Available power transmission capacity evaluation method considering uncertainty of new energy

Abstract

The invention provides an available power transmission capacity evaluation method considering new energy uncertainty, which relates to the field of new energy, and comprises the following steps: firstly, based on output characteristics and historical data of new energy power generation, establishing a probability model of fan power generation and photovoltaic power generation; thirdly, establishing a probabilistic available power transmission capacity calculation model associated with the power transmission network by utilizing a continuous power flow model; and finally, a proxy model is established based on a data-driven polynomial chaos expansion method and is used for rapidly calculating a probability available power transmission capacity model, and the evaluation precision and the calculation efficiency of the power transmission network under the uncertainty of new energy are improved. By comprehensively considering uncertainty factors of new energy power generation, a scientific and reliable basis can be provided for scheduling and planning of a power transmission system.

Description

Technical Field

[0001] The present invention relates to the field of new energy, and in particular to a method for evaluating available power transmission capacity taking into account the uncertainty of new energy. Background Art

[0002] Large-scale development and utilization of new energy sources such as wind and solar is an important means to accelerate my country's transformation to a low-carbon and clean energy structure and achieve the goals of "carbon peak and carbon neutrality". The China National Renewable Energy Center mentioned in the "China Renewable Energy Outlook" that "it is expected that by 2050, China's new energy power generation will account for more than 70%." Large-scale new energy grid-connected power generation will inevitably become a development trend and an important feature of the future power system, but the volatility and randomness of new energy output have brought new challenges to the evaluation of available transfer capacity (ATC) between regions.

[0003] Available transmission capacity refers to the remaining power transmission capacity of the transmission network under the condition of meeting certain safety constraints. It is an important indicator for guiding electricity market transactions and evaluating the safety and stability margin of interconnected power grids.

[0004] At present, the ATC evaluation of power systems is mainly divided into deterministic solution methods and probabilistic solution methods. Deterministic ATC solution methods mainly include repeated power flow method, continuous power flow method, optimal power flow method and sensitivity method. Deterministic calculation methods have solved the calculation problem of power system ATC in the past. However, with the large-scale grid connection of new energy such as wind power and photovoltaic power, the participation of renewable energy in power and electricity balance, and the strong uncertainty at the power supply and load ends of the power grid, the operation mode of the power system and the inter-regional transmission capacity have shown probabilistic characteristics. The uncertainty factors in the operation of the power system are increasing, and it is difficult for deterministic calculation methods to effectively take into account the impact of uncertainty factors on ATC.

[0005] In this context, quantifying the impact of uncertainty factors on the ATC of key transmission sections can provide important information and new perspectives for the planning and operation of power systems containing renewable energy. The calculation of probabilistic ATC takes into account the randomness of all components of the power system. It is usually based on probability theory and mathematical statistics to calculate the mean, standard deviation, probability distribution function and other statistical characteristics of the system ATC, and then evaluate the system's ATC level.

[0006] In the probabilistic ATC calculation, ETC is calculated by the grid operator based on existing contracts and service commitments, and CBM is calculated based on emergency power supply needs and backup agreements, which are generally fixed values. The impact of uncertainty factors on the system's transmission capacity is reflected through the reserved transmission power margin indicator TRM.

[0007] The calculation of probabilistic ATC takes into account the randomness of all components of the power system. It is usually based on probability theory and mathematical statistics to calculate the mean, standard deviation, probability distribution function and other statistical characteristics of the system ATC, and then evaluate the system's ATC level.

[0008] Among the probabilistic ATC calculation methods, the enumeration method is often used to deal with the uncertainty of the operating status of electrical equipment such as generators, transformers and lines. However, when the system is large, multiple faults are difficult to exhaust, and if limited faults are selected, it is difficult to cover the entire operating status probability space. Therefore, the enumeration method is difficult to be truly applied to the calculation of the transmission capacity of the power system with a large number of new energy sources. Monte Carlo simulation generates a large number of system operating status samples through random sampling based on the probability distribution function of the uncertainty factors. When the number of input variables is large, it presents the disadvantages of huge calculation amount and long calculation time. Establishing a proxy model for the ATC calculation model has become a suitable choice. Summary of the invention

[0009] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for evaluating available power transmission capacity taking into account the uncertainty of new energy.

[0010] The present invention is achieved through the following technical solutions.

[0011] According to one aspect of the present invention, a method for evaluating available power transmission capacity considering the uncertainty of new energy sources is provided, comprising:

[0012] Based on the output characteristics and historical data of renewable energy power generation, a probability model of wind turbine power generation and photovoltaic power generation is established;

[0013] Based on the continuous power flow model, a probabilistic available transmission capacity calculation model is established;

[0014] Based on data-driven polynomial chaos expansion, a proxy model for ATC calculation is established, and an efficient evaluation method for probabilistic available transmission capacity model is designed.

[0015] Preferably, the probability model of wind turbine power generation and photovoltaic power generation includes:

[0016] The Weibull model is used to approximate the probability distribution of wind speed:

[0017]

[0018] Where v is the wind speed, ξ k and c They represent the shape parameter and scale parameter of the Weibull model, which can be obtained from historical meteorological data. The output power calculation formula of the wind turbine is P WT for

[0019]

[0020] Where P r Indicates the rated active power of the wind turbine. in 、v r and v out They respectively represent the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine.

[0021]

[0022] Assume that the power factor angle during operation remains unchanged, the reactive power output Q WT for

[0023] Q WT =P WT tanφ w

[0024] In photovoltaic systems, the light intensity r approximately follows the Beta distribution:

[0025]

[0026] In the formula, r max Indicates the maximum irradiance. ξ a and b is the shape parameter, Γ(·) is the gamma function. Given the light intensity r, the active power output P of the photovoltaic cell PV for

[0027] P PV = rAη s

[0028] Among them, η s It represents the photoelectric conversion efficiency of photovoltaic cells. A represents the total area of ​​the photovoltaic cell array.

[0029] The output of photovoltaic and wind turbines is used as the input random variables of the probabilistic available transmission capacity model, and its set is represented by ζ.

[0030] Preferably, the probability can be calculated using a transmission capacity model, including:

[0031] Available Transfer Capability (ATC) can be expressed as:

[0032] ATC=TTC-TRM-ETC-CBM

[0033] Among them, the existing transfer capability (ETC) and the capacity benefit margin (CBM) can be regarded as proportional to the maximum transfer capability (TTC). After considering the uncertainty of new energy, the transmission reliability margin (TRM) is generally related to the uncertainty of the system.

[0034] Under deterministic operation conditions, TTC can be calculated based on the continuation power flow method:

[0035] min-λ

[0036]

[0037] P Gi,min ≤P Gi ≤P Gi,max ,i∈S G

[0038] Q Gi,min ≤Q Gi ≤Q Gi,max ,i∈S G

[0039] U i,min ≤U i ≤U i,max ,i∈S B

[0040] S ij ≤S ij,max ,i∈S l

[0041] Where: n is the total number of nodes. B is the set of system nodes. G is the set of generator nodes. l is the set of all branches. λ is the load growth parameterized scalar, which represents the maximum transmission capacity of the system. b is the load power growth direction vector. U i and θ i is the voltage amplitude and phase angle of node i. ij and B ij is the real and imaginary part of the element in the i-th row and j-th column of the node admittance matrix. The load power growth direction b is expressed as:

[0042]

[0043] Next, we consider the uncertainty of new energy sources and use ζ to represent the set of random variables. Since the output of new energy sources will affect the voltage, current, power and other variables in the system, the probability of the system can be expressed by the transmission capacity model as follows:

[0044] min-λ

[0045]

[0046] P Gi,min ≤P Gi (ζ)≤P Gi,max ,i∈S G

[0047] Q Gi,min ≤Q Gi (ζ)≤Q Gi,max ,i∈S G

[0048] U i,min ≤U i (ζ)≤U i,max ,i∈S B

[0049] S ij (ζ)≤S ij,max ,i∈S l

[0050] Preferably, a data-driven polynomial chaos expansion is used to establish an agent model for ATC calculation and to design an efficient evaluation method for a probabilistic available transmission capacity model, including:

[0051] Consider multidimensional independent random variables ζ=[ζ1,ζ2,…,ζ N ]. N is the number of random variables. Let Y = g(ζ) be the ATC calculation model. The random response Y can be approximated by a multidimensional PCE model of order H.

[0052]

[0053] Where M is the number of terms included in the model. In practical applications, we can set M = (H + N)! / (H! N!). In addition, c k Refers to the PCE model coefficients that need to be solved, Φ k is a set of multivariate orthogonal polynomials about the input random variables, which can be represented by a one-dimensional orthogonal polynomial basis P i (ζ i ) yields:

[0054]

[0055] In the formula, α i kThe exponent corresponding to the kth expansion of the ith univariate polynomial basis. The univariate orthogonal polynomial basis for the ith variable can be expressed as follows:

[0056]

[0057] α i k Denoted by l, l={0,...,H},

[0058] According to the definition of orthogonal polynomial basis, each orthogonal polynomial basis needs to satisfy the following conditions:

[0059]

[0060] Assuming that the highest coefficient of all polynomials is 1, substitute the recursive solution to obtain:

[0061]

[0062] For any input variable data, its k-order original moment can be obtained:

[0063]

[0064] F(ζ) is the joint cumulative distribution function of the input variables. Therefore, the orthogonal polynomial basis can be converted to the following matrix form:

[0065]

[0066] Among them, the j-order raw moment of the input data can be calculated by the following formula:

[0067]

[0068] In order to facilitate calculation, normalization is performed:

[0069]

[0070] in for The norm of .

[0071] After establishing the orthogonal polynomial basis, it is also necessary to determine the coefficients c of each term. k , in order to establish a proxy model. The coefficients are calculated using the least squares regression method, that is, the following optimization calculations are completed:

[0072]

[0073] By randomly sampling a large number of input variables and solving the above optimization model, we can obtain the probability characteristics of TTC. Both TRM and ATC can be expressed by the probability distribution of TTC. get:

[0074] P(ATC a ≥(m TTC -TRM)) = α%

[0075] Where ATC a is the actual value of ATC, m TTC is the expected value of TTC, and α is the set confidence level.

[0076] Due to the adoption of the above technical solution, the present invention has the following beneficial effects:

[0077] The available transmission capacity assessment method considering the uncertainty of new energy provided by the present invention establishes a probability model of wind turbine power generation and photovoltaic power generation based on the output characteristics and historical data of new energy power generation, thereby accurately reflecting the randomness of new energy.

[0078] The available transmission capacity evaluation method provided by the present invention combines the continuous power flow model, establishes a probabilistic available transmission capacity calculation model related to the transmission network, and effectively considers the impact of new energy uncertainty on the available transmission capacity;

[0079] The available transmission capacity evaluation method provided by the invention utilizes data-driven polynomial chaos expansion technology to establish an agent model for ATC calculation, designs an efficient evaluation method, improves computing efficiency, and shortens the time and resource consumption in the evaluation process, thereby providing reliable support for the scheduling and planning of power systems under the uncertainty of new energy.

[0080] The available power transmission capacity evaluation method considering the uncertainty of new energy provided by the present invention has great engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A flow chart of a method for evaluating available power transmission capacity considering the uncertainty of new energy sources in a preferred embodiment of the present invention; DETAILED DESCRIPTION

[0082] The following is a detailed description of the embodiments of the present invention: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0083] The present invention provides an available transmission capacity evaluation method considering the uncertainty of new energy sources, establishes a probability model of wind turbine power generation and photovoltaic power generation based on the output characteristics and historical data of new energy power generation, establishes a probability available transmission capacity calculation model based on a continuous power flow model, establishes an agent model for ATC calculation based on data-driven polynomial chaos expansion, and designs an efficient evaluation method for the probability available transmission capacity model. This method has practical theoretical significance and promotion value for the dispatch and planning of power systems under the uncertainty of new energy sources.

[0084] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0085] Please read first Figure 1 , Figure 1 : is a flow chart of a design method of an available power transmission capacity evaluation method considering the uncertainty of new energy provided by an embodiment of the present invention. As shown in the figure, the available power transmission capacity evaluation method considering the uncertainty of new energy provided by an embodiment of the present invention includes the following steps:

[0086] Step 1), establish a probability model of wind turbine power generation and photovoltaic power generation based on historical data.

[0087] As a preferred embodiment, the method of step 1) is:

[0088] The Weibull model is used to approximate the probability distribution of wind speed:

[0089]

[0090] Where v is the wind speed, ξ k and c They represent the shape parameter and scale parameter of the Weibull model, which can be obtained from historical meteorological data. The output power calculation formula of the wind turbine is P WT for

[0091]

[0092] Where P r Indicates the rated active power of the wind turbine. in 、v r and v out They respectively represent the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine.

[0093]

[0094] Assume that the power factor angle during operation remains unchanged, the reactive power output Q WT for

[0095] Q WT =P WT tanφ w

[0096] In photovoltaic systems, the light intensity r approximately follows the Beta distribution:

[0097]

[0098] In the formula, r max Indicates the maximum irradiance. ξ a and b is the shape parameter, Γ(·) is the gamma function. Given the light intensity r, the active power output P of the photovoltaic cell PV for

[0099] P PV = rAη s

[0100] Among them, η s It represents the photoelectric conversion efficiency of photovoltaic cells. A represents the total area of ​​the photovoltaic cell array.

[0101] The output of photovoltaic and wind turbines is used as the input random variables of the probabilistic available transmission capacity model, and its set is represented by ζ.

[0102] Step 2) Establish a probabilistic available transmission capacity calculation model based on the continuous power flow model.

[0103] As a preferred embodiment, the method of step 2) is:

[0104] Available Transfer Capability (ATC) can be expressed as:

[0105] ATC=TTC-TRM-ETC-CBM

[0106] Among them, the existing transfer capability (ETC) and the capacity benefit margin (CBM) can be regarded as proportional to the maximum transfer capability (TTC). After considering the uncertainty of new energy, the transmission reliability margin (TRM) is generally related to the uncertainty of the system.

[0107] Under deterministic operation conditions, TTC can be calculated based on the continuation power flow method:

[0108] min-λ

[0109]

[0110] P Gi,min ≤P Gi ≤P Gi,max ,i∈S G

[0111] Q Gi,min ≤Q Gi ≤Q Gi,max ,i∈S G

[0112] U i,min ≤U i ≤U i,max ,i∈S B

[0113] S ij ≤S ij,max ,i∈S l

[0114] Where: n is the total number of nodes. B is the set of system nodes. G is the set of generator nodes. l is the set of all branches. λ is the load growth parameterized scalar, which represents the maximum transmission capacity of the system. b is the load power growth direction vector. U i and θ i is the voltage amplitude and phase angle of node i. ij and B ij is the real and imaginary part of the element in the i-th row and j-th column of the node admittance matrix. The load power growth direction b is expressed as:

[0115]

[0116] Next, we consider the uncertainty of new energy sources and use ζ to represent the set of random variables. Since the output of new energy sources will affect the voltage, current, power and other variables in the system, the probability of the system can be expressed by the transmission capacity model as follows:

[0117] min-λ

[0118]

[0119] P Gi,min ≤P Gi (ζ)≤P Gi,max ,i∈S G

[0120] Q Gi,min ≤Q Gi (ζ)≤Q Gi,max ,i∈S G

[0121] U i,min ≤U i (ζ)≤U i,max ,i∈S B

[0122] S ij (ζ)≤S ij,max ,i∈S l

[0123] Step 3) Based on the data-driven polynomial chaos expansion, a proxy model for ATC calculation is established, and an efficient evaluation method for the probabilistic available transmission capacity model is designed.

[0124] As a preferred embodiment, the method of step 3) is:

[0125] Consider multidimensional independent random variables ζ=[ζ1,ζ2,…,ζ N ]. N is the number of random variables. Let Y = g(ζ) be the ATC calculation model. The random response Y can be approximated by a multidimensional PCE model of order H.

[0126]

[0127] Where M is the number of terms included in the model. In practical applications, we can set M = (H + N)! / (H! N!). In addition, c k Refers to the PCE model coefficients that need to be solved, Φ k is a set of multivariate orthogonal polynomials about the input random variables, which can be represented by a one-dimensional orthogonal polynomial basis P i (ζ i ) yields:

[0128]

[0129] In the formula, The exponent corresponding to the kth expansion of the ith univariate polynomial basis. The univariate orthogonal polynomial basis for the ith variable can be expressed as follows:

[0130]

[0131] Denoted by l, l={0,...,H},

[0132] According to the definition of orthogonal polynomial basis, each orthogonal polynomial basis needs to satisfy the following conditions:

[0133]

[0134] Assuming that the highest coefficient of all polynomials is 1, substitute the recursive solution to obtain:

[0135]

[0136] For any input variable data, its k-order original moment can be obtained:

[0137]

[0138] F(ζ) is the joint cumulative distribution function of the input variables. Therefore, the orthogonal polynomial basis can be converted to the following matrix form:

[0139]

[0140] Among them, the j-order raw moment of the input data can be calculated by the following formula:

[0141]

[0142] In order to facilitate calculation, normalization is performed:

[0143]

[0144] in for The norm of .

[0145] After establishing the orthogonal polynomial basis, it is also necessary to determine the coefficients c of each term. k , in order to establish a proxy model. The coefficients are calculated using the least squares regression method, that is, the following optimization calculations are completed:

[0146]

[0147] By randomly sampling a large number of input variables and solving the above optimization model, we can obtain the probability characteristics of TTC. Both TRM and ATC can be expressed by the probability distribution of TTC. get:

[0148] P(ATC a ≥(m TTC -TRM)) = α%

[0149] Where ATC a is the actual value of ATC, m TTC is the expected value of TTC, and α is the set confidence level.

[0150] The present invention provides an available transmission capacity evaluation method considering the uncertainty of new energy sources, establishes a probability model of wind turbine power generation and photovoltaic power generation based on the output characteristics and historical data of new energy power generation, establishes a probability available transmission capacity calculation model based on a continuous power flow model, establishes an agent model for ATC calculation based on data-driven polynomial chaos expansion, and designs an efficient evaluation method for the probability available transmission capacity model. Compared with traditional design methods, the present invention fully considers the uncertainty of new energy output, constructs a probabilistic available transmission capacity evaluation model, and constructs an agent model to realize the rapid calculation of the model, effectively improving the evaluation efficiency.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating available transmission capacity considering the uncertainty of new energy sources, characterized in that: The steps include: (1) Based on the output characteristics and historical data of renewable energy power generation, a probability model of wind turbine power generation and photovoltaic power generation is established; (2) Based on the continuous power flow model, a probabilistic available transmission capacity calculation model is established; (3) Based on data-driven polynomial chaos expansion, a proxy model for ATC calculation is established, and an efficient evaluation method for the probabilistic available transmission capacity model is designed.

2. The method for evaluating available power transmission capacity considering the uncertainty of new energy sources according to claim 1 is characterized in that: The method for establishing the probability model of wind turbine power generation and photovoltaic power generation in step (1) is: The Weibull model is used to approximate the probability distribution of wind speed: Where v is the wind speed, ξ k and c They represent the shape parameter and scale parameter of the Weibull model, respectively, which are obtained from historical meteorological data. The output power calculation formula of the wind turbine is P WT for Where P r Indicates the rated active power of the wind turbine, v in 、v r and v out They represent the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively; Assume that the power factor angle during operation remains unchanged, the reactive power output Q WT for: Q WT =P WT tanφ w In photovoltaic systems, the light intensity r approximately follows the Beta distribution: In the formula, r max represents the maximum irradiance, ξ a and b is the shape parameter, Γ(·) is the gamma function, given the light intensity r, the active power output P of the photovoltaic cell PV for: P PV =rAη s Among them, η s It represents the photoelectric conversion efficiency of photovoltaic cells, and A represents the total area of ​​the photovoltaic cell array; The output of photovoltaic and wind turbines is used as the input random variables of the probabilistic available transmission capacity model, and its set is represented by ζ.

3. The method for evaluating available power transmission capacity considering uncertainty of new energy sources according to claim 2 is characterized in that: The method for establishing the probabilistic available transmission capacity calculation model in step (2) based on the continuous power flow model is as follows: Available transmission capacity ATC is expressed as: ATC=TTC-TRM-ETC-CBM Among them, the existing transmission agreement ETC and capacity benefit margin CBM are considered to be proportional to the maximum transmission capacity TTC. After considering the uncertainty of new energy, the transmission reliability margin TRM is related to the uncertainty of the system. Under deterministic operation conditions, TTC is calculated based on the continuous power flow method: min-λ P Gi,min ≤P Gi ≤P Gi,max ,i∈S G Q Gi,min ≤Q Gi ≤Q Gi,max ,i∈S G IN i,min ≤U i ≤U i,max ,i∈S B S ij ≤S ij,max ,i∈S l Where: n is the total number of nodes, S B is the set of system nodes, S G is the set of generator nodes, S l is the set of all branches, λ is the load growth parameterized scalar, which represents the maximum transmission capacity of the system, b is the load power growth direction vector, U i and θ i is the voltage amplitude and phase angle at node i, G ij and B ij is the real and imaginary part of the element in the i-th row and j-th column of the node admittance matrix, and the load power growth direction b is expressed as: Considering the uncertainty of new energy, ζ is used to represent the set of random variables, and the probability of the system is expressed by the transmission capacity model as follows: min-λ P Gi,min ≤P Gi (ζ)≤P Gi,max ,i∈S G Q Gi,min ≤Q Gi (ζ)≤Q Gi,max ,i∈S G U i,min ≤U i (ζ)≤U i,max ,i∈S B S ij (ζ)≤S ij,max ,i∈S l 。 4. The method for evaluating available power transmission capacity considering the uncertainty of new energy sources according to claim 3 is characterized in that: The step (3) establishes an agent model for ATC calculation based on data-driven polynomial chaos expansion PCE, and designs an efficient evaluation method for a probabilistic available transmission capacity model, including: Consider multidimensional independent random variables ζ=[ζ1,ζ2,…,ζ N ], N is the number of random variables, let Y = g(ζ) be the ATC calculation model, and the random response Y is approximated by a multidimensional PCE model of order H: Where M is the number of terms included in the model, c k Refers to the PCE model coefficients that need to be solved, Φ k is a set of multivariate orthogonal polynomials about the input random variables, consisting of a one-dimensional orthogonal polynomial basis P i (ζ i ) yields: In the formula, The exponent corresponding to the kth expansion term of the ith univariate polynomial basis is given by the following univariate orthogonal polynomial basis for the ith variable: Denoted by l, l={0,...,H}, According to the definition of orthogonal polynomial basis, each orthogonal polynomial basis satisfies the following conditions: Assuming that the highest coefficient of all polynomials is 1, substitute the recursive solution to obtain: For any input variable data, get its k-order original moment: F(ζ) is the joint cumulative distribution function of the input variables, and the orthogonal polynomial basis is converted to the following matrix form: Among them, the j-order raw moment of the input data is calculated by the following formula: Perform normalization: in for The norm of ; After establishing the orthogonal polynomial basis, determine the coefficients c of each term k , in order to establish a proxy model, the coefficients are calculated using the least squares regression method to complete the following optimization calculations: Randomly sample a large number of input variables, solve the above optimization model, and obtain the probability characteristics of TTC. Both TRM and ATC can represent the probability distribution of TTC. get: P(ATC a ≥(m TTC -TRM))=α% Where ATC a is the actual value of ATC, m TTC is the expected value of TTC, and α is the set confidence level.

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