A method and device for suppressing strategic bidding of power suppliers under a zonal settlement mechanism

Abstract

The application discloses a method and device for inhibiting strategic bidding of power suppliers under a partition settlement mechanism, and the method is established as a three-layer collaborative model: an upper-layer model takes a candidate partition scheme as a decision object, enumerates and compares multiple candidate partition schemes, and determines an optimal partition scheme; a middle-layer model takes the candidate partition scheme as a basis, takes maximization of power supplier revenue as a target, determines a power supplier bidding coefficient through Nash equilibrium iteration, forms a bidding strategy; and a lower-layer model takes the partition scheme and the bidding strategy as a basis, takes minimization of total power generation cost as a target, performs market clearing, obtains unit output, node marginal price and partition settlement price, and feeds back the results to the upper-layer model and the middle-layer model, so as to drive scheme optimization and strategy iteration. The method aggregates node price signals through the partition settlement price, weakens the ability of power suppliers to manipulate local prices, inhibits strategic bidding, alleviates pseudo congestion, reduces system operation cost, and improves market efficiency.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method and apparatus for suppressing strategic bidding by e-commerce platforms under a zoned settlement mechanism. Background Technology

[0002] With the continuous increase in the installed capacity of new energy sources and the ongoing development of the electricity spot market, problems such as uneven spatial and temporal distribution of power sources and loads and frequent network constraints are becoming increasingly prominent in the operation of the power system. Under the nodal marginal price settlement mechanism, nodal prices can reflect transmission constraints and marginal power supply costs relatively well, but at the same time, they also make local nodal prices more susceptible to the strategic bidding behavior of market participants. When some power generators influence the marginal price of local nodes by raising their bids, it may cause a redistribution of unit output and a shift in power flow, resulting in some lines exhibiting obvious constrained characteristics even when their physical capacity has not yet become a decisive bottleneck, thus forming a "pseudo-congestion" phenomenon induced by market behavior. This phenomenon not only increases the total operating cost of the system but also weakens the efficiency of market allocation and the fairness of settlement.

[0003] In existing technologies, analyses of strategic bidding problems by power generators largely focus on bidding game modeling, transmission constraint impact analysis, and clearing optimization. Some methods alleviate transmission congestion through physical capacity expansion, but these have limitations such as huge investments and long construction periods, and are difficult to effectively address the "pseudo-congestion" problem created by power generators through bidding behavior before reaching physical capacity limits. Furthermore, most of the above methods are still based on nodal settlement frameworks, lacking a systematic approach from the perspective of settlement mechanism design to weaken the price manipulation capabilities of local nodes.

[0004] Regional settlement mechanisms, by weighting and aggregating node prices within the same region, can reduce the direct impact of price fluctuations at a single node on the settlement outcome, thereby weakening, to some extent, the ability of power generators to obtain excess profits by manipulating marginal electricity prices at local nodes. However, existing technologies still lack a unified modeling method that can simultaneously consider the coupling relationship between regional scheme design, strategic bidding behavior of power generators, and the market clearing process, making it difficult to systematically reveal the mechanism by which regional settlement mechanisms alleviate pseudo-congestion, reduce system costs, and improve market operating efficiency.

[0005] Therefore, it is necessary to propose a method and device for suppressing strategic bidding in the electricity spot market under a regional settlement mechanism, so as to provide technical support for the optimization of the settlement mechanism and operation decision-making in the electricity spot market. Summary of the Invention

[0006] To address the lack of a unified modeling method in existing technologies that can simultaneously consider the coupling relationship between zoning scheme design, generator strategic bidding behavior, and market clearing process, this invention is proposed.

[0007] This invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a method for suppressing strategic pricing in e-commerce under a partitioned settlement mechanism, comprising the following: the method is established as a three-layer model, wherein...

[0009] The upper-level model takes candidate partitioning schemes as the decision object, enumerates and compares multiple candidate partitioning schemes, and determines the optimal partitioning scheme; the candidate partitioning schemes are used to represent the partitioning settlement structure under different node combination methods, and the partitioning settlement structure corresponding to the optimal partitioning scheme is the optimal partitioning settlement scheme.

[0010] The intermediate model aims to maximize the revenue of the generator. Based on the candidate partitioning scheme given by the upper model, it iteratively updates the current bidding coefficient of the generator to form the current bidding strategy of the generator under the current candidate partitioning scheme. When the iteration converges, the equilibrium bidding coefficient and the equilibrium bidding strategy of the generator corresponding to the candidate partitioning scheme are obtained.

[0011] The lower-level model, based on the candidate partitioning schemes currently given by the upper-level model and the power generator bidding strategies currently given by the middle-level model, performs market clearing with the goal of minimizing total power generation costs. It calculates unit output, nodal marginal electricity price, and partition settlement price, and feeds these figures back to the upper-level and middle-level models to drive the comparison of candidate partitioning schemes and the iteration of power generator bidding strategies, respectively.

[0012] Once all candidate partition schemes have been compared and the generator pricing strategies corresponding to each candidate partition scheme have reached equilibrium, the optimal partition scheme and its corresponding equilibrium pricing coefficient, unit output, nodal marginal price, partition settlement price, and total generation cost are output.

[0013] In a preferred embodiment of the present invention, the partitioning decision process of the upper-level model is based on enumeration and comparison:

[0014] Construct multiple candidate partitioning schemes, which are used to characterize the partitioning settlement structure under different node combinations;

[0015] Based on the node marginal electricity price and the net load weight of each node output by the lower-level model, calculate the zonal settlement price under each candidate zoning scheme;

[0016] Based on the total power generation cost, nodal price differences, and market operation effects under each candidate partition scheme, a comprehensive comparison of each candidate partition scheme is made to determine the optimal partition scheme.

[0017] In a preferred embodiment of the present invention, the zoned settlement price is calculated based on the node marginal electricity price and the net load weight of each node, and the calculation formula is as follows:

[0018]

[0019]

[0020] in, For partitioning The settlement price for each zone, For nodes The marginal electricity price at the node, For nodes Net load weight, For partitioning The set of nodes contained therein For nodes The load, For nodes The power generation output, the marginal electricity price of the node and node power generation output All results are obtained after market clearing in the lower-level model.

[0021] In a preferred embodiment of the present invention, the objective function for maximizing the generator's revenue is:

[0022]

[0023] in, For power generation The benefits, For power generation In partition The settlement price for each zone, For power generation Power generation output, and Generators The primary cost coefficient and the secondary cost coefficient, A collection of generators participating in strategic bidding.

[0024] In a preferred embodiment of the present invention, the iterative update of the generator's current bid coefficient is based on Nash equilibrium.

[0025] In a preferred embodiment of the present invention, obtaining the equilibrium price coefficients of each generator through the Nash equilibrium includes:

[0026] Initialize the bidding coefficients of each generator, where the initial value of the bidding coefficient of generators participating in strategic bidding is a preset initial value, and the bidding coefficient of generators not participating in strategic bidding is fixed at 1.

[0027] Based on the candidate partitioning schemes and the corresponding current bidding coefficients, the lower-level model is invoked to clear the electricity market and calculate the unit output, nodal marginal electricity price and partition settlement price.

[0028] The revenue of each generator operator is calculated based on the regional settlement price and the unit output, and the bidding coefficient of each generator operator is updated based on the principle of maximizing revenue.

[0029] Repeat the above calling and updating steps until the pricing coefficients of each generator remain unchanged or the generator's revenue converges, thus obtaining the equilibrium pricing coefficients of each generator.

[0030] In a preferred embodiment of the present invention, the lower-level model is a power market clearing process, with the objective of minimizing total generation cost, and its objective function is:

[0031]

[0032]

[0033] in, The total cost of electricity generation, For generator sets The pricing coefficient, and Generator sets The primary cost coefficient and the secondary cost coefficient, For generator sets of efforts, The total number of generator sets. For generator set index;

[0034] The constraints of the lower-level model include:

[0035] Power balance constraints:

[0036]

[0037] in, For nodes The load, 0 represents the total number of nodes, and 0 represents the number of generator sets. Minimum output;

[0038] Generator output upper and lower limit constraints:

[0039]

[0040] in, For generator sets Maximum output;

[0041] Power flow constraints on transmission lines:

[0042]

[0043] in, For the line The upper limit of transmission capacity, For the line For generator sets The power generation transfer factor, For the line For nodes Load transfer factor This is a collection of power transmission lines.

[0044] In a preferred improvement of the present invention, after all candidate partition schemes have been compared and the generator pricing coefficients corresponding to each candidate partition scheme have converged iteratively, the optimal partition scheme that makes the comprehensive evaluation result optimal is output, as well as the equilibrium pricing coefficient, unit output result, nodal marginal electricity price result, partition settlement price result and total power generation cost result corresponding to the optimal partition scheme.

[0045] In a second aspect, the present invention provides an electronic 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 partitioned settlement mechanism for suppressing e-commerce strategic pricing as described above.

[0046] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the partitioned settlement mechanism for suppressing e-commerce strategic pricing as described above.

[0047] The beneficial effects of this invention are as follows: This invention proposes a method and device for suppressing strategic bidding by power generators under a zoned settlement mechanism. It establishes a three-layer optimization model integrating market zone design, power generator bidding game, and market clearing process. The upper-layer model design is the core, and the Nash equilibrium iteration of the middle-layer model characterizes the strategic bidding behavior of power generators. The lower-layer model implements market clearing considering transmission constraints, thereby systematically revealing the interaction between market design, market participant behavior, and system operation results. Compared with existing technologies that alleviate transmission congestion through physical capacity expansion, this invention, starting from the perspective of settlement mechanism optimization, does not rely on high grid investment and a long construction period, and utilizes zoned settlement prices to suppress power generation congestion. Aggregating price signals can effectively solve the "pseudo-congestion" problem that physical capacity expansion methods struggle to address. This problem arises when power generators engage in strategic bidding behavior, leading to power flow imbalances and market efficiency losses, before physical transmission lines reach their capacity limits. By linking power generator revenue to regional average prices rather than individual node prices, the invention weakens the incentive for power generators to manipulate local electricity prices, suppresses strategic price increases by power generators, reduces total system operating costs, and improves the fairness and economy of the electricity market. The modeling method and optimization process proposed in this invention can provide technical support for the design of electricity spot market settlement mechanisms, the operation and management of key transmission channels, and the optimization of market operations, and has good prospects for widespread application. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of a method for suppressing strategic pricing in e-commerce under a partitioned settlement mechanism, according to an embodiment of the present invention. Detailed Implementation

[0050] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0051] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0053] like Figure 1 As shown, this is an embodiment of the present invention. In a first aspect, a method for suppressing strategic pricing in e-commerce under a partitioned settlement mechanism is provided. The method is established as a three-layer model, which includes an upper-layer model, a middle-layer model, and a lower-layer model. The three-layer models achieve collaborative optimization through information transmission and feedback.

[0054] Specifically, the upper-level model enumerates the current candidate partitioning schemes one by one and passes them to the middle-level and lower-level models. The middle-level model performs Nash equilibrium iterations under the current candidate partitioning schemes, passing the current bidding coefficients to the lower-level model in each iteration. The lower-level model performs market clearing based on the current candidate partitioning schemes and current bidding coefficients, obtaining the current unit output, the current node marginal price, and the current partition settlement price, and returns the results to the middle-level and upper-level models. The middle-level model calculates the revenue of each generator and updates the current bidding coefficients accordingly, repeatedly calling the lower-level model until the bidding coefficients under the current candidate partitioning schemes converge. The upper-level model evaluates the current candidate partitioning schemes based on the converged total generation cost, node price differences, and market operation performance. After all candidate partitioning schemes have been evaluated, the upper-level model determines the optimal partitioning scheme through comparison.

[0055] The upper-level model addresses the market zoning scheme design problem. Its goal is to weaken the ability of power generators to manipulate local electricity prices and improve system operating efficiency. The upper-level model takes candidate zoning schemes as the decision object, enumerates and compares multiple candidate zoning schemes, and determines the optimal zoning scheme.

[0056] In one embodiment of the present invention, the partitioning decision process of the upper-level model is based on enumeration and comparison: multiple candidate partitioning schemes are constructed, which are used to characterize the partitioning settlement structure under different node combinations; based on the node marginal electricity price and the net load weight of each node output by the lower-level model, the partitioning settlement price under each candidate partitioning scheme is calculated; based on the total power generation cost, node price difference and market operation effect under each candidate partitioning scheme, the candidate partitioning schemes are comprehensively compared to determine the optimal partitioning scheme.

[0057] Specifically, the candidate partitioning scheme can be constructed based on at least one of the following methods: power grid topology, administrative region boundaries, node electrical distance, power flow correlation, or historical operation data clustering results. This invention does not limit this method.

[0058] Specifically, for each candidate partitioning scheme, the middle and lower-level models are invoked to perform Nash equilibrium iterations and market clearing to obtain the nodal marginal electricity price and net load weight under that candidate partitioning scheme. A weighted average method is then used to calculate the partition settlement price for that candidate partition. The calculation of the partition settlement price is a crucial intermediate link connecting the upper-level partitioning decision with the middle-level iterations and lower-level clearing. The partition settlement price under each candidate partitioning scheme directly affects the revenue calculation of generators in the middle-level model and the market clearing result of the lower-level model, thus affecting the evaluation indicators of the candidate partitioning scheme, namely total generation cost, nodal price difference, and market operation performance. The upper-level model determines the optimal partitioning scheme by comparing the comprehensive evaluation results of the above evaluation indicators under each candidate partitioning scheme.

[0059] The formula for calculating the settlement price for the designated zones is as follows:

[0060]

[0061]

[0062] in, For partitioning The settlement price for each zone, For nodes The marginal electricity price at the node, For nodes Net load weight, For partitioning The set of nodes contained therein For nodes The load, For nodes The power generation output, the marginal electricity price of the node and node power generation output All of these are obtained after market clearing by the lower-level model, and the power output of a node is obtained by aggregating the power output of each generator unit on the same node according to the corresponding dimensions.

[0063] Specifically, total generation cost refers to the total generation cost obtained by the lower-level model after market clearing under a given zoning scheme; nodal price difference refers to the dispersion between the marginal electricity prices of each node, which can be measured by the variance or standard deviation of the marginal electricity prices of each node; market operation effect includes, but is not limited to, indicators reflecting market operation efficiency such as social welfare and electricity purchase cost. The upper-level model determines the optimal zoning scheme from the candidate zoning schemes by comparing the comprehensive evaluation results of the above evaluation indicators.

[0064] The middle-level model solves the strategic pricing problem for generators, aiming to maximize generator revenue. Based on the candidate partitioning schemes given by the upper-level model, it iteratively updates the generator's current pricing coefficients to form the current generator pricing strategy under the current candidate partitioning scheme. When the iteration converges, the equilibrium pricing coefficients and equilibrium generator pricing strategy corresponding to the candidate partitioning scheme are obtained.

[0065] In one embodiment of the present invention, the objective function for maximizing the generator's revenue is:

[0066]

[0067] in, For power generation The benefits, For power generation In partition The settlement price for each zone, For power generation Power generation output, and Generators The primary cost coefficient and the secondary cost coefficient, For the group of power generators participating in the strategic bidding, the power generation output of each power generator is obtained by aggregating the output of each generator unit of the same power generator according to the corresponding dimensions.

[0068] In one embodiment of the present invention, the iterative update of the generator's current bid coefficient is based on Nash equilibrium.

[0069] Specifically, obtaining the equilibrium bidding coefficients of each power generator through the Nash equilibrium includes: initializing the bidding coefficients of each power generator, wherein the initial value of the bidding coefficients of power generators participating in strategic bidding is a preset initial value, and the bidding coefficients of power generators not participating in strategic bidding are fixed at 1; based on the candidate partition scheme and the corresponding current bidding coefficients, calling the lower-level model to perform electricity market clearing, and calculating the unit output, nodal marginal electricity price and partition settlement price; calculating the revenue of each power generator based on the partition settlement price and unit output, and updating the bidding coefficients of each power generator based on the principle of maximizing revenue; repeating the above calling and updating steps until the bidding coefficients of each power generator remain unchanged or the revenue of the power generator converges, thereby obtaining the equilibrium bidding coefficients of each power generator.

[0070] The pricing strategy of power generators, determined by the middle-level model, is represented by a set of pricing coefficients in practice. The lower-level model performs market clearing based on the power generators' pricing strategy, and in each iteration, it calculates the clearing based on the current pricing coefficients.

[0071] In one specific embodiment of the present invention, the preset initial value is preferably 1.

[0072] The lower-level model, based on the candidate zoning schemes currently given by the upper-level model and the power generator bidding strategies currently given by the middle-level model, performs market clearing with the goal of minimizing total generation cost. It calculates unit output, nodal marginal price, and zoning settlement price, and feeds these figures back to the upper-level and middle-level models to drive the comparison of candidate zoning schemes and the iteration of power generator bidding strategies, respectively. In one embodiment of the invention, the lower-level model represents the electricity market clearing process, with the objective function being:

[0073]

[0074]

[0075] in, The total cost of electricity generation, For generator sets The pricing coefficient, and Generator sets The primary cost coefficient and the secondary cost coefficient, For generator sets of efforts, The total number of generator sets. This is the generator set index.

[0076] The constraints of the lower-level model include power balance constraints, generator output upper and lower limit constraints, and transmission line power flow constraints.

[0077] Specifically, power balance constraints:

[0078]

[0079] in, For nodes The load, 0 represents the total number of nodes, and 0 represents the number of generator sets. Minimum output;

[0080] Generator output upper and lower limit constraints:

[0081]

[0082] in, For generator sets Maximum output;

[0083] Power flow constraints on transmission lines:

[0084]

[0085] in, For the line The upper limit of transmission capacity, For the line For generator sets The power generation transfer factor, For the line For nodes Load transfer factor This is a collection of power transmission lines.

[0086] In one embodiment of the present invention, after all candidate partition schemes have been compared and the generator bidding coefficients corresponding to each candidate partition scheme have converged iteratively, the optimal partition scheme that makes the comprehensive evaluation result optimal is output, as well as the equilibrium bidding coefficient, unit output result, nodal marginal electricity price result, partition settlement price result and total power generation cost result corresponding to the optimal partition scheme.

[0087] Specifically, the comprehensive evaluation result is a comprehensive evaluation of the candidate partitioning scheme after taking into account the total power generation cost after the iterative convergence of the power generator bidding coefficients, the nodal price difference, and the market operation effect.

[0088] Secondly, the present invention also provides an electronic 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-mentioned method for suppressing e-commerce strategic pricing under any of the partitioned settlement mechanisms.

[0089] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for suppressing e-commerce strategic pricing under any of the partitioned settlement mechanisms.

[0090] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for suppressing strategic bidding of power suppliers in a zonal settlement mechanism, characterized in that, The method is established as a three-layer model, including: The upper-level model takes candidate partitioning schemes as the decision object, enumerates and compares multiple candidate partitioning schemes, and determines the optimal partitioning scheme; the candidate partitioning schemes are used to represent the partitioning settlement structure under different node combination methods, and the partitioning settlement structure corresponding to the optimal partitioning scheme is the optimal partitioning settlement scheme. The intermediate model aims to maximize the revenue of the generator. Based on the candidate partitioning scheme given by the upper model, it iteratively updates the current bidding coefficient of the generator to form the current bidding strategy of the generator under the current candidate partitioning scheme. When the iteration converges, the equilibrium bidding coefficient and the equilibrium bidding strategy of the generator corresponding to the candidate partitioning scheme are obtained. The lower-level model, based on the candidate partitioning schemes currently given by the upper-level model and the power generator bidding strategies currently given by the middle-level model, performs market clearing with the goal of minimizing total power generation costs. It calculates unit output, nodal marginal electricity price, and partition settlement price, and feeds these figures back to the upper-level and middle-level models to drive the comparison of candidate partitioning schemes and the iteration of power generator bidding strategies, respectively. Once all candidate partition schemes have been compared and the generator pricing strategies corresponding to each candidate partition scheme have reached equilibrium, the optimal partition scheme and its corresponding equilibrium pricing coefficient, unit output, nodal marginal price, partition settlement price, and total generation cost are output.

2. The method for suppressing strategic pricing in e-commerce under the regional settlement mechanism according to claim 1, characterized in that, The partitioning decision process of the upper-level model is based on enumeration and comparison: Construct multiple candidate partitioning schemes, which are used to characterize the partitioning settlement structure under different node combinations; Based on the node marginal electricity price and the net load weight of each node output by the lower-level model, calculate the zonal settlement price under each candidate zoning scheme; Based on the total power generation cost, nodal price differences, and market operation effects under each candidate partition scheme, a comprehensive comparison of each candidate partition scheme is made to determine the optimal partition scheme.

3. The method for suppressing strategic pricing in e-commerce under the regional settlement mechanism according to claim 2, characterized in that, The regional settlement price is calculated based on the node marginal electricity price and the net load weight of each node, using the following formula: ； ； in, For partitioning The settlement price for each zone, For nodes The marginal electricity price at the node, For nodes Net load weight, For partitioning The set of nodes contained therein For nodes The load, For nodes The power generation output, the marginal electricity price of the node and node power generation output All results are obtained after market clearing in the lower-level model.

4. The method for suppressing strategic pricing in e-commerce under the regional settlement mechanism according to claim 1, characterized in that, The objective function for maximizing the revenue of the power generator is: ； in, For power generation The benefits, For power generation In partition The settlement price for each zone, For power generation Power generation output, and Generators The primary cost coefficient and the secondary cost coefficient, A collection of generators participating in strategic bidding.

5. The method for suppressing strategic pricing in e-commerce under the regional settlement mechanism according to claim 1, characterized in that, The iterative update of the generator's current pricing coefficient is based on Nash equilibrium.

6. The method for suppressing strategic pricing in e-commerce under the regional settlement mechanism according to claim 5, characterized in that, The equilibrium price coefficients for each generator are obtained through the Nash equilibrium, including: Initialize the bidding coefficients of each generator, where the initial value of the bidding coefficient of generators participating in strategic bidding is a preset initial value, and the bidding coefficient of generators not participating in strategic bidding is fixed at 1. Based on the candidate partitioning schemes and the corresponding current bidding coefficients, the lower-level model is invoked to clear the electricity market and calculate the unit output, nodal marginal electricity price and partition settlement price. The revenue of each generator operator is calculated based on the regional settlement price and the unit output, and the bidding coefficient of each generator operator is updated based on the principle of maximizing revenue. Repeat the above calling and updating steps until the pricing coefficients of each generator remain unchanged or the generator's revenue converges, thus obtaining the equilibrium pricing coefficients of each generator.

7. The method for suppressing strategic pricing in e-commerce under the regional settlement mechanism according to claim 1, characterized in that, The lower-level model represents the electricity market clearing process, with the objective of minimizing total generation cost. Its objective function is: ； ； in, The total cost of electricity generation, For generator sets The pricing coefficient, and Generator sets The primary cost coefficient and the secondary cost coefficient, For generator sets of efforts, The total number of generator sets. For generator set index; The constraints of the lower-level model include: Power balance constraints: ； in, For nodes The load, 0 represents the total number of nodes, and 0 represents the number of generator sets. Minimum output; Generator output upper and lower limit constraints: ； in, For generator sets Maximum output; Power flow constraints on transmission lines: ； in, For the line The upper limit of transmission capacity, For the line For generator sets The power generation transfer factor, For the line For nodes Load transfer factor This is a collection of power transmission lines.

8. The method for suppressing strategic pricing in e-commerce under the regional settlement mechanism according to claim 1, characterized in that, Once all candidate partition schemes have been compared and the generator pricing coefficients corresponding to each candidate partition scheme have converged iteratively, the optimal partition scheme that yields the best comprehensive evaluation result is output, along with the equilibrium pricing coefficient, unit output result, nodal marginal electricity price result, partition settlement price result, and total generation cost result corresponding to the optimal partition scheme.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for suppressing e-commerce strategic pricing under the partitioned settlement mechanism as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for suppressing e-commerce strategic pricing under the partitioned settlement mechanism as described in any one of claims 1 to 8.

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