Meteorological factor-based multi-new energy electric field joint output scene generation method

Abstract

The invention relates to a meteorological factor-based multi-new energy electric field joint output scene generation method. The method comprises the following steps of: calculating historical output errors of a wind power plant and a photovoltaic power plant; dividing the historical output error into a plurality of error sample sets; respectively calculating an error probability density function and a distribution function of the wind power output and the photovoltaic output in each error sample set; combining the output error probability distributions to form a density function of the overall combined probability distribution; dividing intervals for predicted output of a wind power plant and a photovoltaic electric field in the future, and performing inverse transformation sampling on functions corresponding to different intervals to obtain a plurality of output error time sequence curves; clustering to obtain a typical output error time sequence curve, and superposing the typical output error time sequence curve and the predicted output time sequence curve to obtain a typical scene; according to the method, meteorological factors and output errors are considered, the output condition of multiple new energy electric fields in the future is effectively predicted, and the absorption capacity of high-uncertainty new energy output is improved.

Description

Technical Field

[0001] The invention relates to a method for generating a multi-renewable energy electric field joint output scene based on meteorological factors, and belongs to the technical field of new energy electric fields. Background Art

[0002] Due to the increasingly serious environmental problems and the gradual scarcity of fossil fuels, renewable energy generation technologies, such as wind and solar energy, have developed rapidly in recent years. At the same time, under the background of "dual carbon", wind power and photovoltaic green clean energy have shown an explosive growth trend. With the rapid development of wind and photovoltaic power generation and the increasing installed capacity, new energy power generation with characteristics of volatility, randomness and uncertainty has occupied an increasingly high proportion in the new power system. In the case of large-scale new energy fields connected to the grid, it will bring huge challenges to the safe and stable operation and economic dispatch of the power system.

[0003] In order to make full use of renewable energy and reduce its potential threat to the safe and stable operation of the power system, uncertainty prediction of renewable energy output is a key method. At present, there are interval prediction, probability prediction and scenario prediction for the uncertainty prediction of renewable energy output. Among them, the scenario prediction method is more intuitive and specific, so it has become the current research focus. Researchers mainly use probabilistic modeling methods to establish a mathematical model that conforms to the characteristics of random power generation output, and then analyze historical output data and generate scenarios through sampling methods. However, the methods for generating scenarios under the premise of considering meteorological factors and output errors are relatively limited. The existing scenario prediction of the joint output of multiple renewable energy power plants does not generate scenarios under the premise of considering meteorological factors and output errors, and cannot reflect the real scenario of the joint output prediction of multiple renewable energy power plants. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention provides a scene generation method for multiple renewable energy electric fields taking into account meteorological factors and output errors.

[0005] In order to achieve the above-mentioned purpose, the technical solution proposed by the present invention is: a method for generating a multi-renewable energy power field joint output scene based on meteorological factors, obtaining historical measured meteorological data, historical predicted meteorological data, historical actual output of wind farms and photovoltaic farms, and historical predicted output of wind farms and photovoltaic farms: comprising the following steps:

[0006] Step 1: Calculate the historical output error of the wind farm according to the historical actual output of the wind farm and the historical predicted output of the wind farm;

[0007] Calculate the historical output error of the photovoltaic field based on the historical actual output of the photovoltaic field and the historical predicted output of the photovoltaic field;

[0008] Step 2: Divide the area into several intervals according to wind speed, irradiance and time;

[0009] Step 3: Divide the historical output error of the wind farm and the historical output error of the photovoltaic farm into several intervals according to the corresponding wind speed, irradiance and time to form several error sample sets;

[0010] Step 4: Use the kernel density estimation method to obtain the probability density function of the wind power output and photovoltaic output error samples in each error sample set, and then obtain the error probability distribution;

[0011] Step 5: Combine the output error probability distributions of multiple wind farms and photovoltaic farms in each error sample to form a Copula density function of the overall joint probability distribution;

[0012] Step 6: divide the predicted output data of future wind farms and photovoltaic farms into intervals according to the rules of step 2, perform inverse transformation sampling on the Copula functions corresponding to different intervals, and obtain several future wind and photovoltaic output error time series curves;

[0013] Step 7: Cluster the obtained several future wind and solar power output error time series curves to obtain a typical output error time series curve. Superimpose the typical output error time series curve and the predicted output time series curve to obtain a typical scenario.

[0014] A further design of the above technical solution is as follows: in step 1, for N w wind farms and N s The wind power output of the i-th wind farm and the photovoltaic output of the j-th photovoltaic farm in the wind power photovoltaic cluster of the photovoltaic farms are modeled, and the historical output error of the wind farm and the photovoltaic farm is:

[0015] ΔP i，w (t) = P i，w，r (t)-P i，w，f (t)

[0016] ΔP j，s (t) = P j，s，r (t)-P j，s，f (t)

[0017] Among them, P i，w，r , (t), P j，s，r (t) represent the normalized historical actual output of wind farm and photovoltaic farm, P i，w，f (t), P j，s， f(t) represents the normalized historical predicted output of wind farm and photovoltaic farm, t is a certain time in the time period, t∈[1, 2, ..., T], i represents N w A wind farm among wind farms, i∈[1, 2, ..., N w ], j represents N sA photovoltaic field among the photovoltaic fields, j∈[1,2,...,N s ], ΔP i，w (t), ΔP j，s (t) represent the historical output errors of wind farm and photovoltaic farm respectively.

[0018] In step 2, the data is divided into intervals according to three dimensions: wind speed, photovoltaic irradiance, and prediction time. Each dimension is divided into three segments, forming a total of 27 intervals.

[0019] In step 2, the wind speed is divided into three sections with equal intervals from low to high, and the irradiance ranges from the minimum irradiance 0 to the maximum irradiance S max It is divided into the first 10%, 10%-80% and 80%-100%, and the time is divided into the first 6 hours of the day, 6 to 18 hours and 18 to 24 hours.

[0020] In step 4, for each error sample set E k Error samples within ΔP i，w (t) and ΔP j，s (t) To calculate the probability distribution, first assume that the error sample set E k The error sample of the internal wind farm is X′ i，k，1 , X′ i，k，2 , ..., X′ i，k，m , the photovoltaic electric field error sample is Y′ j，k，1 , Y′ j，k，2 , ..., Y′ j，k，n , then the corresponding probability density function Φ i，k (X′), Φ j，k The kernel density estimate of (Y′) is:

[0021]

[0022] Where m and n are sample sizes; h is bandwidth; K(·) is kernel function. The corresponding probability distribution function is φ i，k (x),φ j，k (y):

[0023]

[0024]

[0025] The kernel function is a uniform kernel function, a triangular kernel function or a Gaussian kernel function.

[0026] The step 5 specifically comprises the following steps:

[0027] S1: in the kth error sample set E kThe output error variable x of the i-th wind farm and the j-th photovoltaic farm i and j The probability distribution functions are φ i，k (x),φ j，k (y), x i The domain of is [0, x i,max ]、y j The domain of is [0, y j,max ]; let the discretization interval be ΔP, then the discretized probability sequence X i，k (a) Y j，k (b) is:

[0028]

[0029] Where N i,X =[X i,max / ΔP] is the sequence X i，k (a) Length, N i,Y =[y i,max / ΔP] is the sequence Y i，k (b) The length of [x] represents the integer of x;

[0030] S2: Output error variable x of the i+1th wind farm and the j+1th photovoltaic farm i+1 ,y j+1 The probability distribution function of i+1，k (x i+1 ),φ j+1，k (y j+1 ), the probability sequence is X i+1，k (a) Y j+1，k (b), length N i+1，X 、N j+1，Y ;

[0031] S3: The total output variable Zx of two adjacent wind farms and two adjacent photovoltaic farms i，i+1 =x i +x i+1 , Zy j，j+1 =y j +y j+1 The probability distribution of is:

[0032]

[0033] That is, the dependent probability sequence X i，k (a) with X i+1，k (a) Copula sequence between, c i，i+1 (·) is the output variable x of two adjacent wind farms i and x i+1Copula density function between; That is, the dependent probability sequence Y j，k (b) With Y j+1，k (b) Copula sequence between, c j，j+1 (·) is the output variable y of two adjacent photovoltaic fields j and j+1 Copula density function between;

[0034] S4: If i+1 <N w 、j+1 <N s , then repeat S2 to S3 to obtain the output error variable X of the i+2nd wind farm i+2 The probability distribution function of i+2，k (x i+2 ), the probability sequence is X i+2,k (a), length N i+2,X , and obtain the j+2th photovoltaic field output error variable y j+2 The probability distribution function of j+2，k (y j+2 ), the probability sequence is Y j+2，k (a), length N j+2，Y , and continue with S5;

[0035] If i+1=N w 、j+1=N s , then we get all N w The probability distribution of the combined output error of wind farms and N s The probability distribution of the combined output error of the photovoltaic electric fields is calculated, and S7 is executed;

[0036] S5: Let the total output variable of the first i+1 wind farm be Zx e , the probabilistic sequence is zx e (a), length N e =N 1，X +...+N i+1，X , e=1+...+i+i+1;

[0037] Let the total output variable of the first j+1 photovoltaic power fields be Zy f , the probability sequence is zy f (b), length N f =N 1，Y +...+N j+1，Y , f=1+...+j+j+1;

[0038] The total output error variable Zx between the first i+1 wind farm and the i+2 wind farm is e，i+2 =Zx e +x i+2The probability distribution of the total output error variable Zy between the first j+1 photovoltaic field and the j+2 photovoltaic field f，j+2 =Zy f +y j+2 The probability distribution of is:

[0039]

[0040] That is, the dependent probability sequence zx e (a) with X i+2,m (a) Copula sequence between, c e，i+2 (·) is the output variable Zx of the first i+1 wind farm and the i+2 wind farm e and X i+2 Copula density function between; That is, the dependent probability sequence zy f (b) With Y j+2，k (b) Copula sequence between, c f，j+2 (·) is the output variable Zy of the first j+1 photovoltaic field and the j+2 photovoltaic field f and j+2 Copula density function between;

[0041] S6: Return to S4;

[0042] S7: Obtain the probability distribution of the joint output error of the wind power cluster and the probability distribution of the joint output error of the photovoltaic cluster under the kth error set;

[0043] In the kth error sample set E k The error variable ww of the wind power cluster combined output g and the photovoltaic cluster joint output error variable ss h The probability distribution functions are φ g，k (ww),φ h，k (ss), ww g The domain of is [0, ww g,max ]、ss h The domain of is [0, ss h,max ]; let the discretization interval be ΔP, then the discretized probability sequence WW g，k (a) SS h，k (b) is:

[0044]

[0045]

[0046] Where N g,ww =[ww g,max / ΔP] is the sequence WW g，k (a) Length, N h,ss =[ss h,max / ΔP] is the sequence SS h,k (b) The length of [x] represents the integer of x.

[0047] S8: Error sample set E k The combined output error variable ws of the wind power cluster and the photovoltaic cluster g，h =ww g +ss h The probability distribution of

[0048] for:

[0049]

[0050] That is, the dependent probability sequence WW g，k (a) and SS h，k (b) Copula sequence between, c g，h (·) is the combined output error variable ww of the wind power cluster and the photovoltaic cluster g and ss h Copula density function between;

[0051] S9: If k<27, repeat S1 to S8 to obtain the joint output error probability distribution of the wind power cluster and the photovoltaic cluster under the kth error set; if k=27, obtain the joint output error probability distribution of the wind power cluster and the photovoltaic cluster under all error sets.

[0052] The step 6 specifically comprises the following steps:

[0053] S1: At the i-th moment, the wind power cluster joint output error variable ww g and the photovoltaic cluster joint output error variable ss h The probability distribution function of g，j (ww),φ h，j (ss) satisfies the jth classification, the combined output error variable ww of the wind power cluster and the photovoltaic cluster g and ss h The correlation coefficient matrix of the Copula density function between is A, and the standard normal distribution sample Z is constructed: (Z1, Z2) ~ N (0, I2), where I2 is a 2D unit matrix and a chi-square distribution sample χ with v degrees of freedom. 2 (v), and finally form the τ distribution sample T:

[0054] Cumulative distribution function F using τ distribution T (T, v) transforms sample T into a uniformly distributed sample (Ui , V i ), and then the wind and solar output error samples (ΔP w (i), ΔP s (i)), the specific formula is as follows:

[0055]

[0056] S2: If i<96, repeat S1 to obtain the wind and solar output error sample (ΔP w (i), ΔP s (i)); if i = 96, continue with S3;

[0057] S3: The obtained wind and solar output error samples (ΔP w (i), ΔP s (i)) are connected in sequence to form the wind and solar output error time series curve ΔP = (ΔP w , ΔP s ), the specific formula is as follows:

[0058]

[0059] In step 7, a plurality of future wind and solar power output error time series curves are clustered based on the k-means theory to obtain a typical output error time series curve.

[0060] A device for generating a multi-renewable energy electric field joint output scene based on meteorological factors, comprising a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the above-mentioned multi-renewable energy electric field joint output scene generation method based on meteorological factors.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention proposes a scenario generation method for multiple renewable energy power fields that takes into account meteorological factors and output errors. This method effectively considers meteorological factors, can effectively predict the output of multiple renewable energy power fields in the future, improves the ability to absorb highly uncertain renewable energy output, reduces dependence on conventional power sources, and can provide energy managers with more comprehensive information to help them better understand and evaluate the accuracy and reliability of energy forecasts. By analyzing different scenarios, managers can formulate more effective energy scheduling strategies to cope with various possible situations, achieve efficient operation of the power grid and sustainable use of energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0064] The present invention is described in detail below with reference to specific embodiments.

[0065] The method for generating a multi-renewable energy field joint output scene based on meteorological factors of the present invention is as follows: Figure 1 As shown, the following steps are included:

[0066] Step 1: Obtain historical measured meteorological data and historical predicted meteorological data, including wind speed and irradiance; obtain historical actual output of wind farms and photovoltaic farms, historical predicted output of wind farms and photovoltaic farms, and calculate the historical output error of wind farms based on the historical actual output of wind farms and the historical predicted output of wind farms;

[0067] Yes, N w wind farms and N s The technical solution for modeling the wind power output of the i-th wind farm and the photovoltaic output of the i-th photovoltaic farm in the wind power photovoltaic cluster of the photovoltaic farms is characterized in that the error output is obtained by using the normalized historical measured output and predicted output of the wind farm and photovoltaic farm respectively, and the specific expression is:

[0068] ΔP i，w (t) = P i，w，r (t)-P i，w，f (t) (1)

[0069] ΔP j，s (t) = P j，s，r (t)-P j，s，f (t) (2)

[0070] Among them, P i，w，r (t), P j，s，r (t) represent the normalized historical actual output of wind farm and photovoltaic farm, P i，w，f (t), P j，s，f (t) represents the normalized historical predicted output of wind farm and photovoltaic farm, t is a certain time in the time period, t∈[1, 2, ..., T], i represents N w A wind farm among wind farms, i∈[1, 2, ..., N w ], j represents N s A photovoltaic field among the photovoltaic fields, j∈[1,2,...,N s ], ΔP i，w (t), ΔP j，s (t) represent the historical output errors of wind farm and photovoltaic farm respectively.

[0071] Step 2: Classify the data according to the three dimensions of wind speed, photovoltaic irradiance, and prediction time, and divide them into 27 categories: high, medium and low wind speed, high, medium and low irradiance, short, medium and long term. The high, medium and low wind speeds correspond to the minimum wind speed 0 to the maximum wind speed V max There are three wind speed intervals divided at equal intervals, and the high, medium and low irradiances correspond to the minimum irradiance 0 to the maximum irradiance S max The three irradiance intervals are divided into the first 10%, 10%-80% and 80%-100%. The short, medium and long term correspond to the first 6 hours, the middle 12 hours and the last 6 hours of the day respectively. This is because there is a strong correlation between irradiance and time. The irradiance is almost 0 before sunrise and after sunset, so there is no error in the photovoltaic output during this period. This classification method can simulate the actual situation more realistically.

[0072] Assume that the actual wind speed V of the i-th wind farm at time t in the historical period is i (t) belongs to the mvth wind speed interval V mv , the photovoltaic irradiance S of the jth wind farm j (t) belongs to the msth wind speed interval S ms , the prediction time belongs to the mtth time interval T mt , its specific expression is as follows.

[0073]

[0074] Step 3: Divide the historical output error of the wind farm and the historical output error of the photovoltaic farm into several intervals according to the corresponding wind speed, irradiance and time to form several error sample sets.

[0075] From the first and second steps, we can get that the wind speed of the i-th wind farm at time t is in the m-th wind speed interval V m , the output error is ΔP i，w (t), the irradiance of the jth photovoltaic field is in the mth irradiance interval S m , the output error is ΔP j，s (t), assuming that there are 27 error sample sets E k , the kth set is recorded as E k , k∈[1, 2, ..., 27], let the output error ΔP of the i-th wind farm at time t be i，w (t) and the output error ΔP of the jth photovoltaic field j，s (t) belongs to the kth error sample empty set E k , the expression is:

[0076]

[0077]

[0078]

[0079] If \(t < T\), repeat steps 1 to 3 to add the output power error \(\Delta P\) of the \(i\)-th wind farm and the output power error \(\Delta P\) of the \(j\)-th photovoltaic farm at the \((t + 1)\)-th moment to the corresponding error sample set \(E_k\); if \(t = T\), obtain the entire error sample set \(E_k\) of the \(i\)-th wind farm and the \(i\)-th photovoltaic farm, and proceed to the next step. i，w (t) and the output power error \(\Delta P\) of the \(j\)-th photovoltaic farm j，s (t) into the corresponding error sample set \(E_k\); if \(t = T\), then obtain the entire error sample set \(E_k\) of the \(i\)-th wind farm and the \(i\)-th photovoltaic farm, and enter the next step.

[0080] Step 4: Use the kernel density estimation method for the error samples in each error sample set to obtain the probability density function, and then obtain the error probability distribution;

[0081] For each error sample set \(E\) k in the error samples \(\Delta P\) i，w (t) and \(\Delta P\) j，s (t) perform probability distribution calculations. First, assume that the wind farm error samples in the error sample set \(E\) k are \(x\) i，k，1 , \(x\) i，k，2 ,..., \(x\) i，k，m , and the photovoltaic farm error samples are \(y\) j，k，1 , \(y\) j，k，2 ,..., \(y\) j，k，n . Then, the kernel density estimates of the corresponding probability density functions \(\varphi\) i，k (x), \(\varphi\) j，k (y) are:

[0082]

[0083] where \(m\) and \(n\) are the sample sizes; \(h\) is the bandwidth; \(K(\cdot)\) is the kernel function. The kernel function used in this embodiment is a uniform kernel function, a triangular kernel function, or a Gaussian kernel function. The above three kernel functions are as follows:

[0084]

[0085] The probability distribution functions corresponding to the probability density functions are \(\varphi\) i，k (x), \(\varphi\) j，k (y):

[0086]

[0087] If \(i < N\) w , \(j < N\) s then repeat steps 1 to 5 to obtain the error probability distributions of the \((i + 1)\)-th wind farm and the \((j + 1)\)-th photovoltaic farm; if \(i = N\) w , \(j = N\) s , obtain the error probability distributions of all \(N\) w wind farms and \(N\)s The output error probability distribution of each photovoltaic field is obtained and the next step is entered.

[0088] Step 5: Combine the probability distributions of output errors of multiple wind farms and photovoltaic farms to form a Copula density function of the overall joint probability distribution; including the following steps:

[0089] S1: In the kth error sample set E k The output error variable x of the i-th wind farm and the j-th photovoltaic farm i and j The probability distribution functions are φ i，k (x),φ j，k (y), x i The domain of is [0, x i,max ]、y i The domain of is [0, y j,max ]. Assuming the discretization interval is ΔP, the discretized probability sequence X i，k (a) Y j，k (b) is:

[0090]

[0091] Where N i,X =[X i,max / ΔP] is the sequence X i，k (a) Length, N i，Y =[y i,max / ΔP] is the sequence Y i，k (b) The length of [x] represents the integer of x.

[0092] S2: Output error variable x of the i+1th wind farm and the j+1th photovoltaic farm i+1 ,y j+1 The probability distribution function of i+1，k( x i+1) ,φ j+1，k (y j+1 ), the probability sequence is X i+1，k (a) Y j+1，k (b), length N i+1，X 、N j+1，Y .

[0093] S3: Consider the total output variable Zx of two adjacent wind farms and two adjacent photovoltaic farms i，i+1 =x i +x i+1 , Zy j，j+1 =y j +y j+1 The probability distribution of the output variable x of two adjacent wind farms i and xi+1 They are not independent of each other. After derivation, we can get Zx i,i+1 The probability sequence of X i,m (a) with X i+1，m (a) The convolution process multiplies the sequence X i,m (a) with X i+1，m (a) The corresponding elements of the Copula sequence between them are obtained. Since it is different from the ordinary volume and budget, it is called the dependent volume and between these two dependent probability sequences, denoted as Due to the output variable y of two adjacent photovoltaic fields j and j+1 They are not independent of each other. After derivation, we can get Zy j，j+1 The probability sequence of j，n (a) With Y j+1，n (a) The convolution process multiplies the sequence Y j，n (a) With Y j+1，n (a) The corresponding elements of the Copula sequence between them are obtained. Since it is different from the ordinary volume and budget, it is called the dependent volume and between these two dependent probability sequences, denoted as The specific formula is as follows:

[0094]

[0095] That is, the dependent probability sequence X i，k (a) with X i+1，k (a) Copula sequence between, c i，i+1 (·) is the output variable x of two adjacent wind farms i and x i+1 Copula density function between; That is, the dependent probability sequence Y j,k (a) With Y j+1，k (a) Copula sequence between, c j，j+1 (·) is the output variable y of two adjacent photovoltaic fields j and j+1 Copula density function between .

[0096] S4: If i+1 <N w 、j+1 <N s , then repeat S2 to S3 to obtain the output error variable x of the i+2nd wind farm i+2 The probability distribution function of i+2，k (x i+2 ), the probability sequence is X i+2,k (a), length N i+2,X , get the i+2th photovoltaic field output error variable y j+2The probability distribution function of j+2，k (y j+2 ), the probability sequence is Yj+ 2，k (a), length N j+2，Y , and continue S5; if i+1=N w 、i+1=N s , then we get all N w The probability distribution of the combined output error of wind farms and N s The probability distribution of the combined output error of the photovoltaic power fields is calculated, S5 is skipped, and S7 is executed.

[0097] S5: First, let the total output variable of the first i+1 wind farm be Zx e , the probabilistic sequence is zxe(a), and its length is N e =N 1，X +...+N i+1，X , e=1+...+i+i+1, considering the total output error variable Zx between the first i+1 wind farm and the i+2nd wind farm e，i+2 =Zx e +x i+2 The probability distribution of the first i+1 wind farm and the i+2 wind farm output variable Zx e and X i+2 They are not independent of each other. After derivation, we can get Zx e,i+2 The probabilistic sequence of zx e (a) with X i+2,k (a) The convolution process multiplies the sequence zx e (a) with X i+2,k (a) The corresponding elements of the Copula sequence between them are obtained. Since it is different from the ordinary volume and budget, it is called the dependent volume and between these two dependent probability sequences, denoted as Secondly, let the total output variable of the first j+1 photovoltaic field be Zy f , the probability sequence is zy f (b), length N f =N 1,Y +...+N j+1，Y , f=1+...+j+j+1, considering the total output error variable Zy between the first j+1 photovoltaic field and the j+2 photovoltaic field f，j+2 =Zy f +y j+2 The probability distribution of the first j+1 photovoltaic field and the j+2 photovoltaic field output variable Zy f and j+2 They are not independent of each other. After derivation, we can get Zy f，j+2 The probability sequence of zy f (a) With Y j+2，k(a) The convolution process is multiplied by the sequence zy f (a) With Y j+2，k (a) The corresponding elements of the Copula sequence between them are obtained. Since it is different from the ordinary volume and budget, it is called the dependent volume and between these two dependent probability sequences, denoted as The specific formula is as follows:

[0098]

[0099] That is, the dependent probability sequence zx e (a) with X i+2,k (a) Copula sequence between, c e，i+2 (·) is the output variable Zx of the first i+1 wind farm and the i+2 wind farm e and x i+2 Copula density function between; That is, the dependent probability sequence zy f (b) With Y j+2，k (b) Copula sequence between, c f，j+2 (·) is the output variable Zy of the first j+1 photovoltaic field and the j+2 photovoltaic field f and j+2 Copula density function between .

[0100] S6: Return to S4.

[0101] S7: Obtain the probability distribution of the joint output error of the wind power cluster and the probability distribution of the joint output error of the photovoltaic cluster under the kth error set. In the kth error sample set Ek, the wind power cluster joint output error variable ww g and the photovoltaic cluster joint output error variable ss h The probability distribution functions are φ g，k (ww),φ h，k (ss), ww g The domain of is [0, ww g，max ]、ss h The domain of is [0, ss h,max ]. Assuming the discretization interval is ΔP, the discretized probability sequence WW g，k (a) SS h，k (b) is:

[0102]

[0103] Where N g,ww =[ww g，max / ΔP] is the sequence WW g，k (a) Length, N h,ss=[ss h,max / ΔP] is the sequence SS h，k (b) The length of [x] represents the integer of x.

[0104] S8: Consider the error sample set E k The combined output error variable ws of the wind power cluster and the photovoltaic cluster g，h =ww g +ss h The probability distribution of the wind power cluster and the photovoltaic cluster output error variable ww g and ss h They are not independent of each other. After derivation, we can get ws g，h The probabilistic sequence of g，k (a) and SS h，k (a) The volume and process are multiplied by the sequence WW g，k (a) and SS h，k (a) The corresponding elements of the Copula sequence between them are obtained. Since it is different from the ordinary volume and budget, it is called the dependent volume and between these two dependent probability sequences, denoted as The specific formula is as follows:

[0105]

[0106] That is, the dependent probability sequence WW g，k (a) and SS h，k (b) Copula sequence between, c g，h (·) is the combined output error variable ww of the wind power cluster and the photovoltaic cluster g and ss h Copula density function between;

[0107] S9: If k<27, repeat S1 to S8 to obtain the joint output error probability distribution of the wind power cluster and the photovoltaic cluster under the kth error set; if k=27, obtain the joint output error probability distribution of the wind power cluster and the photovoltaic cluster under all error sets.

[0108] Step 6: Divide the predicted output data of future wind farms and photovoltaic farms into intervals according to the rules of step 2, and calculate the Co pu la function is used to perform inverse transformation sampling to obtain several future wind and solar output error time series curves;

[0109] Select the wind speed and irradiance data of a certain day in the next year to generate the predicted output data of wind farms and photovoltaic power plants, perform normalization processing, classify the predicted output data of future wind farms and photovoltaic power plants and the wind speed, irradiance, and time data according to the conditions of step 2, and classify the Co corresponding to different categories. pula function is used to perform inverse transformation sampling to obtain a number of future wind and solar output error time series curves; the steps include:

[0110] S1: The wind power cluster joint output error variable w at the i-th moment w g and the PV cluster joint output error variable ss h The probability distribution function of g，j (ww),φ h，j (ss) satisfies the jth classification, the combined output error variable ww of the wind power cluster and the photovoltaic cluster g and ss h The correlation coefficient matrix of the Copula density function between is A, and the standard normal distribution sample Z is constructed: (Z1, Z2) ~ N (0, I2), where I2 is a 2D unit matrix and a chi-square distribution sample χ with v degrees of freedom. 2 (v), and finally form the τ distribution sample T: Cumulative distribution function F using τ distribution T (T,u) transforms sample T into a uniformly distributed sample (U i , V i ), and then the wind and solar output error samples (ΔP w (i), ΔP s (i)), the specific formula is as follows:

[0111]

[0112] S2: If i<96, repeat S1 to obtain the wind and solar output error sample (ΔP w (i), ΔP s (i)); if i=96, continue with S3.

[0113] S3: The obtained wind and solar output error samples (ΔP w (i), ΔP s (i)) are connected in sequence to form the wind and solar output error sample ΔP = (ΔP w , ΔP s ), the specific formula is as follows:

[0114]

[0115] Step 7: Cluster several future wind and solar power output error time series curves based on the k-means theory to obtain a typical output error time series curve. Superimpose it with the predicted output time series curve to obtain a typical scenario, and calculate the probability of occurrence of the obtained typical scenario.

[0116] Embodiment 2

[0117] The present embodiment discloses a device for generating a multi-renewable energy electric field joint output scene based on meteorological factors, comprising a memory, a processor and a computer program. The computer program is stored in the memory and is configured to be executed by the processor to implement the multi-renewable energy electric field joint output scene generation method based on meteorological factors described in Example 1.

[0118] The technical solutions of the present invention are not limited to the above-mentioned embodiments, and any technical solutions obtained by equivalent replacement methods fall within the scope of protection required by the present invention.

Claims

1. A method for generating a scenario of joint output of multiple renewable energy power plants based on meteorological factors, obtaining historical measured meteorological data, historical forecasted meteorological data, historical actual output of wind farms and photovoltaic power plants, and historical forecasted output of wind farms and photovoltaic power plants: characterized in that, The steps include: Step 1: Calculate the historical output error of the wind farm according to the historical actual output of the wind farm and the historical predicted output of the wind farm; Calculate the historical output error of the photovoltaic field based on the historical actual output of the photovoltaic field and the historical predicted output of the photovoltaic field; Step 2: Divide the area into several intervals according to wind speed, irradiance and time; Step 3: Divide the historical output error of the wind farm and the historical output error of the photovoltaic farm into several intervals according to the corresponding wind speed, irradiance and time to form several error sample sets; Step 4: Use the kernel density estimation method to obtain the probability density function of the error samples of wind power output and photovoltaic output in each error sample set, and then obtain the error probability distribution; Step 5: Combine the output error probability distributions of multiple wind farms and photovoltaic farms to form a Copula density function of the overall joint probability distribution; Step 6: divide the predicted output data of future wind farms and photovoltaic farms into intervals according to the rules of step 2, perform inverse transformation sampling on the Copula functions corresponding to different intervals, and obtain several future wind and photovoltaic output error time series curves; Step 7: Cluster the obtained several future wind and solar power output error time series curves to obtain a typical output error time series curve. Superimpose the typical output error time series curve and the predicted output time series curve to obtain a typical scenario.

2. According to claim 1, the method for generating a multi-renewable energy field joint output scenario based on meteorological factors is characterized by: In step 1, there are N w wind farms and N s The wind power output of the i-th wind farm and the photovoltaic output of the j-th photovoltaic farm in the wind power photovoltaic cluster of the photovoltaic farms are modeled, and the historical output error of the wind farm and the photovoltaic farm is: △P i,w (t)=P i,w,r (t)-P i,w,r (t) △P j,s (t)=P j,s,r (t)-P j,s，r (t) Among them, P i，w，r (t), P j，s，r (t) represent the normalized historical actual output of wind farm and photovoltaic farm, P i，w，f (t), P j，s，f (t) represents the normalized historical predicted output of wind farm and photovoltaic farm respectively, t is a certain time in the time period, t∈[1,2,...,T], i represents N w A wind farm among wind farms, i∈[1,2,...,N w ], j represents N s A photovoltaic field among the photovoltaic fields, j∈[1,2,...,N s ], ΔP i，w (t), ΔP j，s (t) represent the historical output errors of wind farm and photovoltaic farm respectively.

3. The method for generating a multi-renewable energy field joint output scenario based on meteorological factors according to claim 1 is characterized by: In step 2, the data is divided into intervals according to three dimensions: wind speed, photovoltaic irradiance, and prediction time. Each dimension is divided into three segments, forming a total of 27 intervals.

4. The method for generating a multi-renewable energy field joint output scenario based on meteorological factors according to claim 3 is characterized by: In step 2, the wind speed is divided into three sections with equal intervals from low to high, and the irradiance ranges from the minimum irradiance 0 to the maximum irradiance S max It is divided into the first 10%, 10%-80% and 80%-100%, and the time is divided into the first 6 hours of the day, 6 to 18 hours and 18 to 24 hours.

5. According to claim 1, the method for generating a multi-renewable energy field joint output scenario based on meteorological factors is characterized by: In step 4, for each error sample set E k Error samples within ΔP i，w (t) and ΔP j，s (t) To calculate the probability distribution, first assume that the error sample set E k The error sample of the internal wind farm is X′ i，k，1 X′ i，k，2 , ..., X′ i，k，m , the photovoltaic electric field error sample is Y′ j，k，1 Y′ j，k，2 , ..., Y′ j，k，n , then the corresponding probability density function Φ i，k (X′), Φ j，k The kernel density estimate of (Y′) is: Where m and n are sample sizes; h is bandwidth; K(·) is kernel function. The corresponding probability distribution function is φ i，k (x),φ j，k (y):

6. The method for generating a multi-renewable energy field joint output scenario based on meteorological factors according to claim 5 is characterized by: The kernel function is a uniform kernel function, a triangular kernel function or a Gaussian kernel function.

7. The method for generating a multi-renewable energy field joint output scenario based on meteorological factors according to claim 6 is characterized by: The step 5 specifically comprises the following steps: S1: in the kth error sample set E k The output error variable x of the i-th wind farm and the j-th photovoltaic farm i and j The probability distribution functions are φ i，k (x),φ j，k (y), x i The domain of the i,max ]、y j The domain of the j,max ]; let the discretization interval be ΔP, then the discretized probability sequence X i,k (a) Y j,k (b) is: Where N i,X =[x i,max / ΔP] is the sequence X i,k (a) Length, N i,Y =[y i,max / ΔP] is the sequence Y i,k (b) The length of [x] represents the integer of x; S2: Output error variable x of the i+1th wind farm and the j+1th photovoltaic farm i+1 ,y j+1 The probability distribution function of i+1，k (x i+1 ),φ j+1，k (y j+1 ), the probability sequence is X i+1,k (a) Y j+1,k (b), length N i+1,X 、N j+1,Y ; S3: The total output variable Zx of two adjacent wind farms and two adjacent photovoltaic farms i，i+1 =x i +x i+1 , Zy j，j+1 =y j +y j+1 The probability distribution of is: That is, the dependent probability sequence X i,k (a) with X i+1,k (a) Copula sequence between, c i，i+1 (·) is the output variable x of two adjacent wind farms i and x i+1 Copula density function between; That is, the dependent probability sequence Y j,k (b) With Y j+1,k (b) Copula sequence between, c j，j+1 (·) is the output variable y of two adjacent photovoltaic fields j and j+1 Copula density function between; S4: If i+1 <N w 、j+1 <N s , then repeat S2 to S3 to obtain the output error variable x of the i+2nd wind farm i+2 The probability distribution function of i+2，k (x i+2 ), the probability sequence is X i+2,k (a), length N i+2,X , and obtain the j+2th photovoltaic field output error variable y j+2 The probability distribution function of j+2，k (y j+2 ), the probability sequence is Y j+2,k (a), length N j+2,Y , and continue with S5; If i+1=N w 、j+1=N s , then we get all N w The probability distribution of the combined output error of wind farms and N s The probability distribution of the combined output error of the photovoltaic electric fields is calculated, and S7 is executed; S5: Let the total output variable of the first i+1 wind farm be Zx e , the probabilistic sequence is zx e (a), length N e =N 1,X +...+N i+1,X , e=1+...+i+i+1; Let the total output variable of the first j+1 photovoltaic power fields be Zy f , the probability sequence is zy f (b), length N f =N 1,Y +...+N j+1,Y , f=1+...+j+j+1; The total output error variable Zx between the first i+1 wind farm and the i+2 wind farm is e，i+2 =Zx e +x i+2 The probability distribution of the total output error variable Zy between the first j+1 photovoltaic field and the j+2 photovoltaic field f，j+2 =Zy f +y j+2 The probability distribution of is: That is, the dependent probability sequence zx e (a) with X i+2,m (a) Copula sequence between, c e，i+2 (·) is the output variable Zx of the first i+1 wind farm and the i+2 wind farm e and x i+2 Copula density function between; That is, the dependent probability sequence zy f (b) With Y j+2,k (b) Copula sequence between, c f，j+2 (·) is the output variable Zy of the first j+1 photovoltaic field and the j+2 photovoltaic field f and j+2 Copula density function between; S6: Return to S4; S7: Obtain the probability distribution of the joint output error of the wind power cluster and the probability distribution of the joint output error of the photovoltaic cluster under the kth error set; In the kth error sample set E k The error variable ww of the wind power cluster combined output g and the photovoltaic cluster joint output error variable ss h The probability distribution functions are φ g，k (ww),φ h，k (ss), ww g The domain of is [0,ww g,max ]、ss h The domain of is [0,ss h,max ]; let the discretization interval be ΔP, then the discretized probability sequence WW g,k (a) SS h,k (b) is: Where N g,ww =[ww g,max / ΔP] is the sequence WW g,k (a) Length, N h,ss =[ss h,max / ΔP] is the sequence SS h,k (b) The length of [x] represents the integer of x. S8: Error sample set E k The combined output error variable ws of the wind power cluster and the photovoltaic cluster g，h =ww g +ss h The probability distribution of for: That is, the dependent probability sequence WW g,k (a) and SS h,k (b) Copula sequence between, c g，h (·) is the combined output error variable ww of the wind power cluster and the photovoltaic cluster g and ss h Copula density function between; S9: If k<27, repeat S1 to S8 to obtain the joint output error probability distribution of the wind power cluster and the photovoltaic cluster under the kth error set; if k=27, obtain the joint output error probability distribution of the wind power cluster and the photovoltaic cluster under all error sets.

8. The method for generating a multi-renewable energy field joint output scenario based on meteorological factors according to claim 7 is characterized by: The step 6 specifically comprises the following steps: S1: At the i-th moment, the wind power cluster joint output error variable ww g and the photovoltaic cluster joint output error variable ss h The probability distribution function of g，j (ww),φ h，j (ss) satisfies the jth classification, the combined output error variable ww of the wind power cluster and the photovoltaic cluster g and ss h The correlation coefficient matrix of the Copula density function between the two is A, and the standard normal distribution sample Z is constructed: (Z1, Z2) ~ N (0, I2), where I2 is a 2D unit matrix and a chi-square distribution sample χ with v degrees of freedom. 2 (υ), finally forming the τ distribution sample T: Cumulative distribution function F using τ distribution T (T,υ) transforms sample T into a uniformly distributed sample (U i , V i ), and then the wind and solar output error samples (ΔP w (i), ΔP s (i)), the specific formula is as follows: S2: If i<96, repeat S1 to obtain the wind and solar output error sample (ΔP w (i), ΔP s (i)); if i = 96, continue with S3; S3: The obtained wind and solar output error samples (ΔP w (i), ΔP s (i)) are connected in sequence to form the wind and solar output error time series curve ΔP = (ΔP w , ΔP s ), the specific formula is as follows:

9. The method for generating a multi-renewable energy field joint output scenario based on meteorological factors according to claim 7, characterized in that: In step 7, a plurality of future wind and solar power output error time series curves are clustered based on the k-means theory to obtain a typical output error time series curve.

10. A device for generating a multi-renewable energy field joint output scene based on meteorological factors, characterized in that: It includes a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method for generating a multi-renewable energy field joint output scenario based on meteorological factors as described in any one of claims 1 to 9.

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