EANS-Shapley-based power system comprehensive cost calculation and allocation method and system

Abstract

The invention belongs to power supply structure planning, particularly relates to an EANS-Shapley-based comprehensive cost calculation and allocation method and system for a power system, and converts the cost allocation problem of the power system into a more systematized and modularized calculation method through an EANS-Shapley method. Compared with a traditional method, the EANS-Shapley method can avoid complex multi-dimensional resource allocation and calculation by focusing on cost allocation between power supply investment planning and a market subject, so that the overall calculation efficiency is improved. The EANS-Shapley method is combined with different investment and construction scenes of the power system and corresponding investment and construction cost, and the cost can be accurately allocated to different market subjects based on the actual contribution of the system capacity and the auxiliary service.

Description

Technical Field

[0001] The present invention belongs to the power structure planning, and specifically relates to a method and system for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley. Background Art

[0002] In recent years, new energy has been developed on a large scale, resulting in an increase in the peak-valley difference of system electricity consumption and insufficient system regulation ability. Conventional power sources are required to provide peak shaving and standby auxiliary services for it. Currently, the on-grid electricity prices of various types of power sources are formed through a competitive electricity energy market, and it is difficult for the electricity energy market cleared based on marginal cost to fully recover the upfront fixed investment costs of generating units. In the case of large-scale access of new energy to the power system in the future, calculating the system capacity cost and auxiliary service cost is of great significance for market mechanism design and future power structure planning.

[0003] ‌EANS-Shapley is an improved Shapley value calculation method used to solve the income distribution problem in cooperative games. EANS-Shapley combines the advantages of the EANS value and the Shapley value, aiming to distribute income more fairly and reasonably. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiency that the existing method cannot quickly and effectively calculate the system capacity cost and auxiliary service cost, and provide a method and system for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a method for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley, including the following steps: Based on an investment decision model, conduct power source investment planning and establish an investment decision model; According to the investment decision model, obtain the power source structure investment and construction plans under different scenarios, and form different investment and construction costs according to the power source structure investment and construction plans under different scenarios; Based on the EANS-Shapley method, allocate different investment and construction costs to different market entities.

[0006] A further improvement of the present invention is that the investment decision model takes minimizing the investment and construction cost as the objective function, and uses the unit, line, reliability, and power balance as constraints to optimize various alternative units and lines under the given EANS scenarios.

[0007] A further improvement of the present invention is that the investment decision model is as follows:

[0008]

[0009]

[0010]

[0011]

[0012] Among them, C Inv is the investment and construction cost, C OMFix is the fixed operation and maintenance cost, C OMVar is the variable operation and maintenance cost, k t is the discount factor. CG is the set of candidate units, c i inv is the investment and construction cost of the unit, c l inv is the investment and construction cost of the line, c e inv is the investment and construction cost of the energy storage, c i ful is the fuel cost of the unit, c e fix is the fixed operation cost of the energy storage, c e SUSD is the unit start-up and shutdown constraint. x i,t , x l,t , and x e,t respectively represent the decision variables of not investing and constructing. CG is the set of candidate units, CL is the set of candidate lines, and CE is the set of candidate energy storage devices. P l,h,t is l the line t annual h transmission power at time sd i,h,t and su i,h,t are the unit start-up and shutdown decision variables, DT t,i is t annual i unit operation duration, F i is i the fuel consumption rate function of unit No. Pi,h,t For i Unit t in h the output at

[0013] A further improvement of the present invention lies in that the power supply structure investment and construction plan aims to minimize the operating cost, takes the start-up and shutdown, ramping, power balance, line constraints, energy storage operation constraints and unit output of thermal power as constraints, combines the uncertainty of new energy output, and calculates the start-up and shutdown, ramping mileage, fixed and variable operation costs of thermal power under different scenarios given by the Shapley method, while verifying the feasibility of the investment and construction plan and returning the investment scenario for construction.

[0014] A further improvement of the present invention lies in that the operation scheduling model for the uncertainty of new energy output is:

[0015]

[0016] Wherein, ρ s represents the probability of the scenario s occurring, R U i,h,t,s and R D i,h,t,s are respectively the upward and downward adjustment amounts of thermal power caused by the uncertainty of wind and light in the s scenario, c RU i and c RD i are respectively the upward and downward adjustment costs, P i,h,t,s , P w,h,t,s , , P v,h,t,s and P v,h,t,s are respectively s the output of thermal power, wind power, photovoltaic and energy storage in the scenario. The constraints of the operation scheduling model for the uncertainty of new energy output include power balance constraints, network constraints, energy storage operation constraints, unit output constraints and up and down ramping constraints of thermal power units.

[0017] A further improvement of the present invention lies in that based on the EANS-Shapley method, the specific method for allocating different investment costs to different market players is as follows: Use the EANS method to calculate the investment costs under different new energy penetration rates; Based on the investment scenario, according to the fluctuations of different new energy sources, calculate the operating costs of each market entity using the Shapley method; Allocate the new energy capacity cost and the ancillary service cost.

[0018] A further improvement of the present invention lies in that the specific method for calculating the investment cost under different new energy penetration rates using the EANS method is as follows:

[0019]

[0020]

[0021] Among them, SC k is the separable cost that a certain type of new energy needs to bear, indicating the additional cost incurred by connecting this type of new energy k The additional cost incurred, k C ( U ) is the total cost increased after connecting the new energy, C ( U \ k ) is the total cost excluding the new energy, NSC is the inseparable cost, C k is the cost that the participating unit k needs to bear.

[0022] A further improvement of the present invention lies in that the specific method for calculating the operating costs of each market entity using the Shapley method is as follows:

[0023] Among them, C i is the cost that the participating unit i needs to bear; S is all the coalitions that the unit i participates in; n is the number of all participating units in the grand coalition;| s | represents i the number of participating units in the coalition that C k ( S ) - C k ( S -{ i}) is the marginal contribution of the participating unit i to the coalition that S it participates in.

[0024] ​Second aspect, the present invention provides a power system comprehensive cost measurement and sharing system based on EANS-Shapley, including: An investment decision model construction module, configured to perform power source investment planning based on an investment decision model and establish an investment decision model; An investment and construction cost calculation module, configured to obtain power source structure investment and construction plans under different scenarios according to the investment decision model, and form different investment and construction costs according to the power source structure investment and construction plans under different scenarios; A cost sharing module, configured to allocate different investment and construction costs to different market entities based on the EANS-Shapley method.

[0025] Third aspect, the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the power system comprehensive cost measurement and sharing method based on EANS-Shapley are implemented.

[0026] Compared with the prior art, the present invention has the following beneficial effects: The present invention transforms the power system cost sharing problem into a more systematic and modular calculation method through the EANS-Shapley method. Compared with traditional methods, the EANS-Shapley method can avoid complex multi-dimensional resource allocation and calculation by focusing on the cost sharing between power source investment planning and market entities, thereby improving the overall calculation efficiency. The EANS-Shapley method combines different investment and construction scenarios of the power system and their corresponding investment and construction costs, and can accurately allocate costs to different market entities based on the actual contributions of system capacity and ancillary services. Compared with traditional cost sharing methods, the EANS-Shapley method can better reflect the specific role of each market entity in the power system and reduce the deviation of cost sharing. The EANS-Shapley method can comprehensively consider the investment and construction costs in each scenario and effectively allocate them when facing different investment and construction plans. This makes the method more flexible in dealing with complex investment decisions and can adapt to diverse changes in power source structures and fluctuations in market demands. Using this method, decision-makers can more accurately understand the cost performance of each investment plan under different scenarios, providing clearer and quantifiable support for the planning and investment decisions of the power system. Especially when facing multi-objective optimization problems, the EANS-Shapley method can provide better decision-making solutions for investors, helping them achieve a better balance between costs and benefits. In summary, the EANS-Shapley method provides a more effective and scientific method for the investment planning of the power system and the cost sharing of market entities by improving calculation efficiency, accurately allocating costs, flexibly dealing with complex scenarios, and comprehensively considering system capacity and ancillary service costs. Description of the Drawings

[0027] Figure 1 is the method flow chart of the present invention; Figure 2a is the power supply structure under different penetration rates with energy storage; Figure 2b is the power supply structure under different penetration rates without energy storage; Figure 3a is the average cost per kilowatt-hour of each power supply under different penetration rates with energy storage; Figure 3b is the average cost per kilowatt-hour of each power supply under different penetration rates without energy storage; Figure 4 is the structure diagram of Embodiment 1. Specific Embodiments

[0028] To further understand the content of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and not for limiting it.

[0029] See Figure 1 , the present invention includes the following steps: Step 1, investment - operation collaborative simulation of the power system.

[0030] The first step is to conduct power supply investment planning based on the investment decision model, and establish an expression with the goal of minimizing the construction cost and operation and maintenance cost: (1) (2) (3) (4) (5) Among them, C Inv is the construction cost, C OMFix is the fixed operation and maintenance cost, C OMVar is the variable operation and maintenance cost, k t is the discount factor. CG is the set of candidate units, c i inv is the construction cost of the unit, c l inv is the construction cost of the line, c e inv is the construction cost of the energy storage, c i fulis the fuel cost of the unit, c e fix is the fixed operating cost of energy storage, c e SUSD is the unit start-up and shutdown constraint. x i,t , x l,t , and x e,t respectively represent the decision variables of non-investment and construction. CG is the set of candidate units, CL is the set of candidate lines, and CE is the set of candidate energy storage devices. P l,h,t is l line t annual h transmission power at time sd i,h,t and su i,h,t are the unit start-up and shutdown decision variables, DT t,i is t annual i operating duration of the unit, F i is i fuel consumption rate function of unit No. P i,h,t is i unit No. t annual h output at time

[0031] The constraint conditions of the investment decision model include investment constraints, power balance constraints, minimum unit start-up and shutdown constraints, reserve constraints, network constraints, energy storage operation constraints, etc.

[0032] (6) (7) (8) Among them, equations (6), (7), and (8) represent investment constraints, reflecting the irreversibility of investment and construction. Once the unit, line, and device are invested and constructed, they exist in the system for use.

[0033] (9) Among them, equation (9) represents the power balance constraint, where P d,h,t is the load demand. N ( b ) is the b node. R Res is the system reserve coefficient.P i,h,t is the output of the unit. P l,h,t, is the line transmission power. P e,h,t, is the charging power of the energy storage, which is negative during discharging. The power balance constraint is based on the KCL law, representing the power balance at each node.

[0034] (10) (11) (12) (13) (14) Among them, equation (10) is the network constraint, representing the relationship between the active power transmitted by the candidate line and the phase angles at both ends. Among them, M is a large number. X l is the line reactance. θ s(l),h,t represents the phase angle of the l starting node of the line. θ r(l),h,t is the l phase angle of the ending node of the line at year t. P max l is the l maximum transmission power of the line, indicating that the line power limit is related to the thermal stability and dynamic stability of the line. Equation (12) represents the relationship between the active power of the existing line and the phase angles at both ends of the line. Equations (13) and (14) represent the maximum active power limit of the existing line and the phase angle range limit of each node in the system, which are related to the power angle stability of the system.

[0035] (15) (16) (17) (18) (19) (20) (21) Among them, equations (18) to (21) represent the curtailment of wind power and load shedding constraints. Among them φ d,h,t is the load shedding amount, and the load shedding amount at the load node cannot exceed the total load. ∆ D t is the total load. ∆SW t is the total amount of curtailed wind power; ∆ SW t is the total amount of curtailed solar power. P Fore w,t and P Fore v,t are the predicted available power outputs of wind power and photovoltaic power, P w,t and P v,t are the actual power outputs of wind power and photovoltaic power. R Spill is the upper limit of the curtailment rate of wind and solar power. Considering the large-scale access of renewable energy and energy storage to the future power system.

[0036] (22) (23) (24) (25) (26) (27) (28) (29) Among them, equations (22) to (29) represent the operating constraints of energy storage. Among them, u cha e,h,t and u dis e,h,t。 E e,h,t are the discharge and charge decision variables respectively, η e is the charge-discharge efficiency of the energy storage. E min e and E max e are the e minimum and maximum charge levels of the energy storage. The energy storage needs to cycle charge and discharge during 24-hour operation. It is assumed that the state of charge at 0 o'clock and 24 o'clock needs to be the same. Figure 2 shows the power source structure under different new energy installation penetration rates (40.6%, 46.8%, 54.9%, 58.1%, 62.5 and 66.2%).

[0037] Step 2: Obtain the investment and construction plans for various power sources and lines by optimizing the investment and construction decision-making model. Based on the results of investment and day-ahead scheduling, considering the real-time optimization of the operation and scheduling with uncertainties, calculate the variable operation cost of the system. The objective function is as follows: (30) (31) where ρ s represents the probability of the scenario s occurring, R U i,h,t,s and R D i,h,t,s are the upward and downward adjustment amounts of thermal power caused by the uncertainties of wind and light in scenario s. c RU i and c RD i are the upward and downward adjustment costs. P i,h,t,s , P w,h,t,s , P v,h,t,s and P v,h,t,s are s the outputs of thermal power, wind power, photovoltaic power, and energy storage in scenario

[0038] Step 3: The present invention uses the multi-scenario technology to describe the uncertainties of renewable energy output. To reflect the actual situation as much as possible, there are usually a large number of samples, generating a large number of scenarios. To avoid the difficulties brought by the large number of scenarios, the present invention is based on the scenario reduction algorithm of the clustering method. As Figure 3a and Figure 3b shown, with the increase in the new energy penetration rate, the thermal power generation decreases, the new energy generation increases, the total investment cost of the corresponding system increases and the operation cost decreases. However, the new energy cost per kWh is lower, and the total system cost shows a downward trend. When the new energy penetration rate is greater than 60%, the downward trend of the total system cost slows down. With the further increase in the penetration rate, the total system cost will gradually increase. The system cost per kWh decreases from 0.469 yuan / kWh to 0.394 yuan / kWh when the penetration rate ranges from 0 to 60%.

[0039] Step 2: Conduct cost measurement and allocation based on the EANS-Shapley method.

[0040] The first step is to perform an Equal Allocation of Non-separable Costs (EANS) based on the results obtained in Step A. The core idea is to calculate the separable costs that different types of units need to bear and then allocate the non-separable costs to each type of energy. It does not take into account coalitions other than the grand coalition and coalitions containing (n - 1) participants. Denote a certain type of new energy k The separable cost that needs to be borne is SC k , which represents the additional cost incurred by connecting this type of new energy k . The expression is as follows: (32) where C ( U ) is the total additional cost after connecting all types of new energy, C ( U \[ k ) is the total additional cost incurred by connecting the remaining new energy except k type.

[0041] The non-separable cost is obtained by subtracting the sum of the separable costs increased by the new energy connection from the total additional cost after the new energy connection, denoted as NSC . Its calculation formula is: (33) The non-separable cost is evenly allocated to different types of units, and adding the separable costs of each unit can obtain the cost that each type of unit should bear. Its calculation formula is: (34) where C k is the cost that the participating unit k needs to bear.

[0042] Referring to Table 1, taking a new energy penetration rate of 46.8% as an example, compared with the service cost of 34.4 yuan / MWh without wind and light, each MWh of new energy power generation will add a service cost of 5.9 yuan. This part of the cost is generated due to the connection of new energy. In line with the principle of "who provides, who benefits; who benefits, who bears", it is allocated among photovoltaic, wind power, and load according to the EANS-Shapley value method. For each degree of new energy power generation cost, wind power should be allocated 29.6%, photovoltaic should be allocated 24.8%, and load should be allocated 45.5%. This results in an increase in the cost per MW of wind power by 11.97 yuan, the cost of photovoltaic is about 10.03 yuan / MWh, and the load is allocated 18.39 yuan / MW.

[0043] In the second step, the disadvantage of the EANS method is that it does not consider the proper subsets of the unit coalition that can affect the allocation result, while the Shapley value method overcomes this disadvantage by considering the impact of all possible subsets on the sharing result.

[0044] Refer to Table 2. The Shapley value method is used to allocate the cost increased due to the grid connection of new energy units according to the marginal contribution of the participating units to all possible coalitions, and the cost is fairly allocated.

[0045] The calculation formula of the Shapley value is as follows: (35) Where, C i is the cost that the participating unit i needs to bear; S is all the coalitions that the unit i participates in; n is the number of all participating units in the grand coalition; | s | represents i the number of participating units in the coalition that C k ( S ) - C k ( S -{ i}) is the marginal contribution of the participating unit i to the coalition S it participates in.

[0046] Taking the 46.8% penetration rate as an example, wind power, photovoltaic power, and load respectively allocate the fixed costs that cannot be recovered from the electricity energy market and the ancillary service market according to the ratios of 17.8%, 40.7%, and 41.5%.

[0047] Table 1 Decomposition and change of thermal power cost under different scenarios

[0048] Table 2 Thermal power-related expenses and cost changes under different scenario combinations

[0049] The power system comprehensive cost measurement and sharing system based on EANS-Shapley includes: The power system investment-operation simulation module is used to carry out power source investment planning based on the investment decision model and establish the investment decision model; The EANS-Shapley cost measurement and allocation module is used to obtain the power structure investment and construction plans under different scenarios according to the investment decision model, form different investment and construction costs based on the power structure investment and construction plans under different scenarios, and allocate different investment and construction costs to different market players based on the EANS-Shapley method.

[0050] Embodiment 1: Please refer to Figure 4 As shown, the present invention also provides an electronic device 100 for the integrated cost measurement and allocation method of a power system based on EANS-Shapley; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0051] The memory 101 can be used to store the computer program 103. The processor 102 realizes the steps of the integrated cost measurement and allocation method of the power system based on EANS-Shapley described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device 100 (such as audio data, etc.). In addition, the memory 101 may include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0052] The at least one processor 102 can be a Central Processing Unit (CPU), or can 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 processor 102 can be a microprocessor or the processor 102 can also be any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and connects various parts of the entire electronic device 100 through various interfaces and lines.

[0053] The memory 101 in the electronic device 100 stores multiple instructions to implement the method for calculating and allocating the comprehensive cost of the power system based on EANS-Shapley. The processor 102 can execute the multiple instructions to achieve: Perform power investment planning based on the investment decision model and establish the investment decision model; Obtain the power structure investment and construction plans under different scenarios according to the investment decision model, and form different investment and construction costs according to the power structure investment and construction plans under different scenarios; Based on the EANS-Shapley method, allocate different investment and construction costs to different market players.

[0054] Embodiment 2: If the modules / units integrated in the electronic device 100 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above embodiment methods of the present invention, it can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above various 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 that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, and Read-Only Memory (ROM).

[0055] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0056] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0057] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent substitutions. Any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. The comprehensive cost calculation and allocation method of power system based on EANS-Shapley is characterized by: The following steps are involved: Carry out power investment planning based on the investment decision model and establish an investment decision model; According to the investment decision model, the power structure investment and construction schemes under different scenarios are obtained, and different investment and construction costs are formed according to the power structure investment and construction schemes under different scenarios; Based on the EANS-Shapley method, different investment and construction costs are allocated to different market players.

2. The method for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley according to claim 1 is characterized in that: The investment decision model takes minimizing the investment and construction cost as the objective function, and takes the reward units, lines, reliability, and power balance as constraints to optimize various alternative units and lines under the given EANS scenario.

3. The method for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley according to claim 1 or 2, characterized in that: The investment decision model is as follows: in, C Inv For construction costs, C OMFix For fixed operation and maintenance costs, C OMVar is the variable operation and maintenance cost, k t is the discount factor, CG is the candidate unit set, c i inv The construction cost of the unit, c l inv The cost of line construction. c e inv The investment cost of energy storage is c i ful is the unit fuel cost, c e fix Fixed operating costs for energy storage, c e SUSD It is the start and stop constraints of the unit. x i,t , x l,t ,and x e,t They represent the decision variables of whether to invest or not, CG is the candidate unit set, CL is the candidate line set, CE is the candidate energy storage device set, P l,h,t for l line t Year h The transmission power at sd i,h,t and su i,h,t is the unit start-up and shutdown decision variable, DT t,i for t Year i The running time of the unit, F i for i The fuel consumption rate function of unit No. P i,h,t for i Unit No. t Year h Output at the time.

4. The method for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley according to claim 1 is characterized in that: The power structure investment and construction plan aims to minimize operating costs, with thermal power start-up and shutdown, ramping, power balance, line constraints, energy storage operation constraints and unit output as constraints. Combined with the uncertainty of new energy output, the start-up and shutdown, ramping mileage, fixed and variable operation costs of thermal power are calculated under different scenarios given by the Shapley method. At the same time, the feasibility of the investment and construction plan is verified and the investment scenario is returned for construction.

5. The method for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley according to claim 4 is characterized in that: The operation and dispatch model of the uncertainty of renewable energy output is: in, ρ s Representation scene s The probability of occurrence, R U i,h,t,s and R D i,h,t,s are the upward and downward adjustments of thermal power caused by the uncertainty of wind and solar power in scenario s, c RU i and c RD i They are upward and downward adjustment fees, P i,h,t,s , P w,h,t,s , , P v,h,t,s and P v,h,t,s Don't s The output of thermal power, wind power, photovoltaic power and energy storage in the scenario, and the constraints of the operation and scheduling model for the uncertainty of new energy output include power balance constraints, network constraints, energy storage operation constraints, unit output constraints and up and down climbing constraints of thermal power units.

6. The method for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley according to claim 1 is characterized in that: Based on the EANS-Shapley method, the specific method of allocating different investment and construction costs to different market entities is as follows: The EANS method is used to calculate the investment cost under different new energy penetration rates; Based on the investment scenario, the operating costs of each market player are calculated according to the Shapley method according to the fluctuations of different new energy sources; Split the cost of new energy capacity and ancillary service costs.

7. The method for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley according to claim 1 is characterized in that: The specific method of using the EANS method to calculate the investment cost under different new energy penetration rates is as follows: in, SC k For a new energy source k The divisible costs that need to be borne represent the cost of accessing this type of new energy k The additional costs C ( U ) is the total additional cost after connecting to new energy, C ( U \ k ) is the total cost excluding new energy, NSC For indivisible costs, C k For participating crew k The costs to be borne.

8. The method for calculating and allocating the comprehensive cost of a power system based on EANS-Shapley according to claim 1 is characterized in that: The specific method for calculating the operating costs of each market entity according to the Shapley method is as follows: in, C i For participating crew i Costs to be incurred; S For the crew i All alliances involved; n is the number of all participating units in the major alliance; | s | indicates i The number of participating units in the alliance; C k ( S )- C k ( S -{ i }) for the participating units i For participating alliances S marginal contribution.

9. The comprehensive cost calculation and allocation system of power system based on EANS-Shapley is characterized by: include: Power system investment-operation simulation module, used to plan power investment based on the investment decision model and establish the investment decision model; The EANS-Shapley cost estimation and allocation module is used to obtain power structure investment and construction plans under different scenarios based on the investment decision-making model, form different investment and construction costs according to the power structure investment and construction plans under different scenarios, and allocate different investment and construction costs to different market entities based on the EANS-Shapley method.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power system comprehensive cost estimation and allocation method based on EANS-Shapley described in any one of claims 1 to 8 are implemented.

Images