Distributed energy supply cluster division method, medium and electronic device

Abstract

The present invention discloses a method, medium and electronic device for partitioning a distributed energy supply cluster. The method includes: determining regional distributed energy supply cluster partitioning indicators; constructing an objective function with the optimal comprehensive weighted indicator as the goal based on the determined regional distributed energy supply cluster partitioning indicators; and using a recursive partitioning clustering tree algorithm to solve the objective function to obtain an optimal distributed energy supply cluster partitioning result including the loads of each cluster. The present invention comprehensively considers factors such as the geographical distribution of sources and loads, the endowment of local clean energy resources in the region, and the matching degree of sources and loads, and can efficiently and stably achieve a reasonable partitioning of the regional distributed energy supply cluster.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-energy system planning, and in particular to a distributed energy supply cluster division method, medium and electronic equipment. Background Art

[0002] With the development of renewable energy, the future energy system will present distributed characteristics. The regional energy supply network will make full use of local clean energy such as solar energy, wind energy, low-grade waste heat and other energy resources to form an intelligent distributed multi-energy complementary energy supply mode. This allows the planned area to contain more types of local energy and multiple types of energy loads. Therefore, it is necessary to fully consider factors such as the endowment of local clean energy resources in the region, the geographical distribution of sources and loads, and the degree of source-load matching, and explore the optimal form of energy supply. In addition, it is necessary to propose an efficient, stable and reasonable regional distributed energy supply cluster division standard and strategy. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first purpose of the present invention is to provide a distributed energy supply cluster division method, which comprehensively considers the geographical distribution of sources and loads, the endowment of local clean energy resources in the region, and the degree of source-load matching, and can efficiently and stably realize the reasonable division of regional distributed energy supply clusters.

[0004] A second object of the present invention is to provide a computer-readable storage medium.

[0005] A third object of the present invention is to provide an electronic device.

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

[0007] A distributed energy supply cluster division method, comprising:

[0008] Determine the indicators for dividing regional distributed energy supply clusters;

[0009] Based on the determined regional distributed energy supply cluster division index, an objective function with the goal of optimizing the comprehensive weighted index is constructed;

[0010] The objective function is solved by using a recursive partitioning clustering tree algorithm to obtain the optimal distributed energy supply cluster division result including the loads of each cluster.

[0011] Preferably, the regional distributed energy supply cluster division indicators include a heat source temperature to transmission distance ratio indicator, a multi-energy load matching indicator and an average Euclidean distance indicator.

[0012] Preferably, the heat source temperature and transport distance ratio index is expressed as follows:

[0013]

[0014] Among them, I 1,i represents the heat source temperature and transport distance ratio of cluster i, h represents the heat source, T h,i represents the temperature of heat source h in cluster i, k represents the load, and D h,k It represents the distance between the heat source h and the load k, max represents the maximum value function, and ∑ represents the summation function.

[0015] Preferably, the multi-energy charge matching index is expressed as follows:

[0016]

[0017]

[0018]

[0019] Among them, I 2,i represents the multi-energy load matching index of cluster i, E i represents the total available energy of cluster i, L i represents the total load capacity of cluster i, t is the time, T is the supply period, represents the output of heat source h in cluster i, represents the output of distributed photovoltaic e in cluster i, represents the heat load demand in cluster i, Represents the electric load demand in cluster i.

[0020] Preferably, the average Euclidean distance indicator is expressed as follows:

[0021]

[0022] Among them, I 3,i represents the average Euclidean distance index of cluster i, D h,k Indicates the distance between the heat source h and the heat load, D e,k represents the distance between distributed photovoltaic e and electrical load, k n Indicates the total number of source loads.

[0023] Preferably, before constructing an objective function with the goal of optimizing the comprehensive weighted index based on the determined regional distributed energy supply cluster division index, the method further includes standardizing each regional distributed energy supply cluster division index.

[0024] Preferably, the objective function is expressed as follows:

[0025] maxF CP =ω1I 1,i '+ω2I2,i '-ω3I 3,i '

[0026] Among them, F CP represents the objective function, ω1, ω2 and ω3 represent the weights of the heat source temperature and transmission distance ratio index, the multi-energy charge matching index and the average Euclidean distance index respectively, and I 1,i '、I 2,i 'and I 3,i 'represents the standardized heat source temperature and transmission distance ratio index, multi-energy charge matching index and average Euclidean distance index respectively.

[0027] Preferably, the step of solving the problem using a recursive partitioning clustering tree algorithm includes:

[0028] Determine an initial distributed energy cluster area, and divide the initial distributed energy cluster area into two distributed energy cluster sub-areas;

[0029] Based on the two divided distributed energy cluster sub-regions, the objective function is calculated respectively through the corresponding regional distributed energy supply cluster division index, and the distributed energy cluster sub-region that maximizes the gain of the objective function is determined from the two distributed energy cluster sub-regions;

[0030] The determined distributed energy cluster sub-region that can maximize the gain of the objective function is redivided to obtain two new distributed energy cluster sub-regions, and the steps of calculating the objective function are repeated until the objective function has no gain, then the final area division result is output to realize the regional distributed energy supply cluster division.

[0031] To achieve the above-mentioned object, the second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned distributed energy supply cluster division method is implemented.

[0032] To achieve the above-mentioned purpose, the third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned distributed energy supply cluster division method is implemented.

[0033] The present invention has at least the following technical effects:

[0034] The present invention provides a distributed energy supply cluster division method, which determines the regional distributed energy supply cluster division index, then constructs an objective function with the optimal comprehensive weighted index as the goal based on the determined regional distributed energy supply cluster division index, and then uses a recursive partitioning clustering tree algorithm to solve the objective function to obtain the optimal distributed energy supply cluster division result including each cluster load. For a distributed energy system containing multiple types of energy and loads, the present invention can realize the reasonable division of the energy supply area, and based on the recursive idea, the present invention can obtain efficient and stable division results.

[0035] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a method for dividing distributed energy supply clusters according to an embodiment of the present invention.

[0037] FIG. 2( a ) is a schematic diagram of dividing sub-regions according to an embodiment of the present invention.

[0038] FIG. 2( b ) is a schematic diagram of a two-dimensional normal distribution according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present embodiment is described in detail below, and examples of the embodiment are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0040] The distributed energy supply cluster division method, medium and electronic device of this embodiment are described below with reference to the accompanying drawings.

[0041] Figure 1 Flow chart of the distributed energy supply cluster division method according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0042] Step S1: Determine the regional distributed energy supply cluster division indicators.

[0043] In this embodiment, the regional distributed energy supply cluster division indicators include a heat source temperature and transmission distance ratio indicator, a multi-energy load matching indicator, and an average Euclidean distance indicator.

[0044] Specifically, low-grade heat sources come from factories, data centers or sewage treatment plants. Low-grade heat sources often cannot meet heating requirements and require additional auxiliary systems or equipment such as heat pumps. Therefore, it is crucial to find an effective integration strategy such as using a heat pump system to improve the performance of the heat source heating system. On the one hand, the coefficient of performance of the heat pump is positively correlated with the temperature of the heat source. On the other hand, low-grade heat sources are usually arranged in a nearby manner because their heating efficiency is seriously affected by the transportation distance. In order to make heating for the entire region more economical, the heat transfer range of the heat source with high temperature endowment should be larger. Therefore, the heat source temperature to transportation distance ratio indicator is defined as the formula:

[0045]

[0046] Among them, I 1,i represents the heat source temperature and transport distance ratio of cluster i, h represents the heat source, T h,i represents the temperature of heat source h in cluster i, k represents the load, and D h,k It represents the distance between the heat source h and the load k, max represents the maximum value function, and ∑ represents the summation function.

[0047] Multiple types of energy and loads can achieve energy conversion through energy coupling units. In order to enable the cluster to have greater multi-energy self-supply capabilities, the multi-energy load matching index is expressed as follows:

[0048]

[0049]

[0050]

[0051] Among them, I 2,i represents the multi-energy load matching index of cluster i, E i represents the total available energy of cluster i, L i represents the total load capacity of cluster i, t is the time, T is the supply period, represents the output of heat source h in cluster i, represents the output of distributed photovoltaic e in cluster i, represents the heat load demand in cluster i, Represents the electric load demand in cluster i.

[0052] The average Euclidean distance is an important indicator for clustering based on the geographical distribution of sources and loads. The average Euclidean distance indicator is expressed as follows:

[0053]

[0054] Among them, I 3,i represents the average Euclidean distance index of cluster i, Dh,k Indicates the distance between the heat source h and the heat load, D e,k represents the distance between distributed photovoltaic e and electrical load, k n Indicates the total number of source loads.

[0055] Step S2: constructing an objective function with the goal of optimizing the comprehensive weighted index based on the determined regional distributed energy supply cluster division index.

[0056] Before constructing an objective function with the goal of optimizing the comprehensive weighted index based on the determined regional distributed energy supply cluster division index, the method also includes standardizing each regional distributed energy supply cluster division index.

[0057] Specifically, in order to reduce the impact of different indicators on the division results, the following formula can be used to standardize the division indicators of the three regional distributed energy supply clusters:

[0058]

[0059] Among them, I 1,i '、I 2,i '、I 3,i ' are the standardized heat source temperature and transportation distance ratio index, multi-energy charge matching index and average Euclidean distance index respectively.

[0060] In one embodiment of the present invention, an objective function with the goal of optimizing the comprehensive weighted index is constructed based on the determined regional distributed energy supply cluster division index and is expressed as follows:

[0061] maxF CP =ω1I 1,i '+ω2I 2,i '-ω3I 3,i ' (7)

[0062] Among them, F CP represents the objective function, ω1, ω2 and ω3 represent the weights of the heat source temperature and transportation distance ratio index, the multi-energy charge matching index and the average Euclidean distance index respectively, satisfying ω1+ω2+ω3=1.

[0063] Step S3: using a recursive partitioning clustering tree algorithm to solve the objective function and obtain the optimal distributed energy supply cluster division result including each cluster load.

[0064] Among them, the steps of solving the problem by using a recursive partitioning clustering tree algorithm include: determining an initial distributed energy cluster area, dividing the initial distributed energy cluster area into two distributed energy cluster sub-areas; based on the two divided distributed energy cluster sub-areas, respectively calculating the objective function through the corresponding regional distributed energy supply cluster division indicators, and determining the distributed energy cluster sub-area that maximizes the objective function gain from the two distributed energy cluster sub-areas; re-dividing the determined distributed energy cluster sub-area that can maximize the objective function gain to obtain two new distributed energy cluster sub-areas, and repeating the step of calculating the objective function until the objective function has no gain, then outputting the final regional division result to realize the regional distributed energy supply cluster division.

[0065] The RCPT (Recursive Partitioning Clustering Tree Algorithm) algorithm divides a region into two sub-regions at each step. As shown in Figure 2(a), taking the two-dimensional normal distribution as an example, a fixed value is set, and the set of all partition results is traversed on the horizontal axis, with the average distance of the cluster midpoint as the objective function. Take the maximum objective function gain point shown in Figure 2(b), and continue the above operation for the two sub-regions. The detailed steps of the RCPT (Recursive Partitioning Clustering Tree Algorithm) algorithm partitioning are as follows:

[0066] First, the interval is divided by fixed increments. For the horizontal and vertical axes of the coordinate axis, the data space, i.e., the initial distributed energy cluster area, is divided into two sub-areas in the form of fixed increments, i.e., divided into two distributed energy cluster sub-areas.

[0067] Secondly, the maximum target gain division result is obtained. Specifically, based on the two divided distributed energy cluster sub-areas, the target function is calculated respectively through the corresponding regional distributed energy supply cluster division indicators, and the distributed energy cluster sub-area that maximizes the target function gain is determined and retained from the two distributed energy cluster sub-areas.

[0068] Furthermore, the subspace is divided. Specifically, based on the retained division results, the above two steps are repeated for the new distributed energy cluster sub-area.

[0069] Finally, it is determined whether the algorithm is finished. Specifically, if after traversing all sub-regions, the objective function of the division result has no gain, then the convergence condition is met, and the final regional division result is output to realize the regional distributed energy supply cluster division.

[0070] In summary, the present invention provides a distributed energy supply cluster division method, which determines the regional distributed energy supply cluster division index, then constructs an objective function with the optimal comprehensive weighted index as the goal based on the determined regional distributed energy supply cluster division index, and then uses a recursive partitioning clustering tree algorithm to solve the objective function to obtain the optimal distributed energy supply cluster division result including each cluster load. For a distributed energy system containing multiple types of energy and loads, the present invention can realize the reasonable division of the energy supply area, and based on the idea of ​​recursion, the present invention can obtain efficient and stable division results.

[0071] Furthermore, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned distributed energy supply cluster division method is implemented.

[0072] Furthermore, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned distributed energy supply cluster division method is implemented.

[0073] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0074] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. A distributed energy supply cluster division method, characterized in that: include: Determine the indicators for dividing regional distributed energy supply clusters; Based on the determined regional distributed energy supply cluster division index, an objective function with the goal of optimizing the comprehensive weighted index is constructed; The objective function is solved by using a recursive partitioning clustering tree algorithm to obtain the optimal distributed energy supply cluster division result including the loads of each cluster.

2. The distributed energy supply cluster division method according to claim 1, characterized in that: The regional distributed energy supply cluster division indicators include a heat source temperature to transmission distance ratio indicator, a multi-energy load matching indicator and an average Euclidean distance indicator.

3. The distributed energy supply cluster division method according to claim 2, characterized in that: The heat source temperature and transport distance ratio index is expressed as follows: Among them, I 1,i represents the heat source temperature and transport distance ratio of cluster i, h represents the heat source, T h,i represents the temperature of heat source h in cluster i, k represents the load, and D h,k It represents the distance between the heat source h and the load k, max represents the maximum value function, and ∑ represents the summation function.

4. The distributed energy supply cluster division method according to claim 3, characterized in that: The multi-energy load matching index is expressed as follows: Among them, I 2,i represents the multi-energy load matching index of cluster i, E i represents the total available energy of cluster i, L i represents the total load capacity of cluster i, t is the time, T is the supply period, represents the output of heat source h in cluster i, represents the output of distributed photovoltaic e in cluster i, represents the heat load demand in cluster i, Represents the electric load demand in cluster i.

5. The distributed energy supply cluster division method according to claim 4, characterized in that: The average Euclidean distance metric is expressed as follows: Among them, I 3,i represents the average Euclidean distance index of cluster i, D h,k Indicates the distance between the heat source h and the heat load, D e,k represents the distance between distributed photovoltaic e and electrical load, k n Indicates the total number of source loads.

6. The distributed energy supply cluster division method according to claim 5, characterized in that: Before constructing an objective function with the goal of optimizing the comprehensive weighted index based on the determined regional distributed energy supply cluster division index, the method also includes standardizing each regional distributed energy supply cluster division index.

7. The distributed energy supply cluster division method according to claim 6, characterized in that: The objective function is expressed as follows: maxF CP =ω1I 1,i '+ω2I 2,i '-ω3I 3,i ' Among them, F CP represents the objective function, ω1, ω2 and ω3 represent the weights of the heat source temperature and transmission distance ratio index, the multi-energy charge matching index and the average Euclidean distance index respectively, and I 1,i '、I 2,i 'and I 3,i 'represents the standardized heat source temperature and transmission distance ratio index, multi-energy charge matching index and average Euclidean distance index respectively.

8. The distributed energy supply cluster division method according to claim 1, characterized in that: The steps of solving the problem using the recursive partitioning clustering tree algorithm include: Determine an initial distributed energy cluster area, and divide the initial distributed energy cluster area into two distributed energy cluster sub-areas; Based on the two divided distributed energy cluster sub-regions, the objective function is calculated respectively through the corresponding regional distributed energy supply cluster division index, and the distributed energy cluster sub-region that maximizes the gain of the objective function is determined from the two distributed energy cluster sub-regions; The determined distributed energy cluster sub-region that can maximize the gain of the objective function is redivided to obtain two new distributed energy cluster sub-regions, and the steps of calculating the objective function are repeated until the objective function has no gain, then the final area division result is output to realize the regional distributed energy supply cluster division.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the distributed energy supply cluster division method according to any one of claims 1 to 8 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, it implements the distributed energy supply cluster division method according to any one of claims 1-8.

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