Risk Assessment Method for Electricity Spot Trading

Abstract

The present disclosure provides a method for assessing the risk of electricity spot trading, belonging to the field of data processing technology. The method comprises: predicting a first electricity consumption coefficient based on temperature data, economic data, and electricity price data; adjusting a reference value of electricity demand based on the first electricity consumption coefficient to obtain a predicted value of electricity demand; determining a predicted value of electricity supply based on the capacity of each power generation device in the power supply system, operating data of each power generation device in the power supply system, and meteorological data; and determining an electricity spot trading risk assessment value based on the predicted value of electricity demand and the predicted value of electricity supply. The electricity spot trading risk assessment method provided by the present disclosure can improve the accuracy of electricity spot trading risk assessment, thereby improving the stability of the power system.

Description

Technical Field

[0001] The present disclosure belongs to the field of data processing technology, and more specifically, relates to a method for assessing the risks of electricity spot transactions. Background Art

[0002] Electricity spot trading is a method of buying and selling actual electricity quantities currently or within an upcoming period. For example, a power generation company sells its current electricity directly to a power retailer or large user. The electricity product involved in this transaction is called spot electricity.

[0003] The power system must maintain a real-time balance between power supply and demand to ensure stable grid frequency and voltage. However, actual power generation by power generation companies may deviate from planned output due to equipment failures, natural factors (such as water inflows affecting hydropower generation), and other factors. Furthermore, actual power demand from power users may differ from forecasts due to changes in economic activity and emergencies. This imbalance in power supply and demand not only prevents power transactions from being completed as scheduled, but also causes grid voltage and frequency to exceed normal ranges, potentially triggering grid safety incidents.

[0004] Therefore, in order to ensure the smooth progress of electricity spot trading, it is first necessary to accurately predict the supply and demand imbalance risk, so as to help decision makers formulate effective energy policies and market strategies and improve the stability of the power system. Summary of the Invention

[0005] The purpose of the present disclosure is to provide a method for assessing the risk of electricity spot transactions, which can improve the accuracy of risk assessment of electricity spot transactions and thus improve the stability of the power system.

[0006] A first aspect of an embodiment of the present disclosure provides a method for assessing risk in electricity spot trading, comprising:

[0007] predicting a first electricity consumption coefficient based on temperature data, economic data, and electricity price data, and adjusting a reference value of electricity demand based on the first electricity consumption coefficient to obtain a predicted value of electricity demand;

[0008] Determining a predicted value of electricity supply based on the capacity of each power generation device in the power supply system, operating data of each power generation device in the power supply system, and meteorological data;

[0009] The risk assessment value of electricity spot trading is determined based on the predicted value of electricity demand and the predicted value of electricity supply.

[0010] A second aspect of the embodiments of the present disclosure provides a device for assessing risk in electricity spot trading, comprising:

[0011] a first prediction module, configured to predict a first electricity consumption coefficient based on temperature data, economic data, and electricity price data, and adjust a reference value of electricity demand based on the first electricity consumption coefficient to obtain a predicted value of electricity demand;

[0012] a second prediction module, configured to determine a predicted value of electricity supply based on the capacity of each power generation device in the power supply system, operating data of each power generation device in the power supply system, and meteorological data;

[0013] The risk assessment module is used to determine the risk assessment value of electricity spot trading based on the predicted value of electricity demand and the predicted value of electricity supply.

[0014] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned method for risk assessment of electricity spot trading when executing the computer program.

[0015] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for risk assessment of electricity spot trading are implemented.

[0016] The beneficial effects of the power spot transaction risk assessment method provided by the embodiments of the present disclosure are:

[0017] In the embodiment of the present disclosure, considering that temperature data can reflect the impact of seasonal and short-term weather changes on electricity demand, economic data can reflect the long-term trend relationship between macroeconomic activities and electricity demand, and electricity price data can reflect the regulatory effect of the price mechanism on users' electricity consumption behavior, therefore, the first electricity consumption coefficient is predicted based on temperature data, economic data and electricity price data. The first electricity consumption coefficient can comprehensively reflect the changes in electricity demand, thereby achieving a more accurate prediction of electricity demand.

[0018] At the same time, taking into account the capacity and operating status of power generation equipment, as well as the combined impact of external natural factors such as weather on electricity supply, will help to more accurately predict the actual capacity and potential changes in power supply, and achieve more accurate predictions of electricity supply.

[0019] Determining risk assessment values ​​based on accurate electricity demand and supply forecasts can intuitively reflect the supply and demand balance and potential risk level of the electricity market, which is beneficial for decision makers to formulate effective energy policies and market strategies and improve the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 A schematic diagram of a flow chart of a method for risk assessment of electricity spot trading provided in one embodiment of the present disclosure;

[0022] Figure 2 This is a structural block diagram of a power spot transaction risk assessment device provided by an embodiment of the present disclosure;

[0023] Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0025] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0026] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for assessing electricity spot trading risks provided by an embodiment of the present disclosure, the method comprising:

[0027] S101: Predicting a first electricity consumption coefficient based on temperature data, economic data, and electricity price data, and adjusting a reference value of electricity demand based on the first electricity consumption coefficient to obtain a predicted value of electricity demand.

[0028] In this embodiment, the significant impact of temperature on electricity demand is first considered. For example, during high summer temperatures, air conditioning usage in residential and commercial spaces increases significantly, leading to a sharp increase in electricity demand. During cold winter months, heating equipment (such as electric heaters and heat pumps) also consumes significant amounts of electricity. Furthermore, economic data reflects the level of economic activity in a region. For example, growing industrial value added indicates continued expansion of industrial production, which leads to increased electricity consumption for factory machinery, lighting, and other equipment. Furthermore, considering that electricity prices are a key factor influencing user electricity usage, if the peak-valley price difference between time-of-use (TOU) electricity prices is large, users will be more inclined to use electricity during off-peak hours and consume less during peak hours. Industrial value added is the difference between total industrial output and industrial intermediate inputs, and can be obtained from government statistical agencies and specialized databases. The peak-valley price difference is the difference between peak and off-peak electricity prices in the power system.

[0029] Therefore, this embodiment can predict the electricity demand in a certain period of time in the future based on temperature data, economic data and electricity price data.

[0030] Specifically, a reference value for electricity demand can be set in advance. The reference value for electricity demand can be obtained based on historical data on electricity demand (specifically historical data on electricity power). For example, the average value of electricity power in the target area over a period of time (e.g., 1 hour) in spring or autumn can be calculated as a reference value for electricity demand, the industrial growth value during this period can be used as a reference value for economic data, and the peak-valley electricity price difference during this period can be used as a reference value for electricity price data.

[0031] On this basis, the following first formula can be used to predict the second power consumption coefficient corresponding to the temperature data:

[0032]

[0033] in, Represents the second electricity consumption coefficient, Indicates the lower limit of the daily average temperature. Indicates the upper limit of the daily average temperature. represents the measured value of the daily average temperature, and These are preset scaling factors.

[0034] The first formula above indicates that when the daily average temperature is higher than the upper limit of the daily average temperature or lower than the lower limit of the daily average temperature, the second power consumption coefficient will increase. The upper limit and lower limit of the daily average temperature can be obtained based on seasonal temperature statistics. For example, the upper limit of the daily average temperature can be 22°C, and the lower limit of the daily average temperature can be 10°C.

[0035] At the same time, the following second formula can be used to predict the third electricity consumption coefficient corresponding to the economic data:

[0036]

[0037] in, Indicates the third electricity consumption coefficient, Indicates the reference value of economic data, represents the measured value of economic data, is the preset scaling factor.

[0038] The third formula above shows that as economic data increases, the third electricity consumption coefficient will increase accordingly.

[0039] Similarly, the fourth electricity consumption coefficient corresponding to the electricity price data can be predicted using the following third formula:

[0040]

[0041] in, represents the fourth electricity consumption coefficient, Indicates the reference value of electricity price data, Represents the measured value of electricity price data (i.e., peak-valley electricity price difference), Indicates the peak period of electricity price. Indicates the period of trough electricity prices. is the preset scaling factor.

[0042] The third formula above shows that during the peak electricity price period, the fourth electricity consumption coefficient will decrease as the peak-valley electricity price difference increases, and during the trough electricity price period, the fourth electricity consumption coefficient will increase as the peak-valley electricity price difference increases.

[0043] The first electricity consumption coefficient can be obtained by weighted summing of the second electricity consumption coefficient, the third electricity consumption coefficient and the fourth electricity consumption coefficient. The predicted value of the electricity demand can be obtained by multiplying the first electricity consumption coefficient by the reference value of the electricity demand.

[0044] S102: Determine a predicted value of electricity supply based on the capacity of each power generation device in the power supply system, the operating data of each power generation device in the power supply system, and meteorological data.

[0045] In this embodiment, the operating data of each power generation equipment in the power supply system may include real-time power output, operating temperature, vibration conditions, etc. The above data can be obtained through various sensors installed on the power generation equipment (such as temperature sensors, vibration sensors, power sensors, etc.).

[0046] Ideally, the total power supply is calculated by adding the rated capacities of each generating unit in the power supply system. However, given that each generating unit may experience a malfunction or shutdown during operation, if a malfunction occurs, its corresponding power generation will be zero. Therefore, by monitoring the operating data of each generating unit, it is possible to predict its fault data. This data can then be used to determine the actual power generation of each generating unit, and the total power supply of the entire power supply system can be determined based on the actual power generation of each generating unit.

[0047] Furthermore, the actual power generation of renewable energy generation equipment such as hydropower, wind power, and photovoltaics is significantly affected by meteorological factors. For example, for hydropower, data such as precipitation, river runoff, and reservoir water levels determine the generating capacity of hydropower units; wind power relies on information such as wind speed and direction, and wind power generation can be predicted based on these information; photovoltaic power generation is closely related to meteorological factors such as solar radiation intensity and sunshine duration.

[0048] Therefore, by comprehensively considering the capacity of each power generation equipment in the power supply system, the operating data of each power generation equipment in the power supply system and meteorological data, the power supply amount can be accurately predicted.

[0049] S103: Determine a risk assessment value for electricity spot trading based on the predicted value of electricity demand and the predicted value of electricity supply.

[0050] In this embodiment, the predicted value of electricity demand is subtracted from the predicted value of electricity supply to obtain the supply-demand difference. If the supply-demand difference is positive, it indicates that the electricity demand is greater than the electricity supply, and there is a risk of insufficient supply in electricity spot trading, and the larger the supply-demand difference is, the higher the risk of insufficient supply; if the supply-demand difference is negative, it indicates that the electricity demand is less than the electricity supply, and there is a risk of oversupply in electricity spot trading, and the larger the absolute value of the supply-demand difference is, the higher the risk of insufficient supply.

[0051] Therefore, the risk assessment value of electricity spot trading can be determined based on the positive correlation between the absolute value of the supply and demand difference and the risk assessment value of electricity spot trading. For example, the absolute value of the supply and demand difference is multiplied by a preset proportional coefficient. , plus the bias b, we get the risk assessment value of electricity spot trading, and by setting the appropriate proportional coefficient and bias b, the risk assessment value of electricity spot trading can be mapped into a set range, such as between 0 and 100% (insufficient electricity supply) and between -100% and 0 (oversupply of electricity).

[0052] On the basis of obtaining the risk assessment value of electricity spot trading, a corresponding power dispatching strategy can be determined based on the risk assessment value of electricity spot trading to ensure the stable supply of the power system.

[0053] For example, when the risk assessment value of insufficient power supply indicates that the risk of electricity spot trading is at a low risk level (for example, between 40% and 60%), the power generation plan can be fine-tuned to prioritize efficient and flexible power generation equipment (such as gas turbines) to increase a small amount of power generation. Price signals can also be used to guide users, such as pushing time-of-use electricity price information to users through smart meters, reminding users to appropriately reduce the use of non-essential electrical appliances during peak hours. When the risk assessment value of insufficient power supply indicates that the risk of electricity spot trading is at a medium risk level (for example, between 60% and 80%), cross-regional power allocation is required, and power is urgently transferred from surrounding power-rich areas. When the risk assessment value of insufficient power supply indicates that the risk of electricity spot trading is at a high risk level (for example, above 80%), an orderly power consumption plan needs to be implemented, and power cuts and rationing are carried out for industrial users according to certain priorities (such as high-energy-consuming enterprises are shut down first, and enterprises related to people's livelihood are shut down later) to ensure basic electricity consumption for residents and important public facilities.

[0054] When the risk assessment value of oversupply indicates that the risk of electricity spot trading is at a low risk level (for example, between -60% and -40%), the power generation capacity of flexibly adjustable power generation equipment (such as thermal power) can be slightly reduced, thereby reducing power supply. When the risk assessment value of oversupply indicates that the risk of electricity spot trading is at a medium risk level (for example, between -80% and -60%), it is necessary to significantly adjust the power generation plan and arrange for some power generation equipment (such as thermal power units) to be shut down for maintenance in rotation, thereby reducing power generation capacity. When the risk assessment value of oversupply indicates that the risk of electricity spot trading is at a high risk level (for example, below -80%), the operation of more power generation equipment, except for the equipment necessary for the safe and stable operation of the power grid, should be reduced.

[0055] From the above, it can be concluded that this embodiment takes into account that temperature data can reflect the impact of seasonal and short-term weather changes on electricity demand, economic data can reflect the long-term trend relationship between macroeconomic activities and electricity demand, and electricity price data can reflect the regulatory effect of the price mechanism on users' electricity consumption behavior. Therefore, the first electricity consumption coefficient is predicted based on temperature data, economic data and electricity price data. The first electricity consumption coefficient can comprehensively reflect the changes in electricity demand, thereby achieving a more accurate prediction of electricity demand.

[0056] At the same time, taking into account the capacity and operating status of power generation equipment, as well as the combined impact of external natural factors such as weather on electricity supply, will help to more accurately predict the actual capacity and potential changes in power supply, and achieve more accurate predictions of electricity supply.

[0057] Determining risk assessment values ​​based on accurate electricity demand and supply forecasts can intuitively reflect the supply and demand balance and potential risk level of the electricity market, which is beneficial for decision makers to formulate effective energy policies and market strategies and improve the stability of the power system.

[0058] In one embodiment of the present disclosure, predicting a first electricity usage coefficient based on temperature data, economic data, and electricity price data includes:

[0059] Temperature data, economic data and electricity price data are input into a linear regression model to obtain a first electricity consumption coefficient; the linear regression model is constructed based on historical data of temperature data, historical data of economic data, historical data of electricity price data and historical data of the first electricity consumption coefficient.

[0060] In this embodiment, multiple sets of historical data of temperature data, historical data of economic data, and historical data of electricity price data can be used as independent variables, and the historical data of the first electricity consumption coefficient can be used as the dependent variable to construct a linear regression model. On this basis, the temperature data, economic data, and electricity price data are input into the linear regression model to obtain the first electricity consumption coefficient.

[0061] The historical data of the first electricity consumption coefficient can be obtained by calculating the ratio of the historical data of electricity demand to the reference value of electricity demand. To make data of different magnitudes comparable, this embodiment further normalizes the historical data of temperature data, historical data of economic data, and historical data of electricity price data before fitting the linear regression model.

[0062] Taking the historical data of temperature data as an example, the normalization formula is:

[0063]

[0064] in, It represents the normalized temperature data. Indicates the maximum value of temperature data, Indicates the minimum value of the temperature data. The temperature data here still uses the daily average temperature.

[0065] It can be concluded from the above that this embodiment mines the intrinsic relationship between temperature data, economic data, electricity price data and the first electricity consumption coefficient based on the linear regression model, which can improve the accuracy of the prediction of the first electricity consumption coefficient.

[0066] In one embodiment of the present disclosure, determining a predicted value of electricity supply based on the capacity of each power generation device in the power supply system, operating data of each power generation device in the power supply system, and meteorological data includes:

[0067] Predicting fault data of each power generation device based on the operating data of each power generation device in the power supply system;

[0068] selecting a first power generation device from the plurality of power generation devices in the power supply system based on the fault data of each power generation device; the first power generation device refers to a power generation device for which a fault data prediction result indicates that the power generation device is fault-free;

[0069] determining a first power supply amount of a first type of power generation equipment in the first power generation equipment based on meteorological data; the first type of power generation equipment is a new energy power generation equipment;

[0070] determining a second electricity supply amount based on the capacity of a second type of power generation equipment in the first power generation equipment; the second type of power generation equipment is a power generation equipment other than the first type of power generation equipment;

[0071] A predicted value of the electricity supply amount is determined based on the first electricity supply amount and the second electricity supply amount.

[0072] In this embodiment, a fault prediction model can be trained based on the operating data and historical fault records of the power generation equipment to predict the fault status of the power generation equipment. Specifically, machine learning methods (such as decision trees, support vector machines, neural networks, etc.) or statistical analysis methods (such as Markov chain models, reliability analysis methods, etc.) can be used to train the fault prediction model.

[0073] According to the output results of the fault prediction model, the first power generation device without faults among multiple power generation devices can be screened out. The first power generation device is a device that can output actual power generation. Therefore, calculating the power supply based on the capacity of the first power generation device can improve the accuracy of the power supply prediction.

[0074] Furthermore, considering that the first power generation equipment may include new energy power generation equipment (ie, the first type of power generation equipment) and other power generation equipment (ie, the second type of power generation equipment), different types of power generation equipment have different power generation calculation methods.

[0075] Specifically, for the new energy generation equipment in the first power generation device, since its power generation is significantly affected by meteorological data, the power generation of the new energy generation equipment can be predicted based on meteorological data. For example, for wind power generation, the influence of meteorological factors such as wind speed, wind direction, and air density needs to be considered. When calculating the power generation of the wind power generation equipment, the wind speed data can be used to predict the output power of the wind turbine based on a wind speed-power curve model. At the same time, factors such as the effect of wind direction on the wind turbine impeller angle and the effect of air density on power generation efficiency can be considered to accurately calculate the power generation (i.e., the first power supply amount). For solar power generation equipment, its power generation is primarily affected by the intensity of solar radiation. Therefore, the power generation of the solar power generation equipment (i.e., the first power supply amount) can be calculated based on the solar radiation intensity and the photovoltaic conversion efficiency of the solar panel.

[0076] For other power generation equipment in the first power generation equipment, such as thermal power generation equipment, hydropower generation equipment, etc., if it is predicted that there will be no failure, the power generation (that is, the second power supply) can usually be calculated according to its rated capacity or a certain proportion of the rated capacity.

[0077] On the basis of obtaining the first power supply amount and the second power supply amount, the first power supply amount and the second power supply amount are added together to obtain a predicted value of the power supply amount.

[0078] In one embodiment of the present disclosure, determining a risk assessment value for electricity spot trading based on a predicted value of electricity demand and a predicted value of electricity supply includes:

[0079] Calculate the difference between the predicted value of electricity demand and the predicted value of electricity supply;

[0080] Determine the supply-demand imbalance based on the supply-demand difference and the predicted value of electricity demand;

[0081] An electricity spot transaction risk assessment value is determined based on the supply-demand imbalance and a set first benchmark value.

[0082] In this embodiment, the supply-demand gap only reflects the quantitative difference between the predicted electricity demand and the predicted electricity supply, and does not consider the total demand. This can lead to a misjudgment of the risk level. For example, for a small power system, a 100,000-kilowatt supply-demand gap may be a very serious problem, as the total demand may only be 500,000 kilowatts. However, for a large power system with a total demand of 10 million kilowatts, a 100,000-kilowatt supply-demand gap has a smaller impact on the system.

[0083] Therefore, this embodiment combines the supply-demand difference with the electricity demand, and obtains the supply-demand imbalance by calculating the ratio between the supply-demand difference and the predicted value of the electricity demand. This can more comprehensively reflect the relative degree of imbalance in electricity supply and demand, and can be compared between power systems of different sizes to more reasonably compare the severity of the supply-demand imbalance.

[0084] Furthermore, according to the statistical results of the data, the common range of supply and demand imbalance is -50%~50%. Among them, when the supply and demand imbalance is between -10%~10%, it indicates that the supply and demand are basically balanced; when the supply and demand imbalance is between 10%~30% (insufficient power supply) or between -30%~-10% (oversupply of power), it indicates a slight imbalance between supply and demand; when the supply and demand imbalance is between 30%~50% (insufficient power supply) or between -30%~-50% (oversupply of power), it indicates a moderate imbalance between supply and demand; when the supply and demand imbalance is above 50% (insufficient power supply) or below -50% (oversupply of power), it indicates a serious imbalance between supply and demand.

[0085] Therefore, this embodiment adds a first benchmark value based on the supply and demand imbalance, and can map the electricity spot transaction risk assessment value to 0~100% (insufficient electricity supply) and -100%~0 (excess electricity supply), which is more in line with user understanding habits.

[0086] Specifically, if the supply-demand imbalance is greater than zero, the first reference value is set to a first value greater than zero (for example, 30%); if the supply-demand imbalance is less than zero, the first reference value is set to a second value less than zero (for example, -30%).

[0087] From the above, it can be concluded that this embodiment determines the risk assessment value of electricity spot trading based on the supply and demand imbalance, which can more comprehensively reflect the relative degree of electricity supply and demand imbalance, can be compared between power systems of different sizes, and more reasonably compare the severity of supply and demand imbalance.

[0088] In one embodiment of the present disclosure, determining a risk assessment value for electricity spot trading based on a supply-demand imbalance includes:

[0089] Calculating a first adjustment parameter based on the capacity of a specified power generation device among the plurality of power generation devices in the power supply system; the first adjustment parameter is used to characterize the adjustment speed of the power generation;

[0090] Calculating a second adjustment parameter based on the load capacity of a designated user among the plurality of power users; the second adjustment parameter is used to characterize the adjustment speed of the power demand;

[0091] Calculating a third adjustment parameter based on the capacity of the energy storage system in the power supply system; the third adjustment parameter is used to characterize the adjustment speed of the energy storage system;

[0092] Adjusting the first reference value based on the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter to obtain a second reference value;

[0093] An electricity spot transaction risk assessment value is determined based on the second benchmark value and the supply-demand imbalance.

[0094] In this embodiment, the flexibility of power supply systems (e.g., the speed of adjusting power generation and power demand) varies, and so does their tolerance for supply-demand imbalance. A highly flexible grid, capable of rapidly adjusting supply and demand, has a higher tolerance for supply-demand imbalance, and therefore a lower risk assessment value for spot power trading. Conversely, a grid with low flexibility and a weaker tolerance for supply-demand imbalance requires a higher risk assessment value for spot power trading to facilitate timely dispatch of power resources.

[0095] Specifically, the flexibility of the power supply system can be determined by comprehensively considering the regulation speed of power generation, the regulation speed of power demand, and the regulation speed of the energy storage system.

[0096] The speed of regulating power generation (characterized by a first regulating parameter) is positively correlated with the capacity of a designated power generation device. The designated power generation device may include a gas turbine power station, a pumped storage power station, or other power generation device capable of quickly starting, stopping, or changing power generation. The proportion of the capacity of the designated power generation device to the electricity supply is used as the first regulating parameter. A larger first regulating parameter indicates a higher proportion of rapidly adjustable power generation in the power supply system, and a faster speed of regulating power generation.

[0097] The speed of electricity demand adjustment (characterized by the second adjustment parameter) is positively correlated with the load capacity of designated users. Designated users are users participating in the demand-side response program. The demand-side response (DSR) program refers to a power management strategy in which power users change their electricity usage behavior based on the operating conditions of the power system, electricity price signals, or incentives to adjust electricity demand and optimize the allocation of power resources. Therefore, the load capacity of designated users is the adjustable load capacity, and the ratio of the designated user's load capacity to the power demand is used as the second adjustment parameter. The larger the second adjustment parameter, the higher the proportion of load capacity that can be quickly adjusted on the demand side, and the faster the electricity demand adjustment speed.

[0098] The regulation speed of the energy storage system (characterized by the third regulation parameter) is positively correlated with the capacity of the energy storage system. The ratio of the capacity of the energy storage system to the supply-demand difference is used as the third regulation parameter. The larger the third regulation parameter, the faster the regulation speed of the energy storage system.

[0099] On this basis, the first reference value is adjusted based on the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter to obtain the second reference value, which can be described in detail as follows:

[0100] A weighted sum of the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter is obtained to obtain a fourth adjustment parameter;

[0101] Based on the negative correlation between the fourth adjustment parameter and the second reference value, the first reference value is adjusted to obtain the second reference value.

[0102] The fourth adjustment parameter can comprehensively reflect the flexibility of the power supply system. The higher the flexibility of the power supply system, the smaller the second benchmark value can be set, and the smaller the risk assessment value of electricity spot trading is.

[0103] Specifically, the second reference value may be calculated using the following fourth formula:

[0104]

[0105] in, represents the second reference value, represents the first reference value, represents the fourth adjustment parameter.

[0106] From the above, it can be concluded that this embodiment adjusts the electricity spot trading risk assessment value based on the flexibility of the power supply system. The obtained electricity spot trading risk assessment value is more in line with the actual operation of the power supply system, which is conducive to decision makers to formulate more reasonable scheduling strategies.

[0107] In one embodiment of the present disclosure, the fault data of the power generation equipment includes the fault type, and the power spot transaction risk assessment method further includes:

[0108] Predict failure time based on the failure type of power generation equipment;

[0109] determining a first proportional parameter based on the failure time;

[0110] The risk assessment value of the electricity spot transaction is adjusted based on the first proportional parameter.

[0111] In this embodiment, different fault types have different corresponding repair times, resulting in different downtimes (i.e., failure durations) for power generation equipment, and thus varying degrees of impact on power supply. For example, a power generation equipment failure caused by a minor sensor malfunction may require a short repair time of a few hours or even less to restore normal operation. However, a serious mechanical failure, such as a short-circuited generator rotor winding or damaged turbine blades, may require days or even weeks to repair and replace components.

[0112] The failure times corresponding to various common failure types can be counted, and a mapping relationship between failure type and failure time can be pre-built. When a failure is predicted in one or more power generation equipment, the corresponding failure time can be obtained by looking up the mapping relationship.

[0113] On this basis, the first proportional parameter determined based on the fault time can be described in detail as follows:

[0114] Classify multiple faulty power generation equipment into first-category faulty power generation equipment, second-category faulty power generation equipment, and third-category faulty power generation equipment according to the fault duration of each faulty power generation equipment; wherein the fault duration of the first-category faulty power generation equipment is less than or equal to the first duration, the fault duration of the first-category faulty power generation equipment is between the first duration and the second duration, and the fault duration of the first-category faulty power generation equipment is greater than the second duration; calculate the proportion of the first-category faulty power generation equipment, the second-category faulty power generation equipment, and the third-category faulty power generation equipment in all faulty power generation equipment respectively;

[0115] In response to the proportion of first-category faulty power generation equipment being greater than a first threshold, determining a first proportion parameter based on a fifth formula;

[0116] In response to the proportion of third-category faulty power generation equipment being greater than a first threshold, determining a first proportion parameter based on a sixth formula;

[0117] The fifth formula is: ;in, represents the first scale parameter, Indicates the proportion of the first type of faulty power generation equipment, is the preset scale factor;

[0118] The sixth formula is: ;in, represents the first scale parameter, Indicates the proportion of power generation equipment with the third type of failure.

[0119] The first duration and the second duration are both preset constants, which can be obtained by those skilled in the art based on statistical data. For example, the first duration is set to 1 hour and the second duration is set to 3 hours.

[0120] On the basis of obtaining the first proportion parameter, the first proportion parameter is multiplied by the electricity spot transaction risk assessment value to obtain an adjusted electricity spot transaction risk assessment value.

[0121] From the above, it can be concluded that this embodiment considers the impact of the fault type and fault time of the power generation equipment on the power supply system, and further adjusts the power spot trading risk assessment value based on the fault time, which is conducive to the refined management of power spot trading risks.

[0122] In one embodiment of the present disclosure, the power spot transaction risk assessment method further includes:

[0123] Predicting fault data of transmission lines in the power supply system based on status monitoring data of transmission lines in the power supply system;

[0124] The predicted value of the power supply amount is adjusted based on the fault data of the transmission line in the power supply system.

[0125] In this embodiment, considering that if a transmission line fails (grid congestion occurs), part of the electricity cannot be smoothly transmitted to the demand area, thereby affecting the actual electricity supply, it is necessary to monitor the fault data of the transmission line to achieve accurate prediction of the electricity supply.

[0126] Specifically, sensors installed on transmission lines can be used to obtain electrical parameters such as current, voltage, and power. Abnormal changes in these parameters are often associated with line faults. For example, a sudden increase in line current may indicate a short circuit, while abnormal voltage fluctuations may indicate insulation degradation or electromagnetic interference with other lines. Furthermore, line temperature data can be monitored. During normal operation, the temperature of a line is relatively stable. However, when loose connections or conductors are overloaded, the temperature can rise significantly. For example, poor contact at a conductor joint can increase resistance, leading to a temperature increase at the joint. Monitoring this change with a temperature sensor can serve as an important basis for fault prediction.

[0127] A transmission line fault monitoring model can be constructed based on this monitoring data. For example, historical state monitoring data (including data from both normal operation and fault conditions) can be used as training samples to train a support vector machine or neural network model to obtain a transmission line fault monitoring model. Furthermore, the transmission line state monitoring data can be input into the fault monitoring model to obtain the transmission line fault data.

[0128] If the fault data of a transmission line shows that the transmission line has a fault, the transmission line can be marked as a fault line, and the line load of the fault line can be subtracted from the predicted value of the power supply, so as to further achieve accurate calculation of the predicted value of the power supply.

[0129] From the above, it can be concluded that this embodiment predicts fault data based on the status monitoring data of the transmission line and adjusts the predicted value of the power supply, which further improves the accuracy of the predicted value of the power supply and thus improves the accuracy of the risk assessment of electricity spot trading.

[0130] Corresponding to the power spot transaction risk assessment method in the above embodiment, Figure 2 This is a structural block diagram of a power spot transaction risk assessment device provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The power spot transaction risk assessment device 20 includes: a first prediction module 21, a second prediction module 22 and a risk assessment module 23.

[0131] The first prediction module 21 is configured to predict a first electricity consumption coefficient based on temperature data, economic data, and electricity price data, and adjust a reference value of electricity demand based on the first electricity consumption coefficient to obtain a predicted value of electricity demand;

[0132] A second prediction module 22 is configured to determine a predicted value of electricity supply based on the capacity of each power generation device in the power supply system, operating data of each power generation device in the power supply system, and meteorological data;

[0133] The risk assessment module 23 is configured to determine a risk assessment value of the electricity spot transaction based on the predicted value of the electricity demand and the predicted value of the electricity supply.

[0134] In one embodiment of the present disclosure, the first prediction module 21 is specifically configured to:

[0135] Temperature data, economic data and electricity price data are input into a linear regression model to obtain a first electricity consumption coefficient; the linear regression model is constructed based on historical data of temperature data, historical data of economic data, historical data of electricity price data and historical data of the first electricity consumption coefficient.

[0136] In one embodiment of the present disclosure, the second prediction module 22 is specifically configured to:

[0137] Predicting fault data of each power generation device based on the operating data of each power generation device in the power supply system;

[0138] selecting a first power generation device from the plurality of power generation devices in the power supply system based on the fault data of each power generation device; the first power generation device refers to a power generation device for which a fault data prediction result indicates that the power generation device is fault-free;

[0139] determining a first power supply amount of a first type of power generation equipment in the first power generation equipment based on meteorological data; the first type of power generation equipment is a new energy power generation equipment;

[0140] determining a second electricity supply amount based on the capacity of a second type of power generation equipment in the first power generation equipment; the second type of power generation equipment is a power generation equipment other than the first type of power generation equipment;

[0141] A predicted value of the electricity supply amount is determined based on the first electricity supply amount and the second electricity supply amount.

[0142] In one embodiment of the present disclosure, the risk assessment module 23 is specifically configured to:

[0143] Calculate the difference between the predicted value of electricity demand and the predicted value of electricity supply;

[0144] Determine the supply-demand imbalance based on the supply-demand difference and the predicted value of electricity demand;

[0145] An electricity spot transaction risk assessment value is determined based on the supply-demand imbalance and a set first benchmark value.

[0146] In one embodiment of the present disclosure, the risk assessment module 23 is further configured to:

[0147] Calculating a first adjustment parameter based on the capacity of a specified power generation device among the plurality of power generation devices in the power supply system; the first adjustment parameter is used to characterize the adjustment speed of the power generation;

[0148] Calculating a second adjustment parameter based on the load capacity of a designated user among the plurality of power users; the second adjustment parameter is used to characterize the adjustment speed of the power demand;

[0149] Calculating a third adjustment parameter based on the capacity and response speed of the energy storage system in the power supply system; the third adjustment parameter is used to characterize the adjustment speed of the energy storage system;

[0150] Adjusting the first reference value based on the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter to obtain a second reference value;

[0151] An electricity spot transaction risk assessment value is determined based on the second benchmark value and the supply-demand imbalance.

[0152] In one embodiment of the present disclosure, the risk assessment module 23 is further configured to:

[0153] Predict failure time based on the failure type of power generation equipment;

[0154] determining a first proportional parameter based on the failure time;

[0155] The risk assessment value of the electricity spot transaction is adjusted based on the first proportional parameter.

[0156] In one embodiment of the present disclosure, the second prediction module 22 is specifically configured to:

[0157] Predicting fault data of transmission lines in the power supply system based on status monitoring data of transmission lines in the power supply system;

[0158] The predicted value of the power supply amount is adjusted based on the fault data of the transmission line in the power supply system.

[0159] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0160] It should be understood that in the embodiments of the present disclosure, the processor 301 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0161] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0162] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store device type information.

[0163] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the electricity spot trading risk assessment method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0164] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0165] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0166] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0167] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0168] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0169] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present disclosure.

[0170] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0171] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in this disclosure, and such modifications or replacements should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A method for assessing the risk of electricity spot trading, characterized in that: include: predicting a first electricity consumption coefficient based on temperature data, economic data, and electricity price data, and adjusting a reference value of electricity demand based on the first electricity consumption coefficient to obtain a predicted value of electricity demand; Determining a predicted value of electricity supply based on the capacity of each power generation device in the power supply system, operating data of each power generation device in the power supply system, and meteorological data; Calculate the difference between the predicted value of electricity demand and the predicted value of electricity supply; determining a supply-demand imbalance based on the supply-demand difference and the predicted value of the electricity demand; An electricity spot transaction risk assessment value is determined based on the supply-demand imbalance and a set first reference value.

2. The method for assessing risk of electricity spot trading according to claim 1, wherein: Predict the first electricity consumption coefficient based on temperature data, economic data and electricity price data, including: Temperature data, economic data and electricity price data are input into a linear regression model to obtain the first electricity consumption coefficient; the linear regression model is constructed based on historical data of temperature data, historical data of economic data, historical data of electricity price data and historical data of the first electricity consumption coefficient.

3. The method for assessing the risk of electricity spot trading according to claim 1, wherein: The method of determining the predicted value of the power supply amount based on the capacity of each power generation device in the power supply system, the operating data of each power generation device in the power supply system, and meteorological data includes: Predicting fault data of each power generation device based on the operating data of each power generation device in the power supply system; selecting a first power generation device from a plurality of power generation devices in the power supply system based on the fault data of each power generation device; the first power generation device is a power generation device for which a fault data prediction result indicates no fault; determining a first power supply amount of a first type of power generation equipment in a first power generation equipment based on meteorological data; the first type of power generation equipment is a new energy power generation equipment; determining a second electricity supply amount based on the capacity of a second type of power generation equipment in the first power generation equipment; the second type of power generation equipment is a power generation equipment other than the first type of power generation equipment; A predicted value of the power supply amount is determined based on the first power supply amount and the second power supply amount.

4. The method for assessing the risk of electricity spot trading according to claim 1, wherein: Determining the risk assessment value of the electricity spot transaction based on the supply and demand imbalance includes: Calculating a first adjustment parameter based on the capacity of a specified power generation device among the plurality of power generation devices in the power supply system; the first adjustment parameter is used to characterize the adjustment speed of the power generation; Calculating a second adjustment parameter based on the load capacity of a designated user among the plurality of power users; the second adjustment parameter is used to characterize the adjustment speed of the power demand; Calculating a third adjustment parameter based on the capacity and response speed of the energy storage system in the power supply system; the third adjustment parameter is used to characterize the adjustment speed of the energy storage system; Adjusting the first reference value based on the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter to obtain a second reference value; An electricity spot transaction risk assessment value is determined based on the second reference value and the supply-demand imbalance.

5. The method for assessing risk of electricity spot trading according to claim 1 or 4, characterized in that: The fault data of the power generation equipment includes the fault type, and the power spot transaction risk assessment method further includes: predicting a failure time based on a failure type of the power generation equipment; determining a first proportional parameter based on the fault time; The risk assessment value of electricity spot trading is adjusted based on the first proportion parameter.

6. The method for assessing the risk of electricity spot trading according to claim 1, wherein: Also includes: Predicting fault data of transmission lines in the power supply system based on status monitoring data of transmission lines in the power supply system; The predicted value of the power supply amount is adjusted based on the fault data of the transmission line in the power supply system.

7. A device for assessing the risk of electricity spot trading, characterized in that: include: a first prediction module, configured to predict a first electricity consumption coefficient based on temperature data, economic data, and electricity price data, and adjust a reference value of electricity demand based on the first electricity consumption coefficient to obtain a predicted value of electricity demand; a second prediction module, configured to determine a predicted value of electricity supply based on the capacity of each power generation device in the power supply system, operating data of each power generation device in the power supply system, and meteorological data; Risk Assessment Module for: Calculate the difference between the predicted value of electricity demand and the predicted value of electricity supply; determining a supply-demand imbalance based on the supply-demand difference and the predicted value of the electricity demand; An electricity spot transaction risk assessment value is determined based on the supply-demand imbalance and a set first reference value.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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