A method, device and storage medium for grid division of integrated energy system

Abstract

The present invention discloses a method, device and storage medium for grid division of an integrated energy system, belonging to the technical field of integrated energy system planning. First, an energy grid division method based on improved k-means clustering is used to form an initial grid layout. Second, an energy grid correction method considering load characteristics is used to form a final grid layout. Finally, a fuzzy ideal decision method based on expert weights is used to select the optimal number of energy grids. By dividing the energy grid using this method, the peak-to-valley difference rate of the cooling, heating and electricity loads of each grid and the peak load of the entire grid can be effectively reduced, thereby reducing the capacity investment cost of the integrated energy station and its pipeline network.

Description

Technical Field

[0001] The present invention relates to a method, device and storage medium for grid division of an integrated energy system, and belongs to the technical field of integrated energy system planning. Background Art

[0002] Considering that integrated energy system planning involves multiple aspects, including urban planning, transportation planning, and multi-energy production, transportation, and storage, unified city-wide planning often results in excessive workload, low modeling and computational efficiency, and is therefore difficult to meet the requirements of integrated energy planning and smart city development. Furthermore, renewable resource endowments, locational characteristics, and load demands vary from region to region. To meet the practical needs of urban planning and integrated energy system planning, actual planning should be narrowed and precisely tailored to the conditions and needs of different locations.

[0003] In this context, drawing on the experience of urban planning and power grid planning, combining integrated energy system planning with urban functional block planning, and realizing hierarchical and graded management of the integrated energy system through grid planning, can not only greatly reduce the difficulty of integrated energy system planning, but also accurately coordinate with urban block functions, select appropriate energy types according to local conditions, realize the efficient implementation of the integrated energy system, and provide an effective basis for the site selection of integrated energy stations.

[0004] Current research on grid division does not take into account the complementarity of load characteristics, and there is little research on the station-network planning of integrated energy systems, resulting in large equipment investment in integrated energy stations and low economic benefits. Therefore, how to reduce the peak-to-valley difference rate of cooling, heating, and electricity within the grid and the overall load peak of the grid to reduce the capacity construction cost of integrated energy stations and pipelines has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, device and storage medium for grid division of an integrated energy system to solve the technical problems of the peak-to-valley difference in cooling, heating and electricity within the above-mentioned grid and the high peak load of the grid as a whole.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a method for gridding an integrated energy system, comprising the following steps:

[0008] Determine the number of energy grid divisions in the planned area based on the preset energy grid size;

[0009] The k-means clustering algorithm is used to perform initial clustering of load points in the planning area to generate the initial energy grid;

[0010] Considering the predicted maximum and minimum load densities of electricity, heat and cooling in the planned area, the generated initial energy grid is revised.

[0011] Based on the first aspect, in the process of grid division of the integrated energy system, it is first necessary to determine the number of energy grid divisions. Conversely, when the size of the energy grid to be divided and the area of ​​the entire planning area are known, the number of energy grid divisions can be obtained. The scale of the energy grid division should be moderate, neither too large nor too small. If the grid division is too large, the investment cost of the internal pipeline network and the loss of the pipeline network will increase. If the grid division is too small, the load demand within the grid will also decrease, which will cause the integrated energy station within the grid to supply excess energy, resulting in a waste of energy station equipment investment costs. Preferably, the number of energy grid divisions is calculated using the following formula:

[0012]

[0013] Where: n is the number of energy grid divisions determined; int[] is the rounding function, S is the area of ​​the planning area, r max and r min Respectively represent the maximum and minimum demarcation range of the energy grid.

[0014] Based on the first aspect, preferably, the method of using a k-means clustering algorithm to perform initial clustering of load points in the planning area to generate an initial energy grid includes:

[0015] Calculate the distance d between each load point in the planning area ij ;

[0016] The number of load points in the preset energy grid is a constraint value M. A circular area is constructed with each load point in the planned area as the center and a preset radius. It is determined whether the number of load points in the energy grid of the circular area is greater than the constraint value M:

[0017] If yes, then add the load point to set C; if not, then judge the area of ​​the next load point, and finally get the number of load points in set C;

[0018] If the number of load points in set C is less than the number of energy grid divisions, then reduce the constraint value M and re-execute the above steps to redetermine the number of load points in set C;

[0019] If the number of load points in the re-determined set C is greater than the number of energy grid divisions, the load point with the largest neighborhood density in set C is selected as the first initial cluster center point c1;

[0020] Find the load point farthest from c1 in set C as the second initial cluster center point c2; find the load point c3 with the largest sum of distances from c1 and c2 in set C as the third initial cluster center point, and so on, until the number of cluster centers equals the number of energy grid divisions.

[0021] Based on the first aspect, preferably, considering the predicted maximum load density and minimum load density of electricity, heat, and cooling in the area to be planned, the generated initial energy grid is modified including:

[0022] Obtain the hot and cold spot load characteristics within each energy grid and calculate the load weighting factor ω between the load center of each small plot and the cluster center point ij ;

[0023] Calculate the load-weighted distance ω between each load point and each cluster center point ij d ij , and each small plot is assigned to the energy grid with the smallest weighted distance;

[0024] If the addition of a load point to a new energy grid causes the total load of the new energy grid to exceed the grid capacity constraint, or the load of the original energy grid does not reach the grid capacity constraint, the load point will still be included in the original energy grid;

[0025] Repeat the above steps until the small plots within each energy grid no longer change.

[0026] Based on the first aspect, preferably, the calculation formula of the load weighting factor is as follows:

[0027] ω ij =ω ij,e ω ij,h ω ij,c

[0028]

[0029]

[0030]

[0031] Where: ω ij,e 、ω ij,h 、ω ij,c are the load weighting factors between the small plot load center point j and the energy grid cluster center point i when only considering the characteristics of electricity, heat and cooling loads in the energy grid; when the small plot load center point j does not belong to the grid division range to which the cluster center point i belongs, δ ij,e , δ ij,h , δ ij,care the peak-valley differences of the overall electricity, heating, and cooling loads when the load center point j of the small plot is added to the energy grid of the cluster center point i, δ bj,e , δ bj,h , δ bj,c are the overall peak-to-valley differences of electricity, heat, and cooling loads after the load center point j leaves the original grid b; otherwise, they are the peak-to-valley differences of electricity, heat, and cooling loads of the energy grid i itself; δ k,e , δ k,h , δ k,c are the peak-to-valley differences of the overall electricity, heat, and cooling loads within the energy grid k; γ is the amplification factor of the weighting factor of the electricity, heat, and cooling loads; N s is the number of cluster centers.

[0032] Based on the first aspect, preferably, after the initial grid clustering is completed, attention is paid to the total load of each energy grid after division:

[0033] If the total load of each energy grid does not exceed the energy grid constraint capacity, the next post-processing step is performed;

[0034] If the total load of a certain energy grid exceeds the constrained capacity of the energy grid during the clustering process, one or several boundary load points will be divided into adjacent energy grids with load capacity margins closest to the energy grid to prevent exceeding the expected capacity;

[0035] If there is no load capacity margin in the adjacent energy grids during the clustering process, clustering will be performed again by increasing the number of clustering points.

[0036] Based on the first aspect, the method for determining the energy grid constraint capacity includes:

[0037] The product of the maximum load density of electricity, heat and cooling in the planned area and the area of ​​the largest energy grid is used as the upper limit of the electricity, heat and cooling load capacity of the energy grid, and the product of the minimum load density of electricity, heat and cooling and the area of ​​the smallest energy grid is used as the lower limit of the electricity, heat and cooling load capacity of the energy grid;

[0038] The energy grid constraint capacity satisfies:

[0039]

[0040]

[0041]

[0042] Where: P min,e 、P min,h 、P min,c are the minimum load densities of electricity, heating and cooling loads in the area to be planned, P max,e 、Pmax,h 、P max,c are the maximum load densities of electricity, heating and cooling loads in the area to be planned, P e 、P h 、P c are the electricity, heating and cooling load capacities of the energy grid, respectively.

[0043] Based on the first aspect, since the number of energy grids proposed is within a certain range, the final division scheme can be determined by an energy grid division evaluation index. This scheme selects the optimal energy grid division scheme from the revised energy grid through a fuzzy ideal decision method based on expert weights.

[0044] The basis for evaluating whether the energy grid division is reasonable is mainly determined by the following two aspects: First, the overall cooling, heating and electricity load characteristics of the divided integrated energy grid are evaluated to determine whether the result of the energy grid division can effectively alleviate the peak load and reduce the peak-to-valley difference, thereby reducing the equipment investment cost of the integrated energy station within the grid. The average peak-to-valley difference rate index f1 of the cooling, heating and electricity load within the integrated energy grid can be constructed to measure and evaluate the generated initial grid:

[0045] A. Average peak-to-valley difference between hot and cold spots index f1

[0046]

[0047]

[0048]

[0049] P i =P i,e P i,h P i,c

[0050]

[0051] Where: P i,e 、P i,h and P i,c are the peak-to-valley difference rates of the overall electric, heating and cooling within the i-th energy grid; P i,e,max and P i,e,min is the peak and valley value of the overall electric load within the i-th grid; P i,h,max and P i,h,min is the peak and valley value of the overall heat load inside the i-th grid; P i,c,max and P i,c,min is the peak and valley value of the overall cooling load within the i-th grid; P i is the comprehensive peak-to-valley difference rate of the i-th energy grid; N s is the number of cluster center points;

[0052] Secondly, the distance between the load center point of the small plot and the initial clustering point is evaluated by constructing a distance index f2;

[0053] B. Distance index f2

[0054]

[0055]

[0056]

[0057] Where: D is the sum of the Euclidean distances between the load center of the small plot and the corresponding energy grid i cluster center; x ij is a 0-1 variable. If the load center of small plot j belongs to energy grid i, then x ij is 1, otherwise it is 0; N L is the number of small plot load centers; D' represents the sum of the Euclidean distances between the small plot load center and the nearest energy grid i cluster center.

[0058] Based on the hot and cold spot average peak-to-valley difference rate index and the distance index, the fuzzy ideal decision method is used to evaluate and use triangular fuzzy numbers to determine the expert's weight judgment on the two indicators. The corresponding relationship table of triangular fuzzy numbers is shown in Table 1:

[0059] Table 1 Corresponding relationship table of triangular fuzzy numbers

[0060] Language variables triangular fuzzy numbers Very important (0.9,1,1) More important (0.7,0.9,1) important (0.5,0.7,0.9) generally (0.3,0.5,0.7) relatively minor (0.1,0.3,0.5) secondary (0,0.1,0.3) Very minor (0,0,0.1)

[0061] The weights given by experts for each indicator are not necessarily the same, so the weights need to be averaged. The sum of each weight should be 1, so the weights need to be normalized;

[0062] Assume that there are k experts (1≤k≤K) who give fuzzy weights C to j indicators (1≤j≤n) jk , then the average value of the weight is:

[0063]

[0064] The obtained average weight is normalized to obtain the standard fuzzy weight, and a standard fuzzy weight vector C = [C1, C2, ..., C n ],

[0065]

[0066] The essence of the fuzzy ideal decision-making method is to find positive fuzzy ideal solutions and negative fuzzy ideal solutions, and then compare each solution with the positive fuzzy ideal solutions and negative fuzzy ideal solutions. The closer the solution is to the positive fuzzy ideal solution and the farther it is from the negative fuzzy ideal solution, the better the solution is. The specific steps of the fuzzy ideal decision-making method are as follows:

[0067] Assuming there are m options, each option corresponds to n indicators, then a multi-indicator decision matrix F = (f ij ) m×n Since the load peak-to-valley difference rate index and the distance index are both the larger the better, the results of the relative superiority processing of the multi-index decision matrix are as follows:

[0068]

[0069] Construct the weighted standard matrix D = (d ij ) m×n ,

[0070]

[0071] Construct positive fuzzy ideal solution D + and negative fuzzy ideal solution D - ,

[0072]

[0073] Because the average peak-to-valley difference rate index and distance index of hot and cold spots in this scheme are both the larger the better, therefore,

[0074]

[0075] Calculate the distance between each solution and the positive fuzzy ideal solution and the negative fuzzy ideal solution respectively.

[0076] The distance between solution i and the positive ideal fuzzy solution:

[0077]

[0078] The distance between solution i and the negative ideal fuzzy solution:

[0079]

[0080] In the above two expressions, the distance calculation expression between the triangular fuzzy numbers A = (a1, a2, a3) and B = (b1, b2, b3) is as follows:

[0081]

[0082] Calculate the relative closeness,

[0083]

[0084] Each solution is sorted by relative closeness, and the one with the largest relative closeness is the optimal solution.

[0085] In a second aspect, the present invention further provides a device for dividing a grid of an integrated energy system, comprising:

[0086] An energy grid quantity determination module is used to determine the number of energy grid divisions in the area to be planned based on a preset energy grid size;

[0087] The initial clustering module is used to perform initial clustering of load points in the planning area using the k-means clustering algorithm to generate an initial energy grid;

[0088] The energy grid correction module is used to correct the generated initial energy grid by taking into account the predicted maximum and minimum load densities of electricity, heat, and cooling in the planned area.

[0089] In a third aspect, the present invention further provides a computer-readable storage medium storing a program code, wherein the program code is used to enable a computer to execute the grid division method described in any one of the first aspects.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] The present invention discloses a method, device and storage medium for grid division of an integrated energy system. First, an energy grid division method based on improved k-means clustering is used to form an initial grid layout. Second, an energy grid correction method that considers load characteristics is used to form a final grid layout. Finally, a fuzzy ideal decision method based on expert weights is used to select the optimal number of energy grids. This method can effectively reduce the peak-to-valley difference rate of the cooling, heating and electricity loads of each grid and the overall load peak of the grid, thereby reducing the capacity investment cost of the integrated energy station and its pipeline network. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 A schematic diagram of a flow chart of a method for dividing a grid of an integrated energy system provided in the first embodiment of the present invention;

[0093] Figure 2 This is a schematic diagram of a process for performing initial clustering of load points within a planning area according to a first embodiment of the present invention;

[0094] Figure 3 This is a schematic diagram of the structure of the area to be planned in the application scenario of the first embodiment of the present invention;

[0095] Figure 4 This is a simulation result diagram of different numbers of grids in the application scenario of the first embodiment of the present invention;

[0096] Figure 5 Graphs showing mesh division simulation results under different schemes in the application scenario of the first embodiment of the present invention. DETAILED DESCRIPTION

[0097] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific implementation methods in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the technical features of the embodiments of the present application and the implementation methods can be combined with each other.

[0098] The term "and / or" in this document simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this document generally indicates an "or" relationship between the related objects.

[0099] Example 1

[0100] like Figure 1 As shown, this embodiment provides a method for gridding an integrated energy system, comprising the following steps:

[0101] The number of energy grid divisions in the planned area is determined based on the preset energy grid size. In the process of grid division of the integrated energy system, the number of energy grid divisions must be determined first. Conversely, when the size of the energy grid to be divided and the area of ​​the entire planning area are known, the number of energy grid divisions can be obtained. The size of the energy grid division should be moderate, neither too large nor too small. If the grid division is too large, the investment cost of the internal pipeline network and the loss of the pipeline network will increase. If the grid division is too small, the load demand within the grid will also decrease, which will cause the integrated energy station within the grid to supply excess energy, resulting in a waste of investment costs for energy station equipment. The number of energy grid divisions in this embodiment is calculated using the following formula:

[0102]

[0103] Where: n is the number of energy grid divisions determined; int[] is the rounding function, S is the area of ​​the planning area, r max and r min Respectively represent the maximum and minimum demarcation range of the energy grid;

[0104] like Figure 2 As shown in the figure, in this embodiment, the k-means clustering algorithm is used to perform initial clustering of the load points in the planning area to generate the initial energy grid; first, the distance d between each load point in the planning area is calculated. ij, the number of load points in the preset energy grid is a constraint value M, and a circular area is constructed with each load point in the planned area as the center and the preset radius. It is determined whether the number of load points in the energy grid of the circular area is greater than the constraint value M:

[0105] If yes, then add the load point to set C; if not, then judge the area of ​​the next load point, and finally get the number of load points in set C;

[0106] If the number of load points in set C is less than the number of energy grid divisions, then reduce the constraint value M and re-execute the above steps to redetermine the number of load points in set C;

[0107] If the number of load points in the re-determined set C is greater than the number of energy grid divisions, the load point with the largest neighborhood density in set C is selected as the first initial cluster center point c1;

[0108] Find the load point farthest from c1 in set C as the second initial cluster center c2; find the load point c3 with the largest sum of distances from c1 and c2 in set C as the third initial cluster center, and so on, until the number of cluster centers equals the number of energy grid divisions;

[0109] In this embodiment, after the initial grid clustering is completed, the total load of each energy grid after division is focused on:

[0110] If the total load of each energy grid does not exceed the energy grid constraint capacity, the next post-processing step is performed;

[0111] If the total load of a certain energy grid exceeds the constrained capacity of the energy grid during the clustering process, one or several boundary load points will be divided into adjacent energy grids with load capacity margins closest to the energy grid to prevent exceeding the expected capacity;

[0112] If there is no load capacity margin in the adjacent energy grids during the clustering process, clustering is performed again by increasing the number of clustering points. In this embodiment, the energy grid constraint capacity satisfies:

[0113]

[0114]

[0115]

[0116] Where: P min,e 、P min,h 、P min,c are the minimum load densities of electricity, heating and cooling loads in the area to be planned, P max,e 、P max,h 、Pmax,c are the maximum load densities of electricity, heating and cooling loads in the area to be planned, P e 、P h 、P c are the electric, heating and cooling load capacities of the energy grid, respectively;

[0117] In this embodiment, the maximum and minimum load densities of electricity, heat, and cooling in the planned area are predicted, and the generated initial energy grid is modified. Specifically, based on the obtained energy grid, the hot and cold spot load characteristics within each grid are obtained, and then the load weighting factor ω of each small plot load center and cluster center is obtained according to the following formula ij , as follows:

[0118] ω ij =ω ij,e ω ij,h ω ij,c

[0119]

[0120]

[0121]

[0122] Where: ω ij,e 、ω ij,h 、ω ij,c are the load weighting factors between the small plot load center point j and the energy grid cluster center point i when only considering the characteristics of electricity, heat and cooling loads in the energy grid; when the small plot load center point j does not belong to the grid division range to which the cluster center point i belongs, δ ij,e , δ ij,h , δ ij,c are the peak-valley differences of the overall electricity, heating, and cooling loads when the load center point j of the small plot is added to the energy grid of the cluster center point i, δ bj,e , δ bj,h , δ bj,c are the peak-to-valley differences of electricity, heat, and cooling loads after the load center point j leaves the grid b to which it originally belongs. Otherwise, they are the peak-to-valley differences of electricity, heat, and cooling loads of the energy grid i itself; δ k,e , δ k,h , δ k,c are the peak-to-valley differences of the overall electricity, heat, and cooling loads within the energy grid k; γ is the amplification factor of the weighting factor of the electricity, heat, and cooling loads; N s is the number of cluster center points; after obtaining the load weighting factor, calculate the load weighted distance ω between each load point and each cluster center point ij d ij, and each small plot is assigned to the energy grid with the smallest weighted distance. If the total load of a load point exceeds the grid capacity constraint after adding it to the new grid, or the load of the original grid does not reach the grid capacity constraint, the load point will still be assigned to the original grid. Repeat the above steps until the small plots in each energy grid no longer change;

[0123] In this embodiment, based on the above-mentioned finally generated grid, a fuzzy ideal decision method based on expert weight is adopted, and the specific steps are as follows:

[0124] (1) Determination of grid division evaluation indicators. Since the number of energy grids proposed is selected within a certain range, an energy grid division evaluation indicator is needed to determine the final division scheme. The basis for evaluating whether the energy grid division is reasonable is mainly determined by the following two aspects: First, the overall heat and power load characteristics of the integrated energy grid that has been divided are evaluated to determine whether the result of the energy grid division can effectively alleviate the peak load and reduce the peak-to-valley difference, thereby reducing the equipment investment cost of the integrated energy station within the grid. The average peak-to-valley difference rate indicator f1 of the heat and power load within the integrated energy grid can be constructed to measure it.

[0125]

[0126]

[0127]

[0128] P i =P i,e P i,h P i,c

[0129]

[0130] Where: P i,e 、P i,h and P i,c are the peak-to-valley difference rates of the overall electric, heating and cooling within the i-th energy grid; P i,e,max and P i,e,min is the peak and valley value of the overall electric load within the i-th grid; P i,h,max and P i,h,min is the peak and valley value of the overall heat load inside the i-th grid; P i,c,max and P i,c,min is the peak and valley value of the overall cooling load within the i-th grid; P i is the comprehensive peak-to-valley difference rate of the i-th energy grid; N s is the number of cluster centers.

[0131] Secondly, the distance between the load center point of the small plot and the initial clustering point is evaluated and measured by constructing the distance index f2.

[0132]

[0133]

[0134]

[0135] Where: D is the sum of the Euclidean distances between the load center of the small plot and the corresponding energy grid i cluster center; x ij is a 0-1 variable. If the load center of small plot j belongs to energy grid i, then x ij is 1, otherwise it is 0; N L is the number of small plot load centers; D' represents the sum of the Euclidean distances between the small plot load center and the nearest energy grid i cluster center.

[0136] (2) Triangular fuzzy number description. An ideal fuzzy decision method based on expert weights is adopted to determine the final solution for energy grid division. Triangular fuzzy numbers are used to determine the expert's weight judgment on the two indicators. The corresponding relationship table of triangular fuzzy numbers is shown in Table 2:

[0137] Table 2 Corresponding relationship table of triangular fuzzy numbers

[0138]

[0139]

[0140] The weights given by experts for each indicator are not necessarily the same, so the weights need to be averaged. The sum of each weight should be 1, so the weights need to be normalized.

[0141] Assume that there are k experts (1≤k≤K) who give fuzzy weights C to j indicators (1≤j≤n) jk , then the average value of the weight is:

[0142]

[0143] The obtained average weight is normalized to obtain the standard fuzzy weight, and then a standard fuzzy weight vector C = [C1, C2, ..., C n ].

[0144]

[0145] (3) Fuzzy ideal decision-making method. The essence of the fuzzy ideal decision-making method is to find positive fuzzy ideal solutions and negative fuzzy ideal solutions, and compare each solution with the positive fuzzy ideal solution and the negative fuzzy ideal solution. The closer to the positive fuzzy ideal solution and the farther from the negative fuzzy ideal solution, the better the solution. The specific fuzzy ideal decision-making steps are as follows:

[0146] Assuming there are m options, each option corresponds to n indicators, then a multi-indicator decision matrix F = (f ij ) m×n Since the load peak-to-valley difference rate index and the distance index are both considered as larger as possible in this paper, the results of the relative superiority processing of the multi-index decision matrix are as follows:

[0147]

[0148] Construct the weighted standard matrix D = (d ij ) m×n :

[0149]

[0150] Construct positive fuzzy ideal solution D + and negative fuzzy ideal solution D - :

[0151]

[0152] Since the load peak-to-valley difference index and distance index in this article are both the larger the better, we have:

[0153]

[0154] The distance between each solution and the positive fuzzy ideal solution and the negative fuzzy ideal solution is calculated separately.

[0155] The distance between solution i and the positive ideal fuzzy solution:

[0156]

[0157] The distance between solution i and the negative ideal fuzzy solution:

[0158]

[0159] In the above two expressions, the distance calculation expression between the triangular fuzzy numbers A = (a1, a2, a3) and B = (b1, b2, b3) is as follows:

[0160]

[0161] Calculate the relative closeness and sort each solution by the relative closeness. The solution with the largest relative closeness is the optimal solution.

[0162]

[0163] The following is a specific application scenario of a comprehensive energy system grid division method provided in Example 1. A certain area to be planned is used as an example for simulation analysis. The initial structure is as follows: Figure 3 The planned area covers a total area of ​​5.62 square kilometers and includes 68 small load plots. By the 2035 outlook year, load forecast results indicate a total electricity load of 71.34 MW, a heating load of 65.44 MW, and a cooling load of 76.96 MW. The load types for these small plots are divided into four categories: residential, commercial, industrial, and office.

[0164] Design 6 types of energy grid division numbers for simulation calculation, such as Figure 4 As shown in the figure: after the energy grid division results are corrected by taking load characteristics into consideration, some small plot load points no longer belong to the closest cluster center point, but belong to relatively distant cluster center points to form a new energy grid, thereby reducing the peak-to-valley difference rate of cooling, heating and electricity loads within each energy grid, thereby achieving the purpose of this energy grid division.

[0165] Table 3 Evaluation results of different grid number schemes

[0166]

[0167] It can be seen from Table 3 that the solution with the highest relative closeness is the optimal solution. Therefore, the optimal solution for this grid division of the planning area is to divide it into 4 grids.

[0168] In addition, to verify the effectiveness of the planning method, two schemes are designed for comparison, and the settings are as follows:

[0169] Option 1: Divide the energy grid without considering the complementarity of load characteristics.

[0170] Option 2: Divide the energy grid into sections based on the complementarity of load characteristics.

[0171] The simulation results are shown in the figure Figure 5 As shown in the figure, after the planning area is divided by considering the load characteristics, some load points no longer belong to the cluster center point with the closest Euclidean distance to them, but belong to the cluster center point with the closest load weighted distance. This will make the area of ​​some grids larger, but can reduce the peak-to-valley difference rate of each grid, thus achieving the effect of peak shaving and valley filling.

[0172] Table 4 Load peak-valley difference within the energy grid without considering load characteristic complementarity

[0173]

[0174] Table 5 Peak-to-valley difference of internal load of energy grid considering complementary load characteristics

[0175]

[0176] As shown in Tables 4 and 5, considering load complementarity during energy grid segmentation effectively reduces peak-to-valley variations within each grid's capacity constraints. The maximum peak-to-valley variations for electricity, heat, and cooling in each energy grid decreased from 56.9%, 99.6%, and 96.8% to 37.2%, 97.5%, and 94%, respectively. Although the peak-to-valley variations for some loads in some energy grids increased, such as the peak-to-valley variation for heat load in grid three, which increased from 96.6% to 97%, overall, the peak-to-valley variations for cooling, heat, and electricity in each grid improved. Furthermore, the sum of peak electricity, heat, and cooling loads within each grid decreased by 7.94%, 11.53%, and 3.93%, respectively. This demonstrates that considering the characteristics of electricity, heat, and cooling loads during energy grid segmentation can reduce both the overall grid load peak and the peak-to-valley variations within each grid.

[0177] Example 2

[0178] A device for dividing a grid of an integrated energy system, comprising:

[0179] Energy grid division module, used to divide the number range of energy grids in the planning area;

[0180] The load point clustering module uses the improved k-means clustering method to cluster the load points until the number of cluster center points is equal to the number of grids to be divided;

[0181] The energy grid correction module is used to treat the energy grid capacity constraints in the planning area and obtain the maximum and minimum load densities of electricity, heat and cooling in the planning area.

[0182] The integrated energy system grid division device provided in this embodiment can execute the integrated energy system grid division method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For details not described in detail in this embodiment, please refer to Example 1.

[0183] Example 3

[0184] This embodiment provides a computer-readable storage medium, which stores program code. The program code is used to enable a computer to execute the grid division method of the first embodiment.

[0185] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, devices, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0186] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0187] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0189] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for gridding an integrated energy system, characterized in that: The steps include: Determine the number of energy grid divisions in the planned area based on the preset energy grid size; The k-means clustering algorithm is used to perform initial clustering of load points in the planning area to generate the initial energy grid; Considering the predicted maximum and minimum load densities of electricity, heat, and cooling in the planned area, the generated initial energy grid is modified; Considering the predicted maximum and minimum load densities of electricity, heat, and cooling in the planned area, the generated initial energy grid is modified as follows: Obtain the hot and cold spot load characteristics within each energy grid and calculate the load weighting factor ω between the load center of each small plot and the cluster center point ij ; Calculate the load-weighted distance ω between each load point and each cluster center point ij d ij , and each small plot is assigned to the energy grid with the smallest weighted distance; If the addition of a load point to a new energy grid causes the total load of the new energy grid to exceed the grid capacity constraint, or the load of the original energy grid does not reach the grid capacity constraint, the load point will still be included in the original energy grid; Repeat the above steps until the small plots in each energy grid no longer change; The calculation formula of the load weighting factor is as follows: oh ij =ω ij,e oh ij,h oh ij,c Where: ω ij,e 、ω ij,h 、ω ij,c are the load weighting factors between the small plot load center point j and the energy grid cluster center point i when only considering the characteristics of electricity, heat and cooling loads in the energy grid; when the small plot load center point j does not belong to the energy grid division range to which the cluster center point i belongs, δ ij,e , δ ij,h , δ ij,c are the peak-valley differences of the overall electricity, heating, and cooling loads when the load center point j of the small plot is added to the energy grid of the cluster center point i, δ bj,e , δ bj,h , δ bj,c are the overall peak-to-valley differences of electricity, heat, and cooling loads after the load center point j leaves the original energy grid b; otherwise, they are the peak-to-valley differences of electricity, heat, and cooling loads of the energy grid i itself; δ k,e , δ k,h , δ k,c are the peak-to-valley differences of the overall electricity, heat, and cooling loads within the energy grid k; γ is the amplification factor of the weighting factor of the electricity, heat, and cooling loads; N s is the number of cluster centers.

2. The integrated energy system grid division method according to claim 1, characterized in that: The number of energy grid divisions is calculated using the following formula: Where: n is the number of energy grid divisions determined; int[] is the rounding function, S is the area of ​​the planning area, r max and r min Respectively represent the maximum and minimum demarcation range of the energy grid.

3. The integrated energy system grid division method according to claim 1, characterized in that: The k-means clustering algorithm is used to perform initial clustering of the load points in the planning area. The method for generating the initial energy grid includes: constructing a circular area with each load point in the planning area as the center and a preset radius, and presetting the number of load points in the energy grid as a constraint value M. It is determined whether the number of load points in the energy grid of the circular area is greater than the constraint value M: If yes, then add the load point to set C; if not, then judge the area of ​​the next load point, and finally get the number of load points in set C; If the number of load points in set C is less than the number of energy grid divisions, then reduce the constraint value M and re-execute the above steps to redetermine the number of load points in set C; If the number of load points in the re-determined set C is greater than the number of energy grid divisions, the load point with the largest neighborhood density in set C is selected as the first initial cluster center point c1; Find the load point farthest from c1 in set C as the second initial cluster center point c2; find the load point c3 with the largest sum of distances from c1 and c2 in set C as the third initial cluster center point, and so on, until the number of cluster centers equals the number of energy grid divisions.

4. A method for gridding an integrated energy system according to claim 1, characterized in that: After the initial grid clustering is completed, focus on the total load of each energy grid after division: If the total load of each energy grid does not exceed the energy grid constraint capacity, the next post-processing step is carried out; if the total load of an energy grid exceeds the energy grid constraint capacity during the clustering process, one or several boundary load points are divided into the adjacent energy grids closest to the energy grid with load capacity margin; If there is no load capacity margin in the adjacent energy grids during the clustering process, clustering will be performed again by increasing the number of clustering points.

5. The method for gridding an integrated energy system according to claim 1, characterized in that: The method for determining the energy grid constraint capacity includes: The product of the maximum load density of electricity, heat and cooling in the planned area and the area of ​​the largest energy grid is used as the upper limit of the electricity, heat and cooling load capacity of the energy grid, and the product of the minimum load density of electricity, heat and cooling and the area of ​​the smallest energy grid is used as the lower limit of the electricity, heat and cooling load capacity of the energy grid; The energy grid constraint capacity satisfies: Where: P min,e 、P min,h 、P min,c are the minimum load densities of electricity, heating and cooling loads in the area to be planned, P max,e 、P max,h 、P max,c are the maximum load densities of electricity, heating and cooling loads in the area to be planned, P e 、P h 、P c are the electricity, heating and cooling load capacities of the energy grid, respectively.

6. The integrated energy system grid division method according to claim 1, characterized in that: The method further includes: selecting an optimal energy grid partitioning scheme from the revised energy grid using a fuzzy ideal decision method based on expert weights.

7. A grid division device for an integrated energy system, characterized in that: include: An energy grid quantity determination module is used to determine the number of energy grid divisions in the area to be planned based on a preset energy grid size; Initial clustering module: used to perform initial clustering of load points within the planning area using the k-means clustering algorithm to generate an initial energy grid; Energy grid correction module: used to correct the generated initial energy grid by taking into account the predicted maximum and minimum load densities of electricity, heat, and cooling in the planned area; Considering the predicted maximum and minimum load densities of electricity, heat, and cooling in the planned area, the generated initial energy grid is modified as follows: Obtain the hot and cold spot load characteristics within each energy grid and calculate the load weighting factor ω between the load center of each small plot and the cluster center point ij ; Calculate the load-weighted distance ω between each load point and each cluster center point ij d ij , and each small plot is assigned to the energy grid with the smallest weighted distance; If the addition of a load point to a new energy grid causes the total load of the new energy grid to exceed the grid capacity constraint, or the load of the original energy grid does not reach the grid capacity constraint, the load point will still be included in the original energy grid; Repeat the above steps until the small plots in each energy grid no longer change; The calculation formula of the load weighting factor is as follows: oh ij =ω ij,e oh ij,h oh ij,c Where: ω ij,e 、ω ij,h 、ω ij,c are the load weighting factors between the small plot load center point j and the energy grid cluster center point i when only considering the characteristics of electricity, heat and cooling loads in the energy grid; when the small plot load center point j does not belong to the energy grid division range to which the cluster center point i belongs, δ ij,e , δ ij,h , δ ij,c are the peak-valley differences of the overall electricity, heating, and cooling loads when the load center point j of the small plot is added to the energy grid of the cluster center point i, δ bj,e , δ bj,h , δ bj,c are the overall peak-to-valley differences of electricity, heat, and cooling loads after the load center point j leaves the original energy grid b; otherwise, they are the peak-to-valley differences of electricity, heat, and cooling loads of the energy grid i itself; δ k,e , δ k,h , δ k,c are the peak-to-valley differences of the overall electricity, heat, and cooling loads within the energy grid k; γ is the amplification factor of the weighting factor of the electricity, heat, and cooling loads; N s is the number of cluster centers.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, and the program code is used to enable a computer to execute the grid division method according to any one of claims 1 to 6.

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