Method for quantifying operation status of power spot market, storage medium and program product

Abstract

This invention relates to the field of market assessment and decision-making technology, and particularly to a method, storage medium, and program product for quantifying the operational status of the electricity spot market. The method includes: selecting several operational indices for the electricity spot market; determining the entropy weights of the several operational indices using the entropy weight method; and calculating quantitative indicators of the operational status of the electricity spot market based on the several operational indices and their respective entropy weights. The rapid electricity spot market operational indicator evaluation method constructed by this invention selects pre-market power structure analysis indicators, in-process reporting behavior impact analysis indicators, and post-transaction result analysis indicators, and uses the entropy weight method to quickly and accurately evaluate various key aspects of electricity spot market operation. This evaluation method can adapt to the characteristics of electricity spot markets in different regions, providing tailored assessment and decision support for market operations in specific regions.

Description

Technical Field

[0001] This invention relates to the field of market assessment and decision-making technology, and in particular to a method, storage medium, and program product for quantifying the operational status of the electricity spot market. Background Technology

[0002] With the deepening of power system reform, the electricity spot market has developed rapidly. However, current methods for evaluating the operation of the electricity spot market have many shortcomings. Traditional methods often rely excessively on expert experience when determining indicator weights. This means that the evaluation results are largely influenced by the subjective judgment of a few experts. Different experts may assign different weights to the same indicator due to differences in knowledge background, experience level, and personal opinions. Some indicators lack scientific and reasonable calculation methods and clear definition standards, leading to inaccurate and incomparable evaluation results. Existing methods for evaluating the operation of the electricity spot market are also cumbersome in their calculation processes. These methods typically involve numerous mathematical models and complex calculation steps, requiring a considerable amount of time to complete the evaluation process. Calculating all indicators consumes significant computational resources. Furthermore, electricity spot markets in different regions have their own characteristics, and existing methods are ill-suited to this diversity, failing to provide accurate and effective assessment and decision support for market operations in specific regions. Summary of the Invention

[0003] This invention aims to at least partially solve one of the technical problems in the aforementioned technologies, and to this end proposes a method for quantifying the operational status of the electricity spot market, comprising:

[0004] Select several operating indices from the electricity spot market;

[0005] The entropy weights of the aforementioned operational indices are determined using the entropy weight method.

[0006] Based on the aforementioned operating indices and the entropy weight of each operating index, a quantitative indicator of the operating status of the electricity spot market is calculated.

[0007] Furthermore, the operational indices include: the pre-event market power structure index, the in-event reporting behavior impact index, and the post-event transaction result index.

[0008] Furthermore, the aforementioned ex-ante market power structure indices include: the local HHI index, the Top-m index, the MRR index, the key supplier index (PSI), and the residual supply index (RSI); wherein,

[0009] The expression corresponding to the local HHI index is:

[0010]

[0011] in, This represents the local HHI index; P represents the concentration of the k-th local market; Imk P represents the capacity of the k-th local market; s N represents the total market size; N represents the total number of local markets.

[0012] The expression corresponding to the key supplier index is:

[0013]

[0014] Where, ∑ k≠j S k This represents the total power generation of all power generation entities other than power generation entity j.

[0015] The expression for the Residual Supply Index (RSI) is:

[0016]

[0017] in, The remaining supply index (RSI) represents the total power generation capacity of all eligible power generation entities; S0 represents the total power generation capacity of all eligible power generation entities. j D0 represents the power generation capacity of power generation entity j; D0 represents the total market demand during the target trading period.

[0018] Furthermore, the indices affecting the in-process reporting behavior include: the reporting capacity surplus index, the minimum to maximum market share ratio index, and the horizontal price volatility; wherein,

[0019] The expression for the minimum to maximum market share ratio index is:

[0020]

[0021] Among them, I MMIi R represents the ratio of the minimum to the maximum market share. maxi This represents the maximum market share that the target supplier will obtain when all other suppliers submit fixed prices and the target supplier bids at the lowest price; R mini This indicates the minimum market share that the target supplier can obtain when the target supplier bids for all capacity at the highest price;

[0022] The expression corresponding to the horizontal price volatility is:

[0023]

[0024] Among them, C spv This represents the horizontal price volatility; P represents the average historical bid price of the market entity; P represents the bid price submitted by the market entity this time.

[0025] Furthermore, the declared capacity availability index includes: declared capacity rate, capacity retention rate, low-price bid rate for generating units, high-price bid rate for generating units, average bid price for generating units, and bid price distribution for generating units; wherein,

[0026] The expression corresponding to the declared capacity rate is:

[0027] BCR = BC / Pmax

[0028] Wherein, BCR represents the declared capacity rate; BC represents the actual declared capacity; and Pmax represents the maximum available generating capacity of the unit.

[0029] The expression corresponding to the capacity retention rate is:

[0030] PWR = 1 - BCR

[0031] Wherein, PWR represents the capacity retention rate;

[0032] The expression corresponding to the low-price bid rate of the generating unit is:

[0033] BLR=N L / M L

[0034] Where, N L This indicates the number of segments where the unit's declared price is lower than the set percentage of the unit's power generation cost; M L This indicates the total number of segments declared by the generator set;

[0035] The expression corresponding to the high-price bid rate of the generating unit is:

[0036] BUR=N U / M U

[0037] Where, N U This indicates the number of segments where the unit's declared price exceeds the set percentage of the unit's power generation cost; M U This indicates the total number of segments declared by the generator set;

[0038] The expression corresponding to the average price of the generating units is:

[0039]

[0040] in, P represents the average price of units in the market; i Q represents the quote for unit i; i This indicates the declared capacity of unit i.

[0041] Furthermore, the post-transaction result index includes: the Lerner index and the percentage of marginal generating units winning bids; wherein,

[0042] The expression corresponding to the Lerner index is:

[0043]

[0044] Among them, I Li P represents the Lerner index; i Indicates the market clearing price; MC i This represents the marginal cost of the generator;

[0045] The expression corresponding to the percentage of electricity awarded to the marginal generating units is:

[0046] MUQL = Q i / Q all

[0047] Wherein, MUQL represents the percentage of electricity generated by the marginal generating units; Q i Q represents the total winning bid amount for marginal unit i; all This represents the total winning bid capacity of all marginal units within the power system.

[0048] Furthermore, the post-transaction result index also includes: the power system marginal price and average node price analysis index, the daily winning bid rate analyzed by the winning bid volume of generating units, the proportion of generating units reaching the upper limit price, and the marginal unit price reaching the upper limit rate; among which,

[0049] The expression corresponding to the marginal price and average nodal price analysis index of the power system is:

[0050] DR = (P d -P t ) / P d

[0051] Where DR represents the deviation rate between the marginal price and the average nodal price of the power system; P d P represents the marginal price of the electricity system. t This represents the average node price;

[0052] The expression corresponding to the daily success rate of the analysis based on the winning bid electricity of the generating units is:

[0053]

[0054] Wherein, BR represents the daily success rate of the analysis based on the unit's winning bid electricity; P i，j P represents the winning bid power for unit j; b，j This represents the declared power output of unit j; n represents the number of winning bids for unit j; m represents the number of bids submitted by unit j; T represents the entire day's time period;

[0055] The expression corresponding to the proportion of units reaching the upper price ceiling is:

[0056] PCI max =N max / N

[0057] Among them, PCI max N represents the percentage of units that have reached the upper price ceiling; max This represents the number of generating units that won bids at the maximum price limit; N is the total number of generating units that won bids.

[0058] The expression corresponding to the marginal unit price attainment rate is:

[0059] MUPL = N / M

[0060] Wherein, MUPL represents the market marginal unit price limit rate; N represents the period when the winning bid price of the marginal unit reached the price limit; and M represents the total number of periods for the marginal unit.

[0061] Furthermore, the entropy weights of the aforementioned operational indices are determined using the entropy weight method, including:

[0062] Based on the aforementioned operational indices, a feature matrix is ​​constructed;

[0063] The feature matrix is ​​normalized, and the relative entropy value of each operational indicator is calculated;

[0064] The entropy weight of each operational indicator is calculated based on its relative entropy value.

[0065] The expression corresponding to the feature matrix is:

[0066]

[0067] Where X represents the feature matrix; m represents the number of objects to be evaluated; n represents the number of evaluation indicators; x ij This represents the value of the j-th evaluation metric for the i-th object;

[0068] The formula for calculating the relative entropy value is:

[0069]

[0070] Where, p ij E represents the proportion of the i-th sample value under the j-th evaluation indicator to that evaluation indicator; j This represents the entropy value of the j-th evaluation index;

[0071] The calculation formula corresponding to the entropy weight is:

[0072]

[0073] Among them, wi Let represent the entropy weight of the i-th sample value under the j-th evaluation index.

[0074] This application also proposes a computer-readable storage medium storing a computer program or instructions that, when executed by a processor, are at least used to implement the above-described method.

[0075] This application also proposes a computer program product stored in a computer-readable storage medium, characterized in that, when executed by a processor, the computer program product is used to implement at least the above-described method.

[0076] Compared with existing technologies, the beneficial effects of this invention are as follows: The rapid evaluation method for electricity spot market operation indicators based on the entropy weight method constructed in this invention selects a series of fast and scientifically sound indicators, including pre-market power structure analysis indicators, in-process reporting behavior impact analysis indicators, and post-transaction result analysis indicators, and uses the entropy weight method to quickly and accurately evaluate each key aspect of electricity spot market operation. This solves the problem of complex indicator calculations in existing evaluation methods, covering multi-dimensional analysis from market structure to subject behavior and transaction results. This evaluation method can adapt to the characteristics of electricity spot markets in different regions, providing tailored evaluation and decision support for market operation in specific regions, effectively making up for the lack of adaptability in existing general evaluation methods, thereby greatly improving the scientificity and effectiveness of electricity spot market operation evaluation.

[0077] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. The technical solutions of the invention will be further described below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0078] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0079] Figure 1 This is a schematic diagram illustrating the quantitative analysis of the operation of the electricity spot market as an example.

[0080] Figure 2 The diagram shows an electronic device as illustrated in the embodiment.

[0081] Figure 3 This is a schematic diagram of a computer-readable storage medium provided for an embodiment. Detailed Implementation

[0082] The present invention will be described below with reference to the accompanying drawings. The preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0083] Figure 1 The method for quantifying the operational status of the electricity spot market provided by this invention includes:

[0084] Select several operating indices from the electricity spot market;

[0085] The entropy weights of the aforementioned operational indices are determined using the entropy weight method.

[0086] Based on the aforementioned operating indices and the entropy weight of each operating index, a quantitative indicator of the operating status of the electricity spot market is calculated.

[0087] Furthermore, the operational indices include: the pre-event market power structure index, the in-event reporting behavior impact index, and the post-event transaction outcome index.

[0088] According to some embodiments of this application, the solution proposed in this application adopts a complete indicator methodology framework covering three stages: ex-ante market power structure analysis indicators based on capacity control, in-process reporting behavior impact analysis indicators based on price impact testing, and ex-post transaction result analysis indicators based on price mitigation and excess return.

[0089] Furthermore, the ex-ante market power structure indices include: the local HHI index, the Top-m index, the MRR index, the key supplier index (PSI), and the residual supply index (RSI); among them,

[0090] The expression for the local HHI index is:

[0091]

[0092] in, Indicates the local HHI index; P represents the concentration of the k-th local market; Imk P represents the capacity of the k-th local market; s N represents the total market size; N represents the total number of local markets.

[0093] The expression for the key supplier index is:

[0094]

[0095] Where, ∑ k≠j S k This represents the total power generation of all power generation entities other than power generation entity j.

[0096] The expression for the Residual Supply Index (RSI) is:

[0097]

[0098] in, The RSI index represents the remaining supply indicator; S0 represents the total generating capacity of all eligible power generation entities; S j D0 represents the power generation capacity of power generation entity j; D0 represents the total market demand during the target trading period.

[0099] According to some embodiments of this application, the pre-market power structure index includes:

[0100] (1) Local HHI (Herfindahl-Hirschman Index)

[0101] In cases where transmission capacity limitations result in a higher market share for local power generators and a stronger ability to control node electricity prices, the local HHI can be calculated to determine the concentration of power generation enterprises in a region.

[0102]

[0103] in, P is the concentration of the k-th local market. Imk P is the capacity of the k-th local market. s It refers to the total market capacity.

[0104] This allows us to calculate market concentration under constraints.

[0105] (2) Key Supplier Index

[0106] The key supplier index can also be used as a method for screening and detecting structural market power. Producer j is considered a key supplier if the following conditions are met.

[0107]

[0108] Where, ∑ k≠j S k The Power System Index (PSI) refers to the total power generation of all entities other than the main power generator (or power generation group) j. The PSI reflects the power system's dependence on the main power generators; a PSI of 1 indicates that a lack of power generation from any single entity would lead to a power supply shortage.

[0109] (3) Remaining Supplier Index

[0110] By subtracting the supply of a particular market participant from the total market supply and then comparing this with the market demand, the residual supply index (RSI) associated with producer j can be calculated.

[0111]

[0112] Where S0 is the total power generation capacity of all permitted power generation entities, S j Let D0 be the total market demand for power generation entity j, and D0 be the total market demand for the target trading period. The smaller the RSI index of a power generation company, the stronger its ability to control market prices. If... This indicates that the market participant has market power because their supply has a significant impact on market prices. If Then the surplus supply index of power generation entity j is qualified.

[0113] Furthermore, the indices affecting in-process reporting behavior include: the reporting capacity surplus index, the minimum to maximum market share ratio index, and the horizontal price volatility; among which,

[0114] The expression for the minimum to maximum market share ratio index is:

[0115]

[0116] Among them, I MMIi R represents the ratio of the minimum to the maximum market share. maxi This represents the maximum market share that the target supplier will obtain when all other suppliers submit fixed prices and the target supplier bids at the lowest price; R mini This indicates the minimum market share that the target supplier can obtain when the target supplier bids for all capacity at the highest price;

[0117] The expression for horizontal price volatility is:

[0118]

[0119] Among them, C spv Indicates horizontal price volatility; P represents the average historical bid price of the market entity; P represents the bid price submitted by the market entity this time.

[0120] Furthermore, the declared capacity surplus index includes:

[0121] The data includes declared capacity rate, capacity retention rate, low-price bid rate for generating units, high-price bid rate for generating units, average bid price for generating units, and bid price distribution for generating units; among these,...

[0122] The expression corresponding to the declared capacity rate is:

[0123] BCR = BC / Pmax

[0124] Wherein, BCR represents the declared capacity rate; BC represents the actual declared capacity; and Pmax represents the maximum available generating capacity of the unit.

[0125] The expression corresponding to the capacity retention rate is:

[0126] PWR = 1 - BCR

[0127] Wherein, PWR represents the capacity retention rate;

[0128] The expression corresponding to the low-price bid rate for generating units is:

[0129] BLR=N L / M L

[0130] Where, N L This indicates the number of segments where the unit's declared price is lower than the set percentage of the unit's power generation cost; M L This indicates the total number of segments declared by the generator set;

[0131] The expression corresponding to the high-price bid rate for generating units is:

[0132] BUR=N U / M U

[0133] Where, N U This indicates the number of segments where the unit's declared price exceeds the set percentage of the unit's power generation cost; M U This indicates the total number of segments declared by the generator set;

[0134] The expression corresponding to the average unit price is:

[0135]

[0136] in, P represents the average price of units in the market; i Q represents the quote for unit i; i This indicates the declared capacity of unit i.

[0137] According to some embodiments of this application, the indicators for analyzing the impact of in-process reporting behavior include:

[0138] (1) Declared capacity rate of generating units

[0139] BCR = BC / Pmax

[0140] Wherein, BCR represents the declared capacity rate, BC represents the actual declared capacity, and Pmax represents the maximum available generating capacity of the unit.

[0141] A declared capacity participation rate of less than 1 indicates that the supplier is exercising physical holding.

[0142] (2) Unit capacity retention rate

[0143] PWR = 1 - BCR

[0144] PWR represents the capacity retention rate, and BCR represents the declared capacity rate.

[0145] A higher capacity retention rate reflects a greater degree of control that suppliers have over the supply, and a more obvious intention to limit supply and raise prices.

[0146] (3) Low-price bidding rate of generator units

[0147] BLR=N L / M L

[0148] Where, N L This indicates the number of segments where the declared price is lower than a certain set percentage of the power generation cost, M. L This represents the total number of segments submitted. If different time periods are allowed, N represents the total number of segments submitted. L M represents the total number of periods with low prices. L It is the number of segments multiplied by the number of time periods.

[0149] (4) High-price bidding rate of generator units

[0150] BUR=N U / M U

[0151] Where, N U M indicates the number of segments whose declared price exceeds a certain set percentage of their power generation cost. U This indicates the total number of segments submitted.

[0152] (5) Average unit price

[0153]

[0154] in, P represents the average market price for generator sets. i Indicates the quote for unit i, Q i This indicates the declared capacity of unit i.

[0155] (6) Distribution of unit pricing

[0156] By analyzing the declared capacity and price of generating units within different price ranges, the degree of market competition can be determined.

[0157] (7) Horizontal price volatility

[0158] When market entities submit their bids, if they are quoting based on their true marginal cost, their bids for similar output levels should be consistent. Therefore, comparing a market entity's bid with the bids of similar generating units allows for the supervision of the market entity's exercise of market power.

[0159]

[0160] in, P represents the average historical bid price of the market entity; P represents the bid price submitted by the market entity this time.

[0161] (8) Minimum / Maximum Market Share Ratio

[0162]

[0163] Among them, the supplier's largest market share R maxi This refers to the maximum market share a supplier can achieve when it bids the lowest price and all other suppliers submit fixed prices. The supplier's minimum market share is R. mini This refers to the minimum market share that can be obtained when the supplier bids the highest price for all capacity.

[0164] Furthermore, the post-transaction outcome index includes: the Lerner index and the percentage of marginal generating units winning bids; among which,

[0165] The expression for the Lerner index is:

[0166]

[0167] Among them, I Li P represents the Lerner index; i Indicates the market clearing price; MC i This represents the marginal cost of the generator;

[0168] The expression corresponding to the percentage of electricity generated by marginal units is:

[0169] MUQL = Q i / Q αll

[0170] MUQL represents the percentage of electricity generated by marginal generating units; Q i Q represents the total winning bid amount for marginal unit i; all This represents the total winning bid capacity of all marginal units within the power system.

[0171] Furthermore, the post-trade outcome index also includes: the power system marginal price and average nodal price analysis index, the daily winning bid rate analyzed by the amount of electricity won by generating units, the proportion of generating units reaching the upper limit price, and the marginal unit price reaching the upper limit rate; among which,

[0172] The expression corresponding to the marginal price and average nodal price analysis index of the power system is:

[0173] DR = (P d -P t ) / P d

[0174] Where DR represents the deviation rate between the marginal price and the average nodal price of the power system; P d P represents the marginal price of the electricity system. t This represents the average node price;

[0175] The expression corresponding to the daily success rate of the bidding based on the unit's winning bid electricity volume is:

[0176]

[0177] Wherein, BR represents the overall success rate of the bidding based on the winning electricity volume of the generating units throughout the day; P i，j P represents the winning bid power for unit j; b，j This represents the declared power output of unit j; n represents the number of winning bids for unit j; m represents the number of bids submitted by unit j; T represents the entire day's time period;

[0178] The expression corresponding to the percentage of units reaching the upper price ceiling is:

[0179] PCI max =N max / N

[0180] Among them, PCI max Indicates the percentage of units that have reached the upper price ceiling; N max This represents the number of generating units that won bids at the maximum price limit; N is the total number of generating units that won bids.

[0181] The expression corresponding to the marginal unit price limit rate is:

[0182] MUPL = N / M

[0183] Wherein, MUPL represents the market marginal unit price limit rate; N represents the period when the winning bid price of the marginal unit reached the price limit; and M represents the total number of periods for the marginal unit.

[0184] According to some embodiments of this application, post-transaction result analysis includes:

[0185] (1) Lerner Index

[0186] The Lerner metric is used to measure the market clearing price P. i Marginal cost MC of power generator i Indicators of deviation degree:

[0187]

[0188] In a market with oversupply, such as during a downturn, prices are elastic, and the Lerner index is small, even approaching zero, indicating that power generators have no incentive to exert market forces. In a market with undersupply, even if electricity prices rise, power generators are unable to increase power generation, exhibiting price elasticity rigidity. In this case, I...Li →∞ indicates that the generator has extremely strong market power.

[0189] The average Lerner index Ave (I) under the Cournot oligopoly competition model Li The following relationship exists between Ave(I) and HHI and demand elasticity e: Li )==HHI / e.

[0190] (2) Percentage of electricity volume won by marginal units

[0191] Calculating the proportion of winning bids for marginal generating units in the total market competition space can be done by statistically analyzing the proportion of winning bids for marginal generating units within a trading cycle or at different times.

[0192] MUQL = Q i / Q all

[0193] Among them, Q i Q represents the total winning bid electricity of marginal unit i. all This represents the total winning bid capacity of all marginal units within the power system.

[0194] (3) Analysis of marginal price and average nodal price of power system

[0195] DR = (P d -P t ) / P d

[0196] Where DR represents the deviation rate, P d P represents the marginal price of the electricity system. t Represents the average node price. When network losses are not considered, the DR value reflects the severity of power system congestion at different times.

[0197] (4) Analyze the daily success rate based on the unit's winning bid electricity:

[0198]

[0199] Among them, P i，j This indicates that unit j won the bid for power, P b，j This indicates that generating unit j applied for power. n represents the number of sections that generating unit j won the bid for, m represents the number of sections that generating unit j applied for, and T represents the entire day's time period.

[0200] (5) Percentage of units reaching the upper limit price

[0201] PCI max =N max / N

[0202] Where, N maxThis represents the number of generating units that won bids at the maximum price limit, where N is the total number of winning units. The percentage of units reaching the maximum price limit (high-price winning rate) reflects the alignment of a supplier's bidding strategy with their own capabilities by comparing their transaction history with their bids, evaluating the success rate of their strategy and their market power. It also reflects situations where power generators intentionally raise their bid prices while still obtaining electricity volumes close to their bids. A higher value indicates a stronger ability to control market prices and greater market power.

[0203] (6) Marginal unit price limit attainment rate

[0204] By comparing the number of periods during which the winning bid price of marginal units reached the upper limit of the market price with the number of periods during which all marginal units operated, we can analyze the market supply situation or the ability of marginal units to exert market power during the trading period.

[0205] MUPL = N / M

[0206] Wherein, MUPL represents the marginal unit price cap attainment rate, N represents the period when the winning bid price of marginal units reached the price cap, and M represents the total number of periods for marginal units. The marginal unit price cap attainment rate can also be used to analyze the proportion of marginal units that reached the price cap in different periods, thereby analyzing the supply and market power situation in different periods.

[0207] Furthermore, the entropy weights of several operational indices are determined using the entropy weight method, including:

[0208] A feature matrix is ​​constructed based on several operational indices;

[0209] The feature matrix is ​​normalized, and the relative entropy values ​​of each operational indicator are calculated.

[0210] The entropy weight of each operational indicator is calculated based on its relative entropy value.

[0211] The expression corresponding to the feature matrix is:

[0212]

[0213] Where X represents the feature matrix; m represents the number of objects to be evaluated; n represents the number of evaluation indicators; x ij This represents the value of the j-th evaluation metric for the i-th object;

[0214] The formula for calculating the relative entropy is:

[0215]

[0216] Where, p ij E represents the proportion of the i-th sample value under the j-th evaluation indicator to that evaluation indicator; j This represents the entropy value of the j-th evaluation index;

[0217] The formula for calculating entropy weight is:

[0218]

[0219] Among them, w i Let represent the entropy weight of the i-th sample value under the j-th evaluation index.

[0220] According to some embodiments of this application, the weights of each indicator are calculated using the entropy weight method, and the total market score is calculated using the weights to evaluate the operation of the spot market.

[0221] like Figure 2 As shown, this application provides an electronic device, which includes a memory and a processor. The memory stores computer programs or instructions, and when the computer programs or instructions are executed by the processor, they are used to at least implement the above-mentioned method for quantifying the operating status of the electricity spot market.

[0222] like Figure 3 As shown, this application provides a computer-readable storage medium storing a computer program or instructions, which, when executed by a processor, are used to at least implement the above-mentioned method for quantifying the operating status of the electricity spot market.

[0223] This application also provides a computer program product stored in a computer-readable storage medium, which, when executed by a processor, is used to at least implement the above-described method for quantifying the operating status of the electricity spot market.

[0224] It is obvious that those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for quantifying the operational status of the electricity spot market, characterized in that, include: Select several operating indices from the electricity spot market; The entropy weights of the aforementioned operational indices are determined using the entropy weight method. Based on the aforementioned operating indices and the entropy weight of each operating index, a quantitative indicator of the operating status of the electricity spot market is calculated.

2. The method as described in claim 1, characterized in that, The operational indices include: the pre-event market power structure index, the in-event reporting behavior impact index, and the post-event transaction result index.

3. The method as described in claim 2, characterized in that, The aforementioned ex-ante market power structure indices include: the local HHI index, the Top-m index, the MRR index, the key supplier index (PSI), and the residual supply index (RSI); among which... The expression corresponding to the local HHI index is: in, This represents the local HHI index; P represents the concentration of the k-th local market; Imk P represents the capacity of the k-th local market; s N represents the total market size; N represents the total number of local markets. The expression corresponding to the key supplier index is: Where, ∑ k≠j S k This represents the total power generation of all power generation entities other than power generation entity j. The expression for the Residual Supply Index (RSI) is: in, The remaining supply index (RSI) represents the total power generation capacity of all eligible power generation entities; S0 represents the total power generation capacity of all eligible power generation entities. j D0 represents the power generation capacity of power generation entity j; D0 represents the total market demand during the target trading period.

4. The method as described in claim 2, characterized in that, The indices affecting in-process reporting behavior include: the reporting capacity surplus index, the minimum to maximum market share ratio index, and the horizontal price volatility; among which... The expression for the minimum to maximum market share ratio index is: Among them, I MMIi R represents the ratio of the minimum to the maximum market share. maxi This represents the maximum market share that the target supplier will obtain when all other suppliers submit fixed prices and the target supplier bids at the lowest price; R mini This indicates the minimum market share that the target supplier can obtain when the target supplier bids for all capacity at the highest price; The expression corresponding to the horizontal price volatility is: Among them, C spv This represents the horizontal price volatility; P represents the average historical bid price of the market entity; P represents the bid price submitted by the market entity this time.

5. The method as described in claim 4, characterized in that, The declared capacity availability index includes: declared capacity rate, capacity retention rate, low-price bid rate for generating units, high-price bid rate for generating units, average bid price for generating units, and bid price distribution for generating units; among which... The expression corresponding to the declared capacity rate is: Wherein, BCR represents the declared capacity rate; BC represents the actual declared capacity; and Pmax represents the maximum available generating capacity of the unit. The expression corresponding to the capacity retention rate is: PWR = 1 - BCR Wherein, PWR represents the capacity retention rate; The expression corresponding to the low-price bid rate of the generating unit is: BLR=N L / M L Where, N L This indicates the number of segments where the unit's declared price is lower than the set percentage of the unit's power generation cost; M L This indicates the total number of segments declared by the generator set; The expression corresponding to the high-price bid rate of the generating unit is: BUR=N U / M U Where, N U This indicates the number of segments where the unit's declared price exceeds the set percentage of the unit's power generation cost; M U This indicates the total number of segments declared by the generator set; The expression corresponding to the average price of the generating units is: in, P represents the average price of units in the market; i Q represents the quote for unit i; i This indicates the declared capacity of unit i.

6. The method as described in claim 2, characterized in that, The post-transaction result index includes: the Lerner index and the percentage of marginal generating units winning bids; among which... The expression corresponding to the Lerner index is: Among them, I Li P represents the Lerner index; i Indicates the market clearing price; MC i This represents the marginal cost of the generator; The expression corresponding to the percentage of electricity awarded to the marginal generating units is: MUQL=Q i / Q all Wherein, MUQL represents the percentage of electricity generated by the marginal generating units; Q i Q represents the total winning bid amount for marginal unit i; all This represents the total winning bid capacity of all marginal units within the power system.

7. The method as described in claim 6, characterized in that, The post-transaction result index also includes: the power system marginal price and average nodal price analysis index, the daily winning bid rate analyzed by the amount of electricity won by generating units, the proportion of generating units reaching the upper limit price, and the marginal unit price reaching the upper limit rate; among which... The expression corresponding to the marginal price and average nodal price analysis index of the power system is: DR=(P d -P t ) / P d Where DR represents the deviation rate between the marginal price and the average nodal price of the power system; P d P represents the marginal price of the electricity system. t This represents the average node price; The expression corresponding to the daily success rate of the analysis based on the winning bid electricity of the generating units is: Wherein, BR represents the daily success rate of the analysis based on the unit's winning bid electricity; P i , j P represents the winning bid power for unit j; b , j This represents the declared power output of unit j; n represents the number of winning bids for unit j; m represents the number of bids submitted by unit j; T represents the entire day's time period; The expression corresponding to the proportion of units reaching the upper price ceiling is: PCI max =N max / N Among them, PCI max N represents the percentage of units that have reached the upper price ceiling; max This represents the number of generating units that won bids at the maximum price limit; N is the total number of generating units that won bids. The expression corresponding to the marginal unit price attainment rate is: MUPL = N / M Wherein, MUPL represents the market marginal unit price limit rate; N represents the period when the winning bid price of the marginal unit reached the price limit; and M represents the total number of periods for the marginal unit.

8. The method according to any one of claims 1-7, characterized in that, The entropy weights of the aforementioned operational indices are determined using the entropy weight method, including: Based on the aforementioned operational indices, a feature matrix is ​​constructed; The feature matrix is ​​normalized, and the relative entropy value of each operational indicator is calculated; The entropy weight of each operational indicator is calculated based on its relative entropy value. The expression corresponding to the feature matrix is: Where X represents the feature matrix; m represents the number of objects to be evaluated; n represents the number of evaluation indicators; x ij This represents the value of the j-th evaluation metric for the i-th object; The formula for calculating the relative entropy value is: Where, p ij E represents the proportion of the i-th sample value under the j-th evaluation indicator to that evaluation indicator; j This represents the entropy value of the j-th evaluation index; The calculation formula corresponding to the entropy weight is: Among them, w i Let represent the entropy weight of the i-th sample value under the j-th evaluation index.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or instructions, which, when executed by a processor, are at least used to implement the method described in any one of claims 1-8.

10. A computer program product, said computer program product being stored in a computer-readable storage medium, characterized in that, When the computer program product is executed by a processor, it is used to implement at least the method described in any one of claims 1-8.

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