Multi-partition standby resource optimal configuration method and system, terminal equipment and storage medium

Abstract

The invention discloses a multi-partition standby resource optimal configuration method, a multi-partition standby resource optimal configuration system, terminal equipment and a storage medium, and belongs to the field of resource optimal configuration. Respectively calculating the direct cost, the potential cost, the fault risk cost and the area difference coefficient of each area; then, according to the direct cost, the potential cost, the fault risk cost and the area difference coefficient of each area, constructing a function and a constraint which take the minimum total cost of multi-partition shared reserve capacity scheduling as a target; and finally, solving the target function to obtain a standby resource optimal configuration strategy which has the minimum total cost of shared standby capacity scheduling and meets constraint conditions, so that the strategy can fully consider multiple factors, and the accuracy of the strategy is improved. The problem that in the prior art, due to the fact that potential cost, cross-regional line risk cost and the difference between partitions are ignored, the configuration strategy of standby resources is not accurate is solved.

Description

Technical Field

[0001] The present invention relates to the field of resource optimization allocation, and particularly to a multi-zone standby resource optimization allocation method, system, terminal device, and storage medium. Background Art

[0002] With the accelerating construction of the new power system and the continuous advancement of the power marketization process, sharing standby capacity among provinces and regions through sectional tie lines to improve the overall power grid's risk resistance ability has become an industry consensus. Although certain progress has been made in the configuration and allocation of shared standby capacity in the prior art, there are still some significant drawbacks.

[0003] When calculating the cost of overall shared standby capacity in multiple regions in the prior art, only the apparent direct costs are often considered, such as the costs of gas turbines, new energy, adjustable loads, and energy storage as shared standby capacity. However, in actual applications, potential costs such as transmission fees, system losses, and dispatching management, as well as the risk cost of cross-regional lines, are equally important, but these costs are often ignored in the existing methods. Moreover, due to differences in economic capabilities, environmental impacts, resource availability, etc. among regions, the existing methods fail to consider the differences among various zones when considering costs, resulting in uneven distribution of the cost of shared standby capacity.

[0004] Therefore, there is a problem in the prior art that the configuration strategy of standby resources is inaccurate due to the factors of only considering direct costs while ignoring potential costs, cross-regional line risk costs, and differences among various zones. Summary of the Invention

[0005] The present invention provides a multi-zone standby resource optimization allocation method, system, terminal device, and storage medium, which can solve the problem in the prior art that the configuration strategy of shared standby capacity is inaccurate due to the factors of only considering direct costs while ignoring the potential costs of regions, cross-regional line risk costs, and differences among various zones.

[0006] To solve the above technical problems, an embodiment of the present invention provides a multi-zone standby resource optimization allocation method, including:

[0007] Obtaining the standby resource data, transmission dispatching data, cross-regional tie line transmission data, and historical operation data of each region;

[0008] Calculating the direct cost of each region according to the standby resource data of each region; calculating the potential cost of each region according to the transmission dispatching data of each region; calculating the fault risk cost of each region according to the cross-regional tie line transmission data of each region; calculating the zone difference coefficient of each region according to the historical operation data of each region;

[0009] Construct an objective function that minimizes the total cost of multi - partition shared reserve capacity scheduling based on the direct cost, potential cost, failure risk cost, and regional difference coefficient of each region. And construct the total reserve capacity constraint, inter - regional tie - line transmission capacity constraint, and transmission loss constraint of each region according to the reserve resource data, power transmission scheduling data, and cross - regional tie - line transmission data.

[0010] Solve the objective function that minimizes the total cost of multi - partition shared reserve capacity scheduling to obtain the available capacity of reserve resources and the cross - regional transmission capacity when the total cost of shared reserve capacity scheduling is minimized and the constraint conditions are met. And determine the optimal allocation strategy of reserve resources according to the available capacity of reserve resources and the cross - regional transmission capacity.

[0011] Further, calculating the direct cost of each region according to the reserve resource data of each region includes:

[0012] Calculate the direct cost of each region through the following formula according to the unit cost and call capacity of the reserve resources of gas turbines, new energy equipment, energy storage equipment, and adjustable loads in the reserve resource data of each region;

[0013] C direct,i =c gen,i ·P gen,i +c re,i ·P re,i +c ess,i ·P ess,i +c load,i ·P load,i ;

[0014] Among them, C direct，i is the direct cost of the i - th region; i is the region number; c gen，i is the unit cost of the reserve resources of the gas turbine equipment in the i - th region; c re，i is the unit cost of the reserve resources of the new energy equipment in the i - th region; c ess，i is the unit cost of the reserve resources of the energy storage equipment in the i - th region; c load，i is the unit cost of the reserve resources of the adjustable load in the i - th region; P gen，i is the call capacity of the reserve resources of the gas turbine equipment in the i - th region; P re，i is the call capacity of the reserve resources of the new energy equipment in the i - th region; P ess，i is the call capacity of the reserve resources of the energy storage equipment in the i - th region; P load,i is the call capacity of the reserve resources of the adjustable load in the i - th region.

[0015] Further, calculating the potential cost of each region according to the power transmission scheduling data of each region includes:

[0016] Calculate the potential cost of each region according to the unit power transmission cost, unit power transmission loss cost, unit dispatching management cost, cross-region transmission capacity, power transmission loss power and dispatching times of the power transmission dispatching data of each region through the following formula;

[0017] C potential，i =c trans，i ·L i，i′ +c loss，i ·ΔP loss，i +c manage，i ·N dispatch，i ;

[0018] Wherein, C potential,i is the potential cost of the i-th region; i is the region number; c trans，i is the unit power transmission cost of the i-th region; c loss,i is the unit power transmission loss cost of the i-th region; c manage，i is the unit dispatching management cost of the i-th region; L i，i′ is the cross-region transmission capacity; ΔP loss，i is the power transmission loss power; N dispatch，i is the dispatching times.

[0019] Furthermore, calculating the fault risk cost of each region according to the cross-region tie line transmission data of each region includes:

[0020] Calculate the fault risk cost of each region according to the cross-region tie line fault probability, cross-region transmission capacity and unit transmission capacity fault loss cost of the cross-region tie line transmission data of each region through the following formula;

[0021]

[0022] Wherein, C g，i is the fault risk cost of the i-th region; i is the region number; I n，i，i′ is the cross-region tie line fault probability; L i，i′ is the cross-region transmission capacity; C acciden，ti，i′ is the unit transmission capacity fault loss cost.

[0023] Furthermore, calculating the regional difference coefficient of each region according to the historical operation data of each region includes:

[0024] Calculate the relative cost data, relative economic data, relative environmental data and relative resource data between each region according to the historical cost data, historical economic data, historical environmental data and historical resource data of the historical operation data of each region;

[0025] Perform weighted calculation according to the relative cost data, relative economic data, relative environmental data and relative resource data between each region to obtain the regional difference coefficient of each region.

[0026] Furthermore, the expression of the objective function for minimizing the total cost of multi-partition shared reserve capacity scheduling is as follows:

[0027]

[0028] where N is the total number of regions; i is the region number; q i is the regional difference coefficient of the i-th region; C direct，i is the direct cost of the i-th region; C potential，i is the potential cost of the i-th region; C g，i is the failure risk cost of the i-th region.

[0029] Furthermore, the total reserve capacity constraint is:

[0030]

[0031] where P gen，i is the call capacity of the reserve resources of the gas turbine equipment in the i-th region; P re，i is the call capacity of the reserve resources of the new energy equipment in the i-th region; P ess，i is the call capacity of the reserve resources of the energy storage equipment in the i-th region; P load，i is the call capacity of the reserve resources of the adjustable load in the i-th region; L i,i′ is the cross-regional transmission capacity: is the minimum total reserve capacity requirement of the i-th region;

[0032] The cross-regional tie-line transmission capacity constraint is:

[0033]

[0034] where L i，i′ is the cross-regional transmission capacity; is the maximum transmission capacity of the cross-regional tie-line i-i′;

[0035] The transmission loss constraint is:

[0036] ΔP loss,i = f(P k,i , L i,i' );

[0037] where ΔP loss，i is the transmission loss power; f(P k，i , L i，i′ ) is the power grid power flow equation; P k，i represents the call capacity of the reserve resources of the k-th type in the i-th region, and k is used to represent the types of reserve resources. The types of reserve resources include gas turbine equipment, new energy equipment, energy storage equipment, and adjustable load; Li，i′ is the cross-region transmission capacity.

[0038] Based on the above method embodiments, the present invention correspondingly provides system embodiments;

[0039] An embodiment of the present invention provides a multi-region spare resource optimal allocation system, including: a data acquisition module, a direct cost calculation module, a potential cost calculation module, a failure risk cost calculation module, a region difference coefficient calculation module, an objective function and constraint construction module, and an optimal allocation strategy generation module;

[0040] The data acquisition module is used to acquire spare resource data, power transmission scheduling data, cross-region tie-line transmission data, and historical operation data of each region;

[0041] The direct cost calculation module is used to calculate the direct cost of each region according to the spare resource data of each region;

[0042] The potential cost calculation module is used to calculate the potential cost of each region according to the power transmission scheduling data of each region;

[0043] The failure risk cost calculation module is used to calculate the failure risk cost of each region according to the cross-region tie-line transmission data of each region;

[0044] The region difference coefficient calculation module is used to calculate the region difference coefficient of each region according to the historical operation data of each region;

[0045] The objective function and constraint construction module is used to construct an objective function with the minimum total cost of multi-region shared spare capacity scheduling according to the direct cost, potential cost, failure risk cost, and region difference coefficient of each region, and construct the total spare capacity constraint, inter-region tie-line transmission capacity constraint, and power transmission loss constraint of each region according to the spare resource data, power transmission scheduling data, and cross-region tie-line transmission data;

[0046] The optimal allocation strategy generation module is used to solve the objective function with the minimum total cost of multi-region shared spare capacity scheduling, obtain the available capacity of the spare resources and the cross-region transmission capacity when the total cost of the shared spare capacity scheduling is the minimum and meets the constraint conditions, and determine the optimal allocation strategy of the spare resources according to the available capacity of the spare resources and the cross-region transmission capacity.

[0047] Based on the above method embodiments, another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a multi-region spare resource optimal allocation method as described in the above embodiments.

[0048] Based on the above method embodiments, another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute a multi-partition standby resource optimization configuration method described in the above embodiments.

[0049] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0050] The present invention comprehensively obtains standby resource data, power transmission scheduling data, cross-region tie-line transmission data, and historical operation data of each region, covering various aspects of information such as standby resource call cost and capacity, power transmission and scheduling related costs, tie-line failure probability and loss costs, and historical economic environment. On this basis, the direct cost, potential cost, failure risk cost, and regional difference coefficient of each region are calculated respectively. Then, a function and constraints with the goal of minimizing the total cost of multi-partition shared standby capacity scheduling are constructed according to the direct cost, potential cost, failure risk cost, and regional difference coefficient of each region. Finally, through the solution of the objective function, an optimized configuration strategy of shared standby capacity with the minimum total cost of shared standby capacity scheduling and meeting the constraints is obtained. The objective function not only considers the factor of direct cost, but also considers the cross-region line risk cost and the differences between regions, so that the optimized configuration strategy of shared standby capacity obtained by solving the objective function can fully consider various factors, improve the accuracy of the optimized configuration strategy of shared standby capacity, and solve the problem that the existing technology has inaccurate configuration strategies for standby resources due to only considering direct cost and ignoring potential cost, cross-region line risk cost, and the differences between partitions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flowchart of the steps of a multi-partition standby resource optimization configuration method provided by an embodiment of the present invention;

[0052] Figure 2 It is a module diagram of a multi-partition standby resource optimization configuration system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] In the description of the present invention, it should be understood that the term "first" is only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features.

[0055] Embodiment 1:

[0056] Referring to Figure 1 , which is a step flow chart of a multi - partition standby resource optimization configuration method provided by an embodiment of the present invention; in order to solve the problem in the prior art that the configuration strategy of shared standby capacity is inaccurate because only direct costs are considered while potential costs of regions, cross - region line risk costs, and differences between partitions are ignored, the method at least includes the following steps:

[0057] Step S1: Obtain the standby resource data, power transmission scheduling data, cross - region tie - line transmission data, and historical operation data of each region;

[0058] In this embodiment, the standby resource data includes: unit costs and call capacities of standby resources of gas turbine equipment, new energy equipment, energy storage equipment, and adjustable loads; the power transmission scheduling data includes: unit power transmission cost, unit power transmission loss cost, unit dispatching management cost, cross - region transmission capacity, power transmission loss power, and dispatching times; the cross - region tie - line transmission data includes: cross - region tie - line failure probability, cross - region transmission capacity, and unit transmission capacity failure loss cost; the historical operation data includes: historical cost data, historical economic data, historical environmental data, and historical resource data.

[0059] Specifically, the historical cost data includes but is not limited to additional costs of shared standby capacity outside the dispatching area, additional power transmission costs, and additional environmental costs; the historical economic data includes but is not limited to GDP and per capita disposable income; the historical environmental data includes but is not limited to carbon emission costs and pollutant treatment costs; the historical resource data includes but is not limited to wind resources, solar resources, and water resources.

[0060] Step S2: Calculate the direct cost of each region according to the standby resource data of each region; calculate the potential cost of each region according to the power transmission scheduling data of each region; calculate the failure risk cost of each region according to the cross - region tie - line transmission data of each region; calculate the regional difference coefficient of each region according to the historical operation data of each region.

[0061] In this embodiment, calculating the direct cost of each region according to the standby resource data of each region includes:

[0062] Calculate the direct cost of each region through the following formula according to the unit costs and call capacities of standby resources of gas turbine equipment, new energy equipment, energy storage equipment, and adjustable loads in the standby resource data of each region;

[0063] C direct,i = c gen,i ·P gen,i + c re,i ·P re,i + c ess,i ·P ess,i + c load,i ·P load,i ;

[0064] Among them, C direct，i is the direct cost of the i-th area; i is the area number; c gen，i is the unit cost of the standby resource of the gas turbine equipment in the i-th area; c re，i is the unit cost of the standby resource of the new energy equipment in the i-th area; c ess，i is the unit cost of the standby resource of the energy storage equipment in the i-th area; c load，i is the unit cost of the standby resource of the adjustable load in the i-th area; P gen，i is the call capacity of the standby resource of the gas turbine equipment in the i-th area; P re，i is the call capacity of the standby resource of the new energy equipment in the i-th area; P ess，i is the call capacity of the standby resource of the energy storage equipment in the i-th area; P load，i is the call capacity of the standby resource of the adjustable load in the i-th area.

[0065] In this embodiment, calculating the potential cost of each area according to the transmission dispatching data of each area includes:

[0066] Calculating the potential cost of each area according to the unit transmission cost, unit transmission loss cost, unit dispatching management cost, cross-region transmission capacity, transmission loss power and dispatching times of the transmission dispatching data of each area through the following formula;

[0067] C potential，i = c trans，i ·L i，i′ + c loss，i ·ΔP loss，i + c manage，i ·N dispatch，i ;

[0068] Among them, C potential，i is the potential cost of the i-th area; i is the area number; c trans，i is the unit transmission cost of the i-th area; c loss，i is the unit transmission loss cost of the i-th area; c manage，i is the unit dispatching management cost of the i-th area; L i，i′ is the cross-region transmission capacity; ΔP loss，i is the transmission loss power; N dispatch，iis the number of scheduling times.

[0069] In this embodiment, calculating the fault risk cost of each region according to the data transmitted by the inter-region connection lines of each region includes:

[0070] According to the inter-region connection line fault probability, inter-region transmission capacity, and unit transmission capacity fault loss cost of the data transmitted by the inter-region connection lines of each region, calculate the fault risk cost of each region through the following formula;

[0071]

[0072] where C g，i is the fault risk cost of the i-th region; i is the region number; I n，i，i′ is the inter-region connection line fault probability; L i，i′ is the inter-region transmission capacity; C acciden，ti，i′ is the unit transmission capacity fault loss cost.

[0073] Specifically, the inter-region connection line fault probability is calculated from the ratio of the continuous occurrence time of risk accidents to the total continuous occurrence time of risk accidents in the historical process of sharing spare resources between regions, and the calculation formula is: I n，i，i′ =T n，i，i′ / T 总 , where n is the risk accident, I n，i，i′ is the inter-region connection line fault probability, T n，i，i′ is the continuous occurrence time of risk accidents in the historical process of sharing spare resources between regions, T 总 is the total continuous occurrence time of risk accidents.

[0074] Specifically, the unit transmission capacity fault loss cost is mainly calculated from the sum of the fault equipment repair cost, power outage loss cost, and labor cost.

[0075] In this embodiment, calculating the regional difference coefficient of each region according to the historical operation data of each region includes:

[0076] According to the historical cost data, historical economic data, historical environmental data, and historical resource data of the historical operation data of each region, calculate the relative cost data, relative economic data, relative environmental data, and relative resource data between each region;

[0077] According to the relative cost data, relative economic data, relative environmental data, and relative resource data between each region, perform weighted calculation to obtain the regional difference coefficient of each region.

[0078] Exemplarily, based on the historical cost data, historical economic data, historical environmental data, and historical resource data of each region, the relative cost data, relative economic data, relative environmental data, and relative resource data between regions are calculated as follows: The relative cost data is: b ij is the j-th relative cost data of region i relative to its own region, and there are j I relative cost data in total; The relative economic data is: e ij is the j-th relative economic data of region i relative to its own region, and there are j2 relative cost data in total; The relative environmental data is: h ij is the j-th relative environmental data of region i relative to its own region, and there are j3 relative cost data in total; The relative resource data is: z ij is the j-th relative resource data of region i relative to its own region, and there are j4 relative cost data in total;

[0079] The above is a set of relative values of other regions relative to the local region. By selecting different local regions, the other regions will change accordingly, and the relative values will also change correspondingly.

[0080] Exemplarily, based on the relative cost data, relative economic data, relative environmental data, and relative resource data between regions, weighted calculation is performed to obtain the regional difference coefficient of each region, specifically:

[0081]

[0082] where n = j1 + j2 + j3 + j4, that is, the number of weighted weights is equal to the number of relative values of influencing factors; w1, w2,..., w n refers to the weight; q1, q2,..., q i refers to the regional difference coefficient of each region; When different local regions are selected, the other regions will change accordingly, and a series of solved regional difference data will also change.

[0083] Step S3: Based on the direct cost, potential cost, failure risk cost, and regional difference coefficient of each region, construct an objective function with the minimum total cost of multi-partition shared reserve capacity scheduling, and construct the total reserve capacity constraint of each region, the transmission capacity constraint of the inter-regional tie line, and the transmission loss constraint according to the reserve resource data, transmission scheduling data, and cross-regional tie line transmission data;

[0084] In this embodiment, the expression of the objective function with the minimum total cost of multi-partition shared reserve capacity scheduling is:

[0085]

[0086] Among them, N is the total number of regions; i is the region number; q i is the regional difference coefficient of the i-th region; C direct，i is the direct cost of the i-th region; C potential，i is the potential cost of the i-th region; C g,i is the failure risk cost of the i-th region.

[0087] In this embodiment, the total reserve capacity constraint is:

[0088]

[0089] Among them, P gen,i is the call capacity of the reserve resources of the gas turbine equipment in the i-th region; P re,i is the call capacity of the reserve resources of the new energy equipment in the i-th region; P ess,i is the call capacity of the reserve resources of the energy storage equipment in the i-th region; P load,i is the call capacity of the reserve resources of the adjustable load in the i-th region; L i，i′ is the cross-regional transmission capacity; is the minimum total reserve capacity requirement of the i-th region;

[0090] The transmission capacity constraint of the inter-regional tie line is:

[0091]

[0092] Among them, L i，i′ is the cross-regional transmission capacity; is the maximum transmission capacity of the cross-regional tie line i-i′;

[0093] The transmission loss constraint is:

[0094] ΔP loss,i = f(P k,i , L i,i' );

[0095] Among them, ΔP loss，i is the transmission loss power; f(P k，i , L i，i′ ) is the power grid power flow equation; P k，i represents the call capacity of the reserve resources of the k-th type in the i-th region, k is used to represent the type of reserve resources, and the types of reserve resources include gas turbine equipment, new energy equipment, energy storage equipment and adjustable load; L i，i′ is the cross-regional transmission capacity.

[0096] Step S4: Solve the objective function for minimizing the total cost of multi - partition shared reserve capacity scheduling. When the total cost of shared reserve capacity scheduling is minimized and the constraint conditions are met, obtain the callable capacity and cross - region transmission capacity of the reserve resources. Then, based on the callable capacity and cross - region transmission capacity of the reserve resources, determine the optimized configuration strategy of the reserve resources.

[0097] In this embodiment, the method for determining the optimized configuration strategy of the reserve resources according to the callable capacity and cross - region transmission capacity of the reserve resources is as follows:

[0098] Determine the allocation priorities of the reserve resources in each region according to the order of the callable capacity and cross - region transmission capacity of the reserve resources. Then, according to the allocation priorities, preferentially schedule the reserve resources in the regions with large callable capacity and large cross - region transmission capacity of the reserve resources, so as to obtain the optimized configuration strategy of the reserve resources.

[0099] Embodiment 2:

[0100] Refer to Figure 2 , which is a module diagram of a multi - partition reserve resource optimized configuration system provided by an embodiment of the present invention. To solve the problem in the prior art that the configuration strategy of shared reserve capacity is inaccurate because only direct costs are considered while potential costs of regions, cross - region line risk costs, and differences between partitions are ignored, the system at least includes: a data acquisition module, a direct cost calculation module, a potential cost calculation module, a fault risk cost calculation module, a regional difference coefficient calculation module, an objective function and constraint condition construction module, and an optimized configuration strategy generation module;

[0101] The data acquisition module is used to acquire the reserve resource data, power transmission scheduling data, cross - region tie - line transmission data, and historical operation data of each region;

[0102] The direct cost calculation module is used to calculate the direct cost of each region according to the reserve resource data of each region;

[0103] The potential cost calculation module is used to calculate the potential cost of each region according to the power transmission scheduling data of each region;

[0104] The fault risk cost calculation module is used to calculate the fault risk cost of each region according to the cross - region tie - line transmission data of each region;

[0105] The regional difference coefficient calculation module is used to calculate the regional difference coefficient of each region according to the historical operation data of each region;

[0106] The objective function and constraint construction module is used to construct an objective function with the minimum total cost of multi - partition shared reserve capacity scheduling according to the direct cost, potential cost, failure risk cost and regional difference coefficient of each region, and construct the total reserve capacity constraint of each region, the transmission capacity constraint of the inter - regional tie - line and the transmission loss constraint according to the reserve resource data, power transmission scheduling data and cross - regional tie - line transmission data;

[0107] The optimization configuration strategy generation module is used to solve the objective function with the minimum total cost of multi - partition shared reserve capacity scheduling, obtain the available capacity of reserve resources and the cross - regional transmission capacity when the total cost of shared reserve capacity scheduling is the minimum and meets the constraint conditions, and determine the optimization configuration strategy of reserve resources according to the available capacity of reserve resources and the cross - regional transmission capacity.

[0108] Another embodiment of the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a multi - partition reserve resource optimization configuration method as described in the above - mentioned embodiment. The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0109] The processor may be a Central Processing Unit (CPU), or may also be other general - purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field - Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general - purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device through various interfaces and lines.

[0110] The memory can be used to store the computer program. By running or executing the computer program stored in the memory and invoking the data stored in the memory, the processor realizes various functions of the terminal device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, memory, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one magnetic disk storage device, flash memory device or other volatile solid-state storage devices.

[0111] Another embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute a multi-partition spare resource optimization configuration method described in the above embodiment.

[0112] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0113] The specific embodiments described above further elaborate on the purpose, technical solution and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing the allocation of multi - partition backup resources, characterized in that, Including: Obtain the spare resource data, power transmission scheduling data, inter-regional tie-line transmission data, and historical operation data of each region; Calculate the direct cost of each region according to the spare resource data of each region; calculate the potential cost of each region according to the power transmission scheduling data of each region; calculate the fault risk cost of each region according to the inter-regional tie-line transmission data of each region; calculate the regional difference coefficient of each region according to the historical operation data of each region; Construct an objective function with the minimum total cost of multi-region shared reserve capacity scheduling according to the direct cost, potential cost, fault risk cost, and regional difference coefficient of each region, and construct the total reserve capacity constraint, inter-regional tie-line transmission capacity constraint, and power transmission loss constraint of each region according to the spare resource data, power transmission scheduling data, and inter-regional tie-line transmission data; Solve the objective function with the minimum total cost of multi-region shared reserve capacity scheduling to obtain the available capacity of spare resources and the inter-regional transmission capacity when the total cost of shared reserve capacity scheduling is minimized and the constraint conditions are met, and determine the optimal allocation strategy of spare resources according to the available capacity of spare resources and the inter-regional transmission capacity.

2. The multi-partition standby resource optimization configuration method according to claim 1, wherein The calculation of the direct cost of each region according to the spare resource data of each region includes: Calculate the direct cost of each region according to the unit cost and call capacity of the spare resources of gas turbines, new energy equipment, energy storage equipment, and adjustable loads in the spare resource data of each region through the following formula; C direct,i = c gen,i ·P gen,i + c re,i ·P re,i + c ess,i ·P ess,i + c load,i ·P load,i ; Among them, C direct，i is the direct cost of the i-th area; i is the area number; c gen，i is the unit cost of the reserve resources of the gas turbine equipment in the i-th area; c re，i is the unit cost of the reserve resources of the new energy equipment in the i-th area; c ess，i is the unit cost of the reserve resources of the energy storage equipment in the i-th area; C load，i is the unit cost of the reserve resources of the adjustable load in the i-th area; P gen，i is the call capacity of the reserve resources of the gas turbine equipment in the i-th area; P re，i is the call capacity of the reserve resources of the new energy equipment in the i-th area; P ess，i is the call capacity of the reserve resources of the energy storage equipment in the i-th area; P load，i is the call capacity of the reserve resources of the adjustable load in the i-th area.

3. A method for optimizing the allocation of multi-partition backup resources according to claim 2, characterized in that The calculation of the potential cost of each region according to the power transmission scheduling data of each region includes: Calculate the potential cost of each region according to the unit power transmission cost, unit power transmission loss cost, unit scheduling management cost, inter-regional transmission capacity, power transmission loss power, and number of scheduling times in the power transmission scheduling data of each region through the following formula; C potential，i = c trans，i · L i，i′ + c loss，i · ΔP loss，i + c manage，i · N dispatch，i : Among them, C potential，i is the potential cost of the i-th region; i is the region number; c trans，i is the unit transmission cost of the i-th region; c loss，i is the unit transmission loss cost of the i-th region; c manage，i is the unit dispatching management cost of the i-th region; L i，i′ is the cross-regional transmission capacity; ΔP loss，i is the transmission loss power; N dispatch，i is the number of dispatching times.

4. A method for optimizing the allocation of multi-partition standby resources according to claim 3, characterized in that The calculation of the fault risk cost of each region according to the inter-regional tie-line transmission data of each region includes: Calculate the fault risk cost of each region according to the inter-regional tie-line fault probability, inter-regional transmission capacity, and unit transmission capacity fault loss cost in the inter-regional tie-line transmission data of each region through the following formula; Among them, C g，i is the failure risk cost of the i-th region; i is the region number; I n，i，i′ is the failure probability of the inter-region tie line; L i，i ′ is the inter-region transmission capacity; C accidenti，i′ is the failure loss cost per unit transmission capacity.

5. The method for optimizing the allocation of multi - partition backup resources according to claim 4, wherein, The calculation of the regional difference coefficient of each region according to the historical operation data of each region includes: Calculate the relative cost data, relative economic data, relative environmental data, and relative resource data between regions according to the historical cost data, historical economic data, historical environmental data, and historical resource data in the historical operation data of each region; Perform weighted calculation according to the relative cost data, relative economic data, relative environmental data, and relative resource data between regions to obtain the regional difference coefficient of each region.

6. A method for optimizing the allocation of multi - partition backup resources according to claim 5, characterized in that, The expression of the objective function with the minimum total cost of multi-region shared reserve capacity scheduling is: Among them, N is the total number of regions; i is the region number; q i is the regional difference coefficient of the i-th region; C direct,i is the direct cost of the i-th region; c potntial，i is the potential cost of the i-th region; C g，i is the failure risk cost of the i-th region.

7. A method for optimizing the allocation of multi - partition standby resources according to claim 5, characterized in that, The total reserve capacity constraint is: Among them, P gen，i is the call capacity of the standby resources of the gas turbine equipment in the i-th area; P re，i is the call capacity of the standby resources of the new energy equipment in the i-th area; P ess，i is the call capacity of the standby resources of the energy storage equipment in the i-th area; P load，i is the call capacity of the standby resources of the adjustable load in the i-th area; L i，i′ is the cross-regional transmission capacity: is the minimum total standby capacity requirement for the i-th area; The inter-regional tie-line transmission capacity constraint is: Among them, L i,i′ is the cross-region transmission capacity; is the maximum transmission capacity of the cross-region connection line i - i′: The power transmission loss constraint is: ΔP loss，i = f(P k，i , L i，i′ ); Among them, ΔP loss，i is the transmission loss power; f(P k，i , L i，i ) is the power grid power flow equation; P k，i represents the call capacity of the reserve resources of the k-th type in the i-th area, where k is used to represent the types of reserve resources, and the types of reserve resources include gas turbine equipment, new energy equipment, energy storage equipment, and adjustable load; L i,i′ is the cross-regional transmission capacity.

8. A multi - partition standby resource optimization and configuration system, characterized in that, Including: Data acquisition module, direct cost calculation module, potential cost calculation module, fault risk cost calculation module, regional difference coefficient calculation module, objective function and constraint condition construction module, and optimal allocation strategy generation module; The data acquisition module is used to acquire the spare resource data, transmission scheduling data, inter-region tie-line transmission data, and historical operation data of each region; The direct cost calculation module is used to calculate the direct cost of each region according to the spare resource data of each region; The potential cost calculation module is used to calculate the potential cost of each region according to the transmission scheduling data of each region; The fault risk cost calculation module is used to calculate the fault risk cost of each region according to the inter-region tie-line transmission data of each region; The regional difference coefficient calculation module is used to calculate the regional difference coefficient of each region according to the historical operation data of each region; The objective function and constraint construction module is used to construct an objective function with the minimum total cost of multi-region shared reserve capacity scheduling according to the direct cost, potential cost, fault risk cost, and regional difference coefficient of each region, and construct the total reserve capacity constraint, inter-region tie-line transmission capacity constraint, and transmission loss constraint of each region according to the spare resource data, transmission scheduling data, and inter-region tie-line transmission data; The optimization configuration strategy generation module is used to solve the objective function with the minimum total cost of multi-region shared reserve capacity scheduling, obtain the available capacity of the spare resource and the inter-region transmission capacity when the total cost of the shared reserve capacity scheduling is the minimum and meets the constraint conditions, and determine the optimization configuration strategy of the spare resource according to the available capacity of the spare resource and the inter-region transmission capacity.

9. A terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a multi-region spare resource optimization configuration method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the storage medium is located to execute a multi-region spare resource optimization configuration method according to any one of claims 1 to 7.

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