Electric power market transaction risk early warning system and method

Abstract

The invention discloses an electricity market transaction risk early warning system and method. The electricity market transaction risk early warning method comprises the following steps: identifying and classifying risks, and collecting data related to each risk type; calculating a power prediction supply value according to the collected data, and calculating a supply and demand risk rate according to the prediction supply value and an actual demand value; predicting an electric power price increment according to the collected data, and calculating a market price risk rate according to the electric power price increment and an electric power price basic value; calculating a technical equipment risk rate according to the collected data; calculating a total risk rate of electricity market transaction according to the supply and demand risk rate, the market price risk rate and the technical equipment risk rate; if the total risk rate of the electricity market transaction is higher than a threshold value, giving out early warning, otherwise, keeping the current situation; according to the method and the system, multiple risks are comprehensively controlled, so that the transaction security of the electricity market is improved.

Description

Technical Field

[0001] The present invention relates to the field of machine learning algorithms, and in particular to a power market transaction risk early warning system and method. Background Art

[0002] With the transformation of the global energy structure and the opening of the power market, power trading has become more dynamic and complex, and the risks involved are also increasing. These risks may come from multiple aspects, including but not limited to price fluctuations, supply and demand imbalances, changes in power generation costs, extreme weather events, etc. The power market transaction risk early warning system and method is a technology developed against the background of the increasing complexity and uncertainty of the power market. It aims to identify and manage various risks such as financial market fluctuations and supply and demand imbalances through data analysis and prediction models. Such systems use big data and machine learning technologies to conduct in-depth analysis of historical data and real-time market information to achieve multi-dimensional risk assessment, provide timely early warning signals and decision support, help market participants effectively prevent and respond to potential risks, and ensure the stable operation of the power market and the security of power supply. Summary of the invention

[0003] The present invention provides a power market transaction risk early warning method, comprising:

[0004] S110, collecting historical data and identity authentication information of power network equipment;

[0005] S120, calculating the identity authentication success rate of the device accessing the power network based on the collected data;

[0006] S130, calculating the network activity abnormality rate of the device through the collected historical data;

[0007] S140. Determine whether the device identity authentication is qualified based on the device identity authentication success rate and the network activity abnormality rate.

[0008] A method for early warning of risks in power market transactions as described above is characterized in that risk early warnings in power market transactions include supply and demand risks, market price risks, and technical equipment risks. Supply and demand risks mainly stem from the volatility of power demand and the uncertainty of supply capacity. Market price risks stem from the uncertainty of power prices. Technical equipment risks refer to risks caused by equipment failure or performance degradation.

[0009] The above-mentioned power market transaction risk early warning method is characterized in that the method for calculating the supply and demand risk rate includes the following sub-steps:

[0010] Calculate the predicted power supply value based on the collected data;

[0011] The supply and demand risk ratio is calculated based on the predicted supply value and the actual demand value.

[0012] The above-mentioned power market transaction risk early warning method is characterized in that the method for calculating the market price risk rate includes the following sub-steps:

[0013] Based on the collected data, predict the increase in electricity prices;

[0014] The market price risk rate is calculated based on the electricity price increment and the electricity price base value.

[0015] A power market transaction risk early warning method as described above is characterized in that a tree structure is used to calculate the technical equipment risk rate in the process of technical equipment risk quantification, and equipment failure is taken as the top event, i.e., the root node of the tree. Based on the collected data, basic events and the probability of occurrence of basic events are determined. Basic events refer to the failure sources that cannot be further decomposed and cause equipment failures, i.e., leaf nodes. In this case, if any basic event occurs, the top event will occur. The probability that all basic events do not occur is calculated, and then this value is subtracted from 1 to obtain the probability of the top event occurring.

[0016] The present invention also provides a system for quality inspection of 3D printing engineering materials, including: a risk identification and data collection module, a supply and demand risk rate calculation module, a market price risk rate calculation module, a technical equipment risk rate calculation module, a total risk rate calculation module, and an early warning module.

[0017] Risk Identification and Data Collection Module: Identify and classify risks and collect data related to each risk type;

[0018] Supply and demand risk rate calculation module: Calculate the power forecast supply value based on the collected data, and calculate the supply and demand risk rate based on the forecast supply value and actual demand value;

[0019] Market price risk rate calculation module: predict the electricity price increment based on the collected data, and calculate the market price risk rate based on the electricity price increment and the electricity price base value;

[0020] Technical equipment risk rate calculation module: calculates the technical equipment risk rate based on the collected data;

[0021] Total risk rate calculation module: calculates the total risk rate of power market transactions based on supply and demand risk rate, market price risk rate and technical equipment risk rate;

[0022] Early warning module: If the total risk rate of power market transactions is higher than the threshold, an early warning will be issued; otherwise, the status quo will be maintained;

[0023] An electricity market transaction risk warning system as described above is characterized in that risk warnings in electricity market transactions include supply and demand risks, market price risks, and technical equipment risks. Supply and demand risks mainly stem from the volatility of electricity demand and the uncertainty of supply capacity. Market price risks stem from the uncertainty of electricity prices. Technical equipment risks refer to risks caused by equipment failure or performance degradation.

[0024] The power market transaction risk early warning system as described above is characterized in that the method for calculating the supply and demand risk rate includes the following sub-steps:

[0025] Calculate the predicted power supply value based on the collected data;

[0026] The supply and demand risk ratio is calculated based on the predicted supply value and the actual demand value.

[0027] The above-mentioned power market transaction risk early warning system is characterized in that the method for calculating the market price risk rate includes the following sub-steps:

[0028] Based on the collected data, predict the increase in electricity prices;

[0029] The market price risk rate is calculated based on the electricity price increment and the electricity price base value.

[0030] An electricity market transaction risk early warning system as described above is characterized in that a tree structure is used to calculate the technical equipment risk rate during the technical equipment risk quantification process, and equipment failure is taken as the top event, i.e., the root node of the tree. Based on the collected data, basic events and the probability of occurrence of basic events are determined. Basic events refer to the failure sources that cannot be further decomposed and cause equipment failures, i.e., leaf nodes. In this case, if any basic event occurs, the top event will occur. The probability that all basic events do not occur is calculated, and then this value is subtracted from 1 to obtain the probability of the top event occurring.

[0031] The beneficial effects achieved by the present invention are as follows: by comprehensively controlling various risks, the security of power market transactions is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0033] Figure 1 This is a flow chart of a power market transaction risk warning method provided in Example 1 of the present application;

[0034] Figure 2 This is a schematic diagram of an electricity market transaction risk early warning system provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0035] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0036] Embodiment 1

[0037] like Figure 1 As shown, Embodiment 1 of the present application provides a method for early warning of power market transaction risks.

[0038] Step S110: Identify and classify risks, and collect data related to each risk type;

[0039] Risk early warning in power market transactions is a complex and critical process, which covers multiple levels of supply and demand risks, market price risks, and technical equipment risks:

[0040] 1. Supply and demand risks mainly stem from the volatility of electricity demand and the uncertainty of supply capacity. Electricity demand is affected by many factors, such as seasonal changes, weather conditions, economic development level and sudden social events. Electricity supply may be limited by the maintenance plan of power plants, fuel price fluctuations and the intermittent nature of renewable energy. This dynamic imbalance between supply and demand, if not properly managed, may lead to power shortages or surpluses, which in turn may cause price fluctuations and system stability problems.

[0041] 2. Market price risk stems from the uncertainty of electricity prices. Electricity prices are affected by many factors, including supply and demand conditions, fuel costs, and the behavior of market participants. Market price fluctuations not only increase the financial risks of electricity suppliers and consumers, but may also affect investment decisions and market efficiency.

[0042] 3. Technical equipment risk refers to the risk caused by equipment failure or performance degradation. The normal operation of the power system is highly dependent on the stable operation of various equipment, including generators, transmission lines, substations and distribution facilities. Factors such as equipment aging, improper maintenance, natural disasters or man-made damage may lead to equipment failure, thereby affecting the continuity and reliability of power supply.

[0043] In order to effectively identify and manage the above risks, it is necessary to quantify risks through scientific methods, including collecting a large amount of data related to each risk type, such as historical electricity demand records, market price change trends, equipment status monitoring reports, and performing data preprocessing to ensure the accuracy and consistency of the data and eliminate outliers and missing values.

[0044] Step S120: Calculate the power forecast supply value based on the collected data, and calculate the supply and demand risk rate based on the forecast supply value and the actual demand value;

[0045] Based on the collected historical data, statistical and machine learning algorithms are used to calculate the future power supply forecast value. These forecasting models can effectively capture the trends, periodicity and random fluctuations in the historical data, thereby generating accurate supply forecasts. The predicted supply value is then compared with the actual power demand value to quantify the gap between supply and demand. The specific steps are as follows:

[0046] Step S121: Calculate the predicted power supply value based on the collected data;

[0047] In the process of quantifying the risk of power supply and demand, the use of time series analysis is an effective method. It can predict the future power supply situation and then help us evaluate the probability of supply and demand imbalance. This imbalance probability is measured by the supply and demand risk rate. Specifically, the forecast supply value in the current power market can be a combination of the following forms: a fixed base value, a weighted average of the forecast supply in several recent periods, and the cumulative sum of the forecast supply errors in several recent periods. Specifically, the formula is used:

[0048] Calculate the forecast supply value of the current electricity market, where y t represents the current forecast supply value, C represents a constant, p represents the order of the forecast supply value in the most recent time, q represents the order of the forecast supply error value in the most recent time, φ i and θ i are the coefficients of the predicted supply value and the predicted supply error value in the most recent time, y t-i represents the predicted supply value at interval i from now, e t-j It represents the forecast supply error value at the interval j from the present time, i is the independent variable, the value is 1-p, j is the independent variable, the value is 1-q.

[0049] Step S122: Calculate the supply and demand risk rate based on the predicted supply value and the actual demand value;

[0050] The imbalance between supply and demand can be defined as the difference between actual electricity demand and predicted supply. This difference may appear as either a positive value or a negative value. When a positive value appears, it means that market demand exceeds power supply capacity, which will directly lead to power shortages and pose severe challenges to the stability of the power grid and the quality of power services. On the contrary, if the imbalance appears as a negative value, it means that electricity supply exceeds actual demand. Although this situation seems to be abundant, it poses a risk of wasting resources and may cause market prices to fall due to excess supply, affecting the economic benefits and investment returns of the electricity market.

[0051] The supply and demand risk rate is calculated based on the predicted supply value and the actual demand value. Specifically, the formula is used: Calculate the supply and demand risk rate, where UR t represents the supply and demand risk rate, D t represents the actual demand value of the electricity market, y t Represents the forecast supply value.

[0052] Step S130: predicting the electricity price increment based on the collected data, and calculating the market price risk rate based on the electricity price increment and the electricity price base value;

[0053] Quantifying the price risk of the electricity market is essentially a forward-looking prediction of the future volatility of electricity prices. This process has a profound impact on the financial stability of various participants in the power industry chain, including producers, consumers and intermediaries. The dynamic changes in electricity prices not only reflect the real-time adjustment of supply and demand, but are also affected by a variety of factors such as weather and fuel costs. Therefore, its volatility has become a key indicator for measuring market risks, which is divided into the following sub-steps:

[0054] Step S131: predicting the increase in electricity price based on the collected data;

[0055] The calculation of electricity price increment is decomposed into two core components: expected increment and random increment. The expected increment reflects the long-term trend and average growth rate of electricity prices. It is based on historical data and macroeconomic indicators and is predicted through statistical models (such as moving average, exponential smoothing, etc.). The random increment reflects the uncertainty of price fluctuations. It comes from unpredictable factors inside and outside the market, such as extreme weather events, emergencies, etc., and emphasizes the randomness and complexity of electricity price fluctuations. Specifically, the formula is used: Calculate the increment of electricity price, where ΔS represents the increment of electricity price, S represents the basic value of electricity price, μ represents the expected growth rate of electricity price, Δt represents the unit time, μΔt represents the expected growth rate of electricity price per unit time, σ represents the electricity price volatility calculated based on the historical electricity price, T represents the total time span of the selected historical market price data, S trepresents the electricity price at time t, S t-1 represents the electricity price at time t-1, represents the logarithmic rate of return, r represents the average of all logarithmic rates of return, ε represents a random constant, Represents the random growth rate of electricity price per unit time.

[0056] Step S132: Calculate the market price risk rate according to the electricity price increment and the electricity price base value;

[0057] The positive and negative changes in the electricity price increment reflect the different directions of price fluctuations in the electricity market. This dynamic has an important impact on market participants. The electricity price increment is combined with the electricity price base value. By calculating the ratio of the sum of the two to the increment, the market price risk rate can be obtained. This indicator for measuring market price risk selects the electricity market price at a specific time point as the base value, usually the spot market price, contract price or historical average price. The market price risk rate is calculated based on the electricity price increment and the electricity price base value. Specifically, the formula is used: Calculate the market price risk rate, where UN t represents the market price risk rate, ΔS represents the electricity price increment, and S represents the electricity price base.

[0058] Step S140: Calculate the technical equipment risk rate based on the collected data;

[0059] There are many reasons for technical equipment failure, including equipment component failure, control and protection system failure, external factors, material and process defects, etc. In the process of technical equipment risk quantification, a tree structure can be used to calculate the technical equipment risk rate. Equipment failure is taken as the top event, that is, the root node of the tree. According to the collected data, the basic events and the probability of occurrence of the basic events are determined. The basic event refers to the source of failure that cannot be further decomposed that causes the equipment failure, that is, the leaf node. In this case, if any basic event occurs, the top event will occur. The probability of all basic events not occurring is calculated, and then this value is subtracted from 1 to obtain the probability of at least one basic event occurring (that is, the top event occurring). Specifically, the formula is used: Calculate the risk rate of technical equipment, including US t represents the risk rate of technical equipment, p(BE i ) represents the probability of the occurrence of the ith basic event, 1-p(BE i ) represents the probability that the i-th basic event does not occur.

[0060] Step S150: Calculating the total risk rate of power market transactions according to the supply and demand risk rate, the market price risk rate and the technical equipment risk rate;

[0061] In the power trading market, there are correlations and influences between supply and demand risk, market price risk and technical equipment risk. Technical equipment risk may aggravate market price risk, while supply and demand risk may mitigate the impact of technical equipment risk. Therefore, there is a cross-influence between the supply and demand risk rate, market price risk rate and technical equipment risk rate. Specifically, the formula is adopted:

[0062] Calculate the total risk rate of electricity market transactions, where U t represents the total risk rate of power market transactions, They represent the weights of supply and demand risk rate, market price risk rate and technical equipment risk rate, UR t , UN t , US t They represent the supply and demand risk rate, market price risk rate and technical equipment risk rate respectively, α<UR t ,k> indicates UN t , US t About UR t The influence coefficient of α<UN t ,k> indicates UR t , US t To the UN t The influence coefficient of α<US t ,k> indicates UR t , UN t To US t The influence coefficient of .

[0063] Step S160: If the total risk rate of power market transactions is higher than the threshold, an early warning is issued; otherwise, the status quo is maintained;

[0064] In the electricity market, when the monitored total transaction risk rate exceeds the pre-set threshold, it is regarded as a signal of increased market risk. Immediate action is required to issue an early warning to all relevant parties, reminding market participants of potential financial risks and prompting them to take appropriate defensive measures to mitigate or avoid risks. After receiving the early warning, market participants should respond quickly, assess their own risk exposure, adjust trading strategies, and take risk management measures, such as increasing hedging positions, optimizing asset allocation, and strengthening equipment maintenance, to reduce potential financial losses.

[0065] Embodiment 2

[0066] like Figure 2 As shown, Embodiment 2 of the present application provides an electricity market transaction risk early warning system.

[0067] Risk Identification and Data Collection Module 21: Identify and categorize risks and collect data related to each risk type;

[0068] Risk early warning in power market transactions is a complex and critical process, which covers multiple levels of supply and demand risks, market price risks, and technical equipment risks:

[0069] 1. Supply and demand risks mainly stem from the volatility of electricity demand and the uncertainty of supply capacity. Electricity demand is affected by many factors, such as seasonal changes, weather conditions, economic development level and sudden social events. Electricity supply may be limited by the maintenance plan of power plants, fuel price fluctuations and the intermittent nature of renewable energy. This dynamic imbalance between supply and demand, if not properly managed, may lead to power shortages or surpluses, which in turn may cause price fluctuations and system stability problems.

[0070] 2. Market price risk stems from the uncertainty of electricity prices. Electricity prices are affected by many factors, including supply and demand conditions, fuel costs, and the behavior of market participants. Market price fluctuations not only increase the financial risks of electricity suppliers and consumers, but may also affect investment decisions and market efficiency.

[0071] 3. Technical equipment risk refers to the risk caused by equipment failure or performance degradation. The normal operation of the power system is highly dependent on the stable operation of various equipment, including generators, transmission lines, substations and distribution facilities. Factors such as equipment aging, improper maintenance, natural disasters or man-made damage may lead to equipment failure, thereby affecting the continuity and reliability of power supply.

[0072] In order to effectively identify and manage the above risks, it is necessary to quantify risks through scientific methods, including collecting a large amount of data related to each risk type, such as historical electricity demand records, market price change trends, equipment status monitoring reports, and performing data preprocessing to ensure the accuracy and consistency of the data and eliminate outliers and missing values.

[0073] Supply and demand risk rate calculation module 22: calculates the power forecast supply value based on the collected data, and calculates the supply and demand risk rate based on the forecast supply value and the actual demand value;

[0074] Based on the collected historical data, statistical and machine learning algorithms are used to calculate the future power supply forecast value. These forecasting models can effectively capture the trends, periodicity and random fluctuations in the historical data, thereby generating accurate supply forecasts. The predicted supply value is then compared with the actual power demand value to quantify the gap between supply and demand. The specific steps are as follows:

[0075] Power forecast supply value calculation module: calculates the power forecast supply value based on the collected data;

[0076] In the process of quantifying the risk of power supply and demand, the use of time series analysis is an effective method. It can predict the future power supply situation and then help us evaluate the probability of supply and demand imbalance. This imbalance probability is measured by the supply and demand risk rate. Specifically, the forecast supply value in the current power market can be a combination of the following forms: a fixed base value, a weighted average of the forecast supply in several recent periods, and the cumulative sum of the forecast supply errors in several recent periods. Specifically, the formula is used:

[0077] Calculate the forecast supply value of the current electricity market, where y t represents the current forecast supply value, C represents a constant, p represents the order of the forecast supply value in the most recent time, q represents the order of the forecast supply error value in the most recent time, φ i and θ i are the coefficients of the predicted supply value and the predicted supply error value in the most recent time, y t-i represents the predicted supply value at interval i from now, e t-j It represents the forecast supply error value at the interval j from the present time, i is the independent variable, the value is 1-p, j is the independent variable, the value is 1-q.

[0078] Supply and demand risk rate calculation module: calculates the supply and demand risk rate based on the predicted supply value and actual demand value;

[0079] The imbalance between supply and demand can be defined as the difference between actual electricity demand and predicted supply. This difference may appear as either a positive value or a negative value. When a positive value appears, it means that market demand exceeds power supply capacity, which will directly lead to power shortages and pose severe challenges to the stability of the power grid and the quality of power services. On the contrary, if the imbalance appears as a negative value, it means that electricity supply exceeds actual demand. Although this situation seems to be abundant, it poses a risk of wasting resources and may cause market prices to fall due to excess supply, affecting the economic benefits and investment returns of the electricity market.

[0080] The supply and demand risk rate is calculated based on the predicted supply value and the actual demand value. Specifically, the formula is used: Calculate the supply and demand risk rate, where UR t represents the supply and demand risk rate, D t represents the actual demand value of the electricity market, y t Represents the forecast supply value.

[0081] Market price risk rate calculation module 23: predict the power price increment based on the collected data, and calculate the market price risk rate based on the power price increment and the power price base value;

[0082] Quantifying the price risk of the electricity market is essentially a forward-looking prediction of the future volatility of electricity prices. This process has a profound impact on the financial stability of various participants in the power industry chain, including producers, consumers and intermediaries. The dynamic changes in electricity prices not only reflect the real-time adjustment of supply and demand, but are also affected by a variety of factors such as weather and fuel costs. Therefore, its volatility has become a key indicator for measuring market risks, which is divided into the following sub-steps:

[0083] Electricity price increment prediction module: predicts the electricity price increment based on the collected data;

[0084] The calculation of electricity price increment is decomposed into two core components: expected increment and random increment. The expected increment reflects the long-term trend and average growth rate of electricity prices. It is based on historical data and macroeconomic indicators and is predicted through statistical models (such as moving average, exponential smoothing, etc.). The random increment reflects the uncertainty of price fluctuations. It comes from unpredictable factors inside and outside the market, such as extreme weather events, emergencies, etc., and emphasizes the randomness and complexity of electricity price fluctuations. Specifically, the formula is used: Calculate the increment of electricity price, where ΔS represents the increment of electricity price, S represents the basic value of electricity price, μ represents the expected growth rate of electricity price, Δt represents the unit time, μΔt represents the expected growth rate of electricity price per unit time, σ represents the electricity price volatility calculated based on the historical electricity price, T represents the total time span of the selected historical market price data, S t represents the electricity price at time t, S t-1 represents the electricity price at time t-1, represents the logarithmic rate of return, represents the average of all logarithmic returns, ε represents a random constant, Represents the random growth rate of electricity price per unit time.

[0085] Market price risk rate calculation module: calculates the market price risk rate based on the electricity price increment and the electricity price base value;

[0086] The positive and negative changes in the electricity price increment reflect the different directions of price fluctuations in the electricity market. This dynamic has an important impact on market participants. The electricity price increment is combined with the electricity price base value. By calculating the ratio of the sum of the two to the increment, the market price risk rate can be obtained. This indicator for measuring market price risk selects the electricity market price at a specific time point as the base value, usually the spot market price, contract price or historical average price. The market price risk rate is calculated based on the electricity price increment and the electricity price base value. Specifically, the formula is used: Calculate the market price risk rate, where UN trepresents the market price risk rate, ΔS represents the electricity price increment, and S represents the electricity price base.

[0087] Technical equipment risk rate calculation module 24: calculates the technical equipment risk rate according to the collected data;

[0088] There are many reasons for technical equipment failure, including equipment component failure, control and protection system failure, external factors, material and process defects, etc. In the process of technical equipment risk quantification, a tree structure can be used to calculate the technical equipment risk rate. Equipment failure is taken as the top event, that is, the root node of the tree. According to the collected data, the basic events and the probability of occurrence of the basic events are determined. The basic event refers to the source of failure that cannot be further decomposed that causes the equipment failure, that is, the leaf node. In this case, if any basic event occurs, the top event will occur. The probability of all basic events not occurring is calculated, and then this value is subtracted from 1 to obtain the probability of at least one basic event occurring (that is, the top event occurring). Specifically, the formula is used: Calculate the risk rate of technical equipment, including US t represents the risk rate of technical equipment, p(BE i ) represents the probability of the occurrence of the ith basic event, 1-p(BE i ) represents the probability that the i-th basic event does not occur.

[0089] Total risk rate calculation module 25: calculates the total risk rate of power market transactions according to the supply and demand risk rate, the market price risk rate and the technical equipment risk rate;

[0090] In the power trading market, there are correlations and influences between supply and demand risk, market price risk and technical equipment risk. Technical equipment risk may aggravate market price risk, while supply and demand risk may mitigate the impact of technical equipment risk. Therefore, there is a cross-influence between the supply and demand risk rate, market price risk rate and technical equipment risk rate. Specifically, the formula is adopted:

[0091] Calculate the total risk rate of electricity market transactions, where U t represents the total risk rate of power market transactions, They represent the weights of supply and demand risk rate, market price risk rate and technical equipment risk rate, UR t , UN t , US t They represent the supply and demand risk rate, market price risk rate, and technical equipment risk rate respectively. α<i,k> represents the cross-influence coefficient, which indicates the degree of influence of risk i on risk k, where k∈(UR t ,UN t ,US t ).

[0092] Early warning module 26: If the total risk rate of power market transactions is higher than the threshold, an early warning is issued; otherwise, the status quo is maintained;

[0093] In the electricity market, when the monitored total transaction risk rate exceeds the pre-set threshold, it is regarded as a signal of increased market risk. Immediate action is required to issue an early warning to all relevant parties, reminding market participants of potential financial risks and prompting them to take appropriate defensive measures to mitigate or avoid risks. After receiving the early warning, market participants should respond quickly, assess their own risk exposure, adjust trading strategies, and take risk management measures, such as increasing hedging positions, optimizing asset allocation, and strengthening equipment maintenance, to reduce potential financial losses.

[0094] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A power market transaction risk early warning method, characterized in that: include: S110: Identify and categorize risks and collect data related to each risk type; S120: Calculate the power forecast supply value based on the collected data, and calculate the supply and demand risk rate based on the forecast supply value and the actual demand value; S130: predicting the increment of electricity price based on the collected data, and calculating the market price risk rate based on the increment of electricity price and the basic value of electricity price; S140: Calculate the technical equipment risk rate based on the collected data; S150: Calculate the total risk rate of power market transactions based on the supply and demand risk rate, market price risk rate, and technical equipment risk rate; S160: If the total risk rate of electricity market transactions is higher than the threshold, an early warning is issued; otherwise, the status quo is maintained.

2. A power market transaction risk early warning method as claimed in claim 1, characterized in that: Risk warnings in electricity market transactions include supply and demand risks, market price risks, and technical equipment risks. Supply and demand risks mainly stem from the volatility of electricity demand and the uncertainty of supply capacity. Market price risks stem from the uncertainty of electricity prices. Technical equipment risks refer to the risks caused by equipment failure or performance degradation.

3. A power market transaction risk early warning method as claimed in claim 1, characterized in that: The method for calculating the supply and demand risk ratio includes the following sub-steps: Calculate the predicted power supply value based on the collected data; The supply and demand risk ratio is calculated based on the predicted supply value and the actual demand value.

4. A power market transaction risk early warning method as claimed in claim 1, characterized in that: The method for calculating the market price risk rate includes the following sub-steps: Based on the collected data, predict the increase in electricity prices; The market price risk rate is calculated based on the electricity price increment and the electricity price base value.

5. The method for early warning of power market transaction risks according to claim 1, characterized in that: In the process of quantifying technical equipment risks, a tree structure is used to calculate the technical equipment risk rate. Equipment failure is taken as the top event, i.e., the root node of the tree. Based on the collected data, basic events and the probability of occurrence of basic events are determined. Basic events refer to the failure sources that cause equipment failure and cannot be further decomposed, i.e., leaf nodes. In this case, if any basic event occurs, the top event will occur. The probability of no basic events occurring is calculated, and then this value is subtracted from 1 to obtain the probability of the top event occurring.

6. A power market transaction risk early warning system, characterized in that: include: Risk Identification and Data Collection Module: Identify and classify risks and collect data related to each risk type; Supply and demand risk rate calculation module: Calculate the power forecast supply value based on the collected data, and calculate the supply and demand risk rate based on the forecast supply value and actual demand value; Market price risk rate calculation module: predict the electricity price increment based on the collected data, and calculate the market price risk rate based on the electricity price increment and the electricity price base value; Technical equipment risk rate calculation module: calculates the technical equipment risk rate based on the collected data; Total risk rate calculation module: calculates the total risk rate of power market transactions based on supply and demand risk rate, market price risk rate and technical equipment risk rate; Early warning module: If the total risk rate of power market transactions is higher than the threshold, an early warning will be issued; otherwise, the status quo will be maintained.

7. The power market transaction risk early warning system according to claim 6, characterized in that: Risk warnings in electricity market transactions include supply and demand risks, market price risks, and technical equipment risks. Supply and demand risks mainly stem from the volatility of electricity demand and the uncertainty of supply capacity. Market price risks stem from the uncertainty of electricity prices. Technical equipment risks refer to the risks caused by equipment failure or performance degradation.

8. The power market transaction risk early warning system according to claim 6, characterized in that: The method for calculating the supply and demand risk ratio includes the following sub-steps: Calculate the predicted power supply value based on the collected data; The supply and demand risk ratio is calculated based on the predicted supply value and the actual demand value.

9. The power market transaction risk early warning system according to claim 6, characterized in that: The method for calculating the market price risk rate includes the following sub-steps: Based on the collected data, predict the increase in electricity prices; The market price risk rate is calculated based on the electricity price increment and the electricity price base value.

10. The power market transaction risk early warning system according to claim 6, characterized in that: In the process of quantifying technical equipment risks, a tree structure is used to calculate the technical equipment risk rate. Equipment failure is taken as the top event, i.e., the root node of the tree. Based on the collected data, basic events and the probability of occurrence of basic events are determined. Basic events refer to the failure sources that cause equipment failure and cannot be further decomposed, i.e., leaf nodes. In this case, if any basic event occurs, the top event will occur. The probability of no basic events occurring is calculated, and then this value is subtracted from 1 to obtain the probability of the top event occurring.

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