101] to cope with the prediction deviation. In the embodiment of the present invention, the capacities of wind-solar units are all set to a large capacity scale of 1000 MW. The expected value of power shortage of the second system is calculated based on the outage capacity FU,j,t Ignoring the influence of the uncertainty of unit component failures on the expected value of power shortage, the expected value of system power shortage EENS

[0102] EENS FU,j,t = [X FU,j,t -(M t -W PLD,B1,j,t )]P FU,j,t

[0103] In the formula, when X FU,j,t ≤Mt -W L,2,j,t When, the EENS FU,j,t is 0. X FU,j,t is the outage capacity of a set of generating units corresponding to the net load prediction deviation at the t-th moment of the j-th sampling; M t is the total output of thermal power, nuclear power, and gas turbine units at the t-th moment; W PLD,B1,j,t is the day-ahead net load prediction value of the system at the t-th moment of the j-th sampling.

[0104] In one embodiment, obtaining the second expected value of system power shortage for day-ahead net load prediction uncertainty according to the probability distribution of day-ahead net load prediction deviation includes:

[0105] Statistical sampling results, draw the probability distribution curve of the net load prediction deviation according to the core smoothing density, and sort the values of the probability distribution curve from small to large and divide them into several equally spaced arrays;

[0106] Draw the probability density histogram of each equally spaced array. The abscissa of the probability density histogram corresponds to the day-ahead net load prediction deviation values of different sampling times, and use the day-ahead net load prediction deviation value as the outage capacity of the generating units corresponding to the prediction uncertainty;

[0107] Ignore the influence of the uncertainty of unit component failures on the expected value of power shortage, and calculate the second expected value of system power shortage considering the uncertainty of net load prediction.

[0108] It should be noted that the day-ahead net load prediction deviation follows a normal distribution. When it is greater than 0, it means that the actual value is more, and a positive prediction net load correction amount needs to be added; when it is less than 0, it means that the actual value is less, and a negative prediction net load correction amount needs to be added. In the embodiment of the present invention, statistical sampling results are used to draw the probability distribution curve of the net load prediction deviation using the core smoothing density, and sort it from small to large and divide it into h equally spaced arrays, and draw the probability density histogram of each equally spaced array. The abscissa of the probability density histogram corresponds to different sampling times, and the day-ahead net load prediction deviation value can be used as the outage capacity of a set of generating units due to the uncertainty of net load prediction in the forced outage model of thermal power and gas turbine units. The area of the histogram corresponding to this outage capacity is the probability that occurs within this range. When the number of equally spaced arrays divided is small, the probability density histogram will be higher than the probability distribution curve, resulting in a higher fitting probability of the prediction deviation; otherwise, it will result in a lower fitting probability of the prediction deviation; until the probability density histogram corresponding to the equally spaced arrays divided fits the probability distribution curve, the fitting probability of the prediction deviation is meaningful. The product of this outage capacity and the probability that occurs within this range is the planned correction amount for coping with the prediction deviation.

[0109] Preferably, the embodiment of the present invention ignores the impact of the uncertainty of unit component failures on the expected value of energy shortage, and calculates the expected value of energy shortage of the day-ahead system considering the uncertainty of net load prediction.

[0110] In one embodiment, the day-ahead reserve capacity configuration scale is calculated according to the objective function model, including:

[0111] Setting constraint conditions based on system power balance, the output of thermal power and gas turbine units, the ramping of thermal power and gas turbine units, and the output of wind power and photovoltaic units;

[0112] In the embodiment of the present invention, an objective function model is established based on the minimum power outage cost, the fuel cost of thermal-gas turbine units, and the output fluctuations of thermal-gas turbine units; the constraint conditions are system power balance, the output and ramping of thermal-gas turbine units, and the output of wind-solar units.

[0113] The expected value of energy shortage EENS of the day-ahead system at time t considering the uncertainty of unit component failures and the uncertainty of net load prediction CF,FU,t is:

[0114] EENS CF,FU,t = EENS CF,t + EENS FU,t

[0115] Considering the system unit outage (UO) cost C of N users with different attributes UO is:

[0116]

[0117] In the formula, P L,i is the capacity proportion of the i-th type of user in the system; C UO,i is the unit power outage loss value corresponding to the i-th type of user, and the relevant research data of the loss value is shown in Table 2; τ i is the outage loss time constant of the i-th type of user, which characterizes the speed of change of the loss with the duration of the power outage.

[0118] Table 2 Research data of unit power outage loss value

[0119]

[0120] The embodiment of the present invention establishes an objective function model with the minimum power outage cost C O , the fuel cost C of thermal power units TF , the fuel cost C of gas turbine units GF , and the output fluctuations P of thermal power and gas turbine units F ; the constraint conditions set based on system power balance, the output of thermal power and gas turbine units, the ramping of thermal power and gas turbine units, and the output of wind power and photovoltaic units are:

[0121]

[0122]

[0123]

[0124]

[0125] W L,B,1,t = W T,t + W N,t + W G,t + W W,B,1,t + W PV,B,1,t - W FU,1,t - W CF,t - W ch,1,t + W dis,1,t

[0126] W T,G,min,i ≤ W T,G,i,t ≤ W T,G,max,i

[0127] -r D,i Δt ≤ W T,G,i,t - W T,G,i,t-1 ≤ r U,i Δt

[0128] 0 ≤ W W,PV,i,t ≤ W W,PV,max,i

[0129] Wherein, C UO is the unit power outage cost caused by the power loss; u T,i,t , u G,i,t are the start-up and shutdown states of thermal power and gas turbine unit i at time t, 0 for shutdown and 1 for operation; W T,i,t , W G,i,t are the output powers of thermal power and gas turbine unit i at time t respectively; a T,i , b T,i , c T,i are the coal consumption cost coefficients of thermal power unit i respectively; r G,t is the gas consumption cost coefficient at time t; W L,B,1,t , W W,B,1,t , W PV,B,1,t are the predicted values of the load, wind power and photovoltaic output at time t corresponding to the first sampling. In specific implementation, the predicted values that are more consistent can be found in the J - time sampling samples according to the predicted daily weather, wind speed, etc. announced by the local meteorological station. If the workload is to be reduced, the sampling result of a certain time can be randomly selected as the predicted value; W FU,1,t is the planned correction amount for the first sampling to cope with the prediction deviation; W CF,tis the planned correction amount for the outage of thermal power and gas turbine unit components at time t; W ch,1,t is the energy storage charging power at time t of the day-ahead; W dis,1,t is the energy storage discharging power at time t of the day-ahead; W T,G,min,i and W T,N,G,max,i are the minimum and maximum output sizes of thermal power and gas turbine unit i respectively; r D,i and r U,i are the load shedding and loading speed limit values of the i-th unit respectively; W W,PV,max,i is the maximum output size of wind power and photovoltaic unit i.

[0130] According to the constraint conditions and the objective function model, the particle swarm optimization algorithm is used to simulate and obtain the output sizes of thermal power and gas turbine units, and the day-ahead reserve capacity configuration scale is calculated based on the output sizes of thermal power and gas turbine units.

[0131] When both the thermal power unit and the gas turbine unit have been operating without faults for 120 hours, the particle swarm optimization algorithm is used to simulate and obtain the output sizes of thermal power and gas turbine units, so as to obtain the day-ahead reserve capacity configuration scale (Reserve Capacity Configuration Scale, RCCS) at time t W RCCS1,t is:

[0132] W RCCS1,t =W T,all -W T,t +W G,all -W G,t

[0133] In the formula, W T,all and W G,all are the rated capacities of thermal power and gas turbine units respectively; W T,t and W G,t are the planned outputs of the thermal power and gas turbine units called at time t respectively.

[0134] In one embodiment, a confidence interval for the real-time net load prediction deviation is set, the real-time net load prediction deviation is introduced into the confidence interval, and the real-time reserve capacity configuration scale is calculated, including:

[0135] Simulate the real-time situation of the operation day, replace the test set with the real-time true value of the operation day, obtain the improved output sizes of thermal power and gas turbine units, and calculate the real-time net load prediction deviation according to the output sizes of thermal power and gas turbine units;

[0136] In the embodiment of the present invention, the actual load value W L,A2,t of the test set, the actual wind power output value W W,A2,t of the test set, the actual photovoltaic output value W PV,A2,t of the test set, and the actual unit fault outage value W CF,A2,tSimulate the real-time situation on the day of operation. In the specific implementation, replacing the test set with the real-time true values on the day of operation can obtain the improved output of thermal power and gas turbine units and the output of energy storage. According to the test set, the real-time net load prediction deviation ε can be calculated. PLD,B2,t is:

[0137] ε PLD,B2,t =(W L,A2,t -W W,A2,t -W PV,A2,t +W CF,A2,t )-(W L,B,1,t -W W,B,1,t -W PV,B,1,t )-W FU,1,t -W CF,t

[0138] In the formula, W L,A2,t , W W,A2,t , W PV,A2,t , W CF,A2,t are respectively the actual load value, actual wind power output value, actual photovoltaic power output value, and actual unit failure outage value of the training set at time t; W L,B,1,t , W W,B,1,t , W PV,B,1,t are respectively the predicted values of load, wind power, and photovoltaic power output at time t corresponding to the first sampling; W FU,1,t is the planned correction amount for the prediction deviation corresponding to the first sampling; W CF,t is the planned correction amount for the failure outage of thermal power and gas turbine unit components at time t. Please refer to Table 3 for the cost and operation characteristics data tables of thermal power, gas power, and nuclear power units.

[0139] Table 3 Cost and Operation Characteristics of Thermal Power, Gas Power, and Nuclear Power Units

[0140]

[0141] Set the confidence interval of the real-time net load prediction deviation, introduce the real-time net load prediction deviation into the confidence interval, and force the thermal power and gas turbine units to increase or decrease their output within the confidence interval to cope with the real-time net load prediction deviation;

[0142] In the embodiment of the present invention, set the confidence interval of the real-time net load prediction deviation, introduce the real-time net load prediction deviation ε PLD,B2,t into the upper confidence level α U,t and the lower confidence level α D,t . The confidence level is restricted by the unit ramp rate. Force the thermal power and gas turbine units to increase or decrease their output within the confidence range to cope with the real-time net load prediction deviation, and use the energy storage system to meet the real-time net load prediction deviation outside the confidence range. The upper confidence level α U,t and the lower confidence level α D,tAnd the real-time call correction for thermal power - gas - storage is as follows:

[0143]

[0144]

[0145]

[0146]

[0147] Wherein, X T,G,t is the output power that the thermal power and gas turbine units need to increase or decrease at time t to cope with the real - time net load prediction deviation; W ch,1,t is the energy storage charging power at time t of the day - ahead; W dis,1,t is the energy storage discharging power at time t of the day - ahead; W ch,t is the energy storage charging power at real - time t; W dis,t is the energy storage discharging power at real - time t.

[0148] According to the output power that the thermal power and gas turbine units increase or decrease to cope with the real - time load prediction deviation, the rated capacity of the thermal power unit, the rated capacity of the gas turbine unit, and the magnitude of the output power of the thermal power and gas turbine units called, the scale of the real - time reserve capacity configuration is calculated.

[0149] It should be noted that in order to ensure the normal operation of the energy storage system, sufficient charge - discharge space must be reserved at the initial moment. In the embodiment of the present invention, J groups of day - ahead net load prediction deviations ε PLD,B1,j,t are used to simulate the real - time situation. The total number of sampling times of the Monte Carlo sampling method is set to J, and the charging situation W ch,j,t and discharging situation W dis,j,t of the energy storage system at time t of the j - th sampling are calculated, so as to obtain the configuration scale W Bat of the energy storage and the energy storage state u Bat,t at time t, which are:

[0150] W Bat = u ch,0 + u dis,0

[0151]

[0152]

[0153]

[0154] Wherein, W Bat is the configuration scale of the energy storage; u ch,0 is the rechargeable capacity of the energy storage in the initial state; u dis,0 is the dischargeable capacity of the energy storage in the initial state; W MAX,ch,tis the maximum charging value of energy storage at time t; W MAX,dis,t is the maximum discharging value of energy storage at time t; W ch,τ and W dis,τ are the charging and discharging powers of energy storage at each τ moment before and including time t, respectively.

[0155] After being trained by the embodiment, it is more appropriate to select an initial state of energy storage with a rechargeable capacity of 2000 MW and a dischargeable capacity of 600 MW. Solve the configuration scale W RCCS2,t of the real-time reserve capacity at time t considering the new energy with energy storage and setting the confidence interval of the net load prediction deviation as:

[0156] W RCCS2,t = W T,all - W T,t + W G,all - W G,t + X T,G,t + u Bat,t

[0157] In the formula, W T,all and W G,all are the rated capacities of thermal power and gas turbine units respectively; W T,t and W G,t are the planned outputs of the thermal power and gas turbine units called at time t respectively; X T,G,t is the amount of output that needs to be increased or decreased by the thermal power and gas turbine units at time t to cope with the real-time net load prediction deviation; u Bat,t is the energy storage state at time t. Please refer to Table 4, which is the table of the output of real-time energy storage, thermal power, and gas power calls and the real-time reserve capacity configuration.

[0158] Table 4 Output of real-time energy storage, thermal power, and gas power calls and real-time reserve capacity configuration

[0159]

[0160] Implementing the embodiments of the present invention has the following beneficial effects:

[0161] In an embodiment of the present invention, based on a probability model of forced outage scenarios caused by component failures of thermal power and gas turbine units, the first expected value of system power shortage due to the uncertainty of component failures of the unit is calculated. The second expected value of system power shortage due to the uncertainty of day-ahead net load prediction is obtained according to the probability distribution of the day-ahead net load prediction deviation. The third expected value of system power shortage considering the uncertainty of component failures of the unit and the uncertainty of net load prediction is calculated based on the first expected value of system power shortage and the second expected value of system power shortage. The minimum outage cost is calculated according to the third expected value of system power shortage. And an objective function model is established based on the minimum outage cost, fuel cost, and unit output fluctuation. The day-ahead reserve capacity configuration scale is calculated according to the objective function model. And by setting the confidence interval of the real-time net load prediction deviation and introducing the real-time net load prediction deviation into the confidence interval, the real-time reserve capacity configuration scale is calculated. The influence of the expected value of system power shortage considering the uncertainty of component failures of the unit and the uncertainty of net load prediction on capacity configuration is comprehensively considered. And the day-ahead reserve capacity configuration scale is calculated by establishing an objective function model. And by introducing the real-time net load prediction deviation into the confidence interval, the real-time reserve capacity configuration scale is calculated. The day-ahead reserve capacity configuration scale and the real-time reserve capacity configuration scale can be accurately calculated, so as to effectively improve the reserve capacity configuration effect of new energy grid connection.

[0162] Please refer to Figure 5 , based on the same inventive concept as the above embodiment, an embodiment of the present invention provides a reserve capacity configuration device for new energy grid connection, including:

[0163] The first expected value calculation module 10 of system power shortage, which is used to calculate the first expected value of system power shortage due to the uncertainty of component failures of the unit based on a probability model of forced outage scenarios caused by component failures of thermal power and gas turbine units;

[0164] The day-ahead net load prediction deviation acquisition module 20, which is used to ignore the first expected value of system power shortage and perform Monte Carlo sampling simulation on the density function model to obtain the day-ahead net load prediction deviation;

[0165] The second expected value calculation module 30 of system power shortage, which is used to obtain the second expected value of system power shortage due to the uncertainty of day-ahead net load prediction according to the probability distribution of the day-ahead net load prediction deviation;

[0166] The third expected value calculation module 40 of system power shortage, which is used to calculate the third expected value of system power shortage considering the uncertainty of component failures of the unit and the uncertainty of net load prediction based on the first expected value of system power shortage and the second expected value of system power shortage;

[0167] The day-ahead reserve capacity configuration scale calculation module 50 is used to calculate the minimum outage cost according to the expected value of power shortage in the third system, establish an objective function model based on the minimum outage cost, fuel cost, and unit output fluctuation, and calculate the day-ahead reserve capacity configuration scale according to the objective function model;

[0168] The day-ahead reserve capacity configuration scale calculation module 60 is used to set the confidence interval of the real-time net load prediction deviation, introduce the real-time net load prediction deviation into the confidence interval, and calculate the real-time reserve capacity configuration scale.

[0169] In one embodiment, the first system power shortage expected value calculation module 10 is specifically used for:

[0170] Assume that the operating states of thermal power units and gas power units are discretely distributed and follow a (0,1) distribution;

[0171] Construct a probability model for the forced outage scenario caused by the failure of thermal power units and gas power unit components;

[0172] Based on the probability model, calculate the first system power shortage expected value of the uncertainty of unit component failures.

[0173] In one embodiment, the day-ahead net load prediction deviation acquisition module 20 is specifically used for:

[0174] Ignore the first system power shortage expected value, perform Monte Carlo random sampling simulation according to the density function model to obtain the predicted values for each sampling time, calculate the day-ahead load prediction deviation according to the predicted values, and the predicted values include load prediction values, wind speed prediction values, and light intensity prediction values.

[0175] In one embodiment, the second system power shortage expected value calculation module 30 is specifically used for:

[0176] Statistical sampling results, draw the probability distribution curve of the net load prediction deviation according to the kernel smoothing density, and sort the values of the probability distribution curve from small to large and divide them into several equally spaced arrays;

[0177] Draw the probability density histogram of each equally spaced array. The abscissa of the probability density histogram corresponds to the day-ahead net load prediction deviation values for different sampling times, and use the day-ahead net load prediction deviation values as the unit outage capacity corresponding to the prediction uncertainty;

[0178] Ignore the influence of the uncertainty of unit component failures on the power shortage expected value, and calculate the second system power shortage expected value considering the uncertainty of net load prediction.

[0179] In one embodiment, the day-ahead reserve capacity configuration scale calculation module 50 is specifically used for:

[0180] Set constraint conditions based on system power balance, output of thermal power and gas turbine units, ramps of thermal power and gas turbine units, and output of wind power and photovoltaic units;

[0181] According to the constraint conditions and the objective function model, use the particle swarm algorithm to simulate and obtain the output sizes of thermal power and gas turbine units, and calculate the configuration scale of the day-ahead reserve capacity based on the output sizes of thermal power and gas turbine units.

[0182] In one embodiment, the day-ahead reserve capacity configuration scale calculation module 60 is specifically used for:

[0183] Simulate the real-time situation on the operation day, replace the test set with the real-time true value on the operation day, obtain the improved output sizes of thermal power and gas turbine units, and calculate the real-time net load prediction deviation based on the output sizes of thermal power and gas turbine units;

[0184] Set the confidence interval of the real-time net load prediction deviation, introduce the real-time net load prediction deviation into the confidence interval, and force the thermal power and gas turbine units to increase or decrease their output within the confidence interval to cope with the real-time net load prediction deviation;

[0185] Calculate the real-time reserve capacity configuration scale based on the output increases or decreases of thermal power and gas turbine units to cope with the real-time load prediction deviation, the rated capacity of the thermal power unit, the rated capacity of the gas turbine unit, and the output sizes of thermal power and gas turbine units.

[0186] An embodiment of the present invention provides a computer storage medium. The computer-readable storage medium includes a stored computer program; wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the reserve capacity configuration method for new energy grid connection as described above.

[0187] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can still be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A method for configuring reserve capacity for new energy grid connection, characterized in that, it includes: Calculating the first expected value of system power shortage due to the uncertainty of unit component failures based on the probability model of forced outage scenarios caused by failures of thermal power and gas turbine unit components; Ignoring the first expected value of system power shortage, performing Monte Carlo sampling simulation on the density function model to obtain the day-ahead net load prediction deviation; Obtaining the second expected value of system power shortage due to the uncertainty of day-ahead net load prediction according to the probability distribution of the day-ahead net load prediction deviation; The obtaining the second expected value of system power shortage due to the uncertainty of day-ahead net load prediction according to the probability distribution of the day-ahead net load prediction deviation includes: statistically sampling results, drawing a probability distribution curve of the net load prediction deviation according to the core smoothing density, and sorting the values of the probability distribution curve from small to large and dividing them into several equidistant arrays; drawing a probability density histogram for each of the equidistant arrays, where the abscissa of the probability density histogram corresponds to the day-ahead net load prediction deviation values for different sampling times, and using the day-ahead net load prediction deviation values as the unit outage capacity corresponding to prediction uncertainty; ignoring the influence of the uncertainty of unit component failures on the expected value of power shortage, and calculating the second expected value of system power shortage considering the uncertainty of net load prediction; Calculating the third expected value of system power shortage considering the uncertainty of unit component failures and the uncertainty of net load prediction according to the first expected value of system power shortage and the second expected value of system power shortage; Calculating the minimum outage cost according to the third expected value of system power shortage, and establishing an objective function model based on the minimum outage cost, fuel cost, and unit output fluctuation, and calculating the day-ahead reserve capacity configuration scale according to the objective function model; Setting a confidence interval for the real-time net load prediction deviation, introducing the real-time net load prediction deviation into the confidence interval, and calculating the real-time reserve capacity configuration scale.

2. The method for configuring reserve capacity for new energy grid connection according to claim 1, characterized in that, The calculating the first expected value of system power shortage due to the uncertainty of unit component failures based on the probability model of forced outage scenarios caused by failures of thermal power and gas turbine unit components includes: Assuming that the operating states of thermal power and gas turbine units are discrete distributions and follow a (0,1) distribution; Constructing a probability model of forced outage scenarios caused by failures of thermal power and gas turbine unit components; Based on the probability model, calculating the first expected value of system power shortage due to the uncertainty of unit component failures.

3. The method for configuring reserve capacity for new energy grid connection according to claim 1, characterized in that, The density function model includes the probability density function of load, the probability density function of wind speed, and the probability density function of light intensity. The ignoring the first expected value of system power shortage and performing Monte Carlo sampling simulation on the density function model to obtain the day-ahead net load prediction deviation includes: Ignore the expected value of insufficient power of the first system, perform Monte Carlo random sampling simulation according to the density function model to obtain the predicted values for each sampling time, and calculate the day-ahead load prediction deviation according to the predicted values. The predicted values include load prediction values, wind speed prediction values, and light intensity prediction values.

4. The method for configuring reserve capacity for new energy grid connection according to claim 1, wherein, the calculation of the day-ahead reserve capacity configuration scale according to the objective function model includes: setting constraint conditions based on system power balance, the output of thermal power and gas turbine units, the ramping of thermal power and gas turbine units, and the output of wind power and photovoltaic units; according to the constraint conditions and the objective function model, using the particle swarm algorithm to simulate and obtain the output sizes of thermal power and gas turbine units called, and calculating the day-ahead reserve capacity configuration scale according to the output sizes of the thermal power and gas turbine units called.

5. The method for configuring reserve capacity for new energy grid connection according to claim 1, wherein, the setting of the confidence interval of the real-time net load prediction deviation and the introduction of the real-time net load prediction deviation into the confidence interval to calculate the real-time reserve capacity configuration scale includes: simulating the real-time situation of the operation day, replacing the test set with the real-time true value of the operation day to obtain the improved output sizes of thermal power and gas turbine units called, and calculating the real-time net load prediction deviation according to the output sizes of the thermal power and gas turbine units called; setting the confidence interval of the real-time net load prediction deviation, introducing the real-time net load prediction deviation into the confidence interval, and forcing the thermal power and gas turbine units to increase or decrease their output within the confidence interval to cope with the real-time net load prediction deviation; calculating the real-time reserve capacity configuration scale according to the output increase or decrease of the thermal power and gas turbine units to cope with the real-time net load prediction deviation, the rated capacity of the thermal power unit, the rated capacity of the gas turbine unit, and the output sizes of the thermal power and gas turbine units called.

6. A device for configuring reserve capacity for new energy grid connection, wherein, it includes: a first system power shortage expected value calculation module, configured to calculate the first system power shortage expected value of the uncertainty of unit component failures based on the probability model of forced outage scenarios caused by component failures of thermal power and gas turbine units; a day-ahead net load prediction deviation acquisition module, configured to ignore the first system power shortage expected value and perform Monte Carlo sampling simulation on the density function model to obtain the day-ahead net load prediction deviation; The second system power shortage expectation calculation module is used to obtain the second system power shortage expectation of the day-ahead net load prediction uncertainty according to the probability distribution of the day-ahead net load prediction deviation; specifically, it is used for: statistically sampling results, obtaining the probability distribution curve of the net load prediction deviation drawn according to the core smoothing density, sorting the values of the probability distribution curve from small to large and dividing them into several equidistant arrays; drawing the probability density histogram of each equidistant array, the abscissa of the probability density histogram corresponding to the day-ahead net load prediction deviation values of different sampling times, and taking the day-ahead net load prediction deviation values as the unit outage capacity corresponding to the prediction uncertainty; ignoring the influence of unit component failure uncertainty on the power shortage expectation, and calculating the second system power shortage expectation considering the net load prediction uncertainty; The third system power shortage expectation calculation module is used to calculate the third system power shortage expectation considering unit component failure uncertainty and net load prediction uncertainty according to the first system power shortage expectation and the second system power shortage expectation; The day-ahead reserve capacity configuration scale calculation module is used to calculate the minimum outage cost according to the third system power shortage expectation, establish an objective function model according to the minimum outage cost, fuel cost and unit output fluctuation, and calculate the day-ahead reserve capacity configuration scale according to the objective function model; The day-ahead reserve capacity configuration scale calculation module is used to set the confidence interval of the real-time net load prediction deviation, introduce the real-time net load prediction deviation into the confidence interval, and calculate the real-time reserve capacity configuration scale.

7. A computer-readable storage medium, characterized in that, the computer-readable storage medium includes a stored computer program; wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the reserve capacity configuration method for new energy grid connection according to any one of claims 1-5.

Images