Electric power spot transaction risk assessment method

Abstract

The invention provides an electric power spot transaction risk assessment method, and belongs to the technical field of data processing, and the method comprises the steps: predicting a first power consumption coefficient based on temperature data, economic data and electricity price data, and adjusting a reference value of a power consumption demand based on the first power consumption coefficient to obtain a predicted value of the power consumption demand; based on the capacity of each power generation device in the power supply system, the operation data of each power generation device in the power supply system and the meteorological data, determining a predicted value of the power supply amount; and determining an electric power spot transaction risk assessment value based on the predicted value of the power demand quantity and the predicted value of the power supply quantity. According to the electric power spot transaction risk assessment method provided by the invention, the accuracy of electric power spot transaction risk assessment can be improved, so that the stability of an electric power system is improved.

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 spot electricity transactions. Background Art

[0002] Electricity spot trading is a trading method for buying and selling actual electricity in the present or upcoming period of time. For example, a power generation company sells its current electricity directly to a power sales company or a large user, and the electricity product involved in this transaction process is electricity spot.

[0003] The power system needs to maintain a balance between supply and demand of electricity in real time to ensure the stability of the grid frequency and voltage. However, the actual power generation of power generation companies may deviate from the planned power generation due to equipment failures, natural factors (such as the impact of water inflow on hydropower generation), etc. At the same time, the actual electricity demand of power users may also be inconsistent with the predicted demand due to changes in economic activities, emergencies, etc. The imbalance between supply and demand of electricity not only leads to the failure to complete electricity transactions as scheduled, but also the imbalance between supply and demand of electricity leads to the voltage and frequency of the grid exceeding the normal range, which may cause grid safety accidents.

[0004] Therefore, in order to ensure the smooth progress of spot electricity trading, it is first necessary to accurately predict the risk of supply and demand imbalance, 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 risk assessment of electricity spot trading, which can improve the accuracy of risk assessment of electricity spot trading, thereby improving the stability of the power system.

[0006] A first aspect of an embodiment of the present disclosure provides a method for assessing risk of spot electricity trading, comprising: 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; Determine a forecast value of power 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; 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.

[0007] A second aspect of the embodiments of the present disclosure provides a device for assessing risk of spot electricity transactions, including: A first prediction module is used to predict a first power consumption coefficient based on temperature data, economic data and electricity price data, and adjust a reference value of power demand based on the first power consumption coefficient to obtain a predicted value of power demand; A second prediction module is used to determine a predicted value of power supply based on the capacity of each power generation device in the power supply system, the operation data of each power generation device in the power supply system and the meteorological data; 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.

[0008] 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.

[0009] According to a fourth aspect of an embodiment 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.

[0010] The beneficial effects of the power spot transaction risk assessment method provided by the embodiment of the present disclosure are: In the disclosed embodiment, taking 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 the 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.

[0011] 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 electricity supply, and achieve more accurate predictions of electricity supply.

[0012] Determining the risk assessment value 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 conducive to decision makers to formulate effective energy policies and market strategies and improve the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, 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 of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1A schematic diagram of a flow chart of a method for risk assessment of power spot trading provided in an embodiment of the present disclosure; Figure 2 A structural block diagram of a power spot transaction risk assessment device provided by an embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0015] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear 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 obstructing the description of the present disclosure with unnecessary details.

[0016] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0017] Please refer to Figure 1 , Figure 1 A schematic diagram of a flow chart of a power spot transaction risk assessment method provided in an embodiment of the present disclosure, the method comprising: 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.

[0018] In this embodiment, first of all, it is considered that temperature has a significant impact on electricity demand. For example, when the temperature is high in summer, the use of air conditioners in residents and commercial places increases significantly, which will lead to a sharp increase in electricity demand; and when it is cold in winter, the operation of heating equipment (such as electric heaters, heat pumps, etc.) will also consume a lot of electricity. At the same time, considering that economic data reflects the economic activity of the region, for example, the continuous growth of industrial added value indicates that industrial production is expanding, which will increase the electricity consumption of factory machinery and equipment, lighting, etc. In addition, considering that electricity price is an important factor affecting users' electricity consumption behavior, if the peak-valley electricity price difference of time-of-use electricity price is large, users will be more inclined to use electricity during the valley period, and the electricity consumption during the peak period will be relatively reduced. Among them, industrial added value is the balance after deducting industrial intermediate inputs from the total industrial output, and industrial added value can be obtained from government statistical departments and professional databases. The peak-valley electricity price difference is the difference between the peak period electricity price and the valley period electricity price in the power system.

[0019] 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.

[0020] 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.

[0021] On this basis, the following first formula can be used to predict the second power consumption coefficient corresponding to the temperature data:

[0022] in, Represents the second power consumption coefficient, Indicates the lower limit of the daily average temperature. represents the upper limit of the daily average temperature. represents the measured value of the daily average temperature, and These are preset scaling factors.

[0023] 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 of the daily average temperature and the lower limit of the daily average temperature can be obtained based on the statistical data of the temperature of each season. 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.

[0024] At the same time, the following second formula can be used to predict the third electricity consumption coefficient corresponding to the economic data:

[0025] in, represents the third electricity consumption coefficient, Indicates the reference value of economic data, represents the measured value of economic data, is the preset scaling factor.

[0026] The third formula above shows that as the economic data increases, the third electricity consumption coefficient will increase accordingly.

[0027] Similarly, the fourth electricity consumption coefficient corresponding to the electricity price data can be predicted using the following third formula:

[0028] 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., the difference between peak and valley electricity prices), Indicates the peak period of electricity price. Indicates the period of low electricity prices. is the preset scaling factor.

[0029] The above third formula 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.

[0030] 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, and the predicted value of the electricity demand can be obtained by multiplying the first electricity consumption coefficient and the reference value of the electricity demand.

[0031] S102: Determine a predicted value of power supply based on the capacity of each power generation device in the power supply system, the operation data of each power generation device in the power supply system, and meteorological data.

[0032] 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.).

[0033] Ideally, the rated capacity of each power generation equipment in the power supply system is added together to get the power supply. Considering that each power generation equipment may fail or stop during operation, if a power generation equipment fails or stops, its corresponding power generation is zero. Therefore, the fault data of each power generation equipment can be predicted by monitoring its operation data, and then the actual power generation of each power generation equipment can be determined based on its fault data, and then the power supply of the entire power supply system can be determined based on the actual power generation of each power generation equipment.

[0034] Furthermore, for new energy power generation equipment such as hydropower, wind power, and photovoltaic power, their actual power generation is greatly affected by meteorological factors. For example, for hydropower, data such as precipitation, river runoff, and reservoir water level determine the power generation capacity of hydropower units; wind power depends on information such as wind speed and wind direction, and wind power generation can be predicted based on wind speed and wind direction; photovoltaic power generation is closely related to meteorological factors such as solar radiation intensity and sunshine duration.

[0035] 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 the meteorological data, the power supply can be accurately predicted.

[0036] S103: Determine a risk assessment value for spot electricity trading based on the predicted value of electricity demand and the predicted value of electricity supply.

[0037] 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, 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, the higher the risk of insufficient supply.

[0038] Therefore, the risk assessment value of power spot trading can be determined based on the positive correlation between the absolute value of the supply-demand difference and the risk assessment value of power spot trading. For example, the absolute value of the supply-demand difference is multiplied by a preset proportionality 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 (excess electricity supply).

[0039] 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.

[0040] For example, when the risk assessment value of insufficient electricity 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 give priority to 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 to remind users to appropriately reduce the use of non-essential electrical appliances during peak hours. When the risk assessment value of insufficient electricity 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 to urgently transfer electricity from surrounding power-rich areas. When the risk assessment value of insufficient electricity 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 to cut off power to industrial users according to certain priorities (such as high-energy-consuming enterprises should be shut down first, and enterprises related to people's livelihood should be shut down later) to ensure basic electricity consumption for residents and important public facilities.

[0041] When the risk assessment value of oversupply of electricity indicates that the risk of spot electricity 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 to reduce power supply. When the risk assessment value of oversupply of electricity indicates that the risk of spot electricity trading is at a medium risk level (for example, between -80% and -60%), it is necessary to adjust the power generation plan to a large extent, arrange for some power generation equipment (such as thermal power units) to be shut down for maintenance in turn, and reduce the power generation capacity; when the risk assessment value of oversupply of electricity indicates that the risk of spot electricity trading is at a high risk level (for example, below -80%), in addition to the necessary equipment for safe and stable operation of the power grid, reduce the operation of more power generation equipment.

[0042] 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.

[0043] 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 electricity supply, and achieve more accurate predictions of electricity supply.

[0044] Determining the risk assessment value 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 conducive to decision makers to formulate effective energy policies and market strategies and improve the stability of the power system.

[0045] In one embodiment of the present disclosure, predicting a first electricity consumption coefficient based on temperature data, economic data, and electricity price data includes: 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.

[0046] In this embodiment, multiple groups 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.

[0047] The historical data of the first power consumption coefficient can be obtained by calculating the ratio of the historical data of power demand and the reference value of power demand. In order to make data of different magnitudes comparable, this embodiment needs to normalize the historical data of temperature data, historical data of economic data, and historical data of electricity price data before fitting the linear regression model.

[0048] Taking the historical data of temperature data as an example, the normalization processing formula is:

[0049] 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.

[0050] 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.

[0051] In one embodiment of the present disclosure, determining a predicted value of power supply based on the capacity of each power generation device in the power supply system, the operation data of each power generation device in the power supply system, and meteorological data includes: Predicting fault data of each power generation equipment based on the operation data of each power generation equipment 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 refers to a power generation device for which the fault data prediction result indicates that the power generation device is fault-free; determining a first electricity 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; Determining the second electricity supply amount based on the capacity of the 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.

[0052] 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 state of the power generation equipment. Specifically, a machine learning method (such as a decision tree, a support vector machine, a neural network, etc.) or a statistical analysis method (such as a Markov chain model, a reliability analysis method, etc.) can be used to train the above fault prediction model.

[0053] According to the output result 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 electricity supply based on the capacity of the first power generation device can improve the accuracy of the electricity supply prediction.

[0054] 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.

[0055] Specifically, for the new energy power generation equipment in the first power generation equipment, since its power generation is greatly affected by meteorological data, the power generation of the new energy power generation equipment can be predicted based on meteorological data. For example, for wind power generation, it is necessary to consider the influence of meteorological factors such as wind speed, wind direction, and air density. When calculating the power generation of wind power generation equipment, the output power of the wind turbine generator can be predicted through wind speed data based on the model of the wind speed-power curve; at the same time, the influence of wind direction on the angle of the fan impeller and the influence of air density on power generation efficiency are considered to accurately calculate the power generation (that is, the first power supply). For solar power generation equipment, its power generation is mainly affected by the intensity of solar radiation. Therefore, the power generation of solar power generation equipment (that is, the first power supply) can be obtained based on the intensity of solar radiation and the photoelectric conversion efficiency of solar panels.

[0056] For other power generation equipment in the first power generation equipment, such as thermal power generation equipment, hydropower generation equipment, etc., when it is predicted that there will be no failures, 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.

[0057] 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.

[0058] In one embodiment of the present disclosure, determining a risk assessment value of a spot electricity transaction based on a predicted value of electricity demand and a predicted value of electricity supply includes: Calculate the difference between the predicted value of electricity demand and the predicted value of electricity supply; Determine the supply-demand imbalance based on the supply-demand difference and the predicted value of electricity demand; An electricity spot transaction risk assessment value is determined based on the supply-demand imbalance and a set first benchmark value.

[0059] In this embodiment, considering that the supply-demand difference can only reflect the quantitative difference between the predicted value of electricity demand and the predicted value of electricity supply, the total demand is not taken into account, which may lead to a misjudgment of the risk level. For example, for a small power system, a supply-demand difference of 100,000 kilowatts may be a very serious problem because its total demand may be only 500,000 kilowatts; while for a large power system, the total demand is 10 million kilowatts, and the supply-demand difference of 100,000 kilowatts has less impact on the system.

[0060] 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.

[0061] Furthermore, according to the statistical results, the common range of supply-demand imbalance is -50%~50%. When the supply-demand imbalance is between -10%~10%, it indicates that the supply and demand are basically balanced; when the supply-demand imbalance is between 10%~30% (insufficient power supply) or between -30%~-10% (excess power supply), it indicates a slight imbalance between supply and demand; when the supply-demand imbalance is between 30%~50% (insufficient power supply) or between -30%~-50% (excess power supply), it indicates a moderate imbalance between supply and demand; when the supply-demand imbalance is above 50% (insufficient power supply) or below -50% (excess power supply), it indicates a serious imbalance between supply and demand.

[0062] Therefore, this embodiment adds a first benchmark value based on the supply-demand imbalance, and can map the risk assessment value of electricity spot trading to 0~100% (insufficient electricity supply) and -100%~0 (excess electricity supply), which is more in line with users' understanding habits.

[0063] Specifically, if the supply-demand imbalance is greater than zero, the first reference value is set to a first value greater than zero (eg, 30%); if the supply-demand imbalance is less than zero, the first reference value is set to a second value less than zero (eg, -30%).

[0064] 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.

[0065] In one embodiment of the present disclosure, determining a risk assessment value of spot electricity trading based on the supply-demand imbalance includes: Calculating a first adjustment parameter based on the capacity of a specified power generation device among a 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 a 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 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; Adjust 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 benchmark value and the supply-demand imbalance.

[0066] In this embodiment, considering the different flexibility of the power supply system (for example, the adjustment speed of power generation, the adjustment speed of power demand, etc.), the tolerance to the imbalance between supply and demand is different. If the power grid is highly flexible, it can quickly adjust the supply and demand balance, and the tolerance to the imbalance between supply and demand is stronger, that is, the risk assessment value of power spot trading is set lower; on the contrary, if the power grid is less flexible, the tolerance to the imbalance between supply and demand is weak, and the risk assessment value of power spot trading needs to be increased to dispatch power resources in a timely manner.

[0067] 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.

[0068] Among them, the regulation speed of power generation (characterized by the first regulation parameter) is positively correlated with the capacity of the designated power generation equipment. The designated power generation equipment may include gas turbine power stations, pumped storage power stations and other power generation equipment that can quickly start, stop or change the power generation capacity. The proportion of the capacity of the designated power generation equipment to the electricity supply is taken as the first regulation parameter. The larger the first regulation parameter is, the higher the proportion of power generation that can be quickly adjusted in the power supply system is, and the faster the regulation speed of power generation is.

[0069] The regulation speed of electricity demand (characterized by the second regulation parameter) is positively correlated with the load capacity of the designated user. The designated user is a user participating in the demand-side response plan. The demand-side response (DSR) plan refers to a power management strategy in which power users change their electricity consumption behavior according to the operating conditions of the power system, electricity price signals or incentives to adjust power demand and optimize power resource allocation. Therefore, the load capacity of the designated user is the adjustable load capacity, and the proportion of the load capacity of the designated user to the power supply demand is taken as the second regulation parameter. The larger the second regulation parameter, the higher the proportion of the load capacity that can be quickly adjusted on the demand side, and the faster the regulation speed of the electricity demand.

[0070] 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 difference between supply and demand is taken as the third regulation parameter. The larger the third regulation parameter, the faster the regulation speed of the energy storage system.

[0071] 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: The first adjustment parameter, the second adjustment parameter and the third adjustment parameter are weighted and summed to obtain a fourth adjustment parameter; 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.

[0072] 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.

[0073] Specifically, the second reference value may be calculated using the following fourth formula:

[0074] in, represents the second reference value, represents the first reference value, represents the fourth adjustment parameter.

[0075] From the above, it can be concluded that this embodiment adjusts the risk assessment value of electricity spot trading based on the flexibility of the power supply system, and the obtained risk assessment value of electricity spot trading 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.

[0076] 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: Predict failure time based on the failure type of power generation equipment; determining a first proportional parameter based on the failure time; The risk assessment value of electricity spot trading is adjusted based on the first proportional parameter.

[0077] In the embodiment, considering that different types of faults have different corresponding maintenance times, the downtime of the power generation equipment (i.e., the fault time) is different, and therefore, the degree of impact on the power supply is different. For example, if the fault of a power generation equipment is caused by some minor sensor faults, the repair time is short, and it only takes a few hours or even less to resume normal operation; while if it is a serious mechanical fault such as a short circuit in the rotor winding of the generator or a damaged blade of the turbine, it may take days or even weeks to repair and replace parts.

[0078] The failure times corresponding to various common failure types can be counted, and a mapping relationship between the failure type and the 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.

[0079] On this basis, the first proportional parameter determined based on the fault time can be described in detail as follows: According to the failure time of each faulty power generation equipment, multiple faulty power generation equipment are divided into first-category faulty power generation equipment, second-category faulty power generation equipment and third-category faulty power generation equipment; wherein, the failure time of the first-category faulty power generation equipment is less than or equal to the first duration, the failure time of the first-category faulty power generation equipment is between the first duration and the second duration, and the failure time of the first-category faulty power generation equipment is greater than the second duration; respectively 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 the faulty power generation equipment; In response to the proportion of first-type faulty power generation equipment being greater than a first threshold, determining a first proportion parameter based on a fifth formula; In response to the proportion of third-type faulty power generation equipment being greater than a first threshold, determining a first proportion parameter based on a sixth formula; 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 proportional coefficient; The sixth formula is: ;in, represents the first scale parameter, Indicates the proportion of the third type of faulty power generation equipment.

[0080] Among them, the first duration and the second duration are both preset constants, which can be obtained by technicians in this field based on statistical data. For example, the first duration is set to 1 hour and the second duration is set to 3 hours.

[0081] On the basis of obtaining the first proportion parameter, the first proportion parameter is multiplied by the power spot transaction risk assessment value to obtain an adjusted power spot transaction risk assessment value.

[0082] It can be concluded from the above that this embodiment takes into account 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.

[0083] In one embodiment of the present disclosure, the power spot transaction risk assessment method further includes: Predicting fault data of the transmission line in the power supply system based on the status monitoring data of the transmission line 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.

[0084] In this embodiment, considering that if a transmission line fails (grid congestion), part of the electricity cannot be smoothly transmitted to the demand area, thereby affecting the actual power supply, it is necessary to monitor the fault data of the transmission line to achieve accurate prediction of the power supply.

[0085] Specifically, electrical parameters such as current, voltage, and power of the line can be obtained through sensors installed on the transmission line. Abnormal changes in these parameters are often related to line faults. For example, a sudden increase in line current may indicate a short circuit risk, and abnormal voltage fluctuations may indicate a degradation in insulation performance or electromagnetic interference with other lines. In addition, the temperature data of the line can also be monitored. The temperature of the line is relatively stable during normal operation. When the connection point is loose or the wire is overloaded, the temperature will rise significantly. For example, the wire joint has poor contact and increased resistance, which leads to an increase in temperature at the joint. This change can be monitored by the temperature sensor and can serve as an important basis for fault prediction.

[0086] A fault monitoring model for a transmission line can be constructed based on the above monitoring data. For example, historical state monitoring data (including data in normal operation and fault state) can be used as training samples to train a support vector machine, a neural network model, etc., to obtain a fault monitoring model for a transmission line. On this basis, the state monitoring data of the transmission line can be input into the fault monitoring model to obtain the fault data of the transmission line.

[0087] If the fault data of a transmission line shows that the transmission line is faulty, the transmission line can be marked as a faulty line, and the line load of the faulty line can be subtracted from the predicted value of the power supply, so as to further realize the accurate calculation of the predicted value of the power supply.

[0088] It can be concluded from the above that this embodiment predicts fault data based on the status monitoring data of the transmission line and adjusts the predicted value of the electricity supply, which further improves the accuracy of the predicted value of the electricity supply, and thus improves the accuracy of the risk assessment of electricity spot trading.

[0089] 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. The first prediction module 21 is used to predict the first power consumption coefficient based on the temperature data, the economic data and the electricity price data, and adjust the reference value of the power demand based on the first power consumption coefficient to obtain the predicted value of the power demand; A second prediction module 22, for determining a predicted value of power supply based on the capacity of each power generation device in the power supply system, the operation data of each power generation device in the power supply system and meteorological data; The risk assessment module 23 is used to determine the 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.

[0090] In one embodiment of the present disclosure, the first prediction module 21 is specifically used to: 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.

[0091] In one embodiment of the present disclosure, the second prediction module 22 is specifically used to: Predicting fault data of each power generation equipment based on the operation data of each power generation equipment 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 refers to a power generation device for which the fault data prediction result indicates that the power generation device is fault-free; determining a first electricity 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; Determining the second electricity supply amount based on the capacity of the 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.

[0092] In one embodiment of the present disclosure, the risk assessment module 23 is specifically used to: Calculate the difference between the predicted value of electricity demand and the predicted value of electricity supply; Determine the supply-demand imbalance based on the supply-demand difference and the predicted value of electricity demand; An electricity spot transaction risk assessment value is determined based on the supply-demand imbalance and a set first benchmark value.

[0093] In one embodiment of the present disclosure, the risk assessment module 23 is further configured to: Calculating a first adjustment parameter based on the capacity of a specified power generation device among a 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 a 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; Adjust 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 benchmark value and the supply-demand imbalance.

[0094] In one embodiment of the present disclosure, the risk assessment module 23 is further configured to: Predict failure time based on the failure type of power generation equipment; determining a first proportional parameter based on the failure time; The risk assessment value of electricity spot trading is adjusted based on the first proportional parameter.

[0095] In one embodiment of the present disclosure, the second prediction module 22 is specifically used to: Predicting fault data of the transmission line in the power supply system based on the status monitoring data of the transmission line 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.

[0096] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The 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 each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0097] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also 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 the processor may also be any conventional processor, etc.

[0098] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0099] The memory 304 may include a read-only memory and a random access memory, and provide 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 information about the device type.

[0100] 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.

[0101] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0102] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also 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 may also be used to temporarily store data that has been output or is to be output.

[0103] Those of ordinary skill 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 composition and steps of each example have been generally described in terms of function in the above description. 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.

[0104] Those skilled in the art can 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.

[0105] In the several embodiments provided in the present 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 only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, 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 it can be an electrical, mechanical or other form of connection.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0107] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0108] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for assessing the risk of spot electricity 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; Determine a forecast value of power 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; 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.

2. The method for assessing the risk of spot electricity trading according to claim 1, characterized in that: 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 spot electricity trading according to claim 1, characterized in that: 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 operation data of each power generation device in the power supply system and the meteorological data includes: Predicting fault data of each power generation equipment based on the operation data of each power generation equipment in the power supply system; Selecting a first power generation device from multiple 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 the fault data prediction result is that there is no fault; determining a first electricity 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 an amount of electricity supplied is determined based on the first amount of electricity supplied and the second amount of electricity supplied.

4. The method for assessing the risk of spot electricity trading according to claim 1, characterized in that: The step of determining the risk assessment value of spot electricity trading based on the predicted value of electricity demand and the predicted value of electricity supply includes: 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.

5. The method for assessing the risk of spot electricity trading according to claim 4, characterized in that: The step of determining the risk assessment value of spot power trading based on the supply and demand imbalance comprises: Calculating a first regulating parameter based on the capacity of a designated power generation device among a plurality of power generation devices in the power supply system; the first regulating parameter is used to characterize the regulating speed of the power generation; Calculating a second adjustment parameter based on the load capacity of a designated user among a 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; Adjust 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.

6. The method for assessing the risk of spot electricity trading according to claim 4 or 5, 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 spot electricity trading is adjusted based on the first proportion parameter.

7. The method for assessing the risk of spot electricity trading according to claim 1, characterized in that: Also includes: Predicting fault data of the transmission line in the power supply system based on the status monitoring data of the transmission line 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.

8. A device for assessing the risk of spot electricity trading, characterized in that: include: A first prediction module is used to predict a first power consumption coefficient based on temperature data, economic data and electricity price data, and adjust a reference value of power demand based on the first power consumption coefficient to obtain a predicted value of power demand; A second prediction module is used to determine a predicted value of power supply based on the capacity of each power generation device in the power supply system, the operation data of each power generation device in the power supply system and the meteorological data; 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.

9. 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 7 are implemented.

10. 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 7 are implemented.

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