Multi-level spot market risk identification method and related device

Abstract

The invention belongs to the technical field of electricity market risk identification, and discloses a multi-level spot market risk identification method and a related device, and the method comprises the steps: reading the market data of a multi-level spot market; based on the market data, risk assessment index values of the multi-level spot market are calculated, and the risk assessment index values comprise a market price risk index value, a multi-level market association risk index value and a market power risk index value; calculating a weight coefficient of the risk assessment index value of the multi-level spot market by adopting an improved entropy weight method; and based on the risk assessment index value and the weight coefficient of the multi-level spot market, calculating a market risk comprehensive assessment value, and identifying a risk type and a risk level according to the market risk comprehensive assessment value. An improved entropy weight method is adopted to determine various risk index weights, and the improved entropy weight method can effectively overcome the defects of a traditional entropy weight method and can also enable the entropy weight method to keep the capability of distinguishing entropy value differences.

Description

Technical Field

[0001] This invention belongs to the field of power market risk identification technology, and specifically relates to a multi-level spot market risk identification method and related apparatus. Background Technology

[0002] With the construction of new power systems and the increase in the proportion of new energy sources, the risks to the safe operation of the power grid have further increased. The continuous and rapid development of uncertain power sources has exceeded the carrying capacity of local power grids in some provinces, and the pressure on new energy consumption and power balance of the large power grid is very prominent. Electricity trading through inter-provincial and provincial multi-level spot markets is characterized by high frequency, diverse product types, and increasing market operation complexity. Inter-provincial electricity spot trading covers a wide range, involves numerous market participants, and connects multiple provinces via trading channels. The close electrical connections strengthen the power flow coupling of inter-provincial interconnections, making the coupling constraints of these interconnections more complex. Due to the coupling relationships between upstream and downstream power grids and the spot markets in various provinces at the sending and receiving ends, the operational risks of inter-provincial and provincial spot markets influence each other. Existing technology proposes a risk assessment method and system for electricity spot markets based on risk identification. This method collects data on the grid structure, generator parameters, and system operation predictions of the analyzed power system. It then uses the Monte Carlo method to simulate basic risk events, generating six market risk operation scenarios. A single-period clearing model with cost minimization as its objective is constructed to calculate market clearing for various risk scenarios. Scenario analysis is used to identify the risks in each scenario, establishing a three-level risk assessment index system for the spot market. Five evaluation levels (extremely low, low, medium, high, and extremely high) are set, and the fuzzy comprehensive evaluation method is used to calculate the overall market risk score to determine the risk assessment level.

[0003] While the fuzzy comprehensive evaluation method can be used to calculate the overall market risk score, it cannot determine the specific type of market risk. Furthermore, the existing technology only assesses the risk of the primary spot market and does not consider assessing the overall operational risk of inter-provincial and provincial multi-level spot markets, nor does it evaluate the associated risks between inter-provincial spot market transactions and provincial spot market transactions. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-level spot market risk identification method and related apparatus to solve the problems that existing technologies cannot determine the specific types of market risks and cannot conduct multi-level risk assessments.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a multi-level spot market risk identification method, comprising: Read market data from multi-level spot markets, including bidding information, market clearing results, available capacity of inter-regional channels, and unit parameters of inter-provincial and provincial markets. Based on market data, risk assessment index values ​​for multi-level spot markets are calculated. These risk assessment index values ​​include market price risk index values, multi-level market correlation risk index values, and market power risk index values. The weighting coefficients of risk assessment indicators for multi-level spot markets are calculated using an improved entropy weighting method. Based on the risk assessment index values ​​and weighting coefficients of the aforementioned multi-level spot market, a comprehensive market risk assessment value is calculated, and risk types and risk levels are identified according to the comprehensive market risk assessment value.

[0006] Furthermore, the calculation of market price risk indicators based on market data includes: The difference between the transaction price and the quoted price, the peak-valley difference of the transaction price, the basic statistical indicators of the transaction price, the difference between the declared price and the average declared price, the growth rate of thermal power generation costs, the Lerner Index, the historical price volatility, the proportion of high transaction prices and high declared prices, the price limit rate, and the day-ahead / intra-day transaction volume ratio. Difference between transaction price and quoted price This is the difference between the final average bid price at the seller's node and the adjusted average transaction price.

[0007] In the formula, The average price quoted by the seller's node. This is the average transaction price after conversion at this node; Peak-valley difference in transaction electricity price This is the difference between the peak-valley difference of the daily transaction electricity price and the average peak-valley difference of the monthly transaction electricity price.

[0008] In the formula, The peak-valley difference in the electricity price traded on that day. This represents the average peak-to-valley difference in monthly electricity transaction prices. The basic statistical indicators for transaction electricity prices are the average electricity price, the standard deviation of the electricity price, and the extreme values ​​of the electricity price. The average electricity price is the average transaction price within the analysis period. The standard deviation of the electricity price is used to measure the volatility of the electricity price; the larger the standard deviation, the more volatile the electricity price. The extreme values ​​of the electricity price are the maximum and minimum transaction prices within the analysis period, which are used to identify the risk of abnormal market prices. Average electricity price:

[0009] In the formula, To analyze the transaction electricity prices for each trading period within the analysis cycle, To analyze the transaction electricity price points within the analysis period; Electricity price standard deviation:

[0010] In the formula, To analyze the transaction electricity prices for each trading period within the analysis cycle, To analyze the transaction electricity price points within the analysis period, This represents the average electricity price. Extreme electricity prices:

[0011] Difference between declared electricity price and average declared electricity price :

[0012] In the formula, For the electricity price declared by market entities, The total number of electricity prices declared by all market participants; Thermal power generation cost growth rate :

[0013] In the formula, Based on the current cost of thermal power generation, This represents the average cost of thermal power generation in the previous year or the cost of thermal power generation as compiled by market operators. Lerner Index The ratio of electricity price to marginal cost.

[0014] In the formula, For electricity price, Marginal cost; Historical price volatility:

[0015] In the formula, It is the price at time i. It is the average price over a historical period. It represents the number of moments in a historical period; The proportion of high-priced electricity transactions and high-priced electricity applications This represents the proportion of trading sessions with high electricity prices within each day's trading session to the total number of trading sessions.

[0016]

[0017] In the formula The set high electricity price threshold; Electricity price limit rate The percentage of periods during which the transaction price is close to the maximum price limit out of the total number of bidding periods;

[0018]

[0019]

[0020] Daily intraday volume ratio :

[0021] In the formula, This refers to the volume of electricity traded in the previous day. This represents the daily trading volume.

[0022] Furthermore, the calculation of multi-level market-related risk index values ​​based on market data includes: Inter-regional power transmission channel utilization rate :

[0023] In the formula, This represents the actual transaction transmission capacity completed by the inter-regional power transmission channel within a certain time period. This represents the theoretical maximum transmission capacity of the inter-regional power transmission channel during this time period. inter-provincial market supply and demand ratio The ratio of power output provided by the seller node to the total electricity load demand of the buyer node in inter-provincial spot market transactions during the trading period:

[0024] In the formula, The total output that the seller node can provide. This represents the total electricity load demand of the buyer's node; Declaring the proportion of clean energy electricity :

[0025] In the formula, For the daily reported clean energy electricity, This refers to the total electricity generated by various types of generators as reported daily. Seller's standby capacity percentage :

[0026] In the formula, This refers to the reserve capacity of generating units that the seller, in addition to meeting the electricity demand of the winning bid, reserves for use in response to insufficient output from new energy sources or unforeseen circumstances. The total installed capacity of the units declared by the seller's node; Buyer / Seller Transaction Completion Rate :

[0027] In the formula, The actual transaction volume for the buyer / seller. The declared electricity volume for the buyer / seller; Inter-provincial market maximum available capacity change rate : The rate of change of the maximum total output that inter-provincial market-based trading nodes can provide within the market trading cycle.

[0028]

[0029] In the formula, This represents the total maximum available capacity for the current transaction period. This represents the total maximum available capacity of the previous trading period. This indicates the relative rate of change of the maximum available capacity between the current trading period and the previous trading period.

[0030] Furthermore, the calculation of market power risk indicators based on market data includes: Market participant participation :

[0031] In the formula, To determine the number of market participants actually involved in spot market transactions during the statistical period, This represents the total number of market participants eligible to engage in spot market transactions within the statistical period. Maximum price difference :

[0032] In the formula, This refers to the highest electricity price quoted by the seller in a single transaction. This refers to the lowest electricity purchase price quoted by the buyer in a single transaction. Retention rate This indicates the ratio of the electricity declared by the seller node to the predicted surplus electricity:

[0033] In the formula, This refers to the actual amount of electricity sold by the seller node in the electricity market. The remaining electricity available for market sale as predicted by the seller node; The ratio of market entities' revenue per kilowatt-hour to the average annual social revenue :

[0034] In the formula, The actual profitability per unit of electricity for a single seller node. To reflect the average profitability level of the whole society; The ratio of electricity purchased in the spot market to electricity purchased under medium- and long-term contracts :

[0035] In the formula, This refers to the amount of electricity purchased or sold as declared by the seller or buyer in the spot market. This refers to the amount of electricity purchased or sold under medium- or long-term contracts. The HHI index is the sum of the squares of the market shares of all participating power-transmitting and power-receiving nodes, used to reflect the market competition situation.

[0036] In the formula, t represents the HHI index for time period t, and n represents the number of power supply nodes / power receiving nodes participating in the competition. This represents the share of available generating capacity of the j-th power transmission node, or the market share of the power transmission node. The TOP-M metric is the sum of the market shares of the m largest power transmission nodes / receiving nodes, reflecting the overall market concentration.

[0037] In the formula, The market share of the j-th power transmission node is the percentage of the available power generation capacity of the power transmission node to the total available power generation capacity participating in the competition, or the percentage of the electricity volume of the contracted users of the power transmission node to the market electricity volume. RSI (Relative Strength Index): The ratio of the sum of the market shares of all power transmission nodes except for a specific node in the electricity market during a given period to the total market demand.

[0038] In the formula, n is the number of power transmission nodes in the market; Let J be the declared capacity of the j-th power transmission node; denoted as the declared capacity of the i-th power transmission node; D represents the maximum value of the market-based electricity load predicted by the provincial market operation agency.

[0039] Furthermore, the weighting coefficients for calculating the risk assessment index values ​​of the multi-level spot market using the improved entropy weight method include: When identifying risks in multi-tiered spot markets, an improved entropy weight method is used to determine the weights of various market risk indicators. After determining the weights, these market risk indicators are substituted into a comprehensive market risk evaluation model to identify the risk type and level. The specific calculation steps are as follows: First, let's look at each risk indicator. Standardization is implemented, and a set of One risk indicator, The evaluation object is calculated. The first item under the indicator The standardized formula for the weighting of each evaluation object is:

[0040] Calculate the first Information entropy value of the indicator The calculation formula is:

[0041] In the formula, when season

[0042] Calculate the first Original indicator weight coefficients of the item The calculation formula is:

[0043] Calculate the index weight coefficients using the improved entropy weight method. The calculation formula is:

[0044] In the formula and The calculation formula is:

[0045]

[0046] in, This represents the number of indicators whose information entropy value is not equal to 1.

[0047] Furthermore, the calculation of a comprehensive market risk assessment value based on the risk assessment index values ​​and weighting coefficients of the multi-tiered spot market, and the identification of risk types and risk levels based on the comprehensive market risk assessment value, includes: The weight coefficients of each indicator were calculated using the improved entropy weight method. Then calculate the risk assessment value T for each market; Risk assessment value of inter-provincial or provincial spot markets The calculation formula is:

[0048] Risk assessment values ​​for spot markets at various levels based on the quartile method Rating; 1> When the value is ≥0.9, the risk level is high; when the value is >0.9, the risk level is high. When the value is ≥0.6, the risk level is relatively high; when the value is >0.6, the risk level is relatively high. When the value is ≥0.3, the risk level is low; when the value is >0.3, the risk level is low. When the value is ≥0, the risk level is low.

[0049] Secondly, the present invention provides a multi-level spot market risk identification system, comprising: The data acquisition module is used to read market data from multi-level spot markets, including bidding information, market clearing results, available capacity of inter-regional channels, and unit parameters in inter-provincial and provincial markets. The indicator calculation module is used to calculate risk assessment indicator values ​​for multi-level spot markets based on market data. The risk assessment indicator values ​​include market price risk indicators, multi-level market correlation risk indicators, and market power risk indicators. The weighting coefficient acquisition module is used to calculate the weighting coefficients of risk assessment index values ​​for multi-level spot markets using the improved entropy weighting method. The early warning output module is used to calculate the comprehensive market risk assessment value based on the risk assessment index values ​​and weighting coefficients of the multi-level spot market, and to identify the risk type and risk level based on the comprehensive market risk assessment value.

[0050] Thirdly, the present invention provides 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 multi-level spot market risk identification method.

[0051] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-level spot market risk identification method.

[0052] Compared with the prior art, the present invention has the following technical effects: This invention proposes a multi-level spot market risk identification method and system. It establishes a multi-level spot market risk assessment index system consisting of three major categories of risk indicators: market price risk, multi-level market correlation risk, and market power risk. An improved entropy weight method is used to determine the weight of each indicator. Then, the values ​​of various market risk indicators are substituted into the formula to calculate the market risk indicator assessment value. The market risk type and risk level are identified based on the assessment values ​​of the three major categories of risk indicators.

[0053] In terms of identifying associated risks in multi-level spot markets, this invention proposes risk assessment indicators such as the utilization rate of inter-regional power transmission channels, the supply-demand ratio of inter-provincial markets, the proportion of declared clean energy power, the proportion of sellers' reserve capacity, the buyer / seller transaction completion rate, and the change rate of the maximum available capacity in inter-provincial markets. These indicators quantify the associated risks in the operation of inter-provincial and provincial spot markets and can effectively identify associated risks in multi-level spot markets. Attached Figure Description

[0054] Figure 1 This is a logic block diagram of the present invention.

[0055] Figure 2 This is a flowchart of the present invention.

[0056] Figure 3 These are the calculated risk index values ​​for the three scenarios described in this invention. Detailed Implementation

[0057] The present invention will be further described below with reference to the accompanying drawings: Explanation of related terms Multi-tiered spot market: my country's electricity spot market consists of two tiers: inter-provincial spot markets and provincial spot markets. These two tiers operate collaboratively through mechanisms such as transaction timing coordination and security verification. In the future, some provincial spot markets will also conduct load-side spot market transactions, forming a multi-tiered spot market with the inter-provincial and provincial spot markets.

[0058] Inter-regional and inter-provincial spot markets: mainly responsible for inter-regional power trading, organized and operated through the State Grid Dispatch and Control Center, mainly carrying out renewable energy power generation trading, and utilizing the surplus transmission capacity of inter-regional channels to conduct inter-regional and inter-provincial trading of clean energy power generation.

[0059] Provincial spot market: mainly responsible for spot market transactions within the province, aiming to minimize the cost of power generation within the province, and conducts real-time or short-term power transactions through market-based means to reflect the immediate changes in power supply and demand.

[0060] Spot market risk identification: refers to the dynamic discovery, screening and identification of various risks that may exist in the spot market trading process, such as price risk, credit risk, market power risk, supply and demand imbalance risk, etc.

[0061] Analysis period: refers to the trading period of the spot market. The trading period of the inter-provincial / provincial day-ahead spot market is 24 hours with one data point every 15 minutes. One analysis period contains a total of 96 points.

[0062] Example 1, please refer to Figure 2 This invention provides a multi-level spot market risk identification method, comprising: Read market data from multi-level spot markets, including bidding information, market clearing results, available capacity of inter-regional channels, and unit parameters of inter-provincial and provincial markets. Based on market data, risk assessment index values ​​for multi-level spot markets are calculated. These risk assessment index values ​​include market price risk index values, multi-level market correlation risk index values, and market power risk index values. The weighting coefficients of risk assessment indicators for multi-level spot markets are calculated using an improved entropy weighting method. Based on the risk assessment index values ​​and weighting coefficients of the aforementioned multi-level spot market, a comprehensive market risk assessment value is calculated, and risk types and risk levels are identified according to the comprehensive market risk assessment value.

[0063] This invention proposes a multi-level spot market risk identification method and system. It establishes a multi-level spot market risk indicator system consisting of three major categories of risk indicators: market price risk, multi-level market correlation risk, and market power risk. An improved entropy weight method is used to determine the weight of each indicator. Then, the values ​​of various market risk indicators are substituted into the formula to calculate the comprehensive market risk assessment value. The market risk type and risk level are identified based on the assessment values ​​of the three major categories of risk indicators.

[0064] Example 2: This invention provides a multi-level spot market risk identification method, comprising: Step 1: Read various market data, including bidding information, market clearing results, available capacity of inter-regional channels, and unit parameters in inter-provincial and provincial markets; Step 2: Calculate market price risk indicators. Substitute the market data from Step 1 into the formula to calculate the risk assessment indicators such as the difference between transaction prices and bids in inter-provincial and provincial markets, the peak-valley difference in transaction electricity prices, basic statistical indicators of transaction electricity prices, the difference between bid electricity prices and average bid electricity prices, the growth rate of thermal power generation costs, the Lerner Index, historical price volatility, the proportion of high transaction electricity prices and high bid electricity prices, the electricity price limit rate, and the day-ahead / intra-day transaction volume ratio. Step 3: Calculate the risk index values ​​of multi-level market associations. Substitute the market data from Step 1 into the formula to calculate the risk assessment index values ​​of inter-provincial market and provincial market, such as the utilization rate of inter-regional transmission channels, the supply-demand ratio of inter-provincial market, the proportion of declared clean energy electricity, the proportion of seller's reserve capacity, the buyer / seller transaction completion rate, and the change rate of maximum available capacity in inter-provincial market. Step 4: Calculate market power risk indicators. Substitute the market data from Step 1 into the formula to calculate the participation rate of market participants in inter-provincial markets and provincial markets, the maximum bid difference, the retention ratio, the ratio of market participants' revenue per kilowatt-hour to the average annual social revenue, the ratio of electricity purchased in the spot market to electricity purchased under medium- and long-term contracts, the HHI index, the TOP-M index, the RSI index, and other risk assessment indicators. Step 5: Calculate the weighting coefficients of each risk indicator using the improved entropy weighting method formula; Step 6: Substitute the various risk assessment index values ​​calculated in Steps 2, 3, and 4, and the weighting coefficients calculated in Step 5, into the formula to calculate the comprehensive market risk assessment value. Based on the comprehensive market risk assessment value, identify the market risk type and risk level. Step 7: Provide market risk warning information for medium and high risk situations in inter-provincial markets and provincial markets.

[0065] Specifically: A multi-level market risk assessment indicator system was constructed based on three major categories of indicators: market price risk indicators, multi-level market linkage risk indicators, and market power risk indicators.

[0066] Market price risk indicators include: the difference between the transaction price and the quoted price, the peak-valley difference in the transaction price, basic statistical indicators of the transaction price, the difference between the declared price and the average declared price, the growth rate of thermal power generation costs, the Lerner Index, historical price volatility, the proportion of high transaction prices and high declared prices, the price limit rate, and the day-ahead / intra-day transaction volume ratio, among other risk assessment indicators.

[0067] Multi-level market-related risk indicators include risk assessment indicators such as the utilization rate of inter-regional power transmission channels, the supply-demand ratio of inter-provincial markets, the proportion of declared clean energy power, the proportion of seller's reserve capacity, the buyer / seller transaction completion rate, and the change rate of maximum available capacity in inter-provincial markets.

[0068] Market power risk indicators include market participant participation, maximum bid-ask spread, retention rate, ratio of market participant revenue per kilowatt-hour to average annual social revenue, ratio of spot market purchase volume to medium- and long-term contract purchase volume, HHI index, TOP-M index, RSI index, and other risk assessment indicators.

[0069] Market price risk indicators Difference between transaction price and quoted price This is the difference between the final average bid price of the seller and the discounted average transaction price, which represents the profit that the seller can obtain. This indicator reflects the difference between the transaction price and the marginal cost.

[0070] (Equation 1) In the formula, Average price quoted by the seller node (unit: yuan / MWh). The average transaction price after conversion at this node (unit: yuan / MWh).

[0071] Peak-valley difference in transaction electricity price This is the difference between the peak-valley difference of the daily transaction electricity price and the average peak-valley difference of the monthly transaction electricity price. This indicator reflects market transparency. When the range is too large, there may be extremely low or extremely high electricity purchase prices, which may affect the stable operation of the market.

[0072] (Equation 2) In the formula, The peak-valley difference in the daily electricity transaction price (unit: yuan / MWh). The average peak-to-valley difference in monthly electricity transaction price (unit: yuan / MWh).

[0073] Basic statistical indicators of transaction electricity price The average electricity price is the average transaction price within the analysis period, which helps to understand the general price trend in the market; the standard deviation of the electricity price is used to measure the volatility of the electricity price, and the larger the standard deviation, the more drastic the price fluctuation; the extreme values ​​of the electricity price are the maximum and minimum transaction prices within the analysis period, which are used to identify abnormal market price risks.

[0074] Average electricity price: (Equation 3) In the formula, The analysis focuses on the transaction electricity price (unit: yuan / MWh) for each trading period within the analysis cycle. To analyze the transaction electricity price points within the analysis period.

[0075] Electricity price standard deviation: (Equation 4) In the formula, The analysis focuses on the transaction electricity price (unit: yuan / MWh) for each trading period within the analysis cycle. To analyze the transaction electricity price points within the analysis period, This represents the average electricity price.

[0076] Extreme electricity prices: (Equation 5) Difference between declared electricity price and average declared electricity price This reflects the different reporting behaviors of market entities. A larger difference indicates that the behavior of market entities is more deviant and the market price risk is greater.

[0077] (Equation 6) In the formula, For the electricity price declared by market entities, The total number of electricity prices declared by all market participants; Thermal power generation cost growth rate The cost of thermal power generation is directly reflected in market prices. The higher the cost of thermal power generation, the greater the risk of rising market prices.

[0078] (Equation 7) In the formula, Based on the current cost of thermal power generation, This indicates the average cost of thermal power generation in the previous year or the cost of thermal power generation as calculated by market operators.

[0079] Lerner Index The Lerner Index is the ratio of electricity price to marginal cost. The higher the Lerner Index, the greater the market price risk.

[0080] (Equation 8) In the formula, For electricity price, This refers to marginal cost.

[0081] Historical price volatility : Reflects the volatility of prices over a certain historical period.

[0082] (Equation 9) In the formula, It is the price at time i. It is the average price over a historical period. It refers to the number of moments in a historical period.

[0083] The proportion of high-priced electricity transactions and high-priced electricity applications This refers to the proportion of high-price trading sessions within a daily trading period to the total number of trading sessions. The same applies to the indicator of the proportion of high-price declarations.

[0084] (Equation 10) (Equation 11) In the formula This is the threshold for setting high electricity prices.

[0085] Electricity price limit rate This refers to the proportion of periods when the transaction price is close to the maximum price limit to the total number of bidding periods. This indicator reflects abnormal electricity prices caused by tight market supply and demand, or the risk of price gouging on the generation side.

[0086] (Equation 12) (Equation 13) (Equation 14) Daily intraday volume ratio This reflects the degree of adjustment in trading volume. The higher the proportion of trading volume within a day, the weaker the stability and the higher the market risk.

[0087] (Equation 15) In the formula, The daily transaction volume is in MWh. The daily transaction volume is expressed in MWh.

[0088] Multi-level market-related risk indicators Inter-regional power transmission channel utilization rate This reflects the proportion of transaction transmission capacity in inter-regional power transmission channels. The higher the utilization rate, the higher the efficiency of inter-provincial transactions.

[0089] (Equation 16) In the formula, This represents the actual transaction transmission capacity completed by the inter-regional power transmission channel within a certain time period. The theoretical maximum transmission capacity of the inter-regional power transmission channel during this time period is usually determined based on the design capacity or scheduling plan.

[0090] inter-provincial market supply and demand ratio This refers to the ratio of the power output provided by the seller node to the total electricity load demand of the buyer node in the inter-provincial spot market transactions during the trading period.

[0091] (Equation 17) In the formula, The total output that the seller node can provide. This represents the total electricity load demand of the buyer's node.

[0092] The risk of a low supply-demand ratio in the inter-provincial market, with insufficient supply potentially leading to the following problems: 1) Power shortages in the selling province, making it difficult to meet the purchasing province's electricity demand, which may trigger power rationing or load adjustments in the provincial power grid. 2) Abnormal fluctuations in electricity prices; under tight market conditions, electricity prices may rise rapidly, affecting the stability of the spot market. 3) Increased dispatching pressure, requiring the use of backup power sources and inter-provincial power support to make up for the power gap, but increasing the complexity of power grid operation and electricity purchase costs.

[0093] The high supply-demand ratio in the inter-provincial market poses a risk, with oversupply potentially leading to the following problems: 1) Waste of electricity resources, as surplus electricity in some provinces cannot be absorbed, potentially resulting in wind and solar power curtailment. 2) Risk of excessively low electricity prices, as oversupply may depress winning bid prices, affecting sellers' revenue and long-term investment incentives.

[0094] Declaring the proportion of clean energy electricity This reflects the situation where the amount of new energy power generation fails to meet the standards due to the discrepancy between the predicted and actual output of clean energy. The greater the proportion of clean energy power generation, the greater this type of risk.

[0095] (Equation 18) In the formula, For the daily reported clean energy electricity, This refers to the total electricity generated by various types of generators as reported daily.

[0096] Seller's standby capacity percentage This reflects the size of the standby generating capacity of the unit when the seller's new energy output is insufficient. The smaller the ratio, the greater the market operation risk.

[0097] (Equation 19) In the formula, This refers to the reserve capacity of generating units that the seller, in addition to meeting the electricity demand of the winning bid, reserves for use in response to insufficient output from new energy sources or unforeseen circumstances. This refers to the total installed capacity of the units declared by the seller's node. If... A smaller value indicates that the seller's reserve capacity is insufficient, which may make it difficult to cope with fluctuations in new energy sources or sudden power demand, and poses a higher risk.

[0098] Buyer / Seller Transaction Completion Rate : Divided into buyer and seller transaction completion rates.

[0099] (Equation 20) In the formula, The actual electricity volume transacted by the buyer / seller (unit: MWh). The declared electricity volume for the buyer / seller (unit: MWh).

[0100] Power balance risk (low buyer agreement rate): A low buyer agreement rate indicates that the demand for electricity cannot be fully met, which may lead to the risk of power shortage, especially when there are large fluctuations in peak load or new energy sources. This indicator needs to be closely monitored.

[0101] Risk of renewable energy consumption (low sellers' achievement rate): A low sellers' achievement rate indicates that the buyer provinces are not willing to consume surplus renewable energy, which may lead to problems such as wind and solar power curtailment in the seller provinces and affect the stable operation of the inter-provincial spot market.

[0102] Inter-provincial market maximum available capacity change rate : The rate of change of the maximum total output that inter-provincial market-based trading nodes can provide within the market trading cycle.

[0103] (Equation 21) In the formula, This represents the total maximum available capacity for the current transaction period. This represents the total maximum available capacity of the previous trading period. This indicates the relative rate of change of the maximum available capacity between the current trading period and the previous trading period.

[0104] The risk of this indicator remaining negative: If A persistently negative value indicates a continuous decrease in the maximum available capacity of the inter-provincial market, which may lead to the following problems: 1) Insufficient power supply, with declining supply capacity making it difficult to meet demand. 2) Abnormal electricity price fluctuations, as insufficient supply may trigger rapid price increases, affecting the price stability of the inter-provincial market. 3) System security risks, as insufficient power supply capacity may lead to frequency fluctuations, increasing the risk of grid operation.

[0105] The risk of excessive volatility in this indicator: If The sharp fluctuations indicate that the maximum available capacity of inter-provincial market-based trading nodes is unstable, which may be caused by the following factors: 1) Fluctuations in renewable energy output: such as wind power and photovoltaic power being significantly affected by weather. 2) Unit failures: such as unit failures or maintenance leading to capacity changes. 3) Changes in load demand: supply and demand imbalances may cause pressure on grid dispatching.

[0106] like A positive value indicates that the maximum available capacity of the inter-provincial market has increased, the power supply capacity has been enhanced, and the market operation risk has been reduced.

[0107] Market power risk indicators Market participant participation The ratio of market participants in the spot market to the total number of market participants eligible to participate in spot trading during the statistical period. The smaller the ratio, the lower the participation rate of market participants and the greater the market risk.

[0108] (Equation 22) In the formula, To determine the number of market participants actually involved in spot market transactions during the statistical period, This refers to the total number of market participants eligible to engage in spot market transactions within the statistical period.

[0109] Maximum price difference The maximum bid-ask spread occurs within a single transaction. This includes extreme actions by buyers, such as purchasing electricity at extremely low prices by violating market rules; and extreme actions by sellers, such as price gouging by sellers when buyers urgently need to access electricity due to unforeseen events or extreme weather. A larger maximum bid-ask spread indicates greater market risk.

[0110] (Equation 23) In the formula, This refers to the highest electricity price quoted by the seller in a single transaction. This represents the lowest electricity purchase price quoted by the buyer in a single transaction.

[0111] Retention rate This represents the ratio of the electricity declared by the seller node to the predicted surplus electricity, reflecting the seller node's control over the electricity sold in the market. The higher the retention ratio, the greater the risk.

[0112] (Equation 24) In the formula, This refers to the actual amount of electricity sold by the seller node in the electricity market. The remaining electricity available for market sale is predicted for seller nodes. It is usually predicted based on information such as generation plans and load forecasts, reflecting their theoretical power supply capacity.

[0113] A higher retention rate indicates that the seller node's declared electricity volume is close to or exceeds its predicted surplus electricity volume, which may reflect its tendency to control electricity sales or its behavior of reducing market supply to push up electricity prices.

[0114] A lower retention ratio indicates that the electricity declared by seller nodes is far lower than their predicted surplus electricity, suggesting limited control over the electricity market and potentially leading to a waste of surplus electricity. An excessively high retention ratio increases market risk, such as abnormal price fluctuations and market manipulation.

[0115] The ratio of market entities' revenue per kilowatt-hour to the average annual social revenue This reflects whether the seller's profit at the node is significantly higher than the social average return for an extended period; the higher the ratio, the greater the risk.

[0116] (Equation 25) In the formula, The actual profitability per unit of electricity for a single seller node. To reflect the average profitability level of the whole society, it is used to compare whether the profits of individual entities are too high.

[0117] The ratio of electricity purchased in the spot market to electricity purchased under medium- and long-term contracts This reflects the degree of drastic changes in the electricity supply and demand situation of market participants; the higher the ratio, the greater the risk.

[0118] (Equation 26) In the formula, This refers to the amount of electricity purchased or sold by the seller or buyer in the spot market (unit: MWh). This refers to the electricity volume (unit: MWh) that has been signed in medium- and long-term contracts for the purchase or sale of electricity.

[0119] HHI index The summation of the squares of the market share of all participating power transmission / receiving nodes is used to reflect the market competition situation.

[0120] (Equation 27) In the formula, t represents the HHI index for time period t, and n represents the number of power supply nodes / power receiving nodes participating in the competition. This represents the available generation capacity share, or market share, of the j-th power transmission node. Available generation capacity refers to the sum of the actual generation capacity and reserve capacity that the power transmission node can provide to the grid during a specific time period. This reflects the power transmission capacity of the power transmission node.

[0121] TOP-M index : This represents the sum of the market share of the m largest power transmission nodes / power receiving nodes, reflecting the overall market concentration.

[0122] (Equation 28) In the formula, The market share of the j-th power transmission node is the percentage of the available generating capacity of the power transmission node to the total available generating capacity participating in the competition, or the percentage of the electricity volume of the contracted users of the power transmission node to the total electricity volume of the market.

[0123] RSI index The ratio of the sum of the market share of all power transmission nodes except for a certain power transmission node to the total market demand in a certain period of the electricity market can be used to identify power transmission nodes with market power and the magnitude of their market power.

[0124] (Equation 29) In the formula, n is the number of power transmission nodes in the market; Let J be the declared capacity of the j-th power transmission node; denoted as the declared capacity of the i-th power transmission node; D represents the maximum value of the market-based electricity load (bidding space) predicted by the provincial market operation agency.

[0125] Multi-tiered spot market risk identification methods When identifying risks in multi-tiered spot markets, an improved entropy weight method is used to determine the weights of various market risk indicators. After determining the weights, the various market risk indicators are substituted into the comprehensive market risk evaluation model to identify the risk type and level.

[0126] In information theory, entropy is a measure of the degree of disorder in a system. Entropy weighting is an objective weighting method that determines the weight of an indicator based on its variability. For a risk indicator, the smaller its entropy value, the greater the information it provides, and the greater its weight; conversely, the larger the entropy value, the smaller the weight. Traditional entropy weighting methods, when calculating the weights of indicators with entropy values ​​approaching 1, suffer from significant variations in objective weights due to small differences in entropy values. The improved entropy weighting method effectively overcomes the shortcomings of traditional methods while maintaining its ability to distinguish between entropy value differences. The specific calculation steps are as follows.

[0127] (1) Standardization of indicators. First, standardize each risk indicator. Standardization is implemented, and a set of One risk indicator, The evaluation object is calculated. The first item under the indicator The standardized formula for the weighting of each evaluation object is: (Equation 30) (2) Calculate the first Information entropy value of the indicator The calculation formula is: (Equation 31) In the formula, when season

[0128] (3) Calculate the first Original indicator weight coefficients of the item The calculation formula is: (Equation 32) (4) Calculate the index weight coefficients using the improved entropy weight method. The calculation formula is: (Equation 33) In the formula and The calculation formula is: (Equation 34) (Equation 35) in, This represents the number of indicators whose information entropy value is not equal to 1.

[0129] (5) The weight coefficients of each indicator are calculated using the improved entropy weight method. Then calculate the risk assessment value T for each market. Risk assessment value of inter-provincial or provincial spot markets The calculation formula is: (Equation 36) Risk assessment values ​​for spot markets at various levels based on the quartile method Rating will be conducted. Rule 1 > When the value is ≥0.9, the risk level is high (red alert); when the value is >0.9, the risk level is low. When the value is ≥0.6, the risk level is relatively high (yellow alert); when the value is >0.6, the risk level is considered high. When the value is ≥0.3, the risk level is low (blue alert); when the value is >0.3, the risk level is low. When the value is ≥0, the risk level is low (no warning).

[0130] A multi-level spot market risk identification method is proposed, and a multi-level spot market risk assessment index system is established, consisting of three major risk indicators: market price risk, multi-level market correlation risk, and market power risk. An improved entropy weight method is used to determine the weights of various risk indicators. The improved entropy weight method can effectively overcome the shortcomings of the traditional entropy weight method, while maintaining the ability of the entropy weight method to distinguish entropy value differences. In terms of identifying associated risks in multi-level spot markets, this invention proposes risk assessment indicators such as the utilization rate of inter-regional power transmission channels, the supply-demand ratio of inter-provincial markets, the proportion of declared clean energy power, the proportion of sellers' reserve capacity, the buyer / seller transaction completion rate, and the change rate of the maximum available capacity in inter-provincial markets. These indicators quantify the associated risks in the operation of inter-provincial and provincial spot markets and can effectively identify associated risks in multi-level spot markets.

[0131] Example 3: A case study of a multi-tiered spot market risk identification method: Market risk indicator weight calculation

[0132] The risk indicator calculation formula is derived by substituting transaction data from the same period over three days. Finally, by inputting transaction data from different scenarios, the risk value of each transaction can be calculated, thereby quantifying the risks involved in trading.

[0133] Risk level identification in different transaction scenarios

[0134] The calculated risk index values ​​are listed for three scenarios to identify the risk level of each scenario, such as... Figure 3 As shown.

[0135] Scenario 1: The risk level is identified as low risk, the supply and demand relationship is relatively balanced, and the market price fluctuates little; market participants behave in a standardized manner, and there is little abuse of market power; the risk of multi-level market linkages is low, and there is little external interference.

[0136] Scenario 2: The risk level is identified as medium risk. The market power and price risks are relatively high compared to other scenarios. There is a risk of significant price fluctuations. Market participants may abuse their market power, leading to unfair competition in the market. This may be positively correlated with price fluctuations.

[0137] Scenario 3: The risk level is identified as low, with some imbalance between supply and demand, but less severe than in Scenario 2. Market participants behave relatively well, but there is some abuse of market power; the risk of multi-level market linkages is lowest.

[0138] In another embodiment of the present invention, a multi-level spot market risk identification system is provided, which can be used to implement the above-described multi-level spot market risk identification method. Specifically, the system includes: The data acquisition module is used to read market data from multi-level spot markets, including bidding information, market clearing results, available capacity of inter-regional channels, and unit parameters in inter-provincial and provincial markets. The indicator calculation module is used to calculate risk assessment indicator values ​​for multi-level spot markets based on market data. The risk assessment indicator values ​​include market price risk indicators, multi-level market correlation risk indicators, and market power risk indicators. The weighting coefficient acquisition module is used to calculate the weighting coefficients of risk assessment index values ​​for multi-level spot markets using the improved entropy weighting method. The early warning output module is used to calculate the comprehensive market risk assessment value based on the risk assessment index values ​​and weighting coefficients of the multi-level spot market, and to identify the risk type and risk level based on the comprehensive market risk assessment value.

[0139] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0140] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a multi-level spot market risk identification method.

[0141] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the multi-level spot market risk identification method in the above embodiments.

[0142] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0144] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0145] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-level spot market risk identification method, characterized by, The method comprises the following steps: reading market data of a multi-level spot market, wherein the market data comprises bidding declaration information, market clearing results, cross-zone passage available capacity and unit parameters of inter-provincial markets and provincial markets; calculating risk assessment index values of the multi-level spot market based on the market data, wherein the risk assessment index values comprise market price risk index values, multi-level market correlation risk index values and market force risk index values; calculating weight coefficients of the risk assessment index values of the multi-level spot market by using an improved entropy weight method; calculating a market risk comprehensive evaluation value based on the risk assessment index values and the weight coefficients of the multi-level spot market, and identifying a risk type and a risk level according to the market risk comprehensive evaluation value.

2. The multi-level spot market risk identification method of claim 1, wherein, The method for calculating the market price risk index values based on the market data comprises the following steps: a difference between a transaction price and a bid price, a difference between a transaction price and a peak-valley price, basic statistical indicators of a transaction price, a difference between a bid price and an average bid price, a thermal power generation cost growth rate, a Lerner index, a price historical fluctuation rate, a proportion of a transaction high price to a bid high price, a price limit rate, and a ratio of a day-ahead transaction electricity quantity to an intraday transaction electricity quantity; Difference between transaction price and offer Difference between average offer and average transaction price after conversion for final seller node: In the formula, is the average offer price of the seller nodes, is the average transaction price of the node after conversion; peak-valley difference of transaction price Difference between peak-valley difference of transaction price for the day and average peak-valley difference of monthly transaction price: In the formula, is the peak-valley difference of the daily transaction price, is the average peak-valley difference of the monthly transaction price; the basic statistical indicators of the transaction price are a price mean value, a price standard deviation and a price extreme value; the price mean value is an average value of transaction prices in an analysis period, the price standard deviation is used to measure the volatility of the prices, and a larger standard deviation indicates a more intense price fluctuation; and the price extreme value is a maximum value and a minimum value of the transaction prices in the analysis period, and is used to identify market price abnormal risk; Electricity price average: In the formula, To analyze the transaction price of each transaction period in the analysis period, To analyze the transaction price points in the analysis period; Electricity price standard deviation: In the formula, To analyze the transaction price per unit of electricity for each transaction period within the analysis period, To analyze the number of points of the transaction price within the analysis period, is the average price of electricity; Price extremum: Difference between declared price and average declared price : In the formula, is the declared price of the market subject, is the total number of declared prices of all market subjects; Cost growth rate of thermal power generation : In the formula, is the current cost of thermal power generation, represents the average value of the cost of thermal power generation in the previous year or the cost of thermal power generation counted by the market operation agency; Lerner index The ratio of price to marginal cost wherein P is the electricity price, Pm is the marginal cost; the price historical fluctuation rate is a ratio of a maximum price fluctuation range to an average price fluctuation range in the analysis period; wherein is the price at the i-th time point, is the average price of the historical time period, is the number of time points of the historical time period; The proportion of high electricity price and declared high electricity price The proportion of the number of high electricity price periods in the total number of trading periods within the daily trading period; In the formula is a set high electricity price threshold, is the total number of transaction periods; Electricity price reaches the limit rate The proportion of the number of periods when the transaction electricity price is close to the highest limit price to the total number of bidding periods ratio of daily intraday traded energy : In the formula, is the day-ahead traded power, is the intraday traded power.

3. The multi-level spot market risk identification method of claim 1, wherein, The method for calculating the multi-level market correlation risk index values based on the market data comprises the following steps: Cross-zone power transmission channel utilization : In the formula, is the actual transmission capacity of the cross-zone transmission channel for the transaction in a time period, is the theoretical maximum transmission capacity of the cross-zone transmission channel in the time period; inter-provincial market supply-demand ratio The ratio of the total power supply of the seller nodes in the inter-provincial spot market transaction in the transaction period to the total demand of the power load of the buyer nodes In the formula, total power available to the seller node, total demand for electricity load by the buyer node; Reported clean energy power ratio : In the formula, is the daily declared clean energy power, is the total power of each type of power generation declared daily; Seller reserve capacity percentage : In the formula, refers to the spare generating capacity of the seller to cope with insufficient new energy output or unexpected situations in addition to meeting the demand for winning electricity, Total installed capacity of the seller node declared Buyer / seller transaction achievement rate : wherein is the actual traded volume for the buyer / seller, is the reported volume for the buyer / seller; inter-provincial market maximum available capacity change rate : the change rate of the total maximum output available for the inter-provincial market transaction node within the market transaction period; wherein is the sum of the maximum available capacities of the current transaction period, is the sum of the maximum available capacities of the previous transaction period, denotes the relative change rate of the maximum available capacities between the current transaction period and the previous transaction period.

4. The multi-level spot market risk identification method of claim 1, wherein, The method for calculating the market force risk index values based on the market data comprises the following steps: Market participant engagement : In the formula, is the number of market participants actually participating in spot market transactions in the statistical period, is the total number of market participants that can participate in spot market transactions in the statistical period; Maximum bid spread : wherein is the highest selling price of electricity by the seller subject in a single transaction, is the lowest buying price of electricity by the buyer subject in a single transaction; Retention ratio Represents the ratio of the amount of electricity declared by the seller node to the predicted rich electricity In the formula, is the actual amount of electricity sold by the seller node in the electricity market, is the remaining amount of electricity predicted by the seller node that can be used for market electricity sales; The ratio of the market subject's electricity income to the average annual income of the society : In the formula, actual profitability of a single seller node per unit of electricity, reflecting the average profitability of the entire society; The ratio of spot market electricity purchase volume to medium and long-term contract electricity purchase volume : wherein is the amount of electricity bought or sold by the seller or buyer in the spot market, is the amount of electricity bought or sold in the medium and long term contract; an HHI index is a sum of squares of market shares of all participating market sending nodes / receiving nodes, and is used to reflect market competition: In the formula, HHI index for time period t, n is the number of power transmitting nodes / power receiving nodes participating in the competition; is the available power generation capacity share of the jth power transmitting node, or the market share of the power transmitting node; a TOP-M index is a sum of market shares of m sending nodes / receiving nodes with the largest market shares in the market, and is used to reflect overall market concentration: In the formula, The market share of the jth power supply node in descending order of market share of the power supply node / power receiving node j, the market share being the proportion of the available power generation capacity of the power supply node to the total available power generation capacity participating in the competition, or the proportion of the electricity scale of the contracted users of the power supply node to the market electricity scale. an RSI index is a ratio of a sum of market shares of all sending nodes except a certain sending node to total market demand in the power market: In the formula, n is the number of power transmission nodes in the market; is the declared capacity of the jth power transmission node; is the declared capacity of the ith power transmission node; and D represents the maximum value of the marketized power load predicted by the provincial market operation agency.

5. The multi-level spot market risk identification method of claim 1, wherein, The method for calculating the weight coefficients of the risk assessment index values of the multi-level spot market by using the improved entropy weight method comprises the following steps: When the multi-level spot market is identified, the improved entropy weight method is used to determine index weights of all types of market risk indexes, the market risk comprehensive evaluation model is used to identify a risk type and a risk level after the weights are determined, and the specific calculation steps are as follows: First, each risk index is standardized , and there are risk indexes, evaluation objects, and the proportion of the first evaluation object under the first index is calculated, and the standardization formula is: The information entropy value of the item index is calculated The information entropy value of the item index is calculated The information entropy value of the item index is calculated wherein when then The original index weight coefficient of the item index is calculated The original index weight coefficient of the item index is calculated The original index weight coefficient of the item index is calculated The improved entropy weight method is used to calculate the index weight coefficient The calculation formula is: In the formula and The calculation formula is: wherein is the number of indices for which the information entropy value is not equal to 1.

6. The multi-level spot market risk identification method of claim 5, wherein, The method for calculating the market risk comprehensive evaluation value based on the risk assessment index values and the weight coefficients of the multi-level spot market, and identifying a risk type and a risk level according to the market risk comprehensive evaluation value comprises the following steps: The weight coefficient of each index is calculated by using the improved entropy weight method The risk assessment value T of each market is calculated The risk assessment value of inter-provincial or provincial spot market The calculation formula is as follows: Risk assessment values of each level spot market based on quartile method Perform rating; 1> ≥ 0.9, risk level is high; 0.9> ≥ 0.6, risk level is higher; 0.6> ≥ 0.3, risk level is lower; 0.3> ≥ 0, risk level is low.

7. A multi-level spot market risk identification system characterized by, The method comprises the following steps: a data acquisition module is configured to read market data of a multi-level spot market, wherein the market data comprises bidding declaration information, market clearing results, cross-zone passage available capacity and unit parameters of inter-provincial markets and provincial markets; An index calculation module is configured to calculate risk assessment index values of a multi-level spot market based on market data, wherein the risk assessment index values include market price risk index values, multi-level market correlation risk index values, and market force risk index values; A weight coefficient acquisition module is configured to calculate weight coefficients of the risk assessment index values of the multi-level spot market by using an improved entropy weight method; An early warning output module is configured to calculate a market risk comprehensive assessment value based on the risk assessment index values and the weight coefficients of the multi-level spot market, and identify a risk type and a risk level according to the market risk comprehensive assessment value.

8. The multi-tiered spot market risk identification system of claim 7, wherein, The market price risk index values are calculated based on the market data, and include: a transaction price and a quoted price difference, a transaction electricity price peak-valley difference, a transaction electricity price basic statistical index, a declared electricity price and an average declared electricity price difference, a thermal power generation cost growth rate, a Lerner index, a price historical volatility rate, a transaction high electricity price and a declared high electricity price ratio, an electricity price limit rate, and a day-ahead / day-in transaction electricity quantity ratio; Difference between transaction price and offer Difference between average offer and average transaction price after conversion for final seller node: In the formula, is the average offer price of the seller nodes, is the average transaction price of the node after conversion; peak-valley difference of transaction price Difference between peak-valley difference of transaction price for the day and average peak-valley difference of monthly transaction price: wherein is the peak-valley difference of the day-ahead electricity price, is the average peak-valley difference of the monthly electricity price; the transaction electricity price basic statistical index is an electricity price average value, an electricity price standard deviation, and an electricity price extreme value; the electricity price average value is an average value of transaction electricity prices in an analysis period, the electricity price standard deviation is used to measure the volatility of the electricity price, and a larger standard deviation indicates a more intense price fluctuation; and the electricity price extreme value is a maximum value and a minimum value of transaction electricity prices in the analysis period, and is used to identify market price abnormal risk; Electricity price average: In the formula, is the transaction price of each transaction period in the analysis period, is the transaction price point number in the analysis period. Electricity price standard deviation: In the formula, To analyze the transaction price per unit of electricity for each transaction period within the analysis period, To analyze the number of points of the transaction price within the analysis period, is the average price of electricity; Price extremum: Difference between declared price and average declared price : In the formula, the declared price of the market subject, the total number of declared prices of all market subjects; Cost growth rate of thermal power generation : In the formula, is the current cost of thermal power generation, represents the average value of the cost of thermal power generation in the previous year or the cost of thermal power generation counted by the market operation agency; Lerner index The ratio of price to marginal cost wherein P is the electricity price, Pm is the marginal cost; the price historical volatility rate is calculated based on historical transaction electricity prices in a certain period of time; wherein is the price at the i-th time point, is the average price of the historical time period, is the number of time points of the historical time period; The proportion of the high electricity price and the declared high electricity price The proportion of the number of high electricity price periods in the total number of trading periods within the daily trading period; In the formula is a set high electricity price threshold; Electricity price reaches the limit rate The proportion of the number of periods when the transaction electricity price is close to the highest limit price to the total number of bidding periods ratio of daily intraday traded energy : In the formula, is the day-ahead traded power, is the intraday traded power.

9. The multi-tiered spot market risk identification system of claim 7, wherein, The multi-level market correlation risk index values are calculated based on the market data, and include: Cross-zone transmission channel utilization rate : In the formula, is the actual transmission capacity of the cross-zone transmission channel for the transaction completed in a certain period of time, is the theoretical maximum transmission capacity of the cross-zone transmission channel in the period of time; inter-provincial market supply-demand ratio The ratio of the total power supply of the seller nodes in the inter-provincial spot market transaction in the transaction period to the total demand of the power load of the buyer nodes: In the formula, total power available to the seller nodes, total demand for electricity load by the buyer nodes; Reported clean energy power ratio : In the formula, is the daily declared clean energy power, is the total power of each type of power generation declared daily; Seller reserve capacity percentage : In the formula, refers to the spare generating capacity of the seller to cope with insufficient new energy output or unexpected situations in addition to meeting the demand for winning electricity, Total installed capacity of the seller node declared Buyer / seller transaction achievement rate : wherein is the actual traded volume for the buyer / seller, is the reported volume for the buyer / seller; inter-provincial market maximum available capacity change rate : the change rate of the total maximum output available for the inter-provincial market transaction node within the market transaction period; In the formula, is the total sum of the maximum available capacities of the current transaction period, is the total sum of the maximum available capacities of the last transaction period, represents the relative change rate of the maximum available capacities between the current transaction period and the last transaction period.

10. The multi-tiered spot market risk identification system of claim 7, wherein, The market force risk index values are calculated based on the market data, and include: Market participant engagement : In the formula, is the number of market participants actually participating in spot market transactions in the statistical period, is the total number of market participants that can participate in spot market transactions in the statistical period; Maximum bid spread : wherein is the highest selling price of electricity by the seller subject in a single transaction, is the lowest buying price of electricity by the buyer subject in a single transaction; Retention ratio Represents the ratio of the amount of electricity declared by the seller node to the predicted amount of rich electricity: In the formula, is the actual amount of electricity sold by the seller node in the electricity market, is the remaining amount of electricity predicted by the seller node to be available for market sale. The ratio of the market subject's electricity income to the average annual income of the society : In the formula, actual profitability of a single seller node per unit of electricity, reflecting the average profitability of the entire society; The ratio of spot market electricity purchase volume to medium and long-term contract electricity purchase volume : In the formula, is the electricity purchase or sale quantity declared by the seller or buyer in the spot market, is the electricity purchase or sale quantity signed in the medium and long-term contract; an HHI index is a sum of squares of market shares of all participating market power supply nodes / power receiving nodes, and is used to reflect market competition; In the formula, HHI index for time period t, n is the number of power transmitting nodes / power receiving nodes participating in the competition; Available power generation capacity share of the jth power transmitting node, or market share of the power transmitting node; a TOP-M index is a sum of market shares of the top m power supply nodes / power receiving nodes with the largest market shares, and reflects overall market concentration; In the formula, The market share of the jth power supply node in descending order of market share of the power supply node / power receiving node j, the market share being the proportion of the available power generation capacity of the power supply node to the total available power generation capacity participating in the competition, or the proportion of the electricity scale of the contracted users of the power supply node to the market electricity scale. an RSI index is a ratio of a sum of market shares of all power supply nodes except for a certain power supply node to total market demand in a power market; In the formula, n is the number of power transmission nodes in the market; is the declared capacity of the jth power transmission node; is the declared capacity of the ith power transmission node; D represents the maximum value of the marketized power load predicted by the provincial market operation agency.

11. The multi-tiered spot market risk identification system of claim 7, wherein, The weight coefficients of the risk assessment index values of the multi-level spot market are calculated by using the improved entropy weight method, and include: When identifying market risks of the multi-level spot market, the improved entropy weight method is used to determine index weights of various market risk indexes, and after the weights are determined, various market risk indexes are substituted into a market risk comprehensive evaluation model to identify a risk type and a risk level, and specific calculation steps are as follows: First, each risk index is standardized, and there are risk indexes, evaluation objects, and the proportion of the first evaluation object under the first index is calculated, and the standardization formula is: The information entropy value of the item index is calculated The information entropy value of the item index is calculated The calculation formula is: wherein when then The original index weight coefficient of the item index is calculated The original index weight coefficient of the item index is calculated The original index weight coefficient of the item index is calculated The improved entropy weight method is used to calculate the index weight coefficient The calculation formula is: In the formula and The calculation formula is: wherein is the number of indices for which the information entropy value is not equal to 1.

12. The multi-tiered spot market risk identification system of claim 11, wherein, The market risk comprehensive assessment value is calculated based on the risk assessment index values and the weight coefficients of the multi-level spot market, and the risk type and the risk level are identified according to the market risk comprehensive assessment value. The weight coefficient of each index is calculated by using the improved entropy weight method The risk assessment value T of each market is calculated again The risk assessment value of inter-provincial or provincial spot market The calculation formula is Risk assessment values of each level spot market based on quartile method Perform rating; 1> ≥ 0.9, risk level is high; 0.9> ≥ 0.6, risk level is higher; 0.6> ≥ 0.3, risk level is lower; 0.3> ≥ 0, risk level is low.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the steps of the multi-level spot market risk identification method according to any one of claims 1 to 7 when executing the computer program.

14. A computer-readable storage medium storing a computer program, wherein the computer program comprises the following steps of: The computer program implements the steps of the multi-level spot market risk identification method according to any one of claims 1 to 7 when executed by the processor.

Images