Substation site selection planning method based on II-type fuzzy credibility constraint planning

Abstract

The invention discloses a transformer substation site selection planning method based on II-type fuzzy credibility constraint planning, and the method comprises the steps: firstly employing an interval II-type fuzzy membership function to represent the output of renewable energy sources, and building an uncertainty model of the output of the renewable energy sources; and determining the number of transformer substations according to hardware conditions in the planning area. And then, taking the minimum load moment as an optimization target, and establishing a transformer substation site selection planning model based on II-type fuzzy credibility constraint planning. And finally, solving the substation site selection planning model by using an optimization algorithm, and outputting coordinates of the substation. According to the method, uncertainty brought by photovoltaic energy is processed by introducing II-type fuzziness, fuzzy credibility constraint programming is fused into a mixed integer linear programming framework, planning schemes under different confidence levels can be obtained, system stability and economic benefits can be balanced conveniently, and the method is suitable for large-scale popularization and application. Unnecessary economic cost caused by excessive refinement is avoided to a certain extent, and actual requirements of different power distribution networks are better met.

Description

Technical Field

[0001] The invention belongs to the technical field of new energy, relates to a site selection planning method for a distribution network, and specifically relates to a substation site selection planning method based on type II fuzzy credibility constraint planning. Background Art

[0002] As an important part of the power system, the distribution network is an important bridge connecting the power supply system and user terminals. A scientific and reasonable distribution network planning scheme can improve the power supply reliability of the distribution network and reduce power outage accidents. It is an important way to ensure the smooth, safe and stable operation of the power system.

[0003] With the continuous development of renewable energy such as hydropower, wind power, and photovoltaics, distributed generation has been widely used due to its advantages of high environmental protection, high flexibility, and low investment. The combination of distributed generation and large power grids is an inevitable trend in the development of future power systems. However, due to the uncertainty and volatility of distributed generation, the decentralized access of a large number of distributed generation has brought a series of challenges to the distribution network that cannot be ignored, mainly reflected in the increase of uncertainty risk factors, the reduction of power supply reliability, and the generation of overvoltage.

[0004] In order to meet these challenges, microgrid technology came into being. Microgrid technology integrates loads, distributed generation, energy storage devices and power electronic equipment to build a system that can be connected to the grid and can operate independently. This system not only realizes the complementary power supply of micro power sources and loads, but also enhances the stability of the system through energy storage devices, promotes the large-scale access of clean renewable energy and distributed energy, and realizes the reliable supply of multiple energy forms and multiple loads, becoming a new idea for solving the problem of distributed power access to the distribution network.

[0005] However, traditional distribution network planning is usually based on a passive distribution network with one-way transmission from the power supply system to the final consumer. The two-way power flow, grid structure, reliability and stability issues brought about by the access of microgrids have greatly affected the stable operation of the distribution network under the traditional planning scheme. In particular, the intermittent and random nature of renewable energy such as photovoltaics will lead to frequent changes in load voltage, further increasing the difficulty of distribution network operation and placing higher requirements on planning and design. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a substation site planning method based on type II fuzzy credibility constraint programming, which adopts type II fuzzy sets to describe the uncertainty characteristics of renewable energy in microgrids. Through fuzzy credibility constraint programming, the uncertain fuzzy quantities are converted into expressions at different confidence levels to more comprehensively describe the uncertainty brought by renewable energy.

[0007] The substation site selection planning method based on type II fuzzy credibility constraint programming specifically includes the following steps:

[0008] Step 1: Use interval II-type fuzzy membership function to represent renewable energy output and establish an uncertainty model for renewable energy output.

[0009] Step 2: Determine the number of substations based on the substation capacity, capacity-load ratio, node load peak, and maximum absorbed power of the microgrid in the planning area.

[0010] Step 3: Taking the minimum load moment as the optimization objective, a substation site selection planning model based on type II fuzzy credibility constrained programming is established.

[0011] Step 4: Use the optimization algorithm to solve the substation site selection planning model and output the substation coordinates.

[0012] The present invention has the following beneficial effects:

[0013] 1. In the substation site selection planning, the bidirectional power flow problem caused by the access to photovoltaics is modeled, and type II fuzzy is introduced to deal with the uncertainty brought by photovoltaic energy, so as to improve the model's ability to describe photovoltaic uncertainty and cater to the development trend of high penetration distribution networks in the future.

[0014] 2. By integrating fuzzy credibility constrained programming into the mixed integer linear programming framework, planning schemes under different confidence levels can be obtained, which is convenient for decision makers to balance system stability and economic benefits, avoid unnecessary economic costs caused by over-refinement to a certain extent, and better adapt to the actual needs of different distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of type II fuzzy membership function;

[0016] Figure 2 This is a schematic diagram of the regional distribution network to be planned in the embodiment;

[0017] Figure 3 It is a schematic diagram of type I fuzzy membership function;

[0018] Figure 4 This is the substation site planning result obtained at 90% confidence level in the embodiment. DETAILED DESCRIPTION

[0019] The present invention will be further explained below with reference to the accompanying drawings;

[0020] The substation site selection planning method based on type II fuzzy credibility constraint programming specifically includes the following steps:

[0021] Step 1: Use interval II-type fuzzy membership function to represent renewable energy output. The uncertainty range is used to quantify the degree of uncertainty of the fuzzy set, such as Figure 1 As shown, it includes two boundary functions, namely the upper membership function and the lower membership function

[0022]

[0023]

[0024] Where x represents the fuzzy variable of renewable energy; P Renew Represents the predicted output power of renewable energy; △P Renew , △P R ' enew Represents the renewable energy prediction error in the upper and lower membership functions.

[0025] Step 2: In the planning of substations in the distribution network connected to the microgrid, the number and capacity planning of substations must not only meet the load requirements of the traditional passive distribution network, but also fully consider the complex impact of microgrid access. Calculate the number of substations required n s :

[0026]

[0027] Where, k represents the capacity ratio, S N represents the substation capacity, represents the peak load of node i, represents the maximum absorbed power of the jth microgrid, which is known information. L represents the set of all load nodes, Φ mg Represents the set of all microgrid nodes.

[0028] Step 3: Using the load moment minimization index, a substation site selection planning model based on type II fuzzy credibility constraint programming is established:

[0029]

[0030] Among them, Φ T represents the substation node set; δ li P is a Boolean variable indicating whether substation l supplies power to node i, 1 for power supply and 0 for no power supply; i represents the power of load node i; The interval type II fuzzy number representing the net power of the jth microgrid; u l 、v l represents the horizontal and vertical coordinates of substation l; xi ,y i represents the horizontal and vertical coordinates of load node i; Cr{·} represents the credibility of the constraint; represent the type II fuzzy numbers of the net power of the j-th microgrid under the upper membership function and the lower membership function respectively; α represents the given confidence level.

[0031] Step 4: The solution to the problem to be optimized, i.e., the location of the substation, is represented by a two-dimensional coordinate set of decision variables, i.e., a solution X of equation (5) i Include s indivual Coordinates. Suppose a set X contains n optimal feasible coordinate sets global =(X 1 ,X 2 ,...,X n ). Each coordinate set is determined by a migration velocity that determines its evolution toward the objective function value Update and adjust the migration speed by combining its own historical migration experience and the migration experience of the global feasible coordinate set, and update to the load moment minimization target:

[0032]

[0033] Where m represents the number of iterative offsets. Indicates the updated offset velocity and coordinates. Represents X i The best position in history, P g Indicates that in the feasible coordinate set X global The global optimal position in . ω is the memory factor, which determines the influence of the slope and growth rate of the last offset of the coordinate point on the current offset decision. The larger the ω, the greater the influence of the coordinate point on individuals and groups, and the greater the effect on the convergence speed of the objective function. The smaller the ω, the more refined the exploration ability of the coordinate point on the search space. In this embodiment, the initial value of the memory factor ω is set to 0.1 and the minimum value is 0.001, and it is dynamically adjusted in combination with the iterative process. c 1 and c 2 is the learning factor, set in the range of [1, 4]. 1 =c 2 When , the proportion in the evolutionary offset update can be balanced. By adjusting the value of the learning factor, the scale of the coordinate point offset can be flexibly changed to ensure that the coordinate cluster maintains a suitable offset change slope and growth rate trend during the search process. In this embodiment, c 1 =c 2 =1.49. 1 、r 2 It is a random number, evenly distributed between 0 and 1, which provides randomness for the offset evolution of the coordinate point and avoids the search falling into the local optimal solution.

[0034] The coordinate points obtained through iterative optimization correspond to the optimal planning locations of each substation.

[0035] like Figure 2 As shown in the figure, a regional distribution network including 6 microgrid nodes and 50 common load nodes is taken as an example, and the method and the Figure 3 The type I fuzzy membership function shown in the figure is used to describe the output of renewable energy and to plan the substation location for the regional distribution network. The triangle represents the common load node and the square represents the microgrid node. At the 90% confidence level, the planning results of this method are as follows: Figure 4 As shown, the substation location is represented by a circle. The load moment results of the two methods at different confidence levels are shown in Table 1:

[0036] Table 1

[0037]

[0038] According to the data in the table, as the confidence level decreases, the reliability of describing the uncertainty of photovoltaic energy decreases, and the constraints of substation site selection planning are lower. Therefore, both models can obtain planning schemes with smaller load moments, and the lower the confidence level, the smaller the difference in load moments between the two models. Under different confidence levels, this method can obtain planning schemes with smaller load moments compared to the method of using type I fuzzy membership function to describe the output of renewable energy.

Claims

1. A substation site selection planning method based on type II fuzzy credibility constraint programming, characterized by: The specific steps include: Step 1: Use interval II-type fuzzy membership function to represent renewable energy output and establish an uncertainty model for renewable energy output. Step 2: Determine the number of substations according to the substation capacity, capacity-load ratio, node load peak, and maximum absorbed power of the microgrid in the planning area. Step 3: Taking the minimum load moment as the optimization objective, a substation site selection planning model based on type II fuzzy credibility constrained programming is established. Step 4: Use the optimization algorithm to solve the substation site selection planning model and output the substation coordinates.

2. The substation site selection planning method based on type II fuzzy credibility constraint programming as claimed in claim 1, characterized in that: The interval II-type fuzzy membership function includes the upper membership function and the lower membership function Where x represents the fuzzy variable of renewable energy; P Renew Represents the predicted output power of renewable energy; ΔP Renew , ΔP′ Renew Represents the renewable energy prediction error in the upper and lower membership functions.

3. The substation site selection planning method based on type II fuzzy credibility constraint programming as claimed in claim 1, characterized in that: Calculate the number of substations n s : Where, k represents the capacity ratio, S N represents the substation capacity, represents the peak load of node i, Represents the maximum absorbed power of the jth microgrid. Φ L represents the set of all load nodes, Φ mg Represents the set of all microgrid nodes.

4. The substation site selection planning method based on type II fuzzy credibility constraint programming as claimed in claim 1, characterized in that: The substation site selection planning model based on type II fuzzy credibility constraint programming is: Among them, Φ T represents the substation node set; δ li P is a Boolean variable indicating whether substation l supplies power to node i; i represents the power of load node i; The interval type II fuzzy number representing the net power of the jth microgrid; u l 、v l represents the horizontal and vertical coordinates of substation l; x i ,y i represents the horizontal and vertical coordinates of load node i; Cr{·} represents the credibility of the constraint; represent the type II fuzzy numbers of the net power of the j-th microgrid under the upper membership function and the lower membership function respectively; α represents the given confidence level.

5. The substation site selection planning method based on type II fuzzy credibility constraint programming as claimed in claim 1 or 4, characterized in that: The particle swarm optimization algorithm is used to solve the substation site selection planning model.

6. The substation site selection planning method based on type II fuzzy credibility constraint programming as claimed in claim 1 or 4, characterized in that: Suppose a set X containing n optimal feasible coordinate sets global =(X1,X2,...,X n ), a solution X i Include s Substation coordinates Coordinates, updated offset speed In the m+1th round, the coordinates are updated using the following formula: in, Indicates the updated offset velocity and coordinates. Represents X i The best position in history, P g Indicates that in the feasible coordinate set X global The global optimal position in . ω is the memory factor; c1, c2∈[1, 4] are learning factors; r1, r2 are random numbers uniformly distributed between 0 and 1.

7. The substation site selection planning method based on type II fuzzy credibility constraint programming as claimed in claim 6, characterized in that: The initial value of the memory factor ω is set to 0.1 and the minimum value is set to 0.001, which is adjusted dynamically during the iteration process.

8. The substation site selection planning method based on type II fuzzy credibility constraint programming as claimed in claim 6, characterized in that: c1=c2=1.

49.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 4.

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