Day-ahead unit commitment optimization method for uncertain scene climbing demand

Abstract

The invention discloses a day-ahead unit combination method for an uncertain scene climbing demand of an electric power system, the day-ahead unit combination method is based on the electric power system, and the electric power system comprises a thermal power generating unit scheduling model, an energy storage scheduling model and a renewable energy output scene set; step 101, establishing a day-ahead economic dispatching model according to output loads of the thermal power generating unit dispatching model and the energy storage dispatching model; step 102, calculating the maximum regulation capability of the thermal power generating unit of the power system at any moment t of the day-ahead economic dispatching model through a linear programming method; 103, sampling the renewable energy output scene set through hypercube to generate a renewable energy output error prediction curve; 104, judging whether the maximum regulation capability of the thermal power generating unit of the electric power system meets the climbing demand power in the renewable energy output error prediction curve or not; step 105, adjusting the maximum adjusting capability of the thermal power generating unit of the power system; the power supply cost of the system can be reduced while stable operation of the system is guaranteed, and the method has good engineering application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimizing power systems, and in particular relates to a day-ahead unit combination optimization method for uncertain scenario ramping demands. Background Art

[0002] In order to achieve the "dual carbon" goal, China's future installed capacity of new energy such as wind power and photovoltaic power will continue to expand. However, the uncertainty characteristics of renewable energy output lead to strong uncertainty in the net load curve, and the peak-shaving pressure of the power system will be further increased. There is an urgent need for a method to deal with the problem of insufficient peak-shaving capacity of the new power system caused by large-scale new energy grid connection.

[0003] As the capacity of new energy grid connection gradually increases, the uncertainty and anti-peak characteristics faced by the power system continue to increase. It is necessary to fully tap the flexible adjustment capabilities of the power system to meet the peak demand while increasing the consumption of renewable energy. The volatility of a high proportion of renewable energy can easily cause frequent start and stop of thermal power units in the system, which in turn leads to power imbalance between the power generation side and the user side. When wind and solar units are generating large amounts of power, full acceptance of wind and solar output requires shutdown of high-efficiency thermal power units; if the wind and solar grid output is restricted, a large amount of wind and solar abandonment will occur. In order to alleviate the plight of power systems with a high proportion of renewable energy, it is necessary to make full use of various flexible resources in the system to carry out optimized scheduling.

[0004] There are some research results in the academic community on the dispatch model with flexible ramping products. As far as the ramping demand design is concerned, different ramping demand design methods will have different effects on the solution results of the dispatch model with flexible ramping reserve. However, the ramping demand described in existing studies is often deterministic, ignoring the feasibility of implementing uncertain factors, which may cause load loss, wind and solar power abandonment, and increased operating costs. At present, the main methods for dealing with the uncertainty of renewable energy power forecasting are: (1) increasing spinning reserve. This method is simple and reliable, but because the current wind power prediction error is still large, the size of the reserved spinning reserve capacity is difficult to determine; (2) robust optimization method. The idea of ​​robust optimization is to transform the uncertainty of wind power into the form of an uncertain solution set. Considering the worst scenario, it often makes the dispatch result more conservative and less economical; (3) random modeling method based on chance constrained programming. This method is difficult to derive an analytical chance constraint model for large-scale power systems, and it is also difficult to objectively determine what confidence level to set; (4) random optimization method based on scenario set. Compared with the previous methods, this method can avoid overly conservative dispatch results and improve the rationality of the optimization scheme while taking into account the probability distribution of renewable energy output. It is suitable for provincial power system ramp demand optimization method with high penetration rate of renewable energy. Summary of the invention

[0005] In view of the difficulties in the prior art, the present invention proposes a method for optimizing the day-ahead unit combination for the ramping demand in uncertain scenarios, which takes into account the strong uncertainty of renewable energy output and optimizes the ramping demand capacity of the provincial system. The present invention solves the technical problems existing in the prior art, namely: ① taking into account the strong uncertainty of renewable energy output, so that the results of the optimization scheduling model are more in line with engineering practice; ② taking into account the power support role of inter-provincial interconnection lines, solving the problem of insufficient ramping power in power systems with high penetration rates.

[0006] In order to solve the problem of insufficient system climbing power caused by strong uncertainty in renewable energy output, the present invention adopts the following technical solution (to be added after the technical solution is determined):

[0007] A method for optimizing the day-ahead unit combination for the ramping demand of power system uncertainty scenarios, the method for optimizing the day-ahead unit combination is based on the power system, the power system includes a thermal power unit scheduling model, an energy storage scheduling model and a renewable energy output scenario set; wherein: the method includes the following steps:

[0008] Step 101, establishing a day-ahead economic dispatch model according to the output load of the thermal power unit dispatch model and the energy storage dispatch model;

[0009] Right now:

[0010] C=C 1 +C 2

[0011] Where: C 1 Represents the operating cost of thermal power units; C 2 represents the energy storage dispatch model;

[0012] The constraints are:

[0013]

[0014] Among them, P t L represents the load of the power grid at time t; P t dis and P t cha Represents the charging and discharging power of the grid energy storage device, P t PV and P t Wind Indicates the output level of photovoltaic and wind turbines in the corresponding scenario; including:

[0015] Peak load power upper limit constraint:

[0016]

[0017] in, represents the peak load capacity provided by the neighboring network at time t, Indicates the upper limit of the peak load capacity provided by the neighboring network.

[0018] Wind turbine and photovoltaic output upper limit constraints

[0019]

[0020] Spinning reserve constraints

[0021]

[0022] Among them, P res,down,t and P res,up,t They represent the lower reserve and upper reserve requirements of the provincial power system at time t respectively;

[0023] Step 102, using a linear programming method to calculate the maximum regulation capacity of the thermal power units in the power system at any time t of the day-ahead economic dispatch model; that is:

[0024]

[0025] Among them, P t s,up and P t s,dn They represent the maximum upward and downward adjustable capacity of the system at time t respectively.

[0026] Step 103: Sampling the renewable energy output scenario set by hypercube to generate a renewable energy output error prediction curve;

[0027] Step 104: determine whether the maximum regulation capacity of the thermal power unit in the power system meets the ramp demand power in the renewable energy output error prediction curve. If the demand is met, output the optimal output model of the thermal power unit in the power system; otherwise, proceed to the next step:

[0028] Step 105: The maximum regulating capacity of the thermal power generating units in the power system is adjusted according to the following steps:

[0029] 501. The day-ahead economic dispatch model taking into account the power support of the tie line is established according to the following formula:

[0030] C=C 1 +C 2 +C 3

[0031] Where: C 3 For regional power grids through inter-provincial interconnection lines, namely:

[0032]

[0033] in, and They respectively represent the real-time electricity prices of the peak-shaving capacity and peak-shaving electricity purchased by the power grid from the upper-level power grid; and Indicates the peak load capacity and peak load electricity purchased by the power grid from the upper power grid;

[0034] Peak load power upper and lower limit constraints:

[0035]

[0036] 502. The maximum value of the difference vector is the upper limit capacity of the tie line Substitute into the day-ahead economic dispatch model in step 101 for calculation.

[0037] Furthermore, the process of establishing a dispatching model for thermal power units includes:

[0038] The operating cost of the thermal power unit is constructed according to the following formula:

[0039]

[0040] Among them, a i , b i and c i is the operating cost coefficient of the i-th thermal power unit; and They represent the active power output and start-up and shutdown costs of the i-th thermal power unit at time t; S i,t Indicates the start and stop status of the i-th thermal power unit at time t, with start being 1 and stop being 0; It represents the start-up and shutdown cost of the unit;

[0041] The constraints are as follows:

[0042]

[0043] and They represent the startup and shutdown duration of the i-th thermal power unit at time t respectively; and They represent the minimum output, maximum output, maximum climbing speed, minimum startup time and minimum shutdown time of the i-th unit respectively.

[0044] Furthermore, the process of establishing the energy storage dispatch model includes:

[0045] The energy storage operating cost is constructed according to the following formula:

[0046]

[0047] Among them, s ESP represents the cost of energy storage equipment per 1kWh of discharge; t Dis represents the discharge power of the energy storage device at time t;

[0048] Constraints on the relationship between the amount of energy stored and the charging and discharging power:

[0049]

[0050] Among them, E 1 and E Last They represent the power of the energy storage device at the initial moment and at the end of the dispatch cycle respectively; E t and P t Cha Indicates the power and charging power of the energy storage device at time t;

[0051] There are upper and lower limits on energy storage capacity:

[0052]

[0053] Among them, E max Indicates the maximum capacity of the energy storage device, m 1 and m 2 is a constant and is related to the type of energy storage device;

[0054] There is an upper limit constraint on the charging and discharging power of energy storage equipment:

[0055]

[0056] Among them, λ is a constant;

[0057] Constraints prohibiting simultaneous charging and discharging of energy storage devices:

[0058] P t Dis *P t Cha =0.

[0059] Furthermore, the ramp demand power process is obtained through the renewable energy output error prediction curve; including:

[0060] 401. Calculate the probability distribution function of the forecast error of renewable energy and load output according to the following formula;

[0061]

[0062] Among them, P t fore , P cap Represent the output of renewable energy and the installed capacity of renewable energy at time t, respectively, and the normal distribution parameters of the prediction error of each time section can be obtained, that is, it obeys the normal distribution

[0063] 402. Generate renewable energy output scenario set based on Latin hypercube sampling:

[0064] P PV =P PV +P PV *X PV,j

[0065] Among them, the prediction error rate of the jth scene is recorded as X j , The predicted output vector of wind turbines, photovoltaics and the next dispatching period is denoted as P PV , P Wind , P L , which are:

[0066] 403. Obtain the renewable energy output error prediction curve according to the following formula:

[0067] P L,t =P t L -P t PV -P t Wind

[0068] Among them, P L,t It represents the power gap caused by the load, photovoltaic and wind turbine forecast deviation at time t.

[0069] 404. Calculate the ramp demand power at any time t in any scenario according to the renewable energy output error prediction curve using the following formula:

[0070]

[0071] Among them, P t L,up and P t L,dn They represent the upward climbing and downward climbing capacities required by the system at time t respectively.

[0072] Beneficial Effects

[0073] Compared with the traditional technical solution, the beneficial effects brought by the present invention are:

[0074] 1. The present invention takes the optimization of ramp demand capacity of provincial power systems as the research object. When using this patent, it is only necessary to input the output data of renewable energy sources that are easily available in the local power system and the capacity of inter-provincial interconnection lines to carry out the optimization of ramp demand capacity.

[0075] 2. The present invention takes into account the strong uncertainty of renewable energy output and the power support role of inter-provincial interconnection lines on the system, optimizes the output of various flexible resources such as thermal power units and energy storage in the system, and can reduce the power supply cost of the system while ensuring stable operation of the system, thus having good engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a flow chart of a method for optimizing the day-ahead unit commitment facing the ramping demand of an uncertain scenario according to the present invention;

[0077] Figure 2 The invention relates to a schematic diagram of a system climbing demand calculation principle. DETAILED DESCRIPTION

[0078] The following is combined with Figures 1-2 The implementation process of the present invention is described in further detail.

[0079] The present invention provides a day-ahead unit combination method for uncertain scenario ramping demand, the day-ahead unit combination method is based on a power system, the power system includes a thermal power unit scheduling model, an energy storage scheduling model and a renewable energy output scenario set; wherein: the method includes the following steps:

[0080] Based on the probability distribution function of renewable energy output forecast error, Latin hypercube sampling is used to generate renewable energy output curves with forecast error.

[0081] Count the difference between the climbing power demand and the system regulation capability, and calculate the maximum climbing power demand;

[0082] Step 101: Determine the maximum regulation capacity of the system based on the day-ahead economic dispatch model

[0083] 1. Modeling of flexible resources in power systems

[0084] 1.1 Establishing a dispatching model for thermal power units

[0085] Operating costs of thermal power units

[0086]

[0087] Among them, a i , b i and c i is the operating cost coefficient of the i-th thermal power unit; and They represent the active power output and start-up and shutdown costs of the i-th thermal power unit at time t; S i,t It indicates the start and stop status of the i-th thermal power unit at time t, where start is 1 and stop is 0. Represents the start-up and shutdown cost of the unit.

[0088] The constraints that exist are as follows:

[0089]

[0090] and They represent the startup and shutdown duration of the i-th thermal power unit at time t respectively; and They represent the minimum output, maximum output, maximum ramp speed, minimum startup time and minimum shutdown time of the i-th unit respectively;

[0091] 1.2 Establishing an energy storage dispatch model

[0092] The energy storage operating cost can be calculated using the following formula:

[0093]

[0094] Among them, s ES P represents the cost of energy storage equipment per 1kWh of discharge; t Dis Represents the discharge power of the energy storage device at time t

[0095] Constraints on the relationship between the amount of energy stored and the charging and discharging power:

[0096]

[0097] Among them, E 1 and E Last They represent the power of the energy storage device at the initial moment and at the end of the dispatch cycle respectively; E t and P t Cha Indicates the power and charging power of the energy storage device at time t;

[0098] There are upper and lower limits on energy storage capacity:

[0099]

[0100] Among them, E max Indicates the maximum capacity of the energy storage device, m 1 and m 2 is a constant and is related to the type of energy storage device.

[0101] There is an upper limit constraint on the charging and discharging power of energy storage equipment:

[0102]

[0103] Among them, λ is a constant, which is related to the energy storage device used.

[0104] Energy storage devices are not allowed to charge and discharge simultaneously:

[0105] P t Dis *P t Cha =0

[0106] 2. Establish a day-ahead economic dispatch model

[0107] The objective function of the provincial power system day-ahead dispatch model can be expressed as follows:

[0108] C=C 1 +C 2

[0109] In addition to the constraints of various flexible resources, the following constraints must also be considered: power balance constraints:

[0110]

[0111] Among them, P t L represents the load of the power grid at time t; P t dis and P t cha Represents the charging and discharging power of the grid energy storage device, P t PV and P t Wind Indicates the output level of photovoltaic and wind turbines in the corresponding scenario.

[0112] Peak load power upper limit constraint:

[0113]

[0114] Wind turbine and photovoltaic output upper limit constraints

[0115]

[0116] Spinning reserve constraints

[0117]

[0118] Among them, P res,down,t and P res,up,t They respectively represent the lower reserve and upper reserve requirements of the provincial power system at time t.

[0119] Step 102, solve the model to calculate the maximum adjustment capacity of the system:

[0120] The linearization method is used to linearize the quadratic function of the operating cost of the thermal power unit. CPLEX is used to solve the linear model of day-ahead economic dispatch to obtain the start-up and shutdown plan of the thermal power unit.

[0121] For any time t, the maximum regulation capacity of the system thermal power unit is calculated as follows:

[0122]

[0123] Step 103: Generate renewable energy output scenarios with prediction errors using Latin hypercube sampling:

[0124] 1. Fitting the probability distribution function of renewable energy and load output forecast error based on moment estimation method;

[0125] The forecast error of renewable energy output at each time section is zero and the standard deviation is σ t The normal distribution of renewable energy output forecast error is σ t Calculated by the following formula:

[0126]

[0127] Among them, P t fore , P cap Represent the output of renewable energy and the installed capacity of renewable energy at time t, respectively, and the normal distribution parameters of the prediction error of each time section can be obtained, that is, it obeys the normal distribution

[0128] 2. Generate renewable energy and load output scenario sets based on Latin hypercube sampling

[0129] Latin hypercube sampling is a stratified sampling method that consists of two steps: generating the original scene using Latin hypercube sampling, the specific steps are as follows:

[0130] 2.1 Sampling

[0131] Assume x 1 , x 2 , …, x s There are s input random variables, where the kth random variable x k The cumulative probability distribution function of (k=1,2,…,s) is:

[0132] μ k =F k (x k )

[0133] Assume the sampling scale is N, and set F k The value space of x (for example, 0-1) is evenly divided into N intervals. A number is randomly selected from each interval. Then the variable x k The sample value of can be calculated using the inverse function method as follows:

[0134] x ki =F k -1 ((i-η) / N)

[0135] Where: x ki is a random variable x k The sample in the i-th interval; η is a random number between 0 and 1; i = 1, 2, ..., N. k The sample values ​​of X are arranged in a row, and we can get k ; Sampling all input random variables can obtain an s×N dimensional sample matrix X.

[0136] 2.2 Arrangement

[0137] After obtaining the sample matrix X, it is necessary to sort the positions of the elements in each row of X. The most basic arrangement method is random arrangement. For independent random variables, the correlation coefficient matrix of its sample matrix should be a unit matrix. If RP is used for sorting, the correlation coefficient matrix of the sample matrix may become a non-unit matrix. For this reason, a more reasonable arrangement method is needed to eliminate the unexpected correlation. Cholesky decomposition has high computational efficiency and excellent correlation control effect. The process of the arrangement method based on Cholesky decomposition is as follows:

[0138] Assume that L is an s×N dimensional sequential matrix, each row of which is a certain arrangement of 1, 2, …, N, which corresponds to the arrangement of each row element of the sample matrix X. A is an s×s dimensional linear correlation matrix of L, which is a pairwise positive definite matrix, so A can be decomposed by Cholesky:

[0139] A=DD T

[0140] Where: D is a lower triangular matrix.

[0141] The s×N dimensional matrix G can be calculated as follows:

[0142] G=D -1 L

[0143] Unlike matrix L, the elements of matrix G are not necessarily positive integers. Generate matrix G' based on G, so that each row of G' is a permutation of 1, 2, ..., N, and its permutation order corresponds to the permutation order of each row of elements in G. According to the permutation of each row of elements in G', update the permutation of each row of elements in sample matrix X to obtain the final sample matrix X'.

[0144] 2.3 Generate a renewable energy output scenario set based on steps 2.1 and 2.2

[0145] According to steps 2.1 and 2.2, the prediction error rate of J scenarios is generated, where the prediction error rate of the jth scenario is recorded as Xj , The predicted output vector of wind turbines, photovoltaics and the next dispatching period is denoted as P PV , P Wind , P L , which are: The output of the photovoltaic meter and the prediction error in the jth scenario can be calculated using the following formula:

[0146] P PV =P PV +P PV *X PV,j

[0147] Similarly, the output of the wind turbine taking into account the prediction error in the jth scenario can be calculated.

[0148] 2.4. For any scenario and any time t, the system net load curve is calculated as follows:

[0149] P L,t =P t L -P t PV -P t Wind

[0150] The system's required capabilities are as follows: Figure 2 As shown, the calculation method is as follows:

[0151]

[0152] Step 104, calculate whether the provincial power system regulation capacity meets the ramp power demand, if yes, output the result, otherwise execute step 105.

[0153] By comparing and analyzing the system regulation capability obtained in step 102, it can be determined whether the system demand capability is within the range of the system regulation capability, and the power difference can be calculated.

[0154] Step 105: Optimize the output of thermal power units by taking into account the inter-provincial interconnection lines, and re-carry out day-ahead economic dispatch for scenarios that do not meet the peak power demand, and output the output curves of various flexible resources such as thermal power units, energy storage, and inter-provincial interconnection lines. Including:

[0155] 1. Establish a day-ahead economic dispatch model taking into account the power support of the tie line

[0156] The regional power grid provides power support for the provincial power system through inter-provincial interconnection lines, with a cost of C 3 It can be calculated by the following formula:

[0157]

[0158] in, and They respectively represent the real-time electricity prices of the peak-shaving capacity and peak-shaving electricity purchased by the power grid from the upper-level power grid;

[0159] and It indicates the peak-shaving capacity and peak-shaving electricity purchased by the power grid from the superior power grid.

[0160] Therefore, the objective function of the provincial power system day-ahead dispatch model can be expressed as follows:

[0161] C=C 1 +C 2 +C 3

[0162] In addition to the constraints described in step 101, the following constraints must also be considered:

[0163] Peak load power upper and lower limits constraints:

[0164]

[0165] 2. For each scenario, the difference between the system demand capacity and the regulation capacity is calculated based on the method in step 103, where the maximum value of the difference vector is the upper limit capacity of the tie line, which is

[0166] 3. Substitute the day-ahead economic dispatch model described in 101, solve the dispatch plan for the thermal power units, and update the regulation capacity of the system.

[0167] Although the present invention has been described above, the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can make many modifications without departing from the purpose of the present invention, which are all within the protection of the present invention.

Claims

1. A day-ahead unit commitment optimization method for power system uncertainty scenario ramping demand, characterized in that: The day-ahead unit combination method is based on a power system, which includes a thermal power unit scheduling model, an energy storage scheduling model, and a renewable energy output scenario set; wherein: the method includes the following steps: Step 101, establishing a day-ahead economic dispatch model according to the output load of the thermal power unit dispatch model and the energy storage dispatch model; Right now: C=C1+C2 Where: C1 represents the operating cost of the thermal power unit; represents the energy storage dispatch model; The constraints are: in, represents the load of the power grid at time t; and Indicates the charging and discharging power of the grid energy storage device, and Indicates the output level of photovoltaic and wind turbines in the corresponding scenario; including: Peak load power upper limit constraint: in, represents the peak load capacity provided by the neighboring network at time t, Indicates the upper limit of the peak load-shaving capacity provided by the neighboring network. Wind turbine and photovoltaic output upper limit constraints Spinning reserve constraints Among them, P res,down,t and P res,up,t They represent the lower reserve and upper reserve requirements of the provincial power system at time t respectively; Step 102, using a linear programming method to calculate the maximum regulation capacity of the thermal power units in the power system at any time t of the day-ahead economic dispatch model; that is: in, and They represent the maximum upward and downward adjustable capacity of the system at time t respectively. Step 103: Sampling the renewable energy output scenario set by hypercube to generate a renewable energy output error prediction curve; Step 104: determine whether the maximum regulation capacity of the thermal power unit in the power system meets the ramp demand power in the renewable energy output error prediction curve. If the demand is met, output the optimal output model of the thermal power unit in the power system; otherwise, proceed to the next step: Step 105: The maximum regulating capacity of the thermal power generating units in the power system is adjusted according to the following steps:

501. The day-ahead economic dispatch model taking into account the power support of the tie line is established according to the following formula: C=C1+C2+C3 Among them: C3 is the regional power grid through the inter-provincial interconnection line, that is: in, and They respectively represent the real-time electricity prices of the peak-shaving capacity and peak-shaving electricity purchased by the power grid from the upper-level power grid; and Indicates the peak load capacity and peak load electricity purchased by the power grid from the upper power grid; Peak load power upper and lower limit constraints:

502. The maximum value of the difference vector is the upper limit capacity of the tie line Substitute into the day-ahead economic dispatch model in step 101 for calculation.

2. The method for optimizing the day-ahead unit commitment for power system uncertainty scenario ramping demand according to claim 1, characterized in that: The process of establishing a thermal power unit dispatch model includes: The operating cost of the thermal power unit is constructed according to the following formula: Among them, a i 、b i and c i is the operating cost coefficient of the i-th thermal power unit; and They represent the active power output and start-up and shutdown costs of the i-th thermal power unit at time t; S i,t Indicates the start and stop status of the i-th thermal power unit at time t, with start being 1 and stop being 0; It represents the start-up and shutdown cost of the unit; The constraints are as follows: and They represent the startup and shutdown duration of the i-th thermal power unit at time t respectively; and They represent the minimum output, maximum output, maximum climbing speed, minimum startup time and minimum shutdown time of the i-th unit respectively.

3. The method for optimizing the day-ahead unit commitment for power system uncertainty scenario ramping demand according to claim 1, characterized in that: The process of establishing an energy storage dispatch model includes: The energy storage operating cost is constructed according to the following formula: Among them, s ES Indicates the cost of each 1kWh of energy storage equipment discharged; Represents the discharge power of the energy storage device at time t; Constraints on the relationship between the amount of energy stored and the charging and discharging power: Among them, E1 and E Last They represent the power of the energy storage device at the initial moment and at the end of the dispatch cycle respectively; E t as well as Indicates the power and charging power of the energy storage device at time t; There are upper and lower limits on energy storage capacity: Among them, E max Indicates the maximum power of the energy storage device. m1 and m2 are constants and are related to the type of energy storage device. There is an upper limit constraint on the charging and discharging power of energy storage equipment: Among them, λ is a constant; Constraints prohibiting simultaneous charging and discharging of energy storage devices:

4. The method for optimizing the day-ahead unit commitment for power system uncertainty scenario ramping demand according to claim 1, characterized in that: The ramp demand power process is obtained through the renewable energy output error prediction curve; include:

401. Calculate the probability distribution function of the forecast error of renewable energy and load output according to the following formula; in, P cap Represent the output of renewable energy and the installed capacity of renewable energy at time t, respectively, and the normal distribution parameters of the prediction error of each time section can be obtained, that is, it obeys the normal distribution 402. Generate renewable energy output scenario set based on Latin hypercube sampling: P PV =P PV +P PV *X PV,j Among them, the prediction error rate of the jth scene is recorded as X j , The predicted output vector of wind turbines, photovoltaics and the next dispatching period is denoted as P PV , P Wind , P L , which are:

403. Obtain the renewable energy output error prediction curve according to the following formula: Among them, P L,t It represents the power gap caused by the load, photovoltaic and wind turbine forecast deviation at time t.

404. Calculate the ramp demand power at any time t in any scenario according to the renewable energy output error prediction curve using the following formula: in, and They represent the upward climbing and downward climbing capacities required by the system at time t respectively.

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