A distribution station planning method based on statistical multiplexing

Abstract

The present invention discloses a distribution station planning method based on statistical multiplexing, which scientifically dispatches distribution stations for electricity demand objects in different time periods: the electricity demand of different electricity demand objects in different time periods is dynamic, and this phenomenon often shows strong correlation in time and space, such as the "tidal effect". According to the periodic changes in electricity demand presented by different objects, fewer "static" distribution stations are respectively configured for them to meet their minimum electricity demand. The remaining distribution stations constitute dynamic resources, and the Pearson correlation coefficient algorithm is used to allocate the remaining distribution stations in real time. The distribution stations are statistically multiplexed according to the needs of electricity users in different time periods to determine the planning scheme of the distribution stations. The present invention performs statistical multiplexing planning on distribution stations in different time periods, effectively improving the resource utilization rate of distribution stations, and the distribution station planning scheme obtained has high application value.

Description

Technical Field

[0001] The invention belongs to the technical field of distribution network planning, and in particular relates to a distribution station planning method based on statistical multiplexing. Background Art

[0002] With the widespread use of the Internet and mobile smart terminal devices, people's demand for electric energy resources is increasing day by day. According to statistics from the National Energy Administration, my country's electricity consumption reached 722.55 billion kilowatts in 2019, a year-on-year increase of 4.5%. The construction of distribution stations, which are the last mile to deliver electricity to thousands of households, deserves special attention and research. Since the beginning of the 21st century, there has not been a very flexible planning scheme for the construction of distribution stations, which has resulted in some distribution stations not being fully utilized, causing unnecessary waste of resources. Therefore, the scientific and reasonable planning of distribution stations in the future is an urgent problem to be solved. Summary of the invention

[0003] Purpose of the invention: In view of the above problems, the present invention proposes a distribution station planning method based on statistical multiplexing. For electricity demand objects with strong temporal and spatial correlation, a scientific, reasonable and effective method is used to flexibly dispatch the distribution stations in the area to allocate the least resources to meet people's electricity needs.

[0004] Technical solution: To achieve the purpose of the present invention, the technical solution adopted by the present invention is: a distribution station planning method based on statistical multiplexing, which specifically includes the following steps:

[0005] Step 1, obtain a set of power demand objects Ω = {1, 2, ..., i, ..., N} and a set of distribution stations B = {1, 2, ..., j, ..., M} in the area to be planned; wherein i is the i-th power demand object, N is the total number of power demand objects in the area to be planned; j is the j-th distribution station, and M is the total number of distribution stations in the area to be planned;

[0006] Step 2: construct a distribution station planning model for the area to be planned and determine the optimization goal and constraint conditions of the distribution station planning model; the optimization goal of the distribution station planning model is to minimize the configuration resources of the distribution station; the constraint conditions include the energy supply constraint of the distribution station and the minimum power constraint of the power user;

[0007] Step 3: Based on the constraints of the distribution station planning model, the distribution station planning model described in step 2 is solved to obtain a decision plan for activation of the distribution station, that is, to obtain a distribution station planning result.

[0008] Furthermore, in step 1, the electric energy demand set of the i-th electric energy demand object is represented as A i ={a i,1 ,a i,2 ,...,ai,t ,...,a i,T}, a i,t is the energy demand of the ith electricity demand object in the tth time period, 1≤t≤T, and T represents the total number of time periods.

[0009] Furthermore, the optimization objective of the distribution station planning model in step 2 is expressed as follows:

[0010]

[0011] Where b j,t represents the activation plan of the jth distribution station in the tth period, b j,t =1 means the jth distribution station is enabled in the tth period, b j,t =0 means that the jth distribution station is not enabled in the tth time period, and T represents the total number of time periods;

[0012] The energy supply constraint of the distribution station is expressed as:

[0013]

[0014] In the formula, c j is the power supply capacity of the jth distribution station, w i,j It represents the electric energy obtained by the i-th electricity consumer at the j-th distribution station;

[0015] The minimum power constraint of the power consumer is expressed as:

[0016]

[0017] In the formula, a i,t is the power demand of the ith power demand object in the tth period, L j i,t It represents the electric energy obtained by the i-th electricity demand object at the j-th distribution station in the t-th time period.

[0018] Furthermore, the method of step 3 is as follows:

[0019] Set the enabled substation set B used , the distribution station set B used All elements in b satisfy j,t =1,1≤t≤T; b j,t represents the activation plan of the jth distribution station in the tth period, b j,t =1 means that the jth distribution station is enabled in the tth time period, and T represents the total number of time periods;

[0020] Identifying the electricity demand object set Ω;

[0021] If the set Ω is an empty set, then the enabled substation set B is output used , get the decision plan for activation of the distribution station;

[0022] If there is only one power demand object in the set Ω, then according to the power demand of the power demand object, the enabled distribution station set B used The distribution station is allocated, and after the allocation is completed, the power demand object is deleted from the set Ω, and the power demand object set Ω is re-identified; wherein the allocated distribution station satisfies the energy supply constraint of the distribution station and the minimum power constraint of the power user;

[0023] Otherwise, all the electricity demand objects in the set Ω are randomly grouped into two groups, and the Pearson correlation coefficient of the power demand of the two electricity demand objects in each group is calculated. The group with the smallest Pearson correlation coefficient is selected, and the electricity demand objects in this group are connected to the enabled distribution station set B. used The distribution station is allocated in the group, and after the allocation is completed, the power demand objects in the group are deleted from the set Ω, and the power demand object set Ω is re-identified; wherein the allocated distribution station meets the energy supply constraint of the distribution station and the minimum power constraint of the power object.

[0024] Furthermore, the step 3 also includes:

[0025] When the enabled substation set B used If the energy supply resources of all distribution stations in the set are occupied, then any distribution station from the distribution station set B is randomly selected and placed in the enabled distribution station set B. used middle.

[0026] Furthermore, the calculation formula of the Pearson correlation coefficient is as follows:

[0027]

[0028]

[0029] In the formula, A α , A β are the power demand sets of power demand objects α and β, respectively, α,β A α With A β Pearson correlation coefficient, α∈Ω, β∈Ω; cov() is the covariance function; σ α , σ β A α With A β The standard deviation of α,t is the power demand of the power demand object α in the tth period, 1≤t≤T; a β,t is the power demand of the power demand object β in the tth period; They represent the expected power demand of all T time periods of power demand objects α and β respectively.

[0030] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0031] The present invention scientifically plans the distribution station based on the statistical multiplexing method, specifically including: in the planning model of the distribution station, the energy supply constraint of the distribution station is limited by the basic equipment of the distribution station, and the boundary condition is established by ensuring that the total amount of electric energy provided to all users does not exceed the electric energy that the distribution station can provide; and the minimum electric energy constraint of the power user is limited by the user's demand, and the boundary condition is set by ensuring that the electric energy provided by all distribution stations exceeds the demand of the power user. Under the condition of multiple power users, the demand of the power users shows a strong correlation in time and space, and resources are reasonably scheduled to determine the planning scheme of the distribution station. The statistical multiplexing planning model of the present invention is based on the premise that the load of the distribution station within a certain period of time can be relatively accurately predicted based on historical operating data, and the distribution station is reasonably and flexibly allocated to meet the needs of user objects, so that the obtained distribution station planning scheme is scientific and effective and can be applied in the actual engineering field. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of a distribution station planning method based on statistical multiplexing under an embodiment;

[0033] FIG2( a ) is a schematic diagram of demand complementarity correlation of electricity demand objects under an embodiment;

[0034] FIG2( b ) is a schematic diagram of the super-demand correlation of an electricity demand object under an embodiment;

[0035] FIG2( c ) is a schematic diagram of low demand correlation of an electricity demand object under an embodiment;

[0036] Figure 3 The present invention is an example diagram of a distribution station planning based on statistical multiplexing under an embodiment. DETAILED DESCRIPTION

[0037] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0038] The present invention discloses a distribution station planning method based on statistical multiplexing, referring to Figure 1 , specifically including the following steps:

[0039] (1) Obtain a set of electricity demand objects Ω = {1, 2, ..., N} from the area to be planned, where the set of electricity demand for electricity demand objects i, i∈Ω in T time periods is represented by A i ={ai,1 ,a i,2 ,...,a i,T}; Get the available distribution station set B = {1, 2, ..., M}, and the power supply capacity set of its corresponding distribution station is expressed as C = {c 1 ,c 2 ,c 3 ,...,c M-1 ,c M};

[0040] (2) Let the set of enabled distribution stations in the area to be planned be B used ={}; Construct the distribution station planning model for the area and determine the objective function of the distribution station planning model:

[0041]

[0042] Where b j,t represents the activation plan of the jth distribution station in the tth period, b j,t =1 means the jth distribution station is enabled in the tth period, b j,t =0 means that the jth distribution station is not enabled in the tth time period, and T represents the total number of time periods;

[0043] (3) Identify the elements in the set Ω to see whether there is an electricity user with demand. If so, proceed to step (4); if not, proceed to step (6);

[0044] (4) If there is more than one power demand object in the set Ω, proceed to step (5); if there is only one power demand object in the set Ω, then select the power distribution station set B that has been turned on according to the power demand of the power demand object. used Distribution substation b j,t ,j∈B used ,1≤t≤T, that is, it must satisfy:

[0045] Energy supply constraints of distribution stations:

[0046]

[0047] In the formula, c j is the power supply capacity of the jth distribution station, w i,j It represents the electric energy obtained by the i-th electricity consumer at the j-th distribution station;

[0048] Minimum power consumption constraints for electricity users:

[0049]

[0050] In the formula, a i,t is the power demand of the ith power demand object in the tth period, L ji,t It represents the electric energy obtained by the i-th electricity demand object at the j-th distribution station in the t-th time period.

[0051] If the set B used If there are not enough distribution stations in the set, a new distribution station b is activated from set B. j,t ,j∈B,1≤t≤T is assigned to it, and the newly enabled distribution station b j,t Add to set B used Middle and b j,t Set to 1; delete the electricity demand object from the set Ω and return to step (3);

[0052] (5) Randomly select two electricity demand objects α, β, α∈Ω, β∈Ω from the set Ω so that the Pearson correlation coefficient p between electricity demand objects α and β is α,β Minimum, for the electricity demand objects α and β from the opened distribution station set B used The allocation of distribution station resources must also meet the following requirements:

[0053] Energy supply constraints of distribution stations:

[0054]

[0055] Minimum power consumption constraints for electricity users:

[0056]

[0057] If the set B used If there are not enough distribution stations in the set, a new distribution station b is activated from set B. j,t ,j∈B,1≤t≤T is assigned to it, and the newly enabled distribution station b j,t Add to set B used Middle and b j,t Set to 1; delete the electricity demand objects α and β from the set Ω and return to step (3);

[0058] (6) Output the enabled distribution substation set B used All elements in the final decision plan for the activation of the distribution station are obtained:

[0059]

[0060] refer to Figure 3 , which is an example diagram of a distribution station planning based on statistical multiplexing under an embodiment.

[0061] In step (5), referring to FIG. 2(a), FIG. 2(b) and FIG. 2(c), the Pearson correlation coefficient is calculated, and the calculation expression of the Pearson correlation coefficient is:

[0062]

[0063] in,

[0064] In the formula, A α , A β are the power demand sets of power demand objects α and β, respectively, α,β A α With A β Pearson correlation coefficient, α∈Ω, β∈Ω; cov() is the covariance function; σ α , σ β A α With A β The standard deviation of α,t is the power demand of the power demand object α in the tth period, 1≤t≤T; a β,t is the power demand of the power demand object β in the tth period; They represent the expected power demand of all T time periods of power demand objects α and β respectively.

Claims

1. A distribution station planning method based on statistical multiplexing, characterized in that: The specific steps include: Step 1, obtain a set of power demand objects Ω = {1, 2, ..., i, ..., N} and a set of distribution stations B = {1, 2, ..., j, ..., M} in the area to be planned; wherein i is the i-th power demand object, N is the total number of power demand objects in the area to be planned; j is the j-th distribution station, and M is the total number of distribution stations in the area to be planned; Step 2: construct a distribution station planning model for the area to be planned and determine the optimization goal and constraint conditions of the distribution station planning model; the optimization goal of the distribution station planning model is to minimize the configuration resources of the distribution station; the constraint conditions include the energy supply constraint of the distribution station and the minimum power constraint of the power user; Step 3, based on the constraints of the distribution station planning model, solving the distribution station planning model described in step 2, and obtaining a decision plan for activation of the distribution station, that is, obtaining a distribution station planning result; The method of step 3 is as follows: Set the enabled substation set B used , the distribution station set B used All elements in b satisfy j,t =1,1≤t≤T; b j,t represents the activation plan of the jth distribution station in the tth period, b j,t =1 means that the jth distribution station is enabled in the tth time period, and T represents the total number of time periods; Identifying the electricity demand object set Ω; If the set Ω is an empty set, then the enabled substation set B is output used , get the decision plan for activation of the distribution station; If there is only one power demand object in the set Ω, then according to the power demand of the power demand object, the enabled distribution station set B used The distribution station is allocated, and after the allocation is completed, the power demand object is deleted from the set Ω, and the power demand object set Ω is re-identified; wherein the allocated distribution station meets the energy supply constraint of the distribution station and the minimum power constraint of the power user; Otherwise, all the electricity demand objects in the set Ω are randomly grouped into two groups, and the Pearson correlation coefficient of the power demand of the two electricity demand objects in each group is calculated. The group with the smallest Pearson correlation coefficient is selected, and the electricity demand objects in this group are connected to the enabled distribution station set B. used The distribution station is allocated in the group, and after the allocation is completed, the power demand objects in the group are deleted from the set Ω, and the power demand object set Ω is re-identified; wherein the allocated distribution station satisfies the energy supply constraint of the distribution station and the minimum power constraint of the power object; The calculation formula of the Pearson correlation coefficient is as follows: In the formula, A α , A β are the power demand sets of power demand objects α and β, respectively, α,β A α With A β Pearson correlation coefficient, α∈Ω, β∈Ω; cov() is the covariance function; σ α , σ β A α With A β The standard deviation of α,t is the power demand of the power demand object α in the tth period, 1≤t≤T; a β,t is the power demand of the power demand object β in the tth period; They represent the expected power demand of all T time periods of power demand objects α and β respectively.

2. A distribution station planning method based on statistical multiplexing according to claim 1, characterized in that: In step 1, the electric energy demand set of the i-th electric energy demand object is represented as A i ={a i,1 ,a i,2 ,...,a i,t ,...,a i,T }, a i,t is the energy demand of the ith electricity demand object in the tth time period, 1≤t≤T, and T represents the total number of time periods.

3. A distribution station planning method based on statistical multiplexing according to claim 1, characterized in that: The optimization objective of the distribution station planning model described in step 2 is expressed as follows: Where b j,t represents the activation plan of the jth distribution station in the tth period, b j,t =1 means the jth distribution station is enabled in the tth period, b j,t =0 means that the jth distribution station is not enabled in the tth time period, and T represents the total number of time periods; The energy supply constraint of the distribution station is expressed as: In the formula, c j is the power supply capacity of the jth distribution station, w i,j It represents the electric energy obtained by the i-th electricity consumer at the j-th distribution station; The minimum power constraint of the power consumer is expressed as: In the formula, a i,t is the power demand of the ith power demand object in the tth period, L j i,t It represents the electric energy obtained by the i-th electricity demand object at the j-th distribution station in the t-th time period.

4. A distribution station planning method based on statistical multiplexing according to claim 1, characterized in that: The step 3 also includes: When the enabled substation set B used If the energy supply resources of all distribution stations in the set are occupied, then any distribution station from the distribution station set B is randomly selected and placed in the enabled distribution station set B. used middle.

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