Power transmission upgrading decision-making method, system and equipment based on multi-subject contribution evaluation

Abstract

The invention relates to the technical field of power transmission upgrading, and discloses a power transmission upgrading decision-making method, system and equipment based on multi-subject contribution evaluation, and the method comprises the steps: obtaining a candidate upgrading scheme set, generating a plurality of simulation scenes, introducing random disturbance in each simulation scene, and obtaining a plurality of sample scenes; simulating each candidate upgrading scheme to obtain a plurality of simulation results under the participation of different power transmission main bodies; according to the performance index and the simulation result, a cooperative game algorithm is adopted to calculate the marginal contribution of each type of power transmission main body, and the main body average marginal contribution of each type of power transmission main body is calculated according to the marginal contribution; and selecting an optimal upgrading scheme from the candidate upgrading scheme set according to the main body average marginal contribution of each type of power transmission main body of each candidate upgrading scheme. According to the method, multi-subject contribution evaluation is accurately quantified, so that the optimization decision of the power transmission upgrading scheme is realized, and the scientificity and rationality of power transmission upgrading planning design are improved.

Description

Technical Field

[0001] This invention relates to the field of power transmission upgrade technology, and in particular to a power transmission upgrade decision-making method, system and equipment based on multi-subject contribution evaluation. Background Technology

[0002] As the green and low-carbon transformation of the power industry continues to accelerate, the installed capacity and power generation of new energy sources have significantly increased their share in the power system. However, the large-scale grid connection of new energy sources has also brought a series of challenges, among which transmission channel congestion and renewable energy curtailment are becoming increasingly prominent. On the one hand, the capacity design of existing transmission networks is difficult to match the intermittent, fluctuating, and geographically distributed characteristics of new energy power generation, leading to transmission bottlenecks in some areas and restricting the external transmission and consumption of new energy power. On the other hand, curtailment not only wastes clean energy resources but also damages the economic interests of new energy power generation companies, hindering the sustainable development of the new energy industry.

[0003] To address these issues, upgrading the power transmission network is a necessary measure. In actual engineering projects, power transmission upgrade projects typically involve multiple stakeholders, including power generation companies, grid companies, and renewable energy entities. The functions and contributions of these different stakeholders vary, and traditional contribution evaluation methods are mostly economically oriented, lacking a quantitative representation of improvements in system physical performance. This results in a lack of necessary technical performance indicators to support the planning and decision-making processes for power transmission upgrades. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method, system, and device for power transmission upgrade decision-making based on multi-entity contribution evaluation. By accurately quantifying the technical contributions of various power transmission entities in power transmission upgrades, the rationality of power transmission upgrade planning decisions is improved.

[0005] In a first aspect, the present invention provides a power transmission upgrade decision-making method based on multi-agent contribution evaluation, the method comprising: A set of candidate upgrade schemes for power transmission upgrade projects is obtained, several simulation scenarios are generated using Monte Carlo simulation, and random disturbances are introduced into each simulation scenario based on the uncertainty of power system operation to obtain several sample scenarios. In each sample scenario, the original power transmission data is used as the baseline data, and various power transmission entities are used as the participants. Each candidate upgrade scheme is simulated to obtain multiple simulation results under the participation of different power transmission entities. Based on the preset performance indicators and the simulation results, a cooperative game algorithm is used to calculate the marginal contribution of each type of power transmission entity in each sample scenario, and based on the marginal contribution, the average marginal contribution of each type of power transmission entity in all sample scenarios is calculated. The optimal upgrade scheme is selected from the set of candidate upgrade schemes based on the average marginal contribution of each type of power transmission entity in each candidate upgrade scheme.

[0006] Furthermore, the step of generating several simulation scenarios using Monte Carlo simulation, and introducing random disturbances into each simulation scenario based on the uncertainties in power system operation to obtain several sample scenarios includes: The number of sampling times for the Monte Carlo simulation is set, and in each sampling round of the simulation scenario, random disturbance values ​​are introduced into the key operating variables according to the uncertainty of power system operation to generate sample scenarios corresponding to each simulation scenario. The key operating variables include wind power output, photovoltaic power output and external load, and the random disturbance values ​​follow a normal distribution.

[0007] Furthermore, the step of calculating the marginal contribution of each type of transmission entity in each sample scenario using a cooperative game algorithm based on preset performance indicators and simulation results includes: The power transmission entities participating in the upgrade in a simulation are treated as a consortium, and the performance evaluation value of each consortium is calculated based on the preset performance indicators and the simulation results corresponding to each consortium. Each type of power transmission entity is designated as the entity to be evaluated. The alliance that does not include the entity to be evaluated is designated as the first alliance. The alliance formed by the first alliance and the entity to be evaluated is designated as the second alliance. The marginal contribution of the subject to be evaluated in each sample scenario is calculated based on the difference between the performance evaluation value of the second alliance and the performance evaluation value of the first alliance.

[0008] Furthermore, the step of calculating the performance evaluation value of each alliance based on preset performance indicators and the simulation results corresponding to each alliance includes: The performance parameters of the corresponding transmission entities included in the alliance are extracted from the simulation results, and the performance indicators of the transmission entities are calculated based on the performance parameters. The main performance indicators of the power transmission main body are obtained by weighted summation of the performance indicators; The sum of the main performance indicators of the transmission entities included in the alliance shall be used as the performance evaluation value of the alliance; The power transmission entities include non-new energy power transmission entities, new energy power transmission entities, and energy storage supply entities. The performance indicators include operation deviation rate, curtailment improvement rate, green power increment rate, reserve capacity adequacy improvement rate, load shedding energy improvement rate, and load shedding time improvement rate.

[0009] Furthermore, the step of calculating the performance indicators of the power transmission main body based on the performance parameters includes: The operating deviation rate is calculated based on the unit output of the non-new energy power transmission entity before and after the upgrade; Based on the curtailment rate and on-grid electricity volume of the new energy transmission entity before and after the upgrade, calculate the curtailment improvement rate and the green electricity increment rate; Based on the backup capacity adequacy, load loss energy, and load loss time of the power transmission and supply entity before and after the upgrade, calculate the improvement degree of the backup capacity adequacy, the improvement degree of the load loss energy, and the improvement degree of the load loss time.

[0010] Furthermore, the step of weighted summing of the performance indicators to obtain the main performance indicators of the power transmission main body includes: The operational deviation rate is used as the main performance indicator of the non-new energy power transmission main body; The main performance indicators of the new energy transmission main body are obtained by weighted summation of the curtailment improvement rate and the green power increment rate; The main performance indicators of the power transmission and supply guarantee entity are obtained by weighted summation of the improvement in reserve capacity adequacy, the improvement in load shedding energy, and the improvement in load shedding time.

[0011] Furthermore, after the step of calculating the average marginal contribution of each type of transmission entity across all sample scenarios, the method further includes: The total marginal contribution of all power transmission entities is calculated based on the average marginal contribution of each type of power transmission entity across all sample scenarios. Based on the performance evaluation values ​​of the alliance including all transmission entities in all sample scenarios, calculate the average performance evaluation value of the alliance including all transmission entities. Determine whether the difference between the total marginal contribution of the subject and the average performance evaluation value satisfies the average consistency constraint. If not, adjust and iteratively verify the simulation scenario until the average consistency constraint is satisfied, and take the average marginal contribution of the subject that satisfies the average consistency constraint as the final average marginal contribution of the subject.

[0012] Secondly, this invention provides a power transmission upgrade decision-making system based on multi-agent contribution evaluation, the system comprising: The scenario generation module is used to obtain a set of candidate upgrade schemes for power transmission upgrade projects. It uses Monte Carlo simulation to generate several simulation scenarios and introduces random disturbances into each simulation scenario based on the uncertainty of power system operation to obtain several sample scenarios. The scheme simulation module is used to simulate each candidate upgrade scheme in each sample scenario, using the original power transmission data as the benchmark data and various power transmission entities as the participants, to obtain multiple simulation results under different power transmission entities. The contribution evaluation module is used to calculate the marginal contribution of each type of transmission entity in each sample scenario based on preset performance indicators and the simulation results, using a cooperative game algorithm, and to calculate the average marginal contribution of each type of transmission entity in all sample scenarios based on the marginal contribution. The planning and decision-making module is used to select the optimal upgrade scheme from the set of candidate upgrade schemes based on the average marginal contribution of each type of power transmission entity for each candidate upgrade scheme.

[0013] Furthermore, the contribution evaluation module is also used to treat the transmission entities participating in the upgrade in a simulation as a consortium, and calculate the performance evaluation value of each consortium based on preset performance indicators and the simulation results corresponding to each consortium. Each type of power transmission entity is designated as the entity to be evaluated. The alliance that does not include the entity to be evaluated is designated as the first alliance. The alliance formed by the first alliance and the entity to be evaluated is designated as the second alliance. The marginal contribution of the subject to be evaluated in each sample scenario is calculated based on the difference between the performance evaluation value of the second alliance and the performance evaluation value of the first alliance.

[0014] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0015] This invention provides a method, system, and equipment for power transmission upgrade decision-making based on multi-agent contribution evaluation. By establishing multi-dimensional evaluation indicators, this invention can accurately quantify the actual contributions of various power transmission entities in power transmission upgrades. By combining Monte Carlo sampling and cooperative game theory, the uncertainty of system operation is incorporated into the evaluation process, improving the stability and robustness of the evaluation results. Through parallel computation of Monte Carlo sampling and categorical decomposition, the algorithm complexity is effectively reduced, significantly improving computational efficiency while ensuring computational accuracy. This invention, by accurately quantifying the multi-agent contribution evaluation, enables optimized decision-making for power transmission upgrade schemes, thereby improving the scientific rigor and rationality of power transmission upgrade planning and design. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the power transmission upgrade decision-making method based on multi-subject contribution evaluation in an embodiment of the present invention. Figure 2 This is a schematic diagram of the power transmission upgrade decision system based on multi-subject contribution evaluation in an embodiment of the present invention; Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention.

[0017] Figure label: 10. Scenario Generation Module; 20. Scheme Simulation Module; 30. Contribution Evaluation Module; 40. Planning and Decision Module. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 The first embodiment of the present invention proposes a power transmission upgrade decision-making method based on multi-subject contribution evaluation, including steps S10 to S40: Step S10: Obtain a set of candidate upgrade schemes for the power transmission upgrade project, generate several simulation scenarios using Monte Carlo simulation, and introduce random disturbances into each simulation scenario based on the uncertainty of power system operation to obtain several sample scenarios. Step S20: In each sample scenario, using the original power transmission data as the baseline data and various power transmission entities as the participants, simulate each candidate upgrade scheme to obtain multiple simulation results under different power transmission entities. Step S30: Based on the preset performance indicators and the simulation results, a cooperative game algorithm is used to calculate the marginal contribution of each type of power transmission entity in each sample scenario, and based on the marginal contribution, the average marginal contribution of each type of power transmission entity in all sample scenarios is calculated. Step S40: Select the optimal upgrade scheme from the set of candidate upgrade schemes based on the average marginal contribution of each type of power transmission entity in each candidate upgrade scheme.

[0020] This invention provides a method for optimizing decision-making in power transmission upgrade projects. It quantifies the technical contributions of different power transmission entities to the upgrade project through multi-entity contribution evaluation, thereby providing accurate data support for the decision-making of candidate upgrade schemes.

[0021] First, the original transmission data and candidate upgrade schemes for the power transmission upgrade project are obtained. The original transmission data includes relevant data from the generation side, grid side, and load side. Among them, the generation side information includes the installed capacity, minimum output, ramp / glide rate, no-load cost, piecewise cost function, emission coefficient, etc. of thermal power units, and the installed capacity, bus location, benchmark output curve, and benchmark curtailment rate of new energy units. The grid side information includes the transmission topology and the capacity of existing transmission channels. The load side information includes load time sequence, geographical distribution, and load level. The candidate upgrade scheme set includes the capacity increment, implementation cost, and construction period of each scheme. These data will be used for subsequent simulation and performance index calculation. To facilitate subsequent calculations, the current power transmission system is divided into several different types of transmission entities. These entities are the ones participating in the upgrade. Preferably, the transmission entities are divided into non-new energy transmission entities, new energy transmission entities, and energy storage supply entities. Non-new energy transmission entities mainly include entities composed of non-new energy units such as thermal power units (cooperating with dispatch). New energy transmission entities mainly include entities composed of new energy units such as wind turbines and photovoltaic units (participating in power consumption). Energy storage supply entities mainly include entities that provide reliable power supply, such as energy storage entities and grid companies (for power transmission channel upgrades), peak-shaving energy storage companies (providing peak-shaving services), and public power plants (providing backup power supply). The specific entity division can be flexibly set according to the actual situation and is not specifically limited here.

[0022] To accurately quantify the technical contributions of different transmission entities in each candidate upgrade scheme, in a preferred embodiment, this invention uses the original transmission data of the current transmission system as benchmark data and employs Monte Carlo simulation to simulate each candidate upgrade scheme. In the actual operation of the power system, there are various uncertainties, such as renewable energy output and load fluctuations. To improve the accuracy of the simulation results, this embodiment introduces random disturbances to generate a large number of random scenarios. Specific steps include: The number of sampling times for the Monte Carlo simulation is set, and in each sampling round of the simulation scenario, random disturbance values ​​are introduced into the key operating variables according to the uncertainty of power system operation to generate sample scenarios corresponding to each simulation scenario. The key operating variables include wind power output, photovoltaic power output and external load, and the random disturbance values ​​follow a normal distribution.

[0023] In this embodiment, the number of samplings K is first set according to the error requirement. For example, K=1200 is taken according to the law of large numbers. Considering the uncertainties such as power output fluctuations during the operation of the power system, for each sampling round k∈{1,2,...,K}, random disturbances are introduced into the key operating variables of the current transmission system to generate a specific system operation scenario. Preferably, assuming that the key operating variables include wind power output, photovoltaic power output, and external load, the introduced random disturbances include wind power fluctuation values, photovoltaic fluctuation values, and load fluctuation values. Multiplying the initial operating variables by (1+random disturbance value) yields the key operating variables after introducing random disturbances. By setting different random disturbance values, multiple sample scenarios under different disturbances can be obtained. Preferably, the random disturbance values ​​follow a normal distribution. For example, the root mean square error of wind power fluctuation values ​​is set to 0.2, the root mean square error of photovoltaic fluctuation values ​​is set to 0.5, and the root mean square error of load fluctuation values ​​is set to 0.05.

[0024] After setting up all simulation scenarios, each candidate upgrade scheme is simulated in each scenario. Different transmission entities are selected for the upgrade each time, resulting in multiple simulation results. In other words, multiple simulations are performed within a single simulation scenario, depending on the participating transmission entities. Specifically, in the k-th simulation scenario, one or more transmission entities are selected for the upgrade each time, and the candidate upgrade scheme is simulated, resulting in multiple simulation results. Taking a non-new energy transmission entity E, a new energy transmission entity G, and an energy storage supply entity S as an example, the combinations of transmission entities participating in the upgrade include {E, G, S}, {E}, {G}, {S}, {E, G}, {E, S}, and {G, S}, a total of 7 combinations. This can be understood as the simulation results including the changes in the transmission data of the participating transmission entities compared to the existing transmission data.

[0025] To accurately quantify the technical contribution of each type of transmission entity in the candidate upgrade scheme, this embodiment pre-sets performance indicators for different types of transmission entities and, based on the simulation results in each sample scenario, uses a cooperative game theory algorithm to calculate the marginal contribution of each type of transmission entity in each sample scenario. Specific steps include: The power transmission entities participating in the upgrade in a simulation are treated as a consortium, and the performance evaluation value of each consortium is calculated based on the preset performance indicators and the simulation results corresponding to each consortium. Each type of power transmission entity is designated as the entity to be evaluated. The alliance that does not include the entity to be evaluated is designated as the first alliance. The alliance formed by the first alliance and the entity to be evaluated is designated as the second alliance. The marginal contribution of the subject to be evaluated in each sample scenario is calculated based on the difference between the performance evaluation value of the second alliance and the performance evaluation value of the first alliance.

[0026] In this embodiment, the entities participating in the upgrade are grouped into an alliance. Then, based on the simulation results and preset performance indicators corresponding to each alliance, the performance evaluation value of each alliance is calculated. The specific steps include: The performance parameters of the corresponding transmission entities included in the alliance are extracted from the simulation results, and the performance indicators of the transmission entities are calculated based on the performance parameters. The main performance indicators of the power transmission main body are obtained by weighted summation of the performance indicators; The sum of the main performance indicators of the transmission entities included in the alliance is used as the performance evaluation value of the alliance.

[0027] In this embodiment, taking any transmission entity alliance participating in the upgrade as an example, when the simulation reaches the upgrade target of the candidate upgrade scheme, such as achieving the preset capacity increase, the transmission data of the transmission entities within the alliance participating in the upgrade will change, while the transmission data of the original transmission entities in the transmission system not participating in the upgrade will not change. To quantify the impact of the transmission system upgrade on system performance, different performance indicators are set for different transmission entities. Then, performance parameters of various transmission entities are obtained from the simulation results, and their performance indicators are calculated based on these parameters.

[0028] In a preferred embodiment, the unit's operating deviation rate is used as the performance indicator for non-new energy power transmission, the curtailment improvement rate and green power increment rate are used as the performance indicators for new energy power transmission, and the reserve capacity adequacy improvement, load shedding energy improvement, and load shedding time improvement are used as the performance indicators for energy storage power supply. The specific calculation steps for these performance indicators include: The operating deviation rate is calculated based on the unit output of the non-new energy power transmission entity before and after the upgrade; Based on the curtailment rate and on-grid electricity volume of the new energy transmission entity before and after the upgrade, calculate the curtailment improvement rate and the green electricity increment rate; Based on the backup capacity adequacy, load loss energy, and load loss time of the power transmission and supply entity before and after the upgrade, calculate the improvement degree of the backup capacity adequacy, the improvement degree of the load loss energy, and the improvement degree of the load loss time.

[0029] In this embodiment, for non-new energy power transmission entities, their technological contribution is characterized by the degree to which the system scheduling after transmission upgrades brings the unit output closer to the optimal point. Therefore, this embodiment uses the operating deviation rate as a performance indicator for non-new energy power transmission entities. When calculating the performance indicator, the unit output value with the lowest heat consumption rate is taken as the optimal operating point. Then, based on the difference between the output value before and after the unit upgrade and the output value at the optimal operating point, the operating deviation rate is calculated, and its expression is: In the formula, Indicates the operating deviation rate. This represents the power output of the i-th non-new energy unit after it participates in the upgrade. This represents the power output of the i-th non-new energy unit before it participated in the upgrade. This represents the output value corresponding to the lowest heat consumption rate of the i-th non-new energy unit. Since this embodiment uses the output value of the unit with the lowest heat consumption rate as the optimal operating point, this operating deviation rate actually reflects the economic efficiency of the unit operation.

[0030] For renewable energy transmission entities, their contribution is mainly reflected in the reduction of renewable energy curtailment rate and the improvement of utilization rate. Therefore, this embodiment uses the curtailment improvement rate and the green power increment rate as performance indicators for renewable energy transmission entities. The curtailment improvement rate can be represented by the difference between the curtailment rate of renewable energy units before and after the upgrade, and its expression is as follows: In the formula, Indicates the improvement rate of power curtailment. This indicates the curtailment rate of electricity generated before the upgrade of new energy generating units. This represents the curtailment rate of new energy generating units after upgrades. To simplify calculations, the curtailment rate here can be calculated by treating all new energy generating units as a whole, and subsequent indicators will also be calculated as a whole.

[0031] Similarly, the green electricity increment rate is calculated based on the difference in the amount of new energy electricity connected to the grid before and after the upgrade, and its expression is: In the formula, Indicates the rate of increase in green electricity consumption. This indicates the amount of electricity generated by new energy sources after the upgrade. This indicates the amount of electricity generated by new energy sources before the upgrade.

[0032] For energy storage entities, their core function is to ensure power supply reliability. Unlike the previous two types of transmission entities, their contributions don't have readily observable performance indicators like "installed capacity / power output." Therefore, it's necessary to select quantitative indicators directly related to safety performance. Thus, this embodiment uses the improvement in reserve capacity adequacy, the improvement in load shedding energy, and the improvement in load shedding time as their performance indicators. Specifically, the improvement in reserve capacity adequacy is calculated based on the difference in reserve capacity adequacy before and after the upgrade of the transmission entity, and its expression is: In the formula, Indicates the degree of improvement in reserve capacity adequacy. This indicates the adequacy of the backup capacity of the main power transmission and supply entities after the upgrade. This indicates the adequacy of the backup capacity of the power transmission and supply entity before the upgrade.

[0033] The degree of improvement in load shedding energy is calculated based on the difference in load shedding energy before and after the upgrade of the power transmission and supply entity. Its expression is as follows: In the formula, Indicates the degree of improvement in energy loss due to load. This indicates the load loss energy of the main power transmission and supply entity before the upgrade. This indicates the energy loss due to power transmission and supply disruptions after the upgrade.

[0034] The improvement in load shedding time is calculated based on the difference in load shedding time before and after the upgrade by the main power transmission and supply entity. Its expression is: In the formula, Indicates the degree of improvement in the time to load reduction. This indicates the time of load loss for the main power transmission and supply entity before the upgrade. This indicates the time of load loss for the main power transmission and supply entity after the upgrade.

[0035] After calculating multiple performance indicators for various types of power transmission entities, and considering the different numbers and types of these indicators for each entity, a weighted summation of the performance indicators for each type is performed to accurately characterize the performance changes before and after the upgrade. This yields the main performance indicators for each type of power transmission entity. Specifically, the operational deviation rate is used as the main performance indicator for non-energy power transmission entities; the curtailment improvement rate and green power increment rate are weighted and summed to obtain the main performance indicators for new energy power transmission entities; and the improvement in reserve capacity adequacy, load shedding energy, and load shedding time are weighted and summed to obtain the main performance indicators for power transmission supply guarantee entities. It should be noted that the performance indicators provided in this embodiment are only preferred options, and other performance indicators can be selected according to actual conditions. When calculating the indicators, the parameters need to be standardized to eliminate dimensional differences, and a preset weight allocation algorithm is used to pre-set the weights of each performance indicator. The weight allocation algorithm can be a conventional algorithm such as the analytic hierarchy process (AHP) or the entropy method. The specific calculation process will not be elaborated here.

[0036] After obtaining the main performance indicators of the various transmission entities included in the alliance participating in the upgrade through the above steps, the performance indicators of each entity are added together to obtain the performance evaluation value of the alliance. This performance evaluation value can be understood as a quantitative measure of the technical contribution of the alliance to the performance improvement of the transmission system during its participation in the upgrade.

[0037] To accurately quantify the system performance improvement brought about by the participation of each type of transmission entity in the upgrade in this sample scenario, this embodiment employs a cooperative game theory algorithm. Based on the performance evaluation values ​​of each alliance, the marginal contribution of each type of transmission entity in each sample scenario is calculated. Specifically, for a given type of transmission entity, it is designated as the entity to be evaluated. Alliances not containing the entity to be evaluated are designated as the first alliance, and the alliance formed by the first alliance and the entity to be evaluated is designated as the second alliance. Since this embodiment includes all combinations of all transmission entities participating in the upgrade during simulation, the performance evaluation values ​​of the first and second alliances can be calculated from the simulation results.

[0038] Then, the difference between the performance evaluation value of the second alliance and the performance evaluation value of the first alliance is taken as the marginal contribution of the entity to be evaluated. Since the only difference between the transmission entities included in the first and second alliances is the entity to be evaluated, the first and second alliances can be considered as a pair. For each entity to be evaluated, there are multiple pairs of alliances. Taking the three types of transmission entities as an example, for each entity to be evaluated, there are 3 first alliances and 3 second alliances, that is, a total of 6 alliances and 3 pairs of alliances. The difference between each pair of alliances is weighted and summed to obtain the marginal contribution of the entity to be evaluated in this sample scenario, and its expression is: In the formula, This represents the marginal contribution of the c-th type of power transmission entity in the k-th sample scenario. This indicates the m-th alliance that does not include transmission entities of type c, where C represents an alliance that includes all transmission entities. This represents the performance evaluation value of the alliance in the k-th sample scenario. This indicates the number of entities in the alliance.

[0039] In the above formula, It can be understood as the first league. This is the second league, corresponding to the first league. This applies to the equal probability of all permutations.

[0040] For the typical scenario where |C|=3, there are 3!=6 permutations within the sample scenario, resulting in relatively low computational complexity, making it suitable for implementation in a parallel computing framework. Through the aforementioned marginal contributions, the technological contribution of each transmission entity to transmission upgrades under the same sample scenario can be quantified. Then, the average marginal contribution of each transmission entity across all sample scenarios is calculated to obtain the average marginal contribution of each type of transmission entity in the candidate upgrade scheme, expressed as: In the formula, Let K represent the average marginal contribution of the c-th type of transmission entity, and K represent the total number of sample scenarios.

[0041] This embodiment fully considers the impact of uncertainties on the operation of the power transmission system and accurately quantifies the technical contribution of each type of power transmission entity in the candidate upgrade scheme by using the average marginal contribution of the main entity.

[0042] After obtaining the average marginal contribution of each type of transmission entity for all candidate upgrade schemes through the above steps, the optimal upgrade scheme can be selected from the set of candidate upgrade schemes according to preset decision rules. These optimal decision rules include new energy priority rules, fair participation rules, or stability rules. The new energy priority rule prioritizes the participation of new energy transmission entities in the upgrade; in this case, the candidate upgrade scheme with the largest average marginal contribution of the new energy transmission entity can be selected as the optimal upgrade scheme to improve the utilization rate of green resources. The fair participation rule ensures fair participation of all transmission entities in the upgrade; in this case, the candidate upgrade scheme with smaller differences in the average marginal contribution of each type of transmission entity can be selected as the optimal upgrade scheme to ensure fair participation. The stability rule prioritizes ensuring the operational stability of the transmission system; in this case, the candidate upgrade scheme with the largest average marginal contribution of non-new energy transmission entities can be selected as the optimal upgrade scheme to reduce the impact of the uncertainty of new energy on the operational stability of the transmission system.

[0043] In a preferred embodiment, to verify the stability and rationality of the calculation results, the present invention also provides a method for constraining and verifying the calculation results, the specific steps of which include: The total marginal contribution of all power transmission entities is calculated based on the average marginal contribution of each type of power transmission entity across all sample scenarios. Based on the performance evaluation values ​​of the alliance including all transmission entities in all sample scenarios, calculate the average performance evaluation value of the alliance including all transmission entities. Determine whether the difference between the total marginal contribution of the subject and the average performance evaluation value satisfies the average consistency constraint. If not, adjust and iteratively verify the simulation scenario until the average consistency constraint is satisfied, and take the average marginal contribution of the subject that satisfies the average consistency constraint as the final average marginal contribution of the subject.

[0044] In this embodiment, whether the sum of the contributions of all types of transmission entities is approximately equal to the average system performance improvement brought about by the joint participation of all entities is used as an average consistency constraint. Then, it is determined whether the above calculation results satisfy this constraint. Specifically, for each candidate upgrade scheme, the average marginal contribution of each type of transmission entity calculated through the above steps under all sample scenarios is added together to obtain the total marginal contribution of all transmission entities. Then, the performance evaluation value under the condition that all transmission entities participate in the upgrade is calculated, that is, the performance evaluation value of the alliance including all transmission entities. Finally, based on the average of the performance evaluation value of the alliance under all sample scenarios, the performance evaluation value of all transmission entities participating in the upgrade is obtained.

[0045] Then, it is determined whether the difference between the total marginal contribution of the subject and the average performance evaluation value satisfies the average consistency constraint. In this embodiment, an allowable error is preset. If the difference is within the allowable error, it indicates that the average consistency constraint is satisfied. The constraint expression is as follows: In the formula, Let K represent the average marginal contribution of the c-th type of transmission entity, where C represents the alliance including all transmission entities, and K represents the total number of sample scenarios. This represents the performance evaluation value of the alliance that includes all transmission entities in the k-th sample scenario. Indicates the tolerance, preferred. Take 5%.

[0046] If the above constraints are met, the calculation results are considered to meet the requirements of stability and rationality. If not, the simulation scenario needs to be adjusted, such as increasing the number of scenarios K or adjusting the disturbance amplitude. Iterative calculations and constraint verifications are then performed based on the adjusted simulation scenario until the calculation results converge. At this point, the average marginal contribution of the subject that meets the convergence condition is taken as the final average marginal contribution of the subject used for the scheme decision.

[0047] This embodiment provides a power transmission upgrade decision-making method based on multi-subject contribution evaluation. It focuses on solving the problems of power transmission channel congestion and renewable energy curtailment caused by large-scale grid connection of new energy sources. By constructing a complete logical system of "data-driven, performance quantification, and contribution attribution", it provides calculable and verifiable basis for the planning and design of power transmission projects from the perspective of technical contribution.

[0048] This embodiment uses power system production simulation to accurately quantify the multi-dimensional physical benefits brought about by transmission upgrades, such as reduced renewable energy curtailment rates, lower system coal consumption, and improved power supply reliability. It integrates green value, supply guarantee value, and economic value into a unified evaluation system, achieving comprehensive quantification of system performance improvement benefits. It objectively reflects the true contributions of various transmission entities in transmission upgrades from the perspective of the physical operation of the transmission system. By combining Monte Carlo sampling and cooperative game theory, the uncertainty of system operation is incorporated into the evaluation process, improving the stability and robustness of the evaluation results. Parallel computation and categorical decomposition using Monte Carlo sampling effectively reduce algorithm complexity, significantly improving computational efficiency while maintaining accuracy. This embodiment, through multi-entity contribution evaluation, not only optimizes transmission upgrade schemes and improves the scientific and rational nature of transmission upgrade planning and design, but also can be applied to various engineering scenarios such as investment assessment and multi-entity cost allocation, demonstrating good adaptability and scalability.

[0049] Please see Figure 2 Based on the same inventive concept, the second embodiment of this invention proposes a power transmission upgrade decision-making system based on multi-subject contribution evaluation, comprising: The scenario generation module 10 is used to obtain a set of candidate upgrade schemes for the power transmission upgrade project. It uses Monte Carlo simulation to generate several simulation scenarios and introduces random disturbances into each simulation scenario based on the uncertainty of power system operation to obtain several sample scenarios. The scheme simulation module 20 is used to simulate each candidate upgrade scheme in each sample scenario, using the original power transmission data as the benchmark data and various power transmission entities as the participants, to obtain multiple simulation results under different power transmission entities. The contribution evaluation module 30 is used to calculate the marginal contribution of each type of transmission entity in each sample scenario based on the preset performance indicators and the simulation results, using a cooperative game algorithm, and to calculate the average marginal contribution of each type of transmission entity in all sample scenarios based on the marginal contribution. The planning and decision-making module 40 is used to select the optimal upgrade scheme from the set of candidate upgrade schemes based on the average marginal contribution of each type of power transmission entity in each candidate upgrade scheme.

[0050] Furthermore, the contribution evaluation module 30 is also used to treat the transmission entities participating in the upgrade in a simulation as a consortium, and calculate the performance evaluation value of each consortium based on the preset performance indicators and the simulation results corresponding to each consortium. Each type of power transmission entity is designated as the entity to be evaluated. The alliance that does not include the entity to be evaluated is designated as the first alliance. The alliance formed by the first alliance and the entity to be evaluated is designated as the second alliance. The marginal contribution of the subject to be evaluated in each sample scenario is calculated based on the difference between the performance evaluation value of the second alliance and the performance evaluation value of the first alliance.

[0051] The technical features and effects of the power transmission upgrade decision-making system based on multi-agent contribution evaluation proposed in this invention are the same as those of the method proposed in this invention, and will not be repeated here. Each module in the aforementioned power transmission upgrade decision-making system based on multi-agent contribution evaluation can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0052] Furthermore, embodiments of the present invention also propose a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0053] Please see Figure 3 The diagram illustrates the internal structure of a computer device in one embodiment. This computer device can specifically be a terminal or a server. The computer device includes a processor, memory, network interface, display, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a power transmission upgrade decision-making method based on multi-agent contribution evaluation. The display screen of the computer device can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0054] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0055] In summary, the present invention proposes a power transmission upgrade decision-making method, system, and device based on multi-entity contribution evaluation. The method obtains a set of candidate upgrade schemes for power transmission upgrade projects, generates several simulation scenarios using Monte Carlo simulation, and introduces random disturbances into each simulation scenario to obtain several sample scenarios based on the uncertainties of power system operation. In each sample scenario, using original power transmission data as baseline data and various power transmission entities as participants, each candidate upgrade scheme is simulated to obtain multiple simulation results with different power transmission entities participating. Based on preset performance indicators and the simulation results, a cooperative game theory algorithm is used to calculate the marginal contribution of each type of power transmission entity in each sample scenario, and based on the marginal contribution, the average marginal contribution of each type of power transmission entity in all sample scenarios is calculated. Based on the average marginal contribution of each type of power transmission entity for each candidate upgrade scheme, the optimal upgrade scheme is selected from the set of candidate upgrade schemes. This invention, taking into full account the uncertainties of power transmission system operation, accurately quantifies the technical contributions of different power transmission entities to the improvement of power transmission system performance through a combination of Monte Carlo sampling and cooperative game theory, providing a decision-making basis for the planning and design of power transmission upgrades.

[0056] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0057] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A power transmission upgrade decision-making method based on multi-agent contribution evaluation, characterized in that, The method comprises the following steps: obtaining a set of candidate upgrading schemes of a power transmission upgrading project, generating a plurality of simulation scenarios by Monte Carlo simulation, introducing random disturbances in each simulation scenario according to the uncertainty of power system operation, and obtaining a plurality of sample scenarios; in each sample scenario, taking the original power transmission data as the baseline data and taking various power transmission subjects as the participating objects, simulating each candidate upgrading scheme to obtain a plurality of simulation results under the participation of different power transmission subjects; according to the preset performance indicators and the simulation results, calculating the marginal contribution of each type of power transmission subject in each sample scenario by using a cooperative game algorithm, and calculating the subject average marginal contribution of each type of power transmission subject in all sample scenarios according to the marginal contribution; selecting the optimal upgrading scheme from the set of candidate upgrading schemes according to the subject average marginal contribution of each type of power transmission subject of each candidate upgrading scheme. 2.The power transmission upgrade decision method based on multi-agent contribution evaluation according to claim 1, wherein, The step of generating a plurality of simulation scenarios by Monte Carlo simulation and introducing random disturbances in each simulation scenario according to the uncertainty of power system operation to obtain a plurality of sample scenarios comprises: setting the sampling times of Monte Carlo simulation, and in each simulation scenario of each sampling round, introducing random disturbance values in key operating variables according to the uncertainty of power system operation to generate a sample scenario corresponding to each simulation scenario, wherein the key operating variables include wind power output, photovoltaic output and external load, and the random disturbance values follow a normal distribution. 3.The power transmission upgrade decision method based on multi-agent contribution evaluation according to claim 1, wherein, The step of calculating the marginal contribution of each type of power transmission subject in each sample scenario according to the preset performance indicators and the simulation results by using a cooperative game algorithm comprises: taking the power transmission subjects participating in the upgrading in one simulation as a coalition, calculating the performance evaluation value of each coalition according to the preset performance indicators and the simulation results corresponding to each coalition; taking each type of power transmission subject as a to-be-evaluated subject, taking the coalition not containing the to-be-evaluated subject as a first coalition, and taking the coalition composed of the first coalition and the to-be-evaluated subject as a second coalition; calculating the marginal contribution of the to-be-evaluated subject in each sample scenario according to the difference between the performance evaluation value of the second coalition and the performance evaluation value of the first coalition. 4.The power transmission upgrade decision method based on multi-agent contribution evaluation according to claim 3, wherein, The step of calculating the performance evaluation value of each coalition according to the preset performance indicators and the simulation results corresponding to each coalition comprises: extracting the performance parameters of the power transmission subjects contained in the corresponding coalition from the simulation results, and calculating the performance indicators of the power transmission subjects according to the performance parameters; performing weighted summation on the performance indicators to obtain the subject performance indicators of the power transmission subjects; taking the sum of the subject performance indicators of the power transmission subjects contained in the coalition as the performance evaluation value of the coalition; wherein the power transmission subjects include non-new energy power transmission subjects, new energy power transmission subjects and energy storage power supply subjects, and the performance indicators include operating deviation rate, power curtailment improvement rate, green power increment rate, standby capacity adequacy improvement degree, load loss energy improvement degree and load loss time improvement degree.

5. The power transmission upgrade decision method based on multi-agent contribution evaluation according to claim 4, characterized in that, The step of calculating the performance indicators of the power transmission subjects according to the performance parameters comprises: According to the unit output of the non-new energy power transmission main body before and after upgrading, the operation deviation rate is calculated; According to the curtailed power rate and the on-grid power of the new energy power transmission main body before and after upgrading, the curtailed power improvement rate and the green power increment rate are calculated; According to the reserve capacity adequacy, the load loss energy and the load loss time of the power transmission security main body before and after upgrading, the reserve capacity adequacy improvement degree, the load loss energy improvement degree and the load loss time improvement degree are calculated.

6. The power transmission upgrade decision method based on multi-agent contribution evaluation according to claim 5, characterized in that, The step of performing weighted summation on the performance indicators to obtain the main body performance indicators of the power transmission main body comprises: The operation deviation rate is taken as the main body performance indicator of the non-new energy power transmission main body; The curtailed power improvement rate and the green power increment rate are performed weighted summation to obtain the main body performance indicator of the new energy power transmission main body; The reserve capacity adequacy improvement degree, the load loss energy improvement degree and the load loss time improvement degree are performed weighted summation to obtain the main body performance indicator of the power transmission security main body. 7.The power transmission upgrade decision method based on multi-agent contribution evaluation according to claim 3, wherein, After the step of calculating the main body average marginal contribution of each type of power transmission main body under all sample scenarios, the method further comprises: According to the main body average marginal contribution of each type of power transmission main body under all sample scenarios, the main body total marginal contribution of all power transmission main bodies is calculated; According to the performance evaluation values of the alliance containing all power transmission main bodies under all sample scenarios, the average performance evaluation value of the alliance containing all power transmission main bodies is calculated; It is judged whether the difference between the main body total marginal contribution and the average performance evaluation value meets the average consistency constraint, if not, the simulation scenario is adjusted and iteratively verified until the average consistency constraint is met, and the main body average marginal contribution meeting the average consistency constraint is taken as the final main body average marginal contribution.

8. A power transmission upgrade decision system based on multi-agent contribution evaluation, characterized by, It comprises: A scenario generation module is configured to obtain a candidate upgrading scheme set of a power transmission upgrading project, generate a plurality of simulation scenarios by using Monte Carlo simulation, introduce random disturbance in each simulation scenario according to the uncertainty of power system operation, and obtain a plurality of sample scenarios; A scheme simulation module is configured to simulate each candidate upgrading scheme in each sample scenario by taking original power transmission data as benchmark data and taking various types of power transmission main bodies as participating objects, and obtain a plurality of simulation results under different power transmission main bodies; A contribution evaluation module is configured to calculate the marginal contribution of each type of power transmission main body under each sample scenario by using a cooperative game algorithm according to preset performance indicators and the simulation results, and calculate the main body average marginal contribution of each type of power transmission main body under all sample scenarios according to the marginal contribution; A planning and decision-making module is configured to select an optimal upgrading scheme from the candidate upgrading scheme set according to the main body average marginal contribution of each type of power transmission main body of each candidate upgrading scheme. 9.The power upgrade decision system based on multi-agent contribution evaluation of claim 8, wherein, The contribution evaluation module is further configured to take the power transmission main body participating in upgrading in one simulation as an alliance, calculate the performance evaluation value of each alliance according to preset performance indicators and the simulation results corresponding to each alliance, and calculate the marginal contribution of each type of power transmission main body under each sample scenario by using a cooperative game algorithm according to the performance evaluation value of each alliance. Each type of power transmission subject is taken as a to-be-evaluated subject, a coalition not containing the to-be-evaluated subject is taken as a first coalition, and a coalition composed of the first coalition and the to-be-evaluated subject is taken as a second coalition; According to a difference between a performance evaluation value of the second coalition and a performance evaluation value of the first coalition, a marginal contribution of the to-be-evaluated subject in each sample scenario is calculated.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the method in any one of claims 1 to 7 when executing the computer program.

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