New energy optimal location planning method

Abstract

The invention relates to the field of power supply, and discloses a new energy optimal location planning method. The method comprises the following steps: for each photovoltaic partition, obtaining a load increment, a new energy increment and a new energy increment upper limit in a planning time period; based on the relationship between the output change of each photovoltaic partition and the river-crossing channel power flow change, establishing the relationship between the source, network, load and new energy planning factors and the power flow through the output power transfer distribution factor of the generator, and establishing an objective function of a new energy distribution optimal model. And determining constraint conditions of the new energy distribution optimal model based on the load increment, the new energy increment and the new energy increment upper limit. And on the basis of the constraint condition, solving the target function, obtaining the optimal increment of the new energy of each photovoltaic partition, obtaining the optimal scheme of the new energy distribution of the source network load, and enabling the sum of the river-crossing channel power flow to be minimum. Through the method, the problems of single consideration factor, model staticization, improper constraint processing and low calculation efficiency in the prior art can be solved, the optimal location selection problem of the newly added new energy capacity is solved, the power flow increment of the key river-crossing channel is minimized, and the power grid load distribution is balanced.

Description

Technical Field

[0001] This application relates to the field of power supply, and particularly to a method for optimal location planning of new energy sources. Background Art

[0002] This section aims to provide background or context for the embodiments of the present application described in the claims. The content in this section is for reference only and does not constitute an admission or confirmation that it is prior art that has been made public.

[0003] With the rapid development of new energy sources such as photovoltaic and wind power, large-scale access to the power grid has made the power flow characteristics of the power system complex, and reasonable location selection has become an important factor in ensuring the safe and stable operation of the power grid. However, the existing new energy location selection methods generally fail to fully consider the impact of power flow distribution among regions on the stability of the power grid, and it is difficult to meet the safety and efficiency requirements of the power grid when the system load increases. For example, due to the geographical layout characteristics of the Jiangsu power grid, large-scale access to new energy in the northern part has led to a large amount of power flow tasks being carried by the north-south river-crossing channels. How to distribute the newly added new energy capacity year by year to balance the power grid load and avoid overloading of local lines and excessive power flow in the river-crossing channels is an urgent problem to be solved in the current power grid planning.

[0004] The prior art mainly uses linear programming or non-linear programming models based on load distribution and power grid structure to achieve power flow optimization. Typical methods include power flow distribution calculation based on simulation and heuristic optimization algorithms. However, these methods have significant defects: the simulation calculation method is costly and difficult to process large-scale data, the heuristic algorithm has low solution efficiency and is prone to falling into local optima. In addition, the existing methods often only consider the new energy resource endowment, neglect the collaborative optimization of the power grid transmission capacity and grid structure, lack adaptability to the dynamic changes of the power grid using static models, and have insufficient processing ability for complex constraints, which may lead to infeasible optimization results. At the same time, the problem of low calculation efficiency is particularly prominent in power grids with a high proportion of new energy access, affecting the real-time scheduling ability and overall optimization effect of the system. Summary of the Invention

[0005] The purpose of this application is to provide a method for optimal location planning of new energy sources, which can overcome the problems of single factor consideration, static model, improper constraint handling and low calculation efficiency in the prior art, solve the problem of optimal location selection of newly added new energy capacity, minimize the power flow increment of key river-crossing channels, balance the power grid load distribution, and ensure that the system can operate stably and efficiently under the condition of high proportion of new energy access.

[0006] For each photovoltaic zone, obtain the load increment Δload, new energy increment and upper limit of new energy increment within the planning period

[0007] Based on the relationship between the output change of each photovoltaic sub-region and the tidal current change of the river-crossing channel, and by using the generator output power transfer distribution factor to establish the connection between multiple planning factors and the tidal current, an objective function of the new energy distribution optimal model is established;

[0008] Based on the load increment, the new energy increment, and the upper limit of the new energy increment, the constraint conditions of the new energy distribution optimal model are determined;

[0009] Based on the constraint conditions, the objective function is solved to obtain the optimal new energy increment of each photovoltaic sub-region, and the optimal scheme of the new energy distribution of the source-grid-load is obtained, so that the total sum of the tidal current of the river-crossing channel is minimized.

[0010] In a preferred example, the objective function for establishing the new energy distribution optimal model further includes:

[0011] Suppose there are n circuits of the river-crossing channel, and their tidal currents are P1, ……, P n , and there are h photovoltaic sub-regions, and the photovoltaic outputs of each photovoltaic sub-region are GH1, ……, GH n ;

[0012] Establish a primary objective function to minimize the total sum of the tidal current of the river-crossing channel:

[0013]

[0014] According to the definition of the generator output power transfer distribution factor, the active power change of the jth photovoltaic sub-region is ΔGH j , where j = 1~h, and the tidal current change of the ith river-crossing channel is ΔP i , then:

[0015] ΔP i = G i-j ΔGH j ; (2)

[0016] If the initial tidal current of the ith river-crossing channel is P i0 , and the tidal current of the ith river-crossing channel is P i , then:

[0017]

[0018] The objective function of the new energy distribution optimal model is obtained:

[0019]

[0020] That is:

[0021]

[0022] Among them, G i-j is a coefficient, obtained through the power flow calculation simulation, and varies with the change of the grid structure.

[0023] In a preferred example, nh new energy location - power flow distribution factors, h load - power flow distribution factors, and h generation - power flow distribution factors are obtained. The new energy location - power flow distribution factor represents the output change ΔGH of the photovoltaic sub - area j j on the power flow change ΔP of the cross - river channel i i The load - power flow distribution factor represents the load increment change Δload of the load sub - area l l on the power flow change ΔP of the cross - river channel i i The generation - power flow distribution factor represents the output change ΔGen of the generator g g on the power flow change ΔP of the cross - river channel i i The influence;

[0024] The parameters of the new energy location - power flow distribution factor, the load - power flow distribution factor, and the generation - power flow distribution factor are related to the grid structure, load distribution, and unit - on distribution, and have the greatest correlation with the grid structure.

[0025] In a preferred example, the constraints for determining the optimal new energy distribution model based on the load increment, the new energy increment, and the upper limit of the new energy increment further include:

[0026] The photovoltaic output of each photovoltaic sub - area does not exceed the upper limit of the new energy increment of this photovoltaic sub - area:

[0027]

[0028] where j = 1~h, GH j0 is the initial photovoltaic output of each photovoltaic sub - area.

[0029] In a preferred example, the constraints for determining the optimal new energy distribution model based on the load increment, the new energy increment, and the upper limit of the new energy increment further include:

[0030] Only change the photovoltaic output distribution of the photovoltaic sub - area, without changing the total photovoltaic output, and the new energy increment is 0:

[0031]

[0032] In a preferred example, the constraints for determining the optimal new energy distribution model based on the load increment, the new energy increment, and the upper limit of the new energy increment further include:

[0033] If the new energy increment is a known value N, then:

[0034]

[0035] In a preferred example, the constraint conditions for determining the optimal model of the new energy distribution based on the load increment, the new energy increment, and the upper limit of the new energy increment further include:

[0036] Each of the river-crossing channels satisfies the upper limit constraint of the line transmission capacity:

[0037]

[0038] In a preferred example, considering the w-year plan in the future, where w is a positive integer greater than or equal to 1, if the new energy increment is not 0, according to the change in the output of the g-th generator in each photovoltaic sub-region and the change in the load increment in the load sub-region, the power flow of the i-th river-crossing channel is re-determined:

[0039]

[0040] where g is the number of the generator in the photovoltaic sub-region, g is a positive integer greater than or equal to 1, l is the number of the load sub-region in the photovoltaic sub-region, l is a positive integer greater than or equal to 1, GP i-g is the distribution factor between each generator and the i-th river-crossing channel, and LP i-l is the distribution factor between each load sub-region and the i-th river-crossing channel;

[0041] If the output reserve of the generators in each photovoltaic sub-region increases or decreases, and the load increment increases in the same proportion, then the power flow of the i-th river-crossing channel is:

[0042]

[0043] In a preferred example, it is characterized in that the linear and non-linear optimization problems in the objective function and the constraint conditions are processed by a non-linear constraint optimization algorithm.

[0044] This application also discloses a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the steps in the method described above are implemented.

[0045] In the embodiments of the present application, by means of the generator output power transfer distribution factor, the relationship between the photovoltaic partition output power change, load increment, new energy increment and its upper limit and the power grid power flow is established, the distribution of sources, grids, loads and new energy is comprehensively optimized, and the collaborative optimization of the power grid and the new energy location is realized. The consideration factors are no longer single. By dynamically obtaining the load increment and new energy increment within the planning period, an optimal new energy distribution model is constructed, making the model more real-time and adaptable. The upper limit of the new energy increment and the power grid power flow limit improve the constraint conditions, ensure the feasibility of the optimization result, and avoid the occurrence of infeasible solutions. In addition, the present application adopts an efficient optimization algorithm to handle various complex constraint conditions, greatly improving the calculation efficiency. With the goal of minimizing the total power flow of the river-crossing channels, the pressure on the key transmission channels is significantly relieved, the balanced distribution of the power grid load is realized, and the safety and operation efficiency of the power grid are ensured.

[0046] Furthermore, by constructing the objective function of the optimal new energy distribution model and combining the new energy location-power flow distribution factor, load-power flow distribution factor and generation-power flow distribution factor, the influence of the photovoltaic partition output power change, load increment and generation output change on the key channel power flow is systematically described. The objective function takes minimizing the total power flow of the river-crossing channels as the core, and uses the transfer distribution factor obtained by simulation calculation to dynamically adjust the optimization scheme, which can achieve accurate modeling for different grid structures and load distributions, effectively improving the accuracy and adaptability of power grid planning. At the same time, this method further defines the new energy increment, load increment and various physical constraint conditions (such as line transmission capacity, upper limit of photovoltaic output, etc.), ensuring the feasibility of model optimization and the safety of actual power grid operation.

[0047] Furthermore, in actual solution, the method of the present application efficiently processes the linear and nonlinear constraint problems in the objective function through a nonlinear constraint optimization algorithm (such as SLSQP, Sequential Least Squares Programming), supports the complex optimization of large-scale power systems. Especially when considering the long-term planning, the model can dynamically adjust the power flow calculation formula, combine the changes of future generator output and load increment, and achieve accurate optimization under multiple years and multiple scenarios. This method takes into account both dynamicity and efficiency, breaks through the defects of static model limitations, improper handling of complex constraints and low calculation efficiency in the existing technology, and provides a fast and stable optimization solution for the high-proportion new energy access to the power grid.

[0048] Each of the technical features disclosed in the above-mentioned Summary of the Invention, each of the technical features disclosed in the following embodiments and examples, and each of the technical features disclosed in the accompanying drawings can be freely combined with each other to form various new technical solutions (all of these technical solutions should be regarded as having been described in this specification), unless the combination of such technical features is technically infeasible. For example, in one example, features A + B + C are disclosed, and in another example, features A + B + D + E are disclosed. Features C and D are equivalent technical means that perform the same function, and only one of them can be used technically and it is impossible to use both simultaneously. Feature E can be combined with feature C technically. Then, the solution of A + B + C + D should not be regarded as having been described because it is technically infeasible, while the solution of A + B + C + E should be regarded as having been described. Description of the Drawings

[0049] Figure 1 It is a schematic flowchart of a method for planning the optimal location of new energy according to an embodiment of the present application.

[0050] Figure 2 It is a schematic diagram of the network of river-crossing channels in 2025 according to an embodiment of the present application. Detailed Embodiments

[0051] In the following description, many technical details are presented for the reader to better understand the present application. However, those of ordinary skill in the art can understand that even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can still be implemented.

[0052] Explanation of some concepts:

[0053] Power Transfer Distribution Factor (PTDF): The change in line power flow caused by a unit change in power generation, load, or other factors.

[0054] SLSQP (Sequential Least Squares Programming) algorithm: A non-linear constrained optimization algorithm used to solve unconstrained or constrained non-linear optimization problems. It solves the optimization problem by transforming it into a series of linear or quadratic programming sub-problems. Specifically, the SLSQP algorithm uses the Newton method to solve each sub-problem and the auxiliary function method to handle the constraint conditions.

[0055] The following briefly describes some innovative points of the embodiments of the present application:

[0056] In this application, an optimization model based on the transfer distribution factor is constructed to establish the relationship between various planning factors such as source - grid - load - new energy and section power flow. Through local linearization, an optimal distribution model of source - grid - load - new energy is established. This model describes how to reasonably allocate the planned capacity to each location within a region (provincial power grid) when the total newly added new energy capacity is determined within a planning period, in order to achieve an optimal goal (such as minimizing the cross - river power flow). If the objective function is the sum of all channel power flows, it is a linear function, and the optimization problem is a linear programming problem. If the objective function is a non - linear function, the optimization problem is a non - linear programming problem. This application uses the SLSQP algorithm, which can handle both linear and non - linear optimization problems and effectively solve the above - mentioned optimization problems. The SLSQP algorithm can flexibly adjust the constraints and objectives in the optimization process by integrating the constraint conditions into the objective function, ensuring the stability of the feasible solution during the solution process.

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following will further describe the implementation manners of this application in detail with reference to the accompanying drawings.

[0058] The first implementation manner of this application relates to a method for optimal location planning of new energy, and its process is as Figure 1 shown. This method includes the following steps:

[0059] In step S1, for each photovoltaic zone, obtain the load increment Δload, new energy increment, and new energy increment upper limit within the planning period.

[0060] In step S2, based on the relationship between the output change of each photovoltaic zone and the cross - river channel power flow change, and by using the generator output power transfer distribution factor to establish the connection between multiple planning factors (such as load, new energy, power generation, etc.) and the power flow, establish the objective function of the optimal new energy distribution model.

[0061] In step S3, based on the load increment, new energy increment, and new energy increment upper limit, determine the constraint conditions of the optimal new energy distribution model.

[0062] In step S4, based on the constraint conditions, solve the objective function to obtain the optimal new energy increment of each photovoltaic zone, and obtain the optimal scheme of source - grid - load - new energy distribution to minimize the sum of the cross - river channel power flows.

[0063] Optionally, step S2 may further include:

[0064] Assume there are n cross - river channels, and their power flows are P1, ……, P n , and there are h photovoltaic zones, and the photovoltaic outputs of each photovoltaic zone are GH1, ……, GHn ;

[0065] Establish a primary objective function to minimize the total power flow of the river-crossing channels:

[0066]

[0067] According to the definition of the generator output power transfer distribution factor, the active power change in the j-th PV sub-region is ΔGH j , where j = 1 to h, and the power flow change in the i-th river-crossing channel is ΔP i , then:

[0068] ΔP i = G i-j ΔGH j ; (2)

[0069] If the initial power flow of the i-th river-crossing channel is P i0 , and the power flow of the i-th river-crossing channel is P i , then:

[0070]

[0071] Obtain the objective function of the optimal new energy distribution model:

[0072]

[0073] That is:

[0074]

[0075] Among them, G i-j is a coefficient, obtained through power flow calculation simulation, and changes with the change of the grid structure.

[0076] Optionally, nh new energy location-power flow distribution factors, h load-power flow distribution factors, and h generation-power flow distribution factors can be calculated. The new energy location-power flow distribution factor represents the influence of the output change ΔGH j of the j-th PV sub-region on the power flow change ΔP i of the i-th river-crossing channel. The load-power flow distribution factor represents the influence of the load increment change Δload l of the l-th load sub-region on the power flow change ΔP i of the i-th river-crossing channel. The generation-power flow distribution factor represents the influence of the output change ΔGen g of the generator g on the power flow change ΔP i of the i-th river-crossing channel;

[0077] The parameters of the new - energy location - power - flow distribution factor, load - power - flow distribution factor, and generation - power - flow distribution factor are related to the grid structure, load distribution, and unit - on distribution, and have the greatest correlation with the grid structure.

[0078] Optionally, step S3 may further include:

[0079] The photovoltaic output of each photovoltaic zone does not exceed the upper limit of new - energy increment in this photovoltaic zone:

[0080]

[0081] where \(j = 1\sim h\), \(G_{H}\) j0 is the initial photovoltaic output of each photovoltaic zone.

[0082] Optionally, based on the load increment, new - energy increment, and upper limit of new - energy increment, the constraints for determining the optimal new - energy distribution model may further include:

[0083] Only changing the photovoltaic - output distribution of the photovoltaic zones without changing the total photovoltaic output, the new - energy increment is 0:

[0084]

[0085] Optionally, step S3 may further include:

[0086] If the new - energy increment is a known value \(N\), then:

[0087]

[0088] Optionally, step S3 may further include:

[0089] Each cross - river channel satisfies the upper - limit constraint of line transmission capacity:

[0090]

[0091] Optionally, considering the future \(w\) - year plan (\(w\) is a positive integer greater than or equal to 1), if the new - energy increment is not 0, according to the output change of the \(g\) - th generator in each photovoltaic zone and the load - increment change of the load zone, the power flow of the \(i\) - th cross - river channel is re - determined:

[0092]

[0093] where \(g\) is the number of the generator in the photovoltaic zone (\(g\) is a positive integer greater than or equal to 1), \(l\) is the number of the load zone in the photovoltaic zone (\(l\) is a positive integer greater than or equal to 1), \(G_{P}\) i-g is the distribution factor between each generator and the \(i\) - th cross - river channel, \(L_{P}\) i-l is the distribution factor between each load zone and the \(i\) - th cross - river channel;

[0094] If the output and other reserves of the generators in each photovoltaic zone increase or decrease, and the load increment increases in the same proportion, the power flow of the i-th river-crossing channel is:

[0095]

[0096] Optionally, a non-linear constraint optimization algorithm can be used to handle the linear and non-linear optimization problems in the objective function and constraint conditions.

[0097] To better understand the technical solution of this application, a specific example is given below for illustration. The details listed in this example are mainly for easy understanding and do not limit the protection scope of this application.

[0098] This application proposes a method for optimal location planning of new energy. By establishing an optimal model for new energy distribution, it realizes the reasonable incremental allocation of photovoltaic zones to minimize the power flow pressure of the key channels of the power grid and ensure the safety and stability of the power grid operation. In the specific implementation process, first, for each photovoltaic zone, the load increment, new energy increment, and upper limit of new energy increment during the planning period are obtained. By analyzing the relationship between the output change of the photovoltaic zone and the power flow change of the river-crossing channel, and combining the generator output power transfer distribution factor, the factors such as photovoltaic zones, load distribution, and generator output are related to the power flow characteristics to construct an objective function. The objective function aims to minimize the total power flow of the river-crossing channel to ensure a more balanced power flow distribution and relieve the transmission pressure of the key channels.

[0099] In determining the constraint conditions, this application fully considers various limiting factors in the actual power grid operation, including the upper limit of the output of each photovoltaic zone, the balance constraint of the total photovoltaic increment, and the upper limit requirement of the line transmission capacity. For example, for the upper limit restriction of photovoltaic output, by calculating the sum of the initial output and increment of each photovoltaic zone, it is ensured that it does not exceed the regional resource potential and planning restrictions. For the total photovoltaic increment, this application supports the planning requirements of different scenarios. When the increment is zero, the output is kept balanced, and when the increment is a known value, the optimal allocation of the total increment is realized. In addition, to adapt to the dynamic requirements of the long-term planning, this application can also expand the power flow model according to the load growth and generator output adjustment in the coming years and dynamically correct the power flow relationship between the photovoltaic zones and the river-crossing channels.

[0100] To efficiently solve this optimization model, this application introduces a nonlinear constraint optimization algorithm (such as SLSQP), which optimizes the objective function through step-by-step iteration while strictly satisfying all constraint conditions. In each iteration, the model adjusts the incremental allocation of the photovoltaic zones based on the linear or nonlinear relationship of the distribution factors, and corrects the solution direction through gradient information until the objective function converges to the optimal value. The optimization process shows high computational efficiency in scenarios with a high proportion of new energy access. Even under complex grid structures and multiple constraint conditions, it can quickly obtain the global optimal solution or a result close to the global optimum.

[0101] Through specific implementation, this application verifies the actual application effect of the method. Taking the Jiangsu Power Grid as an example, the simulation analysis shows that this application can significantly reduce the total power flow of the cross-river channels, make the power flow distribution more balanced, and effectively avoid the problem of line overload. At the same time, the model has good dynamic adaptability. In the multi-year long-term planning, it can adjust according to different load increments and generator outputs, and generate a forward-looking optimization plan.

[0102] The following combines with the example of the high-proportion new energy location optimization of the Jiangsu Power Grid to further elaborate on the specific implementation manner of this application. Taking the example of an additional 15,000 MW of photovoltaic power in the Jiangsu Power Grid in 2026 compared with 2025, this implementation manner illustrates how to use the method of this application to achieve the optimal allocation of the photovoltaic increment in five regions (Xuzhou-Suqian-Huai'an, Lianyungang-Yancheng, Nantong-Taizhou-Yangzhou, Nanjing-Zhenjiang-Changzhou, Suzhou-Wuxi), so as to minimize the total power flow of the cross-river channels while meeting the line safety constraints, thereby achieving the optimal location selection of the new energy increment.

[0103] In the given example, the scenario of "Jiangsu's winter peak, large-scale wind and solar power generation" is selected for power flow calculation. The equivalent output coefficient of photovoltaic power is set to 0.57. The increment of winter high load in 2026 compared to 2025 is 3,600 MW, and the load increases evenly across the province. The newly added photovoltaic capacity in 2026 is 15,000 MW, which is distributed in 5 regions, namely Xuzhou, Suqian, Huai'an, Lianyungang, Yancheng, Nantong, Taizhou, Yangzhou, Nanjing, Zhenjiang, Changzhou, Suzhou, and Wuxi. Among them, the increment ratio in northern Jiangsu is considered to be 74%, and that in southern Jiangsu is 26%. The 500 kV cross-river channels to be considered are: Taixing~Doushan, Fengcheng~Meili, Jiangdu~Dagang, Qiuteng~Qinhuai, and Sanchawan~Longwangshan. In the example, it is assumed that the eastern Nantong GIL channel and the central Yangzhou-Zhenjiang Phase 2 and Phase 3 channels have been built to provide sufficient channel margin for the newly added photovoltaic power. In this scenario, to select the optimal location for the increment of new energy, an objective function needs to be established. The objective function is to minimize the total power flow of the 5 500 kV cross-river channels, and no channel exceeds the channel limit after considering N-1. The thermal power units are adjusted to start up according to a unified ratio to achieve the peak shaving balance of the whole province when wind and solar power are generated in large quantities. For each cross-river channel, the upper limit of the line transmission capacity under the N-1 condition is further given in Table 1 as shown below, and it is used as the constraint upper limit of the optimization model.

[0104]

Table 1

[0105] Line current-carrying capacity of 500 kV cross-river section

[0106] Unit: A, 10,000 kW

[0107]

[0108] The distribution factors described in this application are obtained through power flow calculation tests using the BPA power system simulation analysis tool. There are three types of distribution factors: new energy location - power flow distribution factors (25 parameters), load - power flow distribution factors (5 parameters), and generation - power flow distribution factors (5 parameters), which characterize the change in channel power flow under a unit increment of new energy / load / generation. This series of distribution parameters is affected by the grid structure, load distribution, and unit startup distribution, among which the grid structure has the greatest influence. The grid structure diagram of the cross-river channel in 2025 is as shown in Figure 2 shown. According to Tables 2 - 4, for the distribution factors of the grid without cross-river DC channels and the grid with cross-river DC channels in operation, it can be found that after the DC channels are put into operation, the distribution factors generally decrease significantly. Therefore, before calculating the distribution factors, it is necessary to perform power flow calculation to obtain the distribution factor parameters according to the feasible channel construction plan of the whole province.

[0109]

Table 2

[0110] Table of new energy location - power flow distribution factors of the original grid in 2025

[0111] Unit: MW / MW

[0112]

[0113]

[0114]

Table 3

[0115] New Energy Location - Power Flow Distribution Factor Table after Adding Eastern Nantong GIL and Central Yangzhou - Zhenjiang Phase 2 and 3 Channels in 2025

[0116] Unit: MW / MW

[0117] Busbar 1 Busbar 2 Xuzhou-Suqian-Huaian Lianyungang-Yancheng Nantong-Taizhou-Yangzhou Nanjing-Zhenjiang-Changzhou Suzhou-Wuxi Suzhou Taixing 51 Suzhou Doushan 51 0.0425 0.0929 0.0817 0.0004 -0.0406 Suzhou Taixing 51 Suzhou Doushan 51 0.0421 0.0919 0.0809 0.0005 -0.0401 Suzhou Fengcheng 51 Suzhou Meili 51 0.0517 0.1014 0.0706 0.0136 -0.0287 Suzhou Fengcheng 51 Suzhou Meili 51 0.0517 0.1014 0.0706 0.0136 -0.0287 Suzhou Jiangdu 51 Suzhou Dagang 51 0.0502 0.0766 0.082 -0.076 -0.0135 Suzhou Jiangdu 51 Suzhou Dagang 51 0.0502 0.0766 0.082 -0.076 -0.0135 Suzhou Qiuteng 51 Suzhou Qinhuai 51 0.0611 0.059 0.0263 -0.0155 -0.0148 Suzhou Qiuteng 51 Suzhou Qinhuai 51 0.0611 0.059 0.0263 -0.0155 -0.0148 Suzhou Sancha 51 Suzhou Longwang 51 0.0444 0.0399 0.0115 -0.0371 -0.012 Suzhou Sancha 51 Suzhou Longwang 56 0.0441 0.0395 0.0111 -0.0378 -0.0121

[0118]

Table 4

[0119] Load - Power Flow Distribution Factor and Generation - Power Flow Distribution Factor Table

[0120] Unit: MW / MW

[0121]

[0122]

[0123] According to the optimization model, input the above basic parameters, boundary parameters and distribution factor parameters:

[0124] Establish the objective function:

[0125] Constraints:

[0126]

[0127]

[0128] Δload = 3600;

[0129] N = 15000;

[0130] ΔGen = -(N * 0.57 - Δload) = -4950;

[0131] G i-j is the data in Table 3, GP i and LP i are the data in Table 4, is the channel limit after considering N - 1 in Table 1.

[0132] The above optimization problem is established, and the results are calculated by calling the scipy scientific computing package and the SLSQP algorithm in Python as follows:

[0133] fun: 2917.528180538837

[0134] jac: array([0.49978638, 0.73959351, 0.54421997, -0.22918701, -0.21920776])

[0135] message: 'Optimization terminated successfully'

[0136] nfev: 20

[0137] nit: 3

[0138] njev: 3

[0139] status: 0

[0140] success: True

[0141] x: array([8.63595033e+02, 1.80266686e-16, 5.46340497e+03, 5.82508299e-17, 2.22300000e+03])

[0142] Among them, the values after x: array respectively represent the new energy increments in the five regions of Xuzhou-Suqian-Huai'an, Lianyungang-Yancheng, Nantong-Taizhou-Yangzhou, Nanjing-Zhenjiang-Changzhou, and Suzhou-Wuxi. According to the calculation results, it is recommended to arrange 863 MW of new energy output increase in the Xuzhou-Suqian-Huai'an area, 5463 MW of new energy output increase in the Nantong-Taizhou-Yangzhou area, 2223 MW of new energy output increase in the Suzhou-Wuxi area, and 0 in the Lianyungang-Yancheng area and the Nanjing-Zhenjiang-Changzhou area. Calculated according to the photovoltaic equivalent output rate of 0.57, it is recommended to arrange 1514 MW of new energy capacity increase in the Xuzhou-Suqian-Huai'an area, 9584 MW of new energy capacity increase in the Nantong-Taizhou-Yangzhou area, 3900 MW of new energy capacity increase in the Suzhou-Wuxi area, and 0 in the Lianyungang-Yancheng area and the Nanjing-Zhenjiang-Changzhou area.

[0143] Due to the establishment of the Yangzhou-Zhenjiang Phase 2 and 3 and the Nantong GIL channel, the cross-river tidal current pressure in the middle channel is greatly reduced. Coupled with the fact that a part of the southern Jiangsu photovoltaic can be increased in Suzhou-Wuxi to offset the pressure of northern Jiangsu photovoltaic on the cross-river, under this long-term grid framework, the Nantong-Taizhou-Yangzhou area has a relatively large additional photovoltaic margin. The conclusion of this optimization calculation is consistent with the grid framework planning goal. The optimal scheme for the distribution of the newly planned new energy locations as shown in Table 5 is obtained.

[0144]

Table 5

[0145] Optimal Solution for the Newly Planned Location Distribution of New Energy

[0146]

[0147] Correspondingly, an embodiment of the present application also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method embodiments of the present application. The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media do not include transitory computer-readable media such as modulated data signals and carrier waves.

[0148] It should be noted that in the present application, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one" does not exclude the presence of additional identical elements in the process, method, article or device comprising the element. In the present application, if it is mentioned that an act is performed according to a certain element, it means at least performing the act according to the element, including two cases: performing the act only according to the element and performing the act according to the element and other elements. Expressions such as multiple, multiple times, multiple types, etc. include 2, 2 times, 2 types, and more than 2, more than 2 times, more than 2 types.

[0149] The serial numbers used to describe the steps of a method do not, by themselves, impose any limitation on the order of these steps. For example, steps with larger serial numbers are not necessarily to be executed after steps with smaller serial numbers. It is also possible to first execute steps with larger serial numbers and then execute steps with smaller serial numbers, or they can be executed in parallel, as long as this execution order is reasonable to those skilled in the art. Another example is that multiple steps with consecutive serial numbers (such as step 101, step 102, step 103, etc.) do not restrict other steps from being executed between them. For example, there can be other steps between step 101 and step 102.

[0150] This specification includes combinations of various embodiments described herein. Separate references to embodiments (such as "an embodiment" or "some embodiments" or "preferred embodiments"); however, these embodiments are not mutually exclusive unless indicated as such or clearly understood by those skilled in the art to be mutually exclusive. It should be noted that, unless the context clearly indicates otherwise or requires otherwise, the word "or" is used in a non-exclusive sense in this specification.

[0151] All documents mentioned in this specification are considered to be integrally included in the disclosure of this application so that they can be used as a basis for modification if necessary. In addition, it should be understood that the above are only preferred embodiments of this specification and are not used to limit the protection scope of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included within the protection scope of one or more embodiments of this specification.

Claims

1. A method for optimal location planning of new energy, characterized in that, Including: For each photovoltaic partition, obtain the load increment Δload, new energy increment, and upper limit of new energy increment during the planning period Based on the relationship between the output change of each photovoltaic zone and the power flow change of the river-crossing channel, and establishing a connection between multiple planning factors and the power flow through the generator output power transfer distribution factor, an objective function of the new energy distribution optimal model is established. Based on the load increment, the new energy increment, and the upper limit of the new energy increment, the constraint conditions of the new energy distribution optimal model are determined. Based on the constraint conditions, the objective function is solved to obtain the optimal new energy increment of each photovoltaic zone, and the optimal scheme of the new energy distribution of the source-network-load is obtained, so that the sum of the power flows of the river-crossing channels is minimized.

2. The new energy optimal location planning method according to claim 1, characterized in that The objective function for establishing the new energy distribution optimal model further includes: Let the number of the river-crossing channels be n, and their tidal currents be P1, ……, P n , the number of the photovoltaic zones be h, and the photovoltaic outputs of each photovoltaic zone be GH1, ……, GH n ; Establishing a primary objective function to minimize the sum of the power flows of the river-crossing channels: According to the definition of the generator output power transfer distribution factor, the active power change of the j-th photovoltaic sub-region is ΔGH j , where j = 1 to h, and the power flow change of the i-th river-crossing channel is ΔP i , then: ΔP i = G i-j ΔGH j ; (2) If the initial tidal current of the $i$-th river-crossing channel is $P$ i0 and the tidal current of the $i$-th river-crossing channel is $P$ i , then: Obtaining the objective function of the new energy distribution optimal model: That is: Among them, G i-j is a coefficient, obtained through the power flow calculation simulation, and varies with the change of the grid structure.

3. The optimal location planning method for new energy as described in claim 1, characterized in that, Obtain nh new energy location-power flow distribution factors, h load-power flow distribution factors, and h generation-power flow distribution factors. The new energy location-power flow distribution factor represents the output change ΔGH of the photovoltaic sub-region j j on the power flow change ΔP of the river-crossing channel i i The load-power flow distribution factor represents the load increment change Δload of the load sub-region l l on the power flow change ΔP of the river-crossing channel i i The generation-power flow distribution factor represents the output change ΔGen of the generator g g on the power flow change ΔP of the river-crossing channel i i The influence; The parameters of the new energy location-power flow distribution factor, the load-power flow distribution factor, and the generation-power flow distribution factor are related to the grid structure, load distribution, and unit commitment, and have the largest correlation with the grid structure.

4. The new energy optimal location planning method according to claim 1, wherein The determination of the constraint conditions of the new energy distribution optimal model based on the load increment, the new energy increment, and the upper limit of the new energy increment further includes: The photovoltaic output of each photovoltaic zone does not exceed the upper limit of the new energy increment of the photovoltaic zone. where j = 1 to h, and GH j0 is the initial PV output of each of the PV sub - regions.

5. The new energy optimal location planning method according to claim 1, characterized in that The determination of the constraint conditions of the new energy distribution optimal model based on the load increment, the new energy increment, and the upper limit of the new energy increment further includes: Only changing the photovoltaic output distribution of the photovoltaic zone without changing the total photovoltaic output, the new energy increment is 0.

6. The new energy optimal location planning method according to claim 1, characterized in that The determination of the constraint conditions of the new energy distribution optimal model based on the load increment, the new energy increment, and the upper limit of the new energy increment further includes: If the new energy increment is a known value N, then:

7. The new energy optimal location planning method according to claim 1, wherein The determination of the constraint conditions of the new energy distribution optimal model based on the load increment, the new energy increment, and the upper limit of the new energy increment further includes: Each river-crossing channel satisfies the upper limit constraint of the line transmission capacity.

8. The new energy optimal location planning method according to claim 3, characterized in that Considering the w-year future plan, where w is a positive integer greater than or equal to 1, if the new energy increment is not 0, according to the output change of the g-th generator in each photovoltaic zone and the load increment change of the load zone, the power flow of the i-th river-crossing channel is re-determined. Among them, g is the number of the generator in the photovoltaic sub-region, g is a positive integer greater than or equal to 1, l is the number of the load sub-region in the photovoltaic sub-region, l is a positive integer greater than or equal to 1, GP i-g is the distribution factor of each of the generators and the i-th river-crossing channel, LP i-l is the distribution factor of each of the load sub-regions and the i-th river-crossing channel; If the output equal-spare of the generators in each photovoltaic zone increases or decreases, and the load increment increases in the same proportion, then the power flow of the i-th river-crossing channel is:

9. The new energy optimal location planning method according to any one of claims 1-8, characterized in that, Through a non-linear constraint optimization algorithm, the linear and non-linear optimization problems in the objective function and the constraint conditions are processed.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps in the method according to any one of claims 1 to 9 are implemented.

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