A power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method

Abstract

The present application relates to the technical field of distributed photovoltaic optimization investment decision-making, and relates to a distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method.The method comprises the following steps: S1, taking the lowest investment cost and meeting the photovoltaic power demand as double-layer objectives, and obtaining the total region of power distribution demand; S2, collecting the sunlight irradiation value of the total region.The present application realizes the dynamic balance and accurate landing of double-layer objectives, and through the whole process of double objectives throughout the sub-region planning, supply-demand matching and cost accounting, the photovoltaic power demand of each demand sub-region is ensured to be fully covered through multi-dimensional screening and quantitative calculation, avoiding the power supply gap problem, and through fine cost accounting and optimal scheme screening, the equipment purchase, line construction and whole life cycle operation and maintenance cost are maximally reduced, solving the investment waste or power supply shortage problem caused by double objective imbalance in the prior art, and improving the comprehensive benefit of investment decision-making.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed photovoltaic optimal investment decision-making, in particular to a power distribution network distributed photovoltaic double-layer collaborative optimal investment decision-making method. BACKGROUND

[0002] Under the global trend of clean and low-carbon transformation of energy structure, distributed photovoltaic, as one of the core forms of new energy utilization, has been widely integrated into the power distribution network system due to its flexible arrangement, zero pollution emission, and high energy utilization rate.

[0003] Based on the Chinese invention patent publication number CN117952780B, including: based on the historical annual data of all electricity consumption influencing items of the electricity consumption area and the historical annual electricity consumption data of the electricity consumption area, all influence quantization relationships between the electricity consumption influencing items and the electricity consumption are analyzed; based on all influence quantization relationships and predicted annual data of all electricity consumption influencing items of the electricity consumption area, the electricity consumption data of the electricity consumption area in the current year is predicted; based on the electricity consumption data of the electricity consumption area in the current year and the current available power distribution data of the power distribution network, the photovoltaic power supply input adjustment amount of each divided time period in the current year is determined; based on the photovoltaic power supply input adjustment amount and the unit input investment cost of each distributed photovoltaic input position in the power distribution network, the best distributed photovoltaic input scheme is determined; taking the predicted electricity consumption of the electricity consumption area and the minimization of photovoltaic input cost as the double-layer collaborative decision-making target, the best distributed photovoltaic input scheme is determined;

[0004] As can be seen from the above invention patent, the photovoltaic input scheme of the electricity consumption area is calculated, but the calculation of the photovoltaic input scheme in the patent is relatively vague, only the photovoltaic equipment needed by a region is obtained through the predicted electricity consumption and the predicted photovoltaic power, but the photovoltaic power obtained by installing photovoltaic equipment at different positions is different, which is easy to cause imbalance of power supply, secondly, the input analysis is carried out in a whole region, without involving multi-region correlation, but in actual photovoltaic power supply, most of them involve multi-direction power transmission, only adding photovoltaic equipment in a region, if the topographic conditions of the region do not support the photovoltaic equipment to fully play a role, the installation cost of the photovoltaic equipment will be greatly increased, so the above patent does not clearly provide accurate photovoltaic equipment installation plan and power transmission direction selection for workers, therefore, a power distribution network distributed photovoltaic double-layer collaborative optimal investment decision-making method is proposed. SUMMARY

[0005] The present application aims to provide a power distribution network distributed photovoltaic double-layer collaborative optimal investment decision-making method to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, a power distribution network distributed photovoltaic double-layer collaborative optimal investment decision-making method is provided, comprising the following steps:

[0007] S1, taking the lowest investment cost and meeting the photovoltaic power demand as double-layer goals, and obtaining the total area of power distribution demand;

[0008] S2, collecting the sunlight irradiation value of the total area, planning the installation sub-area set according to the sunlight irradiation value, and calculating the maximum number of photovoltaic devices and the maximum photovoltaic power supply of each installation sub-area;

[0009] S3, obtaining the total area photovoltaic demand power, dividing the demand sub-area set according to the demand position and calculating the photovoltaic demand power of each demand sub-area, calculating the distance difference data of each demand sub-area and each installation sub-area, and completing the initial association of the demand sub-area and the installation sub-area, then carrying out overflow power supply analysis and multi-area association simulation, and preferentially selecting the installation sub-area with high sunlight irradiation value in the association simulation process;

[0010] S4, extracting the association scheme according to the simulation process of S3, summarizing the association scheme and selecting the optimal scheme according to the lowest investment cost, and outputting the number of photovoltaic devices of each installation sub-area, the demand sub-area of power supply association and the total investment cost according to the optimal scheme.

[0011] As a further improvement of the technical solution, in S1, taking the lowest investment cost and meeting the photovoltaic power demand as double-layer goals;

[0012] Then a communication connection is established with the photovoltaic management end, and the total area of power distribution demand is obtained from the photovoltaic management end; wherein the total area is the geographical range covered by the power distribution demand.

[0013] As a further improvement of the technical solution, in S2, the total area is subjected to sunlight irradiation value collection, the sunlight irradiation value of the total area is obtained, and the source area of the sunlight irradiation value is determined in the total area;

[0014] The installation sub-area is planned according to the source area of the sunlight irradiation value, a plurality of installation sub-areas are planned in the total area, then all the planned installation sub-areas of the total area are summarized to form an installation sub-area set;

[0015] Among them, the sunlight irradiation threshold value is set, the area with sunlight irradiation value not lower than the threshold value is selected, then the terrain slope and soil bearing capacity of the area are further selected to obtain the installation sub-area set; wherein the terrain slope selection condition is ≤15°, and the soil bearing capacity selection condition is ≥150kPa.

[0016] As a further improvement of the technical solution, the terrain parameters of the installation sub-regions are extracted, and the equipment parameters of the photovoltaic equipment are obtained, the maximum installation quantity analysis is performed according to the terrain parameters combined with the equipment parameters, the maximum photovoltaic equipment quantity corresponding to each installation sub-region is obtained, and the maximum photovoltaic power supply quantity is analyzed by combining the maximum photovoltaic equipment quantity with the sunlight irradiation value, so that the maximum photovoltaic power supply quantity corresponding to the maximum photovoltaic equipment quantity of each installation sub-region is obtained.

[0017] As a further improvement of the technical solution, in S3, the photovoltaic demand power of the total region is collected, and the photovoltaic demand power corresponding to the total region is obtained, and the source region of the photovoltaic demand power in the total region is determined;

[0018] According to the source region of the photovoltaic demand power, the demand sub-region is planned, a plurality of demand sub-regions are planned in the total region, and then all the planned demand sub-regions in the total region are summarized to form a demand sub-region set;

[0019] Among them, the geographical position, the power of the electric load and the annual electricity consumption hours of each electric node in the total region are collected, and the demand sub-region is divided according to the clustering of the electric load density and the topological correlation of the power grid; the load density threshold is set, the electric nodes with load density not lower than the threshold or distance not more than 1km are classified into the same demand sub-region, and the demand sub-region set is formed.

[0020] As a further improvement of the technical solution, in the demand sub-region set, the corresponding photovoltaic demand power of each demand sub-region is determined;

[0021] The distance difference between each demand sub-region and each installation sub-region is calculated by the power grid topological distance, the distance difference data of the corresponding installation sub-region of each demand sub-region is obtained, then the demand sub-region is taken as the center reference, the installation sub-region with the smallest distance difference data is selected for association, and then the maximum photovoltaic power supply quantity corresponding to the associated installation sub-region is added or subtracted with the photovoltaic demand power of the demand sub-region;

[0022] When the calculation result is positive, it represents power surplus, so it is not necessary to add an associated installation sub-region to the demand sub-region, the power value corresponding to the calculation result is taken as the overflow power supply, and the overflow power supply is bound with the installation sub-region;

[0023] When the calculation result is negative, it represents power shortage, so it is necessary to add an associated installation sub-region to the demand sub-region, and the power value corresponding to the calculation result is taken as the lack of power supply, and the lack of power supply is bound with the demand sub-region.

[0024] As a further improvement of the technical solution, in S3, the demand sub-region with the lack of power supply is associated with the installation sub-region with the overflow power supply for simulation, and the association scheme balancing the lack of power supply and the overflow power supply is obtained according to the simulation result.

[0025] Wherein, the installation sub-regions with overflow power supply are sorted in descending order of sunlight irradiation value, and the installation sub-regions with high order are preferentially selected to match the demand sub-regions with lack of power supply; the matching simulation needs to meet the constraint condition: the total overflow power supply of the installation sub-regions ≥ the total lack of power supply of the demand sub-regions.

[0026] The matching scheme includes the photovoltaic power supply needed to be output by each installation sub-region and the demand sub-region associated with the power supply.

[0027] As a further improvement of the technical solution, in S4, the matching scheme corresponding to S3 is extracted, and the total photovoltaic power supply needed to be output by each installation sub-region is obtained according to the photovoltaic power supply needed to be output by the installation sub-region in the matching scheme and the photovoltaic demand power of the demand sub-region associated with the power supply before the matching simulation.

[0028] The total photovoltaic power supply needed to be output is combined with the device parameters of the photovoltaic device to calculate the installation quantity of the photovoltaic device, and the installation quantity of the photovoltaic device of each installation sub-region is obtained.

[0029] Wherein, the calculation result is rounded up.

[0030] As a further improvement of the technical solution, the investment cost is calculated according to the installation quantity of the photovoltaic device of the installation sub-region and the demand sub-region associated with the power supply, so as to obtain the investment cost of each matching scheme, and then the matching scheme with the lowest investment cost is selected as the optimal scheme, and the double-layer collaborative optimization investment decision of the total region is determined according to the optimal scheme.

[0031] Wherein, the investment cost includes the device purchase cost, the line construction cost and the whole life cycle operation and maintenance cost.

[0032] Compared with the prior art, the beneficial effects of the present application are:

[0033] 1、The double-layer collaborative optimization investment decision method for the distribution network distributed photovoltaic power supply realizes the dynamic balance and precise landing of the double-layer target, and through the whole process of double-layer target throughout the sub-regional planning, supply-demand matching and cost accounting, the photovoltaic power demand of each demand sub-region is ensured to be fully covered through multi-dimensional screening and quantitative calculation, and the power supply gap problem is avoided, and through the fine cost accounting and optimal scheme screening, the device purchase, line construction and whole life cycle operation and maintenance cost is minimized, and the investment waste or power supply shortage problem caused by the imbalance of the double-layer target in the prior art is solved, and the comprehensive benefit of the investment decision is improved.

[0034] 2. In the power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method, through the multi-dimensional mechanism of sunlight irradiation threshold screening + terrain slope, soil bearing capacity secondary screening, combined with the K-means clustering algorithm and the quantitative formula, the installation sub-region is accurately delimited, and the maximum equipment quantity and power supply quantity are calculated, which not only ensures the light resource endowment of the installation area, but also fully considers the adaptability of the construction conditions, avoids the subsequent reconstruction cost caused by unreasonable planning, and provides a solid foundation for the stable output of photovoltaic supply capacity.

[0035] 3. In the power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method, the initial correlation between the demand sub-region and the installation sub-region is realized through the calculation of the grid topology distance, and then a multi-region correlation simulation mechanism is established based on the excess and shortage of power supply, and high-illumination areas are preferentially selected for matching, which not only reduces the actual cost of line laying, but also improves the utilization efficiency of photovoltaic resources, realizes the dynamic balance of supply and demand, and at the same time, the quantitative matching constraint condition ensures that the power supply gap of each demand sub-region can be fully supplemented, avoiding the problem of local supply and demand imbalance. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The flowchart of the power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] Please refer to Figure 1 The present embodiment aims to provide a power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method, comprising the following steps:

[0039] S1, taking the lowest investment cost and meeting the photovoltaic power demand as the double-layer target, and obtaining the total area of power distribution demand; as the starting point and program of the whole decision-making process, the optimization direction is determined and the decision boundary is delimited, which provides a basic framework for subsequent sub-region planning, power supply calculation and correlation optimization;

[0040] In S1, taking the lowest investment cost and meeting the photovoltaic power demand as the double-layer target, a double-constraint system is formed;

[0041] Then, a communication connection is established with the photovoltaic management end, and the total area of power distribution demand is obtained from the photovoltaic management end; wherein the total area is the geographical range covered by the power distribution demand.

[0042] Establish a communication connection with the photovoltaic management end, obtain the total power distribution demand area (i.e. the geographical range covered by the power distribution demand), and demarcate the decision space boundary.

[0043] S2, collect the sunlight irradiation value of the total area, plan the installation sub-area set according to the sunlight irradiation value, calculate the maximum number of photovoltaic devices and the maximum photovoltaic power supply of each installation sub-area; focus on supply end optimization, determine the feasible photovoltaic installation area through multi-dimensional screening, and quantify the maximum power supply potential of each area to provide supply data support for subsequent matching with the demand end;

[0044] In S2, the sunlight irradiation value of the total area is collected, the sunlight irradiation value of the total area is obtained, and the source area of the sunlight irradiation value is determined in the total area;

[0045] According to the source area of the sunlight irradiation value, the installation sub-area is planned, and multiple installation sub-areas are planned in the total area, and then all the planned installation sub-areas in the total area are summarized to form an installation sub-area set;

[0046] Among them, set the sunlight irradiation threshold value, filter out the area whose sunlight irradiation value is not lower than the threshold value, and then obtain the terrain slope and soil bearing capacity of the area for further screening to obtain the installation sub-area set; wherein the terrain slope screening condition is ≤15°, and the soil bearing capacity screening condition is ≥150kPa, and the steps are as follows:

[0047] Collect the total area sunlight irradiation data, obtain the core values such as annual effective sunshine hours and actual irradiance, and clearly define the geographical source area corresponding to each value, while setting the sunlight irradiation threshold value (the best value is 1200h-1500h, adjusted according to regional resource endowment), screening out the area whose sunlight irradiation value is not lower than the threshold value as the candidate installation area, then collecting the terrain slope and soil bearing capacity data of the candidate installation area, further screening according to the conditions of terrain slope ≤15° and soil bearing capacity ≥150kPa, eliminating areas that are not feasible for engineering, then summarizing all the areas that pass the secondary screening, planning multiple independent installation sub-areas, and integrating to form an installation sub-area set.

[0048] Extract the terrain parameters of the installation sub-area, and obtain the device parameters of the photovoltaic device, which will be analyzed according to the terrain parameters combined with the device parameters to obtain the maximum number of photovoltaic devices corresponding to each installation sub-area, and the maximum photovoltaic power supply of the maximum number of photovoltaic devices corresponding to each installation sub-area is obtained by combining the maximum number of photovoltaic devices with the sunlight irradiation value, the steps are as follows:

[0049] Extract the terrain parameters (available installation area) of each installation sub-region, and obtain the rated power, single unit land area and other equipment parameters of the photovoltaic equipment. Based on the available installation area in the terrain parameters, combined with the photovoltaic equipment parameters, the maximum number of photovoltaic equipment that can be accommodated in each installation sub-region is calculated (rounded down) through a quantitative formula, and then the maximum number of photovoltaic equipment in each installation sub-region is combined with the collected sunlight radiation value (including sunlight radiation rate) to calculate the maximum photovoltaic power supply under the corresponding maximum equipment quantity through a quantitative formula, as follows:

[0050] ;

[0051] wherein, is the maximum number of photovoltaic equipment in the jth installation sub-region, is the available installation area of the jth installation sub-region, is the installation coefficient (value 0.7, considering the distance between photovoltaic panels and the reserved maintenance channel), is the land area of a single photovoltaic equipment, is the floor function (to avoid non-integer equipment quantity);

[0052] ;

[0053] wherein, is the maximum photovoltaic power supply of the jth installation sub-region, is the rated power of a single photovoltaic equipment, is the collected annual effective sunshine hours, is the sunlight radiation rate, is the photovoltaic inverter conversion efficiency;

[0054] S3, obtain the total regional photovoltaic demand power, divide the demand sub-region set according to the demand position and calculate the photovoltaic demand power of each demand sub-region, calculate the distance difference data of each demand sub-region and each installation sub-region, and complete the initial association of the demand sub-region and the installation sub-region, then carry out overflow power supply analysis and multi-region association simulation, and preferentially select the installation sub-region with high sunlight radiation value in the association simulation process; Focus on demand end planning and supply-demand matching optimization, first define demand sub-regions and quantify demand, then realize supply-demand balance through initial association + multi-region collaborative association, which is the core hub connecting the supply end and the final decision;

[0055] In S3, the total regional photovoltaic demand power is collected to obtain the total regional photovoltaic demand power, and the source region of the total regional photovoltaic demand power is determined;

[0056] According to the source area of the photovoltaic demand power, a demand sub-area is planned, a plurality of demand sub-areas are planned in the total area, and then all the planned demand sub-areas in the total area are collected to form a demand sub-area set;

[0057] Wherein, the geographical position, power load and annual power consumption of each power consumption node in the total area are collected, and the demand sub-area is divided according to the clustering of power load density and the correlation of power grid topology; the load density threshold is set, the power consumption nodes with load density not lower than the threshold or distance not more than 1km are classified into the same demand sub-area, and the demand sub-area set is formed, the steps are as follows:

[0058] The total area is developed for photovoltaic demand power collection, the total photovoltaic demand power of the total area is obtained, and the geographical source area corresponding to each part of the demand power is determined. The core data of all power consumption nodes in the total area are collected synchronously, including the geographical position, power load, and annual power consumption of the power consumption node. Then, the dual division logic of power load density clustering and power grid topology correlation is adopted, the best load density threshold is set to 5kW / ㎡, and the division standard is determined: the power consumption nodes with load density not lower than 5kW / ㎡, or the adjacent power consumption nodes with distance not more than 1km, are classified into the same demand sub-area.

[0059] According to the above division rule, combined with the geographical position, load data and power grid topology relationship of each power consumption node, a plurality of independent demand sub-areas are gradually planned in the total area, and then all the planned demand sub-areas in the total area are collected to form a complete demand sub-area set.

[0060] In the demand sub-area set, the corresponding photovoltaic demand power of each demand sub-area is determined;

[0061] The distance difference between each demand sub-area and each installation sub-area is calculated by the power grid topology distance, the distance difference data of the corresponding installation sub-area of each demand sub-area is obtained, and then the installation sub-area with the minimum distance difference is selected as the center reference of the demand sub-area to establish the correlation, the steps are as follows:

[0062] Based on the divided demand sub-area set, the corresponding photovoltaic demand power of each demand sub-area is calculated and determined one by one, the demand sub-area-demand power mapping relationship is formed, for each demand sub-area, the power grid topology distance is calculated with all installation sub-areas, the distance difference data between the demand sub-area and each installation sub-area is obtained, and a distance matrix is formed. Then, taking a single demand sub-area as the center reference, the corresponding distance difference data is traversed, the installation sub-area with the minimum distance difference is selected, and the initial correlation between the two is established, the formula is as follows:

[0063] ;

[0064] Wherein, Annual PV demand power of the ith demand sub-region, For the mth power consumption node belongs to the ith demand sub-region, The power consumption load of the mth power consumption node, The annual power consumption hours of the mth power consumption node;

[0065] ;

[0066] Wherein, The distance difference data of the ith demand sub-region and the jth installation sub-region, The central longitude and latitude coordinates of the ith demand sub-region, The central longitude and latitude coordinates of the jth installation sub-region, The path correction coefficient (best value: plain 1.1, mountainous area 1.4), correct the deviation of straight line distance and actual line path;

[0067] Then the maximum PV power supply of the associated installation sub-region is combined with the PV demand power of the demand sub-region to add and subtract calculation;

[0068] When the calculation result is positive, it represents power surplus, so there is no need to add the associated installation sub-region to the demand sub-region, and the power value corresponding to the calculation result is taken as the overflow power supply, and the overflow power supply is bound with the installation sub-region;

[0069] When the calculation result is negative, it represents power shortage, so it is necessary to add the associated installation sub-region to the demand sub-region, and the power value corresponding to the calculation result is taken as the lack of power supply, and the lack of power supply is bound with the demand sub-region, and the formula is as follows:

[0070] ;

[0071] Wherein, The power supply difference of the jth installation sub-region (initial association) to the ith demand sub-region, The power supply difference of the jth installation sub-region (initial association) to the ith demand sub-region, Annual PV demand power of the ith demand sub-region;

[0072] In S3, the demand sub-region with lack of power supply is associated with the installation sub-region with overflow power supply for simulation, and the association scheme balancing the lack of power supply and the overflow power supply is obtained according to the simulation result;

[0073] Wherein, the installation sub-regions with overflow power supply are sorted in descending order of sunlight irradiation value, and the installation sub-regions with high priority are selected to match with the demand sub-regions with lack of power supply; the matching simulation needs to meet the constraint condition: the total overflow power supply of the installation sub-regions ≥ the total lack of power supply of the demand sub-regions;

[0074] The total overflow power supply and the total lack of power supply of all the matched installation sub-regions are calculated in real time, and it is checked whether the constraint condition is met. If not, the next priority installation sub-region is selected to continue matching, until the constraint condition is met. All the matching relationships meeting the constraint condition are summarized, the photovoltaic power supply to be output by each installation sub-region (the matched overflow power supply) and the corresponding power supply associated demand sub-region are determined, and a complete lack of power supply and overflow power supply balancing association scheme is formed;

[0075] The association scheme includes the photovoltaic power supply to be output by each installation sub-region and the power supply associated demand sub-region, and the formula is as follows:

[0076] ;

[0077] ;

[0078] Wherein, is the set of overflow power supply installation sub-regions, j is the index of a single installation sub-region in the set, is the set of demand sub-regions with lack of power supply, i is the index of a single demand sub-region in the set, is the association decision variable (xij) xij=1 indicates that the jth overflow installation sub-region is associated with the ith lack of demand sub-region, xij=0 indicates no association, is the overflow power supply of the jth installation sub-region, is the lack of power supply of the ith demand sub-region.

[0079] S4, the association scheme is extracted according to the simulation process of S3, the association scheme is summarized and the optimal scheme is selected according to the lowest investment cost, and the number of photovoltaic devices of each installation sub-region, the power supply associated demand sub-region and the total investment cost are output according to the optimal scheme. As the end and landing link of the whole process, the cost optimization screening is carried out based on the candidate association scheme, and the final investment decision result which can be directly landed is output, which echoes the lowest investment cost target established in S1;

[0080] In S4, the association scheme corresponding to S3 is extracted, and the total photovoltaic power supply to be output by each installation sub-region is obtained according to the photovoltaic power supply to be output by the installation sub-region in the association scheme and the photovoltaic demand power of the associated demand sub-region before the association simulation;

[0081] All association schemes obtained by the early-stage association simulation S3 are superimposed with the photovoltaic power supply amount to be output in each installation sub-region, and the photovoltaic demand power of the initial association demand sub-region before the association simulation is added, to calculate the total photovoltaic power supply amount to be output in each installation sub-region;

[0082] The total photovoltaic power supply amount to be output is combined with the device parameters of the photovoltaic device to calculate the installation quantity of the photovoltaic device, and the installation quantity of the photovoltaic device required in each installation sub-region is obtained;

[0083] The core parameters (rated power, conversion efficiency, etc.) of the photovoltaic device are obtained, combined with the total photovoltaic power supply amount to be output in each installation sub-region, and the required installation quantity of the photovoltaic device is calculated through a quantitative formula, and the calculation result is rounded up (to ensure that the actual power supply amount is not lower than the total output demand), and the formula is as follows:

[0084] ;

[0085] Wherein, is the installation quantity of the photovoltaic device required in the jthinstallation sub-region, is the rounding up function (to ensure that the device quantity meets the total output power supply demand, and to avoid power supply gap), is the total photovoltaic power supply amount to be output in the jthinstallation sub-region;

[0086] According to the installation quantity of the photovoltaic device in the installation sub-region and the demand sub-region of the power supply association, the investment cost is calculated, so as to obtain the investment cost of each association scheme, and then the association scheme with the lowest investment cost is selected as the optimal scheme, and the total regional double-layer collaborative optimization investment decision is determined according to the optimal scheme;

[0087] For each association scheme, the investment cost of each installation sub-region is calculated, the cost composition covers the device procurement cost, the line construction cost and the whole life cycle operation cost, the total investment cost of each association scheme is obtained, and then in all association schemes meeting the supply and demand balance (the total overflow power supply amount ≥ the total lack of power supply amount), the scheme with the lowest total investment cost is selected as the optimal scheme, and the device quantity, the power supply range of each installation sub-region in the total region are determined based on the optimal scheme, and the final double-layer collaborative optimization investment decision is formed;

[0088] Wherein, the investment cost includes the device procurement cost, the line construction cost and the whole life cycle operation cost, and the formula is as follows:

[0089] ;

[0090] Wherein, is the total device procurement cost of all installation sub-regions, Cost of purchasing a single photovoltaic device, Total number of installation sub-regions;

[0091] ;

[0092] Wherein, Total cost of line construction for all installation sub-regions, Topological distance of the jth installation sub-region from the initial associated demand sub-region, Topological distance of the jth installation sub-region from the ith demand sub-region of the supplementary association, Unit length line construction cost, Set of demand sub-regions to which the jth installation sub-region is supplementary associated;

[0093] ;

[0094] Wherein, Annual operation and maintenance cost, Annual operation and maintenance cost rate.

[0095] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A two-layer collaborative optimization investment decision-making method for distributed photovoltaic power grids, characterized in that: The method comprises the following steps: S1, taking the minimum investment cost and meeting the photovoltaic power demand as double-layer objectives, and obtaining the total area of power distribution demand; S2, collecting the sunlight irradiation value of the total area, planning the installation sub-area set according to the sunlight irradiation value, calculating the maximum photovoltaic equipment quantity and the maximum photovoltaic power supply of each installation sub-area; S3, obtaining the total area photovoltaic demand power, dividing the demand sub-area set according to the demand position, calculating the photovoltaic demand power of each demand sub-area, calculating the distance difference data of each demand sub-area and each installation sub-area, and completing the initial association of the demand sub-area and the installation sub-area, then carrying out the overflow power supply quantity analysis and multi-area association simulation, and preferentially selecting the installation sub-area with high sunlight irradiation value in the association simulation process; In S3, the total area photovoltaic demand power is collected, the corresponding photovoltaic demand power of the total area is obtained, and the source area of the photovoltaic demand power in the total area is determined; According to the source area of the photovoltaic demand power, the demand sub-area is planned, and multiple demand sub-areas are planned in the total area, then all the planned demand sub-areas in the total area are summarized to form a demand sub-area set; Wherein, the geographical position, power load and annual power consumption hours of each power consumption node in the total area are collected, the demand sub-area is divided according to the power load density clustering and the grid topology correlation, a load density threshold is set, the power consumption nodes with load density not lower than the threshold or distance not more than 1km are classified into the same demand sub-area, and the demand sub-area set is formed; In the demand sub-area set, the corresponding photovoltaic demand power of each demand sub-area is determined; The distance difference between each demand sub-area and each installation sub-area is calculated by the grid topology distance, the distance difference data of the corresponding installation sub-area of each demand sub-area is obtained, then the demand sub-area is taken as the center reference, the installation sub-area with the smallest distance difference data is selected for association, and then the maximum photovoltaic power supply corresponding to the associated installation sub-area is added or subtracted with the photovoltaic demand power of the demand sub-area; When the calculation result is positive, it represents power surplus, so there is no need to add an associated installation sub-area to the demand sub-area, the power quantity value corresponding to the calculation result is taken as the overflow power supply, and the overflow power supply is bound with the installation sub-area; When the calculation result is negative, it represents power shortage, so it is necessary to add an associated installation sub-area to the demand sub-area, and the power quantity value corresponding to the calculation result is taken as the lack of power supply, and the lack of power supply is bound with the demand sub-area; In S3, the demand sub-area with the lack of power supply is associated with the installation sub-area with the overflow power supply for association simulation, and the association scheme balancing the lack of power supply and the overflow power supply is obtained according to the association simulation result; Wherein, the installation sub-areas with the overflow power supply are sorted in descending order according to the sunlight irradiation value, and the installation sub-areas with high order are preferentially selected for matching simulation with the demand sub-areas with the lack of power supply; the association simulation needs to meet the constraint condition that the total overflow power supply of the installation sub-areas is greater than or equal to the total lack of power supply of the demand sub-areas; The association scheme includes the photovoltaic power supply quantity that each installation sub-area needs to output and the demand sub-area associated with the power supply; S4, extract the association scheme according to the simulation process of S3, aggregate the association scheme and select the optimal scheme according to the lowest investment cost, output the number of photovoltaic devices of each installation sub-area, the demand sub-area associated with power supply and the total investment cost according to the optimal scheme. 2.The power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method according to claim 1, characterized in that: In S1, the lowest investment cost and the satisfaction of photovoltaic power demand are taken as double-layer objectives. Then, a communication connection is established with the photovoltaic management end to obtain the total area of power distribution demand from the photovoltaic management end; wherein the total area is the geographical range covered by the power distribution demand.

3. The power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method according to claim 1, characterized in that: In S2, the sunlight irradiation value of the total area is collected to obtain the sunlight irradiation value of the total area, and the source area of the sunlight irradiation value is determined in the total area; According to the source area of the sunlight irradiation value, the installation sub-area is planned, a plurality of installation sub-areas are planned in the total area, and then all the planned installation sub-areas of the total area are aggregated to form an installation sub-area set; Wherein, a sunlight irradiation threshold is set, the area whose sunlight irradiation value is not lower than the threshold is selected, and then the terrain slope and soil bearing capacity of the area are further selected to obtain the installation sub-area set; wherein the terrain slope selection condition is ≤15°, and the soil bearing capacity selection condition is ≥150kPa.

4. The power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method according to claim 3, characterized in that: The terrain parameters of the installation sub-area are extracted, and the equipment parameters of the photovoltaic equipment are obtained, the maximum installation quantity analysis is carried out according to the terrain parameters combined with the equipment parameters, the maximum photovoltaic equipment quantity corresponding to each installation sub-area is obtained, and the maximum photovoltaic power supply quantity of the maximum photovoltaic equipment quantity corresponding to each installation sub-area is obtained by combining the maximum photovoltaic equipment quantity with the sunlight irradiation value.

5. The power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method according to claim 1, characterized in that: In S4, the association scheme corresponding to S3 is extracted, the total photovoltaic power supply quantity required to be output by each installation sub-area is obtained by combining the photovoltaic power supply quantity required to be output by the installation sub-area in the association scheme with the photovoltaic demand power of the demand sub-area corresponding to the association before simulation; The installation quantity of photovoltaic equipment is calculated by combining the total photovoltaic power supply quantity required to be output with the equipment parameters of photovoltaic equipment, and the number of photovoltaic equipment required to be installed in each installation sub-area is obtained; Wherein, the calculation result is rounded up.

6. The power distribution network distributed photovoltaic double-layer collaborative optimization investment decision-making method according to claim 5, characterized in that: According to the number of photovoltaic equipment of the installation sub-area, the demand sub-area associated with power supply, the investment cost is calculated, thereby obtaining the investment cost of each association scheme, then the association scheme with the lowest investment cost is selected as the optimal scheme, and the double-layer collaborative optimization investment decision of the total area is determined according to the optimal scheme; Wherein, the investment cost includes equipment purchase cost, line construction cost and whole life cycle operation and maintenance cost.

Images