Transmission network expansion planning method based on wind and solar access

Abstract

The present invention discloses a transmission network expansion planning method based on wind and solar access, relating to the field of smart grid technology, and primarily focusing on improving the efficiency and accuracy of transmission network expansion planning. The method comprises: obtaining wind power attribute data of wind power generation equipment, photovoltaic attribute data of photovoltaic power generation equipment, load attribute data corresponding to the planned transmission network, and grid attribute data; determining wind power output data based on the wind power attribute data, photovoltaic output data based on the photovoltaic attribute data, and load absorption data based on the load attribute data; determining multiple transmission expansion planning schemes based on the aforementioned data; inputting evaluation data composed of the grid attribute data and each transmission expansion planning scheme into a preset transmission expansion planning model to predict planning effect parameters, thereby obtaining each planning effect parameter; and determining, based on each planning effect parameter, a target transmission expansion planning scheme after accessing the wind and solar power generation equipment in each transmission expansion planning scheme.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a transmission network expansion planning method based on wind and solar access. Background Art

[0002] In recent years, my country has vigorously expanded the development and utilization of new energy. Wind power and photovoltaics have been increasingly incorporated into the power transmission network, resulting in a huge change in the energy structure. The integration of these new energy units into the power grid has increased the complexity of operations and posed challenges to the real-time balance of electricity. Based on this, in order to ensure the stable operation of the power grid, it is necessary to plan the expansion of the transmission network.

[0003] Currently, transmission network expansion planning is usually done manually. However, this method is subject to significant human influence, resulting in low accuracy and low efficiency of transmission network expansion planning. Summary of the Invention

[0004] The present invention provides a transmission network expansion planning method based on wind and solar access, which is mainly capable of improving the expansion planning efficiency and expansion planning accuracy of the transmission network.

[0005] According to a first aspect of the present invention, a method for planning transmission network expansion based on wind and solar access is provided, comprising:

[0006] Obtain wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned;

[0007] Based on the wind power attribute data, determine the wind power output data of the wind power generation equipment in a preset planning level year; based on the photovoltaic attribute data, determine the photovoltaic output data of the photovoltaic power generation equipment in a preset planning level year; based on the load attribute data, determine the load absorption data of the load in a preset planning level year;

[0008] Determining multiple transmission expansion planning schemes that meet load power demand based on the wind power output data, photovoltaic output data, load consumption data, and the grid attribute data;

[0009] The grid attribute data and each of the transmission expansion planning schemes are respectively combined into evaluation data, and each of the evaluation data is respectively input into a preset transmission expansion planning model to predict planning effect parameters, thereby obtaining planning effect parameters corresponding to each of the transmission expansion planning schemes, wherein the preset transmission expansion planning model is constructed based on the objective functions of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned;

[0010] Based on each of the planning effect parameters, a target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is determined in each of the transmission expansion planning schemes.

[0011] According to a second aspect of the present invention, there is provided a transmission network expansion planning device based on wind and solar access, comprising:

[0012] An acquisition unit, configured to acquire wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned;

[0013] a first determining unit, configured to determine, based on the wind power attribute data, wind power output data of the wind power generation equipment in a preset planned level year; determine, based on the photovoltaic attribute data, photovoltaic output data of the photovoltaic power generation equipment in a preset planned level year; and determine, based on the load attribute data, load absorption data of the load in a preset planned level year;

[0014] A second determining unit is configured to determine a plurality of transmission expansion planning schemes that meet the load power demand based on the wind power output data, the photovoltaic output data, the load consumption data, and the grid attribute data;

[0015] a prediction unit, configured to combine the grid attribute data with each of the transmission expansion planning schemes to form evaluation data, and input each of the evaluation data into a preset transmission expansion planning model to predict planning effect parameters, thereby obtaining planning effect parameters corresponding to each of the transmission expansion planning schemes, wherein the preset transmission expansion planning model is constructed based on the objective functions of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned;

[0016] The third determining unit is configured to determine, based on each of the planning effect parameters, a target transmission expansion planning scheme after connecting to the wind and solar power generation equipment in each of the transmission expansion planning schemes.

[0017] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for planning transmission network expansion based on wind and solar access.

[0018] According to a fourth aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for planning transmission network expansion based on wind and solar access is implemented.

[0019] According to a transmission network expansion planning method based on wind and solar access provided by the present invention, compared with the current manual transmission network expansion planning method, the present invention obtains wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned; and based on the wind power attribute data, determines the wind power output data of the wind power generation equipment in a preset planning level year, based on the photovoltaic attribute data, determines the photovoltaic output data of the photovoltaic power generation equipment in a preset planning level year, and based on the load attribute data, determines the load absorption data of the load in the preset planning level year; at the same time, based on the wind power attribute data, determines the load absorption data of the load in the preset planning level year. output data, photovoltaic output data, load absorption data, and the grid attribute data are used to determine multiple transmission expansion planning schemes that meet the load power demand; then the grid attribute data are respectively combined with the transmission expansion planning schemes to form evaluation data, and each evaluation data is respectively input into a preset transmission expansion planning model to predict planning effect parameters, so as to obtain planning effect parameters corresponding to each transmission expansion planning scheme, wherein the preset transmission expansion planning model is constructed based on the objective function of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned; finally, based on each planning effect parameter, the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is determined in each transmission expansion planning scheme. Therefore, by comprehensively analyzing the attribute information of wind power generation equipment, photovoltaic power generation equipment, transmission network, load, etc., wind power output data, photovoltaic output data, and load absorption data are determined, and then multiple transmission expansion planning schemes that meet the load power demand are determined based on the above data. Then, the planning effect of each transmission expansion planning scheme is predicted using the model, and finally the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is selected based on the planning effect. That is, the transmission network expansion planning is carried out by comprehensively analyzing the information such as classified power generation, photovoltaic power generation, load and power grid, which can avoid the problems of low planning efficiency and low planning accuracy caused by manual transmission network expansion planning. Therefore, the present invention can improve the expansion planning efficiency and expansion planning accuracy of the transmission network. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0021] Figure 1 A flow chart of a transmission network expansion planning method based on wind and solar access provided by an embodiment of the present invention is shown;

[0022] Figure 2 An example differential algorithm solution iteration graph of 18 nodes provided by an embodiment of the present invention is shown;

[0023] Figure 3 A flow chart of another transmission network expansion planning method based on wind and solar access provided by an embodiment of the present invention is shown;

[0024] Figure 4 A schematic structural diagram of a transmission network expansion planning device based on wind and solar access provided by an embodiment of the present invention is shown;

[0025] Figure 5 A schematic structural diagram of another transmission network expansion planning device based on wind and solar access provided by an embodiment of the present invention is shown;

[0026] Figure 6 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0027] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0028] At present, the manual method of transmission network expansion planning is greatly affected by human subjective factors, which leads to low accuracy of transmission network expansion planning. At the same time, it also leads to low efficiency of transmission network expansion planning.

[0029] In order to solve the above problems, the embodiment of the present invention provides a transmission network expansion planning method based on wind and solar access, such as Figure 1 As shown, the method includes:

[0030] 101. Obtain wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned.

[0031] Among them, wind power attribute data include: cut-in wind speed, cut-out wind speed, rated wind speed, average wind speed, rotor diameter, speed range, rated output power, etc. of wind power generation equipment; photovoltaic attribute data include: maximum light intensity of photovoltaic power generation equipment, total area of ​​solar cells, photoelectric conversion efficiency, shape parameters, etc.; load attribute data include: expected value of load active power, standard deviation of load active power, number of loads, load type, etc.; power grid attribute data include: transmission network nodes of the transmission network to be planned, expected value vector of voltage phase angle of transmission network nodes, expected value vector of node active power injection, active power vector of transmission network generators and load nodes, expected value vector of active power flow of transmission network lines under normal operating mode, vector of number of new lines in transmission network corridors, number of corridors allowed to be selected in the transmission network, investment cost per unit length of new lines in transmission network corridors, geographical location, infrastructure data, etc.

[0032] For the embodiment of the present invention, the wind power attribute data of the wind power generation equipment to be connected to the grid, the photovoltaic attribute data of the photovoltaic power generation equipment to be connected to the grid, the load attribute data corresponding to the transmission network to be planned, and the grid attribute data corresponding to the transmission network to be planned are obtained in the database, and then the expansion planning scheme of the transmission network is determined by performing a comprehensive analysis on the above data, thereby improving the efficiency and accuracy of determining the expansion planning scheme of the grid. The embodiment of the present invention is mainly applicable to the scenario of expansion planning of the transmission network connected to wind and solar power. The execution subject of the embodiment of the present invention is a device or equipment that can perform expansion planning of the transmission network connected to wind and solar power, which can be specifically set on the server side.

[0033] 102. Based on the wind power attribute data, determine the wind power output data of the wind power generation equipment in the preset planning level year; based on the photovoltaic attribute data, determine the photovoltaic output data of the photovoltaic power generation equipment in the preset planning level year; based on the load attribute data, determine the load absorption data of the load in the preset planning level year.

[0034] 103. Based on wind power output data, photovoltaic power output data, load absorption data, and grid attribute data, determine multiple transmission expansion planning schemes that meet the load power demand.

[0035] Among them, the preset planning level year is a value set according to actual needs, for example, the next year. In an embodiment of the present invention, after obtaining wind power attribute data corresponding to a wind power generation device, the wind power output data of the wind power generation device in a preset planning level year can be calculated based on the wind power attribute data, and the photovoltaic output data of the photovoltaic power generation device in a preset planning level year can be calculated based on the photovoltaic attribute data. Based on the load attribute data, the load absorption data of the load in the preset planning level year can be determined. Finally, based on the wind power output data, the photovoltaic output data, the load absorption data, and the grid attribute data, multiple transmission expansion planning schemes that meet the load power demand are determined. The method for specifically determining multiple transmission expansion planning schemes that meet the load power demand includes: respectively determining a wind characteristic vector corresponding to the wind power output data, a photovoltaic characteristic vector corresponding to the photovoltaic output data, a load characteristic vector corresponding to the load absorption data, and a grid characteristic vector corresponding to the grid attribute data; cross-processing the wind characteristic vector, the photovoltaic characteristic vector, the load characteristic vector, and the grid special vector to obtain a planning cross-characteristic vector; and inputting the planning cross-characteristic vector into a pre-constructed scheme prediction model for scheme prediction to obtain multiple transmission expansion planning schemes that meet the load power demand. Since the wind characteristic vector, photovoltaic characteristic vector, load characteristic vector, and power grid special vector are vectors of different fields and dimensions, in order to extract more invisible feature information from the wind characteristic vector, photovoltaic characteristic vector, load characteristic vector, and power grid special vector, it is necessary to cross-process the wind characteristic vector, photovoltaic characteristic vector, load characteristic vector, and power grid special vector. By cross-processing the wind characteristic vector, photovoltaic characteristic vector, load characteristic vector, and power grid special vector, we can fully utilize the relationship between different features and extract more implicit features. At the same time, we take into account both high-order and low-order processing, so that the feature data can be more fully utilized, and the subsequent power grid expansion planning scheme can be more accurate to meet the needs of actual application scenarios.

[0036] 104. The grid attribute data and each transmission expansion planning scheme are respectively combined to form evaluation data, and each evaluation data is respectively input into a preset transmission expansion planning model to predict planning effect parameters, thereby obtaining the planning effect parameters corresponding to each transmission expansion planning scheme. The preset transmission expansion planning model is constructed based on the objective function of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned.

[0037] 105. Based on various planning effect parameters, determine the target transmission expansion planning scheme after connecting to wind and solar power generation equipment in each transmission expansion planning scheme.

[0038] Congestion surplus refers to the transaction surplus caused by transmission congestion in the planned transmission network. In order to improve the prediction accuracy of the preset transmission expansion planning model, the embodiment of the present invention requires pre-training and building the preset transmission expansion planning model. The embodiment of the present invention establishes the preset transmission expansion planning model with the objective function of minimizing the investment cost of new lines and the annual congestion surplus in the preset planning level year, as shown below:

[0039] minF=[f e ,f c ] T

[0040]

[0041]

[0042]

[0043] Among them, at time t, B t is the transmission network node admittance matrix; E(θ t ) is the expected value vector of the voltage phase angle at the transmission network node; and are the active power vectors of the generators and load nodes in the transmission network at period t; is the expected value vector of active power injection at the transmission network nodes; is the expected value vector of active power flow of the transmission network line under normal operation mode; n is the vector of the number of new lines in the transmission network corridor; and They represent the lower limit vector and upper limit vector of the expected value vector of active power flow respectively; f e is the investment cost of the new line, f c is the annual congestion surplus of the new line, represents the upper limit of the number of new lines, and minF is the objective function of minimizing the investment cost and annual congestion surplus. e The calculation formula is as follows:

[0044]

[0045] Among them, N i is the number of corridors available for transmission network; C i is the investment cost per unit length of new lines in transmission corridor i; n i is the number of new lines in transmission corridor i; l i is the length of the new line in transmission corridor i. c The blocking surplus for the year is calculated as follows:

[0046]

[0047]

[0048]

[0049]

[0050] Where t represents time, j represents a node in the transmission network, and N d is the set of wind power generation equipment nodes in the transmission network, m represents the load, N l is the set of load nodes in the transmission network, Represents the attribute data of the wind power generation equipment node, Represents the attribute data of the photovoltaic power generation equipment node, N g represents the set of photovoltaic power generation equipment nodes in the transmission network, represents the active output power of the generator node i in the transmission network at period t; represents the active power of load node j in the transmission network at period t; represents the active power flow of transmission line l at period t; represents the load duration in period t, represents the upper limit of the active output power of the generator node i in the transmission network at period t, represents the lower limit of the active output power of the generator node i in the transmission network at period t, represents the upper limit of the active power of load node j in the transmission network at period t, represents the lower limit of the active power of load node j in the transmission network during period t. The preset transmission expansion planning model in the embodiment of the present invention is a dual-objective model. Therefore, the planning effect function fit in the embodiment of the present invention performs weighted processing on the two objectives, as shown below:

[0051] fit = λ1f e +λ2f c

[0052] Here, λ1 represents the weight coefficient for the planned line investment cost, and λ2 represents the weight coefficient for the annual congestion surplus. Using these formulas, a preset transmission expansion planning model can be constructed. This model can then output the planning effect parameters fit for each planning scheme. This model, constructed with the objective functions of minimizing the planned line investment cost and annual congestion surplus of the transmission network, ensures that the final planning scheme determined by the model meets both the minimum investment cost and the minimum annual congestion surplus, thus satisfying multiple requirements.

[0053] For example, if five transmission expansion planning schemes are determined, namely: Scheme A, Scheme B, Scheme C, Scheme D, and Scheme E, then Scheme A and the grid attribute data are combined into evaluation data 1 and input into the preset transmission expansion planning model for planning effect parameter prediction, and the planning effect parameter 1 corresponding to Scheme A is obtained. Scheme B and the grid attribute data are combined into evaluation data 2 and input into the preset transmission expansion planning model for planning effect parameter prediction, and the planning effect parameter 2 corresponding to Scheme B is obtained. Scheme C and the grid attribute data are combined into evaluation data 3 and input into the preset transmission expansion planning model for planning effect parameter prediction, and the planning effect parameter 3 corresponding to Scheme C is obtained. Scheme D and the grid attribute data are combined into evaluation data 4 and input into the preset transmission expansion planning model. Perform planning effect parameter prediction to obtain planning effect parameter 4 corresponding to scheme d, and input scheme e and grid attribute data into evaluation data 5 into the preset transmission expansion planning model to perform planning effect parameter prediction to obtain planning effect parameter 6 corresponding to scheme e. Then, select the minimum planning parameter among various planning parameters, and determine the transmission expansion planning scheme corresponding to the minimum planning parameter as the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment. Thus, by performing a comprehensive analysis of information such as classified power generation, photovoltaic power generation, load and grid to carry out transmission network expansion planning, the problems of low planning efficiency and low planning accuracy caused by manual transmission network expansion planning can be avoided, so that the present invention can improve the expansion planning efficiency and expansion planning accuracy of the transmission network.

[0054] In another embodiment of the present invention, when determining the target transmission expansion planning scheme after connecting to wind and solar power generation equipment, a differential evolution algorithm can also be used to assist in the prediction of a preset transmission expansion planning model. The implementation process of the differential evolution algorithm is as follows:

[0055] Step 1: Input grid attribute data; set control parameters according to actual needs NP; generating an initial population Pop, wherein the initial population includes various transmission expansion planning schemes.

[0056] Step 2: Evaluate the fitness value of each transmission expansion plan: perform topology correction and power flow calculation on each individual (transmission expansion plan) in turn to obtain the objective function F and fitness function fit that minimize the planned line investment cost and the annual congestion surplus, and save the best fitness individual Corresponding objective function value and fitness function value.

[0057] Step 3: for G=1:G max do. Among them, G max Refers to the preset number of cycles.

[0058] Step 3.1: Calculate the attenuation factor DFGδ (linear decreasing factor).

[0059] Step 3.2: Generate

[0060] Step 3.3: for i=1: NP do: Select two different The three entities (transmission expansion planning schemes with fitness greater than a certain value) will perform mutation operations and crossover recombination operations, verify boundary constraints, perform selection operations and update Pop.

[0061] Step 3.4: Update

[0062] Step 3.5: Evaluate the fitness value of each individual: perform topology correction and flow calculation on each individual in turn to obtain the objective function F and fitness function fit; save the individual with the best fitness Corresponding objective function value and fitness function value.

[0063] Step 3.6: If the iteration passes, the iteration coefficient

[0064] Step 3.7: If there is no improvement in the target value (planning performance parameter) in consecutive iterations, a reinitialization operation of the population is applied.

[0065] Step4: end for.

[0066] The transmission expansion planning algorithm determined by the embodiment of the present invention can solve the problem of balancing the volatility of power grids with high penetration rates of renewable energy. The embodiment of the present invention performs global optimization from a comprehensive perspective. By modeling wind power and photovoltaic power and generating half-order invariants, a preset transmission expansion planning model that considers economy and stability is established based on the dangerousness of grid nodes and the deployment of loads to achieve the goal of balancing grid volatility and reducing overall operational risk. In addition, the embodiment of the present invention selects appropriate algorithms to solve the power system expansion planning problem more quickly and accurately, thereby improving the stability and economy of the grid. The embodiment of the present invention provides a new comprehensive perspective and effective tools to better utilize renewable energy power generation resources and respond to the requirements of power market reform. By introducing an annual congestion surplus fee optimization function to describe the severity of system congestion, a transmission network planning model that considers economy and system congestion level is established, so that the resulting planning scheme can achieve better economy while also having a certain effect on alleviating the congestion level of the transmission network. By studying the relationship between the probabilistic output of renewable energy and power system congestion, the embodiments of the present invention help to improve the overall risk management capabilities of the system and reduce the power grid operation risks caused by the large-scale access of leisure agricultural units. At the same time, it can promote the integration of renewable energy power generation and the power grid. On the premise of ensuring the safety and stability of the power grid, it can guide the application of renewable energy power generation, realize the deep integration of renewable energy power generation and the power system, and fully tap the potential of renewable energy power generation and its role in the power system.

[0067] According to a transmission network expansion planning method based on wind and solar access provided by the present invention, compared with the current manual transmission network expansion planning method, the present invention obtains wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned; and based on the wind power attribute data, determines the wind power output data of the wind power generation equipment in a preset planning level year, based on the photovoltaic attribute data, determines the photovoltaic output data of the photovoltaic power generation equipment in a preset planning level year, and based on the load attribute data, determines the load absorption data of the load in the preset planning level year; at the same time, based on the wind power attribute data, determines the load absorption data of the load in the preset planning level year. output data, photovoltaic output data, load absorption data, and the grid attribute data are used to determine multiple transmission expansion planning schemes that meet the load power demand; then the grid attribute data are respectively combined with the transmission expansion planning schemes to form evaluation data, and each evaluation data is respectively input into a preset transmission expansion planning model to predict planning effect parameters, so as to obtain planning effect parameters corresponding to each transmission expansion planning scheme, wherein the preset transmission expansion planning model is constructed based on the objective function of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned; finally, based on each planning effect parameter, the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is determined in each transmission expansion planning scheme. Therefore, by comprehensively analyzing the attribute information of wind power generation equipment, photovoltaic power generation equipment, transmission network, load, etc., wind power output data, photovoltaic output data, and load absorption data are determined, and then multiple transmission expansion planning schemes that meet the load power demand are determined based on the above data. Then, the planning effect of each transmission expansion planning scheme is predicted using the model, and finally the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is selected based on the planning effect. That is, the transmission network expansion planning is carried out by comprehensively analyzing the information such as classified power generation, photovoltaic power generation, load and power grid, which can avoid the problems of low planning efficiency and low planning accuracy caused by manual transmission network expansion planning. Therefore, the present invention can improve the expansion planning efficiency and expansion planning accuracy of the transmission network.

[0068] For example, there are 27 available corridors in an 18-node system (transmission grid), and nodes 11 and 18 are isolated nodes. Node 18 is set as a balancing node. Based on experience, the load of the entire transmission system is assumed to conform to a normal distribution, with its standard deviation set to 2% of the expected value. Node 8 is connected to a 1000MW photovoltaic system, which uses a Beta distribution model with shape parameters α = 63 and β = 27. Node 13 is connected to a 1000MW wind turbine, and the wind turbine cut-in wind speed is set to v ci =3m / s, rated wind speed is set to v r =14m / s, cut-out wind speed is set to v co=25m / s, control parameter Cr=0.5, objective function value F=0.5, wind speed adopts the on-site measured data of a certain wind farm; line cost is C i =100×10 4 Yuan / km, and the load duration is one year.

[0069] The relevant parameter settings of the algorithm are shown in Table 1. Table 2 shows the expansion planning results obtained by all algorithms. Among them, a larger investment cost represents a poorer economic performance; a larger annual congestion residual cost represents a poorer control of transmission congestion. Excessive congestion conditions may lead to resource mismatch and damage overall social welfare. In terms of the economic investment cost of new lines, the differential algorithm has a lower cost. Regarding the congestion level, the annual congestion residual cost obtained by the differential algorithm indicates better control of congestion. In addition, this chapter also saves the fitness value during the algorithm iteration process, such as Figure 2 As shown in Figure 2, it can be observed that the differential algorithm has a good initial value and has better convergence performance and converges quickly.

[0070] Table 118 Node system parameter simulation settings

[0071]

[0072] Table 218 Node system planning results

[0073]

[0074] Furthermore, in order to better illustrate the above process of planning the expansion of the transmission network based on wind and solar access, as a refinement and extension of the above embodiment, the embodiment of the present invention provides another method for planning the expansion of the transmission network based on wind and solar access, such as Figure 3 As shown, the method includes:

[0075] 201. Obtain wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned.

[0076] For the embodiment of the present invention, the database stores attribute data corresponding to various new energy power generation equipment, such as wind discharge equipment, photovoltaic power generation equipment, etc., and also stores load attribute data and grid attribute data of the transmission network. Specifically, the above data can be obtained in the database.

[0077] 202. Based on the wind power attribute data, determine the wind power output data of the wind power generation equipment in the preset planning level year; based on the photovoltaic attribute data, determine the photovoltaic output data of the photovoltaic power generation equipment in the preset planning level year; based on the load attribute data, determine the load absorption data of the load in the preset planning level year.

[0078] Among them, wind power attribute data include: the rated output power, cut-in wind speed, cut-out wind speed and average wind speed of the wind power generation equipment to be connected to the grid in the preset planning level year; the load attribute data include the expected value and standard deviation of the active power of the load corresponding to the planned transmission network in the preset planning level year.

[0079] For the embodiment of the present invention, in order to determine the transmission expansion planning scheme of the transmission network, it is first necessary to determine the wind output data, photovoltaic output data, and load absorption data. Based on this, step 202 specifically includes: subtracting the rated wind speed from the cut-in wind speed to obtain the cut-in wind speed difference of the wind power generation equipment; dividing the rated output power by the cut-in wind speed difference to obtain the cut-in wind speed evaluation parameter of the wind power generation equipment; multiplying the cut-in wind speed evaluation parameter by the cut-in wind speed to obtain the wind output evaluation parameter of the wind power generation equipment; multiplying the cut-in wind speed evaluation parameter by the average wind speed, and adding the multiplication result to the wind output evaluation parameter to obtain the wind output data of the wind power generation equipment in the preset planning level year.

[0080] Specifically, the wind power output data of the wind power generation equipment in the preset planning level year is calculated according to the following formula:

[0081]

[0082]

[0083] k2=-k l ν ci

[0084] Among them, P wr Represents the rated output power of wind power generation equipment; v el Represents the cut-in wind speed of the wind turbine, v co Represents the cut-out wind speed of the fan, v r represents the rated wind speed of the wind power generation equipment, k1 represents the cut-in wind speed evaluation parameter, k2 represents the wind output evaluation parameter, v represents the average wind speed of the wind power generation equipment, P w (v) represents the wind power output data of the wind power generation equipment in the preset planning level year. Further, the method for determining the photovoltaic output data includes: based on the first shape parameter α, the second shape parameter β, the maximum light intensity b max , calculate the solar irradiance b of the photovoltaic power generation equipment in the preset planning level year, where, Γ represents the gamma function, f s () represents the probability density function of solar irradiance using Beta distribution; based on the solar irradiance b, the total area of ​​solar cells A s, photoelectric conversion efficiency η, determine the photovoltaic output data P of the photovoltaic power generation equipment under the preset planning level year s , where P s =bA S η.

[0085] The calculation formula for photovoltaic output data is as follows:

[0086]

[0087] Where bmax represents the maximum light intensity, As represents the total area of ​​solar cells, η represents the photoelectric conversion efficiency, α represents the first shape parameter, β represents the second shape parameter, Ps represents the photovoltaic output data, b represents the solar irradiance, Γ represents the gamma function, and f s () represents the probability density function of solar irradiance using Beta distribution, and the photovoltaic output data can be calculated by the above formula. Further, the method for determining the load absorption data includes: based on the expected value μ of the active power pLoad , standard deviation δ PLoad , calculate the load absorption data P of the load in the preset planning level year Load ,in, fp Load (P L oad) represents the probability density function of load absorption data. The load absorption data is calculated by the following formula:

[0088]

[0089] Among them, μ pLoad represents the expected value of active power, δ PLoad Indicates the standard deviation of active power, P Load It represents the load absorption data under the preset planning level year. The above formula can be used to calculate the load absorption data under the preset planning level year.

[0090] 203. Based on wind power output data, photovoltaic power output data, load absorption data, and grid attribute data, determine multiple transmission expansion planning schemes that meet the load power demand.

[0091] Specifically, based on wind power output data, photovoltaic output data, load absorption data, and grid attribute data, multiple transmission expansion planning schemes that meet load power demand and grid operation requirements are determined.

[0092] 204. The grid attribute data and each transmission expansion planning scheme are respectively combined to form evaluation data, and each evaluation data is respectively input into a preset transmission expansion planning model to predict planning effect parameters, thereby obtaining the planning effect parameters corresponding to each transmission expansion planning scheme. The preset transmission expansion planning model is constructed based on the objective function of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned.

[0093] The preset transmission expansion planning model includes: an investment cost prediction module, an annual congestion surplus prediction module, and a weighted summation module. In this embodiment of the present invention, the grid attribute data is combined with each transmission expansion planning scheme to form evaluation data, and then the preset transmission expansion planning model is used to perform prediction analysis on the evaluation data. Based on this, step 204 specifically includes: inputting each of the evaluation data into the investment cost prediction module for cost prediction to obtain the investment cost parameters corresponding to each of the transmission expansion planning schemes; inputting the evaluation data into the annual congestion surplus prediction module for prediction to obtain the annual congestion surplus parameters corresponding to each of the transmission expansion planning schemes; determining the weight coefficients of the investment cost parameters and the annual congestion surplus parameters; and based on the weight coefficients, adding the investment cost parameters and the annual congestion surplus parameters to obtain the planning effect parameters corresponding to each of the transmission expansion planning schemes.

[0094] The weight coefficients of the investment cost parameter and the annual congestion surplus parameter are values ​​set according to actual needs. Specifically, taking any transmission expansion planning scheme 1 as an example, the grid attribute data and the evaluation data consisting of the transmission expansion planning scheme 1 are respectively input into the investment cost prediction module and the annual congestion surplus prediction module. The investment cost parameter is output through the investment cost prediction module, and the annual congestion surplus parameter is output through the annual congestion surplus prediction module. If the weight coefficient corresponding to the investment cost parameter is λ1 and the weight coefficient corresponding to the annual congestion surplus parameter is λ2, then λ1 is multiplied by the investment cost parameter to obtain a first product, and λ2 is multiplied by the annual congestion surplus parameter to obtain a second product. Then, the first product and the second product are added to obtain the planning effect parameter of the transmission expansion planning scheme 1. In this way, the planning effect parameters corresponding to each transmission expansion planning scheme can be predicted through the above method.

[0095] 205. Determine a target planning effect parameter greater than a preset parameter threshold value among the various planning effect parameters, and use a preset correction algorithm to perform topology correction on the power transmission expansion planning scheme corresponding to the target planning effect parameter to obtain a corrected power transmission expansion planning scheme.

[0096] Among them, the preset parameter threshold is a value set according to actual needs. For the embodiment of the present invention, in order to improve the accuracy of the determination of the transmission network expansion planning scheme, meet the needs of the power grid expansion planning, and thus ensure the stable operation of the power grid, the expansion planning scheme with a larger planning effect parameter can be revised, and then the revised scheme is judged whether it meets the needs of the expansion planning. Based on this, step 205 specifically includes: randomly selecting a preset number of reference transmission expansion planning schemes from each of the transmission expansion planning schemes, and determining the reference scheme feature vectors corresponding to the preset number of reference transmission expansion planning schemes, and determining the target scheme feature vector of the transmission expansion planning scheme corresponding to the target planning effect parameter; determining the vector difference between any two vectors in each of the reference scheme feature vectors, multiplying the vector difference by the preset difference weight to obtain the coefficient of variation, and multiplying the coefficient of variation by the characteristic vector of each of the reference schemes Add the remaining vectors except for any two vectors to obtain a variant scheme characteristic vector; exchange some vector elements in the variant scheme characteristic vector with some vector elements in the target scheme characteristic vector to obtain a test scheme characteristic vector; determine the test planning effect parameter corresponding to the test scheme characteristic vector, if the test planning effect parameter is less than the target planning effect parameter, then determine the test transmission expansion planning scheme corresponding to the test scheme characteristic vector as the revised transmission expansion planning scheme, if the test planning effect parameter is greater than or equal to the target planning effect parameter, then reselect a preset number of planning schemes from the planning schemes after excluding the reference transmission expansion planning scheme in each of the transmission expansion planning schemes, and determine the revised transmission expansion planning scheme based on the reselected preset number of planning schemes.

[0097] Among them, the preset number is set according to actual needs, such as the preset number can be 3. Specifically, for example, after the planning effect parameters corresponding to each transmission expansion planning scheme are predicted by the preset transmission expansion planning model, the target planning effect parameter greater than the preset parameter threshold is determined among the various planning effect parameters, and the transmission expansion planning scheme corresponding to the target planning effect parameter is determined to be the expansion planning scheme a, and a preset number (3) of reference transmission expansion planning schemes are randomly selected from each of the transmission expansion planning schemes: scheme b, scheme c, and scheme d, and then the reference scheme feature vector δ corresponding to scheme b, the reference scheme feature vector β corresponding to scheme c, and the reference scheme feature vector θ corresponding to scheme d are respectively determined by word embedding and the like, and the target scheme feature vector ε corresponding to the expansion planning scheme a is determined at the same time, and then the variation scheme feature vector is determined by the following formula:

[0098] κ=γ(δ-β)+θ

[0099] Wherein, κ represents the characteristic vector of the variation scheme, γ represents the preset differential weight (wherein, the preset differential weight is set according to actual needs), and γ(δ-β) represents the coefficient of variation. Furthermore, if the variation scheme characteristic vector κ is (e, f, g), and the target scheme characteristic vector ε is (h, i, j), if some of the vector elements are any two elements, the elements e and f in κ can be exchanged with i and j in ε, and the variation scheme characteristic vector after the exchange becomes the experimental scheme characteristic vector (i, j, g). Then, the experimental scheme (experimental transmission expansion planning scheme) and the grid attribute data corresponding to the experimental scheme characteristic vector are input into the preset transmission expansion planning model for parameter prediction to obtain the experimental planning effect parameter. If the experimental planning effect parameter is less than the target planning effect parameter, the experimental transmission expansion planning scheme is determined as the revised transmission expansion planning scheme. If the experimental planning effect parameter is greater than or equal to the target planning effect parameter, three planning schemes are reselected from the planning schemes after excluding the reference transmission expansion planning scheme from each transmission expansion planning scheme, and based on the reselected three planning schemes, the revised transmission expansion planning scheme is determined in the above manner. Therefore, by correcting the transmission planning scheme that does not meet the grid planning requirements, and then selecting the scheme that meets the grid planning requirements from the corrected scheme and the previous remaining schemes, the accuracy of determining the transmission expansion planning scheme can be further improved, so that the final expansion planning scheme can better meet the operation and planning requirements of the grid.

[0100] 206. Input the revised transmission expansion planning scheme and the grid attribute data into a preset transmission expansion planning model to predict planning effect parameters, and obtain revised planning effect parameters corresponding to the revised transmission expansion planning scheme.

[0101] 207. Based on the remaining planning effect parameters and the revised planning effect parameters after excluding the target planning effect parameters from each planning effect parameter, determine the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment in the transmission expansion planning scheme corresponding to the remaining planning effect parameters and the revised transmission expansion planning scheme corresponding to the revised planning effect parameters.

[0102] Specifically, if a total of 6 transmission expansion planning schemes are determined, namely: Scheme a, Scheme b, Scheme c, Scheme d, Scheme e, and Scheme f, among which the target planning effect parameter corresponding to Scheme e is greater than the preset parameter threshold, Scheme e needs to be corrected at this time. The corrected transmission expansion planning scheme corresponding to Scheme e is Scheme m. Then, Scheme m and the grid attribute data are input into the preset transmission expansion planning model for planning effect parameter prediction to obtain the corrected planning effect parameters corresponding to Scheme m. Then, the minimum planning effect parameter is determined among the corrected planning effect parameters, the planning effect parameters corresponding to Scheme a, the planning effect parameters corresponding to Scheme b, the planning effect parameters corresponding to Scheme c, the planning effect parameters corresponding to Scheme d, and the planning effect parameters corresponding to Scheme f. If the minimum planning effect parameter is the corrected planning effect parameter corresponding to Scheme m, Scheme m is finally determined as the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment. Therefore, by correcting the transmission planning scheme that does not meet the grid planning requirements, and then selecting the scheme that meets the grid planning requirements from the corrected scheme and the previous remaining schemes, the accuracy of determining the transmission expansion planning scheme can be further improved, so that the final expansion planning scheme can better meet the operation and planning requirements of the grid.

[0103] According to another transmission network expansion planning method based on wind and solar access provided by the present invention, compared with the current manual transmission network expansion planning method, the present invention obtains wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned; and based on the wind power attribute data, determines the wind power output data of the wind power generation equipment in the preset planning level year, based on the photovoltaic attribute data, determines the photovoltaic output data of the photovoltaic power generation equipment in the preset planning level year, and based on the load attribute data, determines the load absorption data of the load in the preset planning level year; at the same time, based on the wind power attribute data, determines the load absorption data of the load in the preset planning level year. output data, photovoltaic output data, load absorption data, and the grid attribute data are used to determine multiple transmission expansion planning schemes that meet the load power demand; then the grid attribute data are respectively combined with the transmission expansion planning schemes to form evaluation data, and each evaluation data is respectively input into a preset transmission expansion planning model to predict planning effect parameters, so as to obtain planning effect parameters corresponding to each transmission expansion planning scheme, wherein the preset transmission expansion planning model is constructed based on the objective function of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned; finally, based on each planning effect parameter, the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is determined in each transmission expansion planning scheme. Therefore, by comprehensively analyzing the attribute information of wind power generation equipment, photovoltaic power generation equipment, transmission network, load, etc., wind power output data, photovoltaic output data, and load absorption data are determined, and then multiple transmission expansion planning schemes that meet the load power demand are determined based on the above data. Then, the planning effect of each transmission expansion planning scheme is predicted using the model, and finally the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is selected based on the planning effect. That is, the transmission network expansion planning is carried out by comprehensively analyzing the information such as classified power generation, photovoltaic power generation, load and power grid, which can avoid the problems of low planning efficiency and low planning accuracy caused by manual transmission network expansion planning. Therefore, the present invention can improve the expansion planning efficiency and expansion planning accuracy of the transmission network.

[0104] Further, as Figure 1 The embodiment of the present invention provides a transmission network expansion planning device based on wind and solar access, such as Figure 4 As shown, the device includes: an acquisition unit 31, a first determination unit 32, a second determination unit 33, a prediction unit 34, and a third determination unit 35.

[0105] The acquisition unit 31 can be used to acquire wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned.

[0106] The first determination unit 32 can be used to determine the wind power output data of the wind power generation equipment in a preset planning level year based on the wind power attribute data, determine the photovoltaic output data of the photovoltaic power generation equipment in a preset planning level year based on the photovoltaic attribute data, and determine the load absorption data of the load in a preset planning level year based on the load attribute data.

[0107] The second determining unit 33 can be used to determine multiple transmission expansion planning schemes that meet the load power demand based on the wind power output data, photovoltaic power output data, load absorption data, and the grid attribute data.

[0108] The prediction unit 34 can be used to combine the power grid attribute data with each of the transmission expansion planning schemes to form evaluation data, and input each of the evaluation data into a preset transmission expansion planning model to predict planning effect parameters, thereby obtaining planning effect parameters corresponding to each of the transmission expansion planning schemes, wherein the preset transmission expansion planning model is constructed based on the objective functions of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned.

[0109] The third determining unit 35 may be configured to determine, in each of the transmission expansion planning schemes, a target transmission expansion planning scheme after the wind and solar power generation equipment is connected, based on each of the planning effect parameters.

[0110] In specific application scenarios, in order to determine the target transmission expansion planning scheme after connecting to wind and solar power generation equipment, such as Figure 5 As shown, the third determination unit 35 includes a correction module 351 , a prediction module 352 , and a first determination module 353 .

[0111] The correction module 351 can be used to determine a target planning effect parameter greater than a preset parameter threshold among the planning effect parameters, and use a preset correction algorithm to perform topological correction on the power transmission expansion planning scheme corresponding to the target planning effect parameter to obtain a corrected power transmission expansion planning scheme.

[0112] The prediction module 352 can be used to input the revised transmission expansion planning scheme and the grid attribute data into the preset transmission expansion planning model to predict planning effect parameters, and obtain revised planning effect parameters corresponding to the revised transmission expansion planning scheme.

[0113] The first determination module 353 can be used to determine the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment in the transmission expansion planning scheme corresponding to the remaining planning effect parameters and the revised transmission expansion planning scheme corresponding to the revised planning effect parameters based on the remaining planning effect parameters after removing the target planning effect parameters from each of the planning effect parameters.

[0114] In a specific application scenario, in order to perform topological correction on the transmission expansion planning scheme, the correction module 351 can be specifically used to randomly select a preset number of reference transmission expansion planning schemes from each of the transmission expansion planning schemes, and determine the reference scheme characteristic vectors corresponding to the preset number of reference transmission expansion planning schemes, and determine the target scheme characteristic vector of the transmission expansion planning scheme corresponding to the target planning effect parameter; determine the vector difference between any two vectors in each of the reference scheme characteristic vectors, multiply the vector difference by a preset differential weight to obtain a coefficient of variation, and add the coefficient of variation to the remaining vectors in each of the reference scheme characteristic vectors except the any two vectors to obtain a variation scheme characteristic vector; and Part of the vector elements in the variation scheme characteristic vector are exchanged with part of the vector elements in the target scheme characteristic vector to obtain a test scheme characteristic vector; the test planning effect parameter corresponding to the test scheme characteristic vector is determined; if the test planning effect parameter is less than the target planning effect parameter, the test transmission expansion planning scheme corresponding to the test scheme characteristic vector is determined as the revised transmission expansion planning scheme; if the test planning effect parameter is greater than or equal to the target planning effect parameter, a preset number of planning schemes are reselected from the planning schemes after excluding the reference transmission expansion planning scheme in each of the transmission expansion planning schemes, and a revised transmission expansion planning scheme is determined based on the reselected preset number of planning schemes.

[0115] In a specific application scenario, in order to determine the wind power output data of the wind power generation equipment in a preset planned level year, the first determination unit 32 includes a subtraction module 321, a division module 322, a multiplication module 323, and a first addition module 324.

[0116] The subtraction module 321 may be configured to subtract the rated wind speed from the cut-in wind speed to obtain a cut-in wind speed difference of the wind power generation equipment.

[0117] The division module 322 may be configured to divide the rated output power by the cut-in wind speed difference to obtain a cut-in wind speed evaluation parameter of the wind power generation equipment.

[0118] The multiplication module 323 may be configured to multiply the cut-in wind speed evaluation parameter by the cut-in wind speed to obtain a wind power output evaluation parameter of the wind power generation equipment.

[0119] The first adding module 324 may be configured to multiply the cut-in wind speed evaluation parameter by the average wind speed, and add the multiplication result to the wind output evaluation parameter to obtain wind output data of the wind power generation equipment in a preset planned level year.

[0120] In a specific application scenario, in order to determine the photovoltaic output data of the photovoltaic power generation equipment under the preset planning level year, the first determining unit 32 can be specifically used to determine the photovoltaic output data of the photovoltaic power generation equipment under the preset planning level year based on the first shape parameter α, the second shape parameter β, the maximum light intensity b max , calculate the solar irradiance b of the photovoltaic power generation equipment in the preset planning level year, where, Γ represents the gamma function, f s () represents the probability density function of solar irradiance using Beta distribution; based on the solar irradiance b, the total area of ​​solar cells A s , photoelectric conversion efficiency η, determine the photovoltaic output data P of the photovoltaic power generation equipment under the preset planning level year s , where P s =bA S η.

[0121] In a specific application scenario, in order to determine the load consumption data of the load under the preset planning level year, the first determining unit 32 can be specifically used to calculate the expected value μ of the active power based on the load consumption data. pLoad , standard deviation δ PLoad , calculate the load absorption data P of the load in the preset planning level year Load ,in, fp Load (P L oad) represents the probability density function of load absorption data.

[0122] In a specific application scenario, in order to predict planning effect parameters, the prediction unit 34 includes a prediction module 341 , a second determination module 342 , and a second addition module 343 .

[0123] The prediction module 341 can be used to input each of the evaluation data into the investment cost prediction module for cost prediction, so as to obtain the investment cost parameters corresponding to each of the transmission expansion planning schemes.

[0124] The prediction module 341 may also be configured to input the evaluation data into the annual congestion surplus prediction module for prediction, thereby obtaining annual congestion surplus parameters corresponding to each of the transmission expansion planning schemes.

[0125] The second determination module 342 may be configured to determine weight coefficients of the investment cost parameter and the annual blocking surplus parameter, respectively.

[0126] The second adding module 343 may be configured to add the investment cost parameter and the annual congestion surplus parameter based on the weight coefficient to obtain planning effect parameters corresponding to each of the transmission expansion planning schemes.

[0127] It should be noted that for other corresponding descriptions of the functional modules involved in the wind-solar access-based transmission network expansion planning device provided in the embodiment of the present invention, please refer to Figure 1 The corresponding description of the method shown will not be repeated here.

[0128] Based on the above Figure 1 The method shown, accordingly, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented: obtaining wind power attribute data of the wind power generation equipment to be connected to the grid, photoelectric attribute data of the photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned; based on the wind power attribute data, determining the wind power output data of the wind power generation equipment in a preset planning level year, based on the photoelectric attribute data, determining the photovoltaic output data of the photovoltaic power generation equipment in a preset planning level year, and based on the load attribute data, determining the load absorption data of the load in the preset planning level year; based on the Wind power output data, photovoltaic output data, load absorption data, and the grid attribute data are used to determine multiple transmission expansion planning schemes that meet the load power demand; the grid attribute data are respectively combined with each of the transmission expansion planning schemes to form evaluation data, and each of the evaluation data is respectively input into a preset transmission expansion planning model to predict planning effect parameters, so as to obtain planning effect parameters corresponding to each of the transmission expansion planning schemes, wherein the preset transmission expansion planning model is constructed based on the objective function of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned; based on each of the planning effect parameters, a target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is determined in each of the transmission expansion planning schemes.

[0129] Based on the above Figure 1 The method shown and Figure 4 The embodiment of the device shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 6As shown, the computer device includes: a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor, wherein the memory 42 and the processor 41 are both arranged on a bus 43. When the processor 41 executes the program, the following steps are implemented: obtaining wind power attribute data of the wind power generation equipment to be connected to the grid, photoelectric attribute data of the photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned; based on the wind power attribute data, determining the wind output data of the wind power generation equipment in a preset planned level year, based on the photoelectric attribute data, determining the photovoltaic output data of the photovoltaic power generation equipment in a preset planned level year, and based on the load attribute data, determining the load output data in a preset planned level year. load absorption data in the planned year; based on the wind power output data, photovoltaic output data, load absorption data, and the grid attribute data, determine multiple transmission expansion planning schemes that meet the load power demand; respectively form evaluation data with the grid attribute data and each of the transmission expansion planning schemes, and respectively input each of the evaluation data into a preset transmission expansion planning model to predict planning effect parameters, and obtain planning effect parameters corresponding to each of the transmission expansion planning schemes, wherein the preset transmission expansion planning model is constructed based on the objective function of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned; based on each of the planning effect parameters, determine the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment in each of the transmission expansion planning schemes.

[0130] Through the technical solution of the present invention, the present invention obtains the wind power attribute data of the wind power generation equipment to be connected to the grid, the photoelectric attribute data of the photovoltaic power generation equipment to be connected to the grid, the load attribute data corresponding to the transmission network to be planned, and the grid attribute data corresponding to the transmission network to be planned; and based on the wind power attribute data, determines the wind power output data of the wind power generation equipment in the preset planning level year, based on the photoelectric attribute data, determines the photovoltaic output data of the photovoltaic power generation equipment in the preset planning level year, and based on the load attribute data, determines the load absorption data of the load in the preset planning level year; at the same time, based on the wind power output data, photovoltaic output data, load absorption data, The grid attribute data is used to determine multiple transmission expansion planning schemes that meet the load power demand; then the grid attribute data is respectively combined with each of the transmission expansion planning schemes to form evaluation data, and each of the evaluation data is respectively input into a preset transmission expansion planning model to predict planning effect parameters, so as to obtain planning effect parameters corresponding to each of the transmission expansion planning schemes, wherein the preset transmission expansion planning model is constructed based on the objective function of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned; finally, based on each of the planning effect parameters, the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is determined in each of the transmission expansion planning schemes. Therefore, by comprehensively analyzing the attribute information of wind power generation equipment, photovoltaic power generation equipment, transmission network, load, etc., wind power output data, photovoltaic output data, and load absorption data are determined, and then multiple transmission expansion planning schemes that meet the load power demand are determined based on the above data. Then, the planning effect of each transmission expansion planning scheme is predicted using the model, and finally the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is selected based on the planning effect. That is, the transmission network expansion planning is carried out by comprehensively analyzing the information such as classified power generation, photovoltaic power generation, load and power grid, which can avoid the problems of low planning efficiency and low planning accuracy caused by manual transmission network expansion planning. Therefore, the present invention can improve the expansion planning efficiency and expansion planning accuracy of the transmission network.

[0131] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, centralized on a single computing device, or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0132] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A transmission network expansion planning method based on wind and solar access, characterized in that: include: Obtain wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned; Based on the wind power attribute data, determine the wind power output data of the wind power generation equipment in a preset planning level year; based on the photovoltaic attribute data, determine the photovoltaic output data of the photovoltaic power generation equipment in a preset planning level year; based on the load attribute data, determine the load absorption data of the load in a preset planning level year; Determining multiple transmission expansion planning schemes that meet load power demand based on the wind power output data, photovoltaic output data, load consumption data, and the grid attribute data; The grid attribute data and each of the transmission expansion planning schemes are respectively combined into evaluation data, and each of the evaluation data is respectively input into a preset transmission expansion planning model to predict planning effect parameters, thereby obtaining planning effect parameters corresponding to each of the transmission expansion planning schemes, wherein the preset transmission expansion planning model is constructed based on the objective functions of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned; Based on each of the planning effect parameters, a target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is determined in each of the transmission expansion planning schemes.

2. The method according to claim 1, characterized in that Determining, in each of the transmission expansion planning schemes, a target transmission expansion planning scheme after connecting to the wind and solar power generation equipment based on each of the planning effect parameters, includes: Determining a target planning effect parameter greater than a preset parameter threshold value among the planning effect parameters, and performing topological correction on the power transmission expansion planning scheme corresponding to the target planning effect parameter using a preset correction algorithm to obtain a corrected power transmission expansion planning scheme; Inputting the revised transmission expansion planning scheme and the grid attribute data into the preset transmission expansion planning model to perform planning effect parameter prediction, and obtaining revised planning effect parameters corresponding to the revised transmission expansion planning scheme; Based on the remaining planning effect parameters after removing the target planning effect parameters from each of the planning effect parameters and the revised planning effect parameters, the target transmission expansion planning scheme after connecting to the wind and solar power generation equipment is determined in the transmission expansion planning scheme corresponding to the remaining planning effect parameters and the revised transmission expansion planning scheme corresponding to the revised planning effect parameters.

3. The method according to claim 2, characterized in that The topological correction of the power transmission expansion planning scheme corresponding to the target planning effect parameter by using a preset correction algorithm to obtain a corrected power transmission expansion planning scheme includes: Randomly selecting a preset number of reference transmission expansion planning schemes from each of the transmission expansion planning schemes, and determining reference scheme characteristic vectors corresponding to the preset number of reference transmission expansion planning schemes, and determining a target scheme characteristic vector of the transmission expansion planning scheme corresponding to the target planning effect parameter; Determine a vector difference between any two vectors in each of the reference solution feature vectors, multiply the vector difference by a preset difference weight to obtain a coefficient of variation, and add the coefficient of variation to the remaining vectors in each of the reference solution feature vectors except the any two vectors to obtain a variation solution feature vector; Exchanging some vector elements in the variant solution feature vector with some vector elements in the target solution feature vector to obtain a test solution feature vector; Determine the experimental planning effect parameter corresponding to the experimental scheme characteristic vector; if the experimental planning effect parameter is less than the target planning effect parameter, determine the experimental transmission expansion planning scheme corresponding to the experimental scheme characteristic vector as the revised transmission expansion planning scheme; if the experimental planning effect parameter is greater than or equal to the target planning effect parameter, reselect a preset number of planning schemes from the planning schemes after excluding the reference transmission expansion planning scheme in each of the transmission expansion planning schemes, and determine the revised transmission expansion planning scheme based on the reselected preset number of planning schemes.

4. The method according to claim 1, wherein The wind power attribute data includes: the rated output power, cut-in wind speed, cut-out wind speed, and average wind speed of the wind power generation equipment to be connected to the grid in a preset planning level year; The determining, based on the wind power attribute data, wind power output data of the wind power generation equipment in a preset planned level year includes: subtracting the cut-in wind speed from the rated wind speed to obtain a cut-in wind speed difference of the wind turbine generator system; Dividing the rated output power by the cut-in wind speed difference to obtain a cut-in wind speed evaluation parameter of the wind power generation equipment; multiplying the cut-in wind speed evaluation parameter by the cut-in wind speed to obtain a wind power output evaluation parameter of the wind power generation equipment; The cut-in wind speed evaluation parameter is multiplied by the average wind speed, and the multiplication result is added to the wind output evaluation parameter to obtain the wind output data of the wind power generation equipment in a preset planning level year.

5. The method according to claim 1, wherein The photoelectric attribute data includes: the maximum light intensity b of the photovoltaic power generation equipment in the preset planning level year max 、Total area of ​​solar cells A s , photoelectric conversion efficiency η, a first shape parameter α, a second shape parameter β; based on the photoelectric attribute data, determining the photovoltaic output data P of the photovoltaic power generation equipment in the preset planning level year s ,include: Based on the first shape parameter α, the second shape parameter β, the maximum light intensity b max , calculate the solar irradiance b of the photovoltaic power generation equipment in the preset planning level year, where, Γ represents the gamma function, f s () represents the probability density function of solar irradiance using Beta distribution; Based on the solar irradiance b, the total area of ​​solar cells A s , photoelectric conversion efficiency η, determine the photovoltaic output data P of the photovoltaic power generation equipment under the preset planning level year s , where P s =bA S η.

6. The method according to claim 1, characterized in that The load attribute data includes the expected value of the active power of the load corresponding to the planned transmission network in the preset planning level year. Standard deviation The step of determining the load absorption data of the load in a preset planning level year based on the load attribute data includes: Based on the expected value of the active power Standard deviation Calculate the load absorption data P of the load in the preset planning level year Load ,in, Represents the probability density function of load absorption data.

7. The method according to claim 1, characterized in that The preset transmission expansion planning model includes: an investment cost prediction module, an annual congestion surplus prediction module, and a weighted summation module; the evaluation data are respectively input into the preset transmission expansion planning model to predict planning effect parameters, and the planning effect parameters corresponding to each transmission expansion planning scheme are obtained, including: Inputting each of the evaluation data into the investment cost prediction module for cost prediction, and obtaining investment cost parameters corresponding to each of the transmission expansion planning schemes; Inputting the evaluation data into the annual congestion surplus prediction module for prediction, and obtaining annual congestion surplus parameters corresponding to each of the transmission expansion planning schemes; respectively determining weight coefficients of the investment cost parameter and the annual blocking surplus parameter; Based on the weight coefficient, the investment cost parameter and the annual congestion surplus parameter are added together to obtain planning effect parameters corresponding to each of the transmission expansion planning schemes.

8. A transmission network expansion planning device based on wind and solar access, characterized in that: include: An acquisition unit, configured to acquire wind power attribute data of wind power generation equipment to be connected to the grid, photovoltaic attribute data of photovoltaic power generation equipment to be connected to the grid, load attribute data corresponding to the transmission network to be planned, and grid attribute data corresponding to the transmission network to be planned; a first determining unit, configured to determine, based on the wind power attribute data, wind power output data of the wind power generation equipment in a preset planned level year; determine, based on the photovoltaic attribute data, photovoltaic output data of the photovoltaic power generation equipment in a preset planned level year; and determine, based on the load attribute data, load absorption data of the load in a preset planned level year; A second determining unit is configured to determine a plurality of transmission expansion planning schemes that meet the load power demand based on the wind power output data, the photovoltaic output data, the load consumption data, and the grid attribute data; a prediction unit, configured to combine the grid attribute data with each of the transmission expansion planning schemes to form evaluation data, and input each of the evaluation data into a preset transmission expansion planning model to predict planning effect parameters, thereby obtaining planning effect parameters corresponding to each of the transmission expansion planning schemes, wherein the preset transmission expansion planning model is constructed based on the objective functions of minimizing the planned line investment cost and minimizing the annual congestion surplus of the transmission network to be planned; The third determining unit is configured to determine, based on each of the planning effect parameters, a target transmission expansion planning scheme after connecting to the wind and solar power generation equipment in each of the transmission expansion planning schemes.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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