Electric power market transaction risk automatic early warning method based on Bayesian network

Abstract

The invention relates to the technical field of electricity market transaction, in particular to an electricity market transaction risk automatic early warning method based on a Bayesian network, which comprises the following steps: S1, collecting multi-dimensional risk indexes such as market transaction, credit settlement, operation, regulation rules and new energy access to generate a set; s2, performing quantification processing on the risk index set; s3, calculating index correlation by using a Bayesian network, and outputting a power market transaction risk estimation value; and S4, based on the estimated value, dividing early warning grades and formulating a response strategy to realize risk grading early warning. According to the invention, through constructing the multi-dimensional risk index system and introducing the Bayesian network estimation and automatic early warning mechanism, accurate identification and hierarchical response of the transaction risk of the electricity market are realized, and the stability of market operation and the scientificity of decision making are improved.

Description

Technical Field

[0001] This invention relates to the field of electricity market trading technology, and in particular to an automatic early warning method for electricity market trading risks based on Bayesian networks. Background Technology

[0002] Against the backdrop of the rapid development of the electricity spot market, the market trading mechanism is becoming increasingly complex. Affected by factors such as supply and demand fluctuations, new energy access, and changes in electricity pricing mechanisms, electricity market trading risks are gradually emerging, becoming an important factor restricting the stable operation and fair competition of the market. Effectively identifying and warning of potential risks in the electricity trading process is of great significance for protecting the interests of market participants, optimizing trading strategies, and enhancing market resilience.

[0003] However, existing technologies in power market risk assessment mostly adopt static indicator analysis or prediction methods based on a single algorithm. These methods have problems such as weak ability to model the correlation of risk factors, inability to dynamically depict risk propagation paths, and coarse classification of early warning levels. They are difficult to support the real-time decision-making needs in complex trading scenarios. In particular, when facing uncertain events such as new energy fluctuations and rule adjustments, the problems of delayed early warning response and poor strategy adaptability are particularly prominent. Summary of the Invention

[0004] This invention provides an automatic early warning method for electricity market transaction risks based on Bayesian networks. By constructing a multi-dimensional risk indicator system covering transactions, credit, operation and maintenance, regulation, and new energy access, it quantifies the importance and correlation of various risks and outputs transaction risk estimates based on Bayesian networks, thereby achieving automatic risk level classification and response strategy matching. This improves the accuracy of risk identification and the intelligence level of early warning response in the electricity market, and promotes the healthy and orderly operation of the market.

[0005] An automatic early warning method for electricity market transaction risks based on Bayesian networks includes the following steps: S1, based on the current operation of the electricity market, collects multi-dimensional risk indicators including market transaction risk, credit and settlement risk, operational risk, policy and rule risk, and new energy access risk, and generates a risk indicator set; S2, quantifies the generated set of risk indicators; S3 uses a Bayesian network to calculate the correlation between different risk indicators and outputs a transaction risk estimate that reflects the overall risk level of the electricity market. S4 classifies the early warning levels of risk based on the estimated transaction risk value, and formulates corresponding early warning response strategies according to the early warning level to achieve graded early warning of risk status.

[0006] Optionally, the market trading risks include price volatility, spot price standard deviation, peak-to-valley price difference rate, frequency of price anomalies, real-time supply and demand deviation rate, reserve capacity margin, load forecasting error, market concentration, and dependence on cross-regional transactions. The credit and settlement risks include historical default rate, margin coverage ratio, frequency of credit rating downgrades, electricity bill collection cycle, bad debt rate, and cash flow gap rate. The operational risks include platform failure frequency, data anomaly rate, number of cybersecurity incidents, frequency of illegal transactions, compliance rate of operating procedures, and emergency response time. The policy and rule risks mentioned include the frequency of policy adjustments, fluctuations in renewable energy quotas, the carbon price-electricity price linkage coefficient, the frequency of rule revisions, and the rate of dispute arbitration. The risks associated with new energy access include power output prediction errors, daily power output variations, wind and solar curtailment rates, frequency regulation resource adequacy, and energy storage regulation efficiency.

[0007] Optionally, the quantitative processing of the generated risk indicator set in S2 is represented as follows: ; in, This represents the quantified value of the i-th risk factor. This represents the liability arising from the i-th risk factor. This represents the change in trading volume affected by the i-th risk factor. This represents the probability of the i-th risk factor occurring. This indicates the total amount of risk that arises during the entire transaction process.

[0008] Optionally, S3 includes: S31. Based on the collected multidimensional risk indicators, establish the conditional dependencies between risk indicators through a Bayesian network and calculate the importance value of each risk indicator. S32, Establish the conditional probability distribution among risk indicators in a Bayesian network; S33, calculate the estimated value of trading risk based on the importance values ​​and conditional probability distribution of each risk indicator.

[0009] Optionally, the importance values ​​of each risk indicator are expressed as follows: ; in, This indicates the importance values ​​of various risk indicators in electricity market transactions. This indicates the likelihood of losses due to electricity market risk factors. This represents the value of losses caused by risk factors in the electricity market. This indicates the likelihood of risk exposure in the electricity market.

[0010] Optionally, the conditional probability distribution among the risk indicators is represented as follows: ; in, This represents the probability of any two risk indicators occurring simultaneously. , These represent any two different indicators of electricity market trading risk. Indicates in Given the occurrence of trading risks, trading risk indicators The probability of occurrence Indicates transaction risk The probability, Indicates transaction risk The probability of occurrence Indicating transaction risk Transaction risk if it does not occur The probability of occurrence Indicates transaction risk The probability of it not occurring.

[0011] Optionally, the estimated transaction risk is expressed as: ; in, This represents the estimated risk of electricity market transactions, where n represents the number of identified electricity risk factors.

[0012] Optionally, S4 includes: When 0 < When the value is less than 0.1, the warning level is no warning. When 0.1≤ When the value is less than 0.2, the warning level is a mild warning. When 0.2≤ When the value is less than 0.5, the warning level is moderate. When 0.5≤ When the value is less than 0.8, the warning level is a high level of alert. When 0.8≤ When the value is less than 1.0, the warning level is a severe warning.

[0013] The beneficial effects of this invention are: This invention constructs a multi-dimensional risk indicator system comprising five sub-items: market transactions, credit settlement, operational procedures, regulatory rules, and new energy access. By combining Bayesian networks to model and quantify the conditional probability relationships and importance values ​​between risk factors, it can achieve comprehensive modeling and dynamic estimation of electricity market transaction risks, effectively improving the accuracy and foresight of risk identification and ensuring the structured expression and interpretability of risk information.

[0014] This invention, by setting up a multi-level automatic risk warning mechanism and realizing a detailed graded response based on the estimated value of transaction risk, can help electricity market participants perceive the level of potential transaction risks in real time, formulate differentiated response strategies, thereby reducing the probability of market imbalance and operational fluctuations, optimizing resource allocation efficiency, improving the stability and resilience of the overall market operation, and promoting the healthy and orderly development of the electricity market. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the early warning method according to an embodiment of the present invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0018] like Figure 1 As shown, an automatic early warning method for electricity market transaction risks based on Bayesian networks includes the following steps: 1. Establish a risk management system for the operation of the electricity spot market: First, based on the actual situation of the electricity market, a risk system for electricity spot market transactions was constructed. This system is divided into market transaction risk, credit and settlement risk, operational risk, policy and regulatory risk, and renewable energy integration risk.

[0019] Market trading risks include: price volatility, spot price standard deviation, peak-to-valley price difference, frequency of price anomalies, real-time supply and demand deviation rate, reserve capacity margin, load forecasting error, market concentration, and dependence on inter-regional transactions.

[0020] Credit and settlement risks: historical default rate, margin coverage ratio, frequency of credit rating downgrades, electricity bill collection cycle, bad debt rate, and cash flow gap rate.

[0021] Operational risks: platform failure frequency, data anomaly rate, number of cybersecurity incidents, frequency of illegal transactions, compliance rate of operational procedures, and emergency response time.

[0022] Policy and regulatory risks: frequency of policy adjustments, fluctuations in renewable energy quotas, carbon price-electricity price linkage coefficient, frequency of rule revisions, and dispute arbitration rate.

[0023] Risks associated with renewable energy integration include: power output forecasting errors, extremely poor daily power output, wind and solar curtailment rates, frequency regulation resource adequacy, and energy storage regulation efficiency.

[0024] The above indicators form the indicator set X.

[0025] 2. Quantitative processing of risk indicators: The aforementioned risk factor X is quantified. The quantification process is as follows: ; In the formula, This represents the quantified value of the i-th risk factor. This represents the liability that may arise from the i-th risk factor. This represents the change in trading volume affected by the i-th risk factor. This represents the probability of the i-th risk factor occurring. This represents the total amount of risk that may arise during the entire transaction process. Based on the above formula, the identified transaction risks are quantified, laying the foundation for subsequent estimation of electricity market transaction risk values. At this point, the design for electricity market transaction risk identification is complete.

[0026] 3. Estimation of the risk value of electricity market transactions based on Bayesian networks: Based on the identified electricity market transaction risks, a Bayesian network is used to calculate the correlation between different electricity market transaction risk factors, thereby estimating the electricity market transaction risk value. The specific calculation process is as follows: In the formula, This indicates the importance value of risk factors in electricity market transactions. This indicates the likelihood of losses due to electricity market risk factors. This represents the value of losses caused by risk factors in the electricity market. This indicates the likelihood of risk exposure in the electricity market.

[0027] Based on the above formula, the importance of different electricity market risk trading factors is calculated. Furthermore, the conditional probability distribution values ​​of different risk factors are calculated. The specific calculation process is as follows: In the formula, This represents the probability that any two trading risks will occur simultaneously. , Each represents any two different risk factors in electricity market transactions. Indicates in Given the occurrence of trading risks, the factors contributing to trading risks. The probability of occurrence Indicates transaction risk The probability, Indicates transaction risk The probability of occurrence Indicating transaction risk Transaction risk if it does not occur The probability of occurrence Indicates transaction risk The probability of it not occurring.

[0028] Based on the above formula, the conditional probability distribution values ​​of different trading risk factors are calculated. Further analysis of the correlation between these factors allows for an estimation of the trading risk value. The specific estimation process is shown below: In the formula, This represents the estimated risk value for electricity market transactions, where n represents the number of identified electricity risk factors. The above formula is used to estimate the risk value for electricity market transactions. The estimated result is then used as the basis for this estimation.

[0029] 4. Automatic early warning mechanism for electricity market transaction risks: Based on the above research, an automatic early warning mechanism for electricity market transaction risks is further constructed. This mechanism classifies risks into different early warning levels according to the aforementioned risk estimates, with specific classification criteria shown in Table 1.

[0030] Table 1 Classification of Risk Warning Levels for Electricity Market Transactions Based on the warning levels set in Table 1, tiered early warning systems can be implemented for electricity market transaction risks. Differentiated early warning response strategies are adopted for different risk levels.

[0031] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0032] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for automatic early warning of power market transaction risk based on Bayesian network, characterized in that, The method comprises the following steps: S1, based on the operation status of the electricity market, collecting multi-dimensional risk indicators including market transaction risk, credit and settlement risk, operation operation risk, policy and rule risk, and new energy access risk, and generating a risk indicator set; S2, quantitatively processing the generated risk indicator set; S3, calculating the correlation between different risk indicators by using the Bayesian network, and outputting a transaction risk estimate value reflecting the overall risk level of the electricity market; S4, based on the transaction risk estimate value, dividing the warning level of the risk level, and formulating the corresponding warning response strategy according to the warning level, and realizing the graded warning of the risk state.

2. The method of claim 1, wherein the method further comprises: The market transaction risk includes price fluctuation rate, spot price standard deviation, peak-valley price difference rate, price anomaly frequency, real-time supply-demand deviation rate, reserve capacity margin, load prediction error, market concentration, and cross-region transaction dependence; The credit and settlement risk includes historical default rate, margin coverage rate, credit rating downgrading frequency, electricity fee recovery period, bad debt rate, and cash flow gap rate; The operation operation risk includes platform failure frequency, data anomaly rate, network security event number, illegal transaction frequency, operation process compliance rate, and emergency response time; The policy and rule risk includes policy adjustment frequency, renewable energy quota fluctuation, carbon price-electricity price linkage coefficient, rule revision frequency, and dispute arbitration rate; The new energy access risk includes output prediction error, intraday output range, wind and light curtailment rate, frequency modulation resource adequacy, and energy storage regulation efficiency.

3. The method of claim 2, wherein the method further comprises: The quantitative processing of the generated risk indicator set in S2 is represented as: ； wherein, represents a quantified value of the i-th risk factor, represents a liability generated by the i-th risk factor, represents a transaction volume change affected by the i-th risk factor, represents a probability of occurrence of the i-th risk factor, represents a total amount of risks occurred throughout the transaction process.

4. The method of claim 3, wherein the method further comprises: S3 comprises: S31, according to the collected multi-dimensional risk indicators, the conditional dependence relationship between the risk indicators is established by the Bayesian network, and the importance value of each risk indicator is calculated; S32, the conditional probability distribution between the risk indicators is established in the Bayesian network; S33, based on the importance value of each risk indicator and the conditional probability distribution, the transaction risk estimate value is calculated.

5. The method for automatic early warning of electricity market transaction risks based on Bayesian networks according to claim 4, characterized in that, The importance value of each risk indicator is represented as: ； wherein, represents the importance value of each risk indicator of the power market transaction, represents the possibility of loss caused by the risk factor of the power market, represents the loss value caused by the risk factor of the power market, represents the risk exposure possibility of the power market.

6. The method of claim 5, wherein the method further comprises: The conditional probability distribution between the risk indicators is represented as: ； in, This represents the probability of any two risk indicators occurring simultaneously. , These represent any two different indicators of electricity market trading risk. Indicates in Given the occurrence of trading risks, trading risk indicators The probability of occurrence Indicates transaction risk The probability, Indicates transaction risk The probability of occurrence Indicating transaction risk Transaction risk if it does not occur The probability of occurrence Indicates transaction risk The probability of it not occurring.

7. The method of claim 6, wherein the method further comprises: The transaction risk estimate value is represented as: ； wherein, represents the power market transaction risk estimate value, n represents the number of identified power risk factors.

8. The method of claim 7, wherein the method further comprises: S4 comprises: When 0 < When the value is less than 0.1, the warning level is no warning. When 0.1≤ When <0.2, the warning level is a mild warning; When 0.2≤ When 0.5 < x < 1, the warning level is a high warning. When 0.5≤ When <0.8, the early warning level is high early warning. When 0.8≤ When <1.0, the warning level is severe warning.

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