Method and system for assisting power market transaction decision based on strategy recommendation

Abstract

The application relates to the technical field of power market transaction, and discloses a power market transaction decision assistance method and system based on strategy recommendation. The method comprises the following steps: acquiring weather, new energy output, power load, electricity price and power transmission channel data; determining the sequence of different new energy stations affected according to the moving path of the weather system, and constructing a new energy output fluctuation propagation chain; analyzing the output fluctuation propagation and determining the response mismatch degree in combination with the power load, determining the transaction exposure change degree according to the response mismatch degree, and analyzing the change of the residual available transmission capacity of the power transmission channel; determining the transaction risk urgency degree, the recommended transaction time and the transaction volume according to the transaction exposure change degree, the electricity price and the change of the residual available transmission capacity of the power transmission channel, and generating a transaction decision assistance strategy. The application improves the scientificity and timeliness of the power market transaction decision by constructing the output fluctuation propagation chain and combining the dynamic change of the power transmission channel.

Description

Technical Field

[0001] This invention relates to the field of electricity market trading technology, and in particular to a method and system for assisting electricity market trading decisions based on strategy recommendations. Background Technology

[0002] Against the backdrop of ongoing power market reforms, market participants such as renewable energy power generation companies and electricity retailers face an increasingly complex trading decision-making environment. They need to comprehensively consider multiple factors, including fluctuations in renewable energy output, changes in electricity load, electricity price trends, and transmission channel constraints, when formulating trading strategies. Existing methods for formulating power market trading strategies typically determine market supply and demand based on renewable energy output forecasts, electricity load forecasts, and electricity price forecasts, and then develop corresponding power trading strategies to mitigate the impact of changes in renewable energy output and electricity load on trading plans.

[0003] A Chinese patent application with publication number CN122048405A discloses a power market transaction auxiliary decision-making method and system for renewable energy power plants. This method processes multi-source data affecting power market supply, demand and price, constructs a renewable energy output prediction model and a load demand prediction model and calculates the supply and demand balance index. Based on the supply and demand balance index and multi-source influencing factors, it constructs a price trend prediction model to predict market price trends and quantifies risks. Then, it constructs a dynamic trading strategy optimization model and sets constraints, and uses a fusion algorithm to solve for the optimal trading strategy. However, existing technologies primarily rely on multi-source data prediction and static optimization models for trading strategy formulation. They fail to adequately consider how the spatial movement of weather systems can cause different renewable energy power plants to be sequentially affected by the same weather processes. Specifically, as a weather system moves to the areas of different renewable energy power plants, the renewable energy output of each plant will change sequentially according to the direction of the system's movement. Existing methods struggle to determine the propagation sequence and subsequent changes of renewable energy output fluctuations based on the spatial movement of weather systems. Furthermore, existing methods typically determine market supply and demand based on renewable energy output and electricity load forecasts, without further considering the different response speeds of renewable energy output and electricity load to the same weather change as renewable energy output fluctuations gradually propagate. This can lead to a supply-demand mismatch for a certain period, and existing methods struggle to determine the source-load response mismatch window based on the temporal relationship between changes in renewable energy output and electricity load. Additionally, existing methods often treat the remaining available transmission capacity of transmission channels as a fixed constraint, failing to consider the dynamic changes in transmission channel power as renewable energy output fluctuations propagate between different renewable energy power plants. This results in the correctable electricity volume determined based on market trading opportunities potentially exceeding the actual capacity of the transmission channels.

[0004] The existing technologies described above have the following problems: they do not consider the propagation process of renewable energy output fluctuations caused by the spatial movement of weather systems between different renewable energy power plants and the resulting source-load response mismatch, making it difficult to determine the propagation of renewable energy output fluctuations and the resulting trading impact based on the movement of weather systems; they do not establish a correlation mechanism between the propagation of renewable energy output fluctuations and the dynamic changes in the remaining capacity of transmission channels, which may lead to the recommended trading volume exceeding the actual carrying capacity of transmission channels; and they do not comprehensively consider multiple dimensions such as the source-load response mismatch window, remaining market trading opportunities, correctable electricity volume, and the evolution trend of trading exposure to quantify the urgency of trading risks, making it difficult to determine the recommended trading timing and volume, and failing to meet the timeliness and accuracy requirements of power market trading decisions under complex weather processes and grid constraints. Summary of the Invention

[0005] This invention aims to overcome the shortcomings of existing technologies and provide a strategy-based decision-making support method and system for electricity market transactions. By determining the order in which each renewable energy power station is affected by the same weather system based on the direction and path of the weather system's movement, a propagation chain of renewable energy output fluctuations is constructed, and the degree of response mismatch between renewable energy output and electricity load is determined. This, in turn, determines the degree of change in transaction exposure and the change in the remaining available transmission capacity of transmission channels. The urgency of transaction risks is comprehensively determined, and transaction timing and volume are recommended accordingly. This generates a transaction decision-making support strategy, improving the timeliness and accuracy of electricity market transaction decisions.

[0006] In a first aspect, the present invention provides a strategy-recommendation-based decision-making assistance method for electricity market transactions, comprising: Acquire weather data, renewable energy output data, electricity load data, electricity price data, and power transmission channel data between different renewable energy power plants for each renewable energy power plant; Based on weather data, determine the direction and path of the weather system, and based on the direction and path of the weather system, determine the order in which each new energy power station is affected by the same weather system, and construct the propagation chain of new energy output fluctuations according to the order of the weather system. Based on the analysis of renewable energy output data, the propagation process of renewable energy output fluctuations along the renewable energy output fluctuation propagation chain is analyzed, and the degree of response mismatch between renewable energy output and electricity load is determined in combination with electricity load data. The degree of change in transaction exposure is determined based on the degree of response mismatch, and the changes in the transmission power of each transmission channel are analyzed based on the propagation chain of new energy output fluctuations to determine the changes in the remaining available transmission capacity of the transmission channels. Based on the degree of change in trading exposure, electricity price data, and changes in the remaining available transmission capacity of transmission channels, the urgency of trading risks corresponding to different trading periods is determined, and the recommended trading timing and corresponding recommended trading volume are determined based on the urgency of trading risks. Trading decision support strategies are generated based on recommended trading timing and volume.

[0007] Furthermore, determining the direction and path of the weather system's movement based on weather data includes: According to the order of weather data collection time, the spatial location of the weather system corresponding to each consecutive sampling time is determined. The spatial location of the weather system is the location of the weather system represented by the weather data at the corresponding consecutive sampling time. The speed of the weather system movement is obtained by dividing the distance between the spatial locations of the weather system corresponding to adjacent sampling times by the time interval between adjacent sampling times. The direction and path of the weather system's movement are determined based on the system's speed of movement and the direction of its spatial location change at each continuous sampling time.

[0008] Furthermore, the step of determining the direction and path of the weather system based on weather data, and determining the order in which each new energy power station is affected by the same weather system based on the direction and path of the weather system, and constructing a propagation chain of new energy output fluctuations according to the order of the weather system, includes: Based on the direction and path of the weather system's movement, the time when the weather system arrives at each new energy power station is determined. Then, according to the order in which the weather system arrives at each new energy power station, the order in which different new energy power stations are affected by the same weather system is determined, and adjacent new energy power stations are regarded as adjacent propagation nodes. Based on the renewable energy output data, the difference between the renewable energy output data of adjacent renewable energy power plants at the same collection time is calculated, and the difference is used as the change in renewable energy output. According to the order in which each new energy power station is affected by the same weather system, the new energy power station that is affected by the same weather system first among adjacent new energy power stations is designated as the preceding new energy power station, and the new energy power station that is affected by the same weather system later is designated as the following new energy power station. Divide the change in renewable energy output corresponding to the subsequent renewable energy power station by the change in renewable energy output corresponding to the preceding renewable energy power station to obtain the power output propagation change ratio. According to the order in which each new energy power station is affected by the same weather system, adjacent propagation nodes and their corresponding output propagation change ratios are connected to form a new energy output fluctuation propagation chain.

[0009] Furthermore, the step of analyzing the propagation process of renewable energy output fluctuations along the renewable energy output fluctuation propagation chain based on renewable energy output data, and determining the degree of response mismatch between renewable energy output and electricity load in conjunction with electricity load data, includes: According to the connection order of adjacent propagation nodes in the propagation chain of new energy power output fluctuations, the new energy power output changes corresponding to adjacent propagation nodes are sequentially correlated to obtain the propagation process of new energy power output fluctuations. Based on the electricity load data, calculate the difference between the electricity load data corresponding to adjacent sampling times, and use the difference as the change in electricity load. Calculate the difference between the change in new energy output and the change in electricity load corresponding to the same sampling time, and use the difference as the source-load change difference; The sum of the absolute values ​​of the source-load change differences at each sampling time is divided by the sum of the absolute values ​​of the new energy output change and the absolute values ​​of the electricity load change at each sampling time, and the resulting ratio is used as the degree of response mismatch.

[0010] Furthermore, determining the degree of change in transaction exposure based on the degree of response mismatch includes: According to the trading period corresponding to the electricity price data, obtain the new energy output data and electricity load data corresponding to each trading period; Match the new energy output data with the electricity load data corresponding to each trading period, and multiply the absolute value of the difference between the new energy output data and the electricity load data by the length of the trading period. Use the product as the planned trading electricity for each trading period. The product of the response mismatch degree and the planned transaction volume is taken as the transaction deviation volume; The difference between the trading deviation electricity volume in the current trading period and the trading deviation electricity volume in the previous trading period is taken as the change in trading deviation electricity volume. The ratio of the change in trading deviation electricity volume to the trading deviation electricity volume of the previous trading period is used as the degree of change in trading exposure.

[0011] Furthermore, the step of analyzing the changes in transmission power of each transmission channel based on the propagation chain of new energy power output fluctuations to determine the changes in the remaining available transmission capacity of the transmission channels includes: Based on the transmission channel data, the corresponding transmission channel for each new energy power station is determined. According to the order in which each new energy power station is affected by the same weather system in the propagation chain of new energy power output fluctuation, the sum of the new energy power output changes of each new energy power station corresponding to the same transmission channel is taken as the power change of the transmission channel. The transmission capacity and current transmission power of each transmission channel are obtained from the transmission channel data, and the difference between the transmission capacity and the current transmission power is taken as the current remaining available transmission capacity. The difference between the current remaining available transmission capacity and the change in power of the transmission channel is taken as the remaining available transmission capacity after propagation; The difference between the remaining available transmission capacity after propagation and the current remaining available transmission capacity is used as the change in the remaining available transmission capacity of the transmission channel.

[0012] Furthermore, determining the urgency of transaction risks for different trading periods based on changes in transaction exposure, electricity price data, and changes in the remaining available transmission capacity of transmission channels includes: The ratio of the degree of change in trading exposure to the benchmark value of trading risk is used as the trading exposure risk ratio; The ratio of the absolute value of the change in the remaining available transmission capacity of the transmission channel to the current remaining available transmission capacity is taken as the transmission channel capacity change ratio. The inverse of the number of trading sessions between the current trading session and subsequent trading sessions is used as the time urgency ratio; The sum of the risk exposure ratio, the transmission channel capacity change ratio, and the time urgency ratio is used as the degree of risk urgency of the transaction.

[0013] Furthermore, the step of determining the recommended trading timing and corresponding recommended trading volume based on the urgency of trading risk includes: Compare the urgency of trading risks in each trading session according to the time sequence of the trading sessions, and select the trading session with the highest urgency of trading risks as the recommended trading opportunity. Compare the trading deviation electricity volume with the product of the remaining available transmission capacity of the transmission channel corresponding to the recommended trading time and the length of the trading period, and take the smaller value between the trading deviation electricity volume and the product as the initial adjustable trading electricity volume; The trading period adjacent to the recommended trading opportunity will be selected as the candidate trading period; The product of the initial adjustable trading volume and the electricity price data corresponding to the recommended trading time and the electricity price data corresponding to the candidate trading period is divided by the electricity price data corresponding to the candidate trading period to obtain the electricity price adjustment volume. The sum of the initial adjustable trading volume and the price-adjusted trading volume is used as the recommended trading volume.

[0014] Secondly, the present invention provides a strategy-recommendation-based power market trading decision support system, comprising: The data acquisition module is used to acquire weather data, new energy output data, electricity load data, electricity price data, and power transmission channel data between different new energy power stations. The propagation chain construction module is used to determine the direction and path of the weather system based on weather data, and to determine the order in which each new energy power station is affected by the same weather system based on the direction and path of the weather system, and to construct the propagation chain of new energy output fluctuations in the order of the weather system. The response mismatch determination module is used to analyze the propagation process of renewable energy output fluctuations along the renewable energy output fluctuation propagation chain based on renewable energy output data, and to determine the degree of response mismatch between renewable energy output and electricity load based on electricity load data. The transaction exposure and transmission capacity determination module is used to determine the degree of change in transaction exposure based on the degree of response mismatch, and to analyze the changes in the transmission power of each transmission channel based on the propagation chain of new energy output fluctuations, thereby determining the changes in the remaining available transmission capacity of the transmission channel. The transaction risk and recommendation strategy determination module is used to determine the urgency of transaction risks for different transaction periods based on the degree of change in transaction exposure, electricity price data, and changes in the remaining available transmission capacity of transmission channels, and to determine the recommended transaction timing and corresponding recommended transaction volume based on the urgency of transaction risks. The decision support strategy generation module is used to generate trading decision support strategies based on recommended trading timing and recommended trading volume.

[0015] Thirdly, the present invention provides an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the computer program to implement the above-described strategy-recommendation-based power market transaction decision support method.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: (1) This invention determines the order in which each new energy power station is affected by the same weather system based on the direction and path of the weather system, constructs a new energy output fluctuation propagation chain, and realizes a quantitative characterization of the new energy output fluctuation propagation process and its trading impact. (2) This invention analyzes the propagation process of new energy output fluctuations based on the propagation chain of new energy output fluctuations and determines the degree of response mismatch between new energy output and electricity load by combining electricity load data, thereby realizing the quantitative determination of the degree of source-load response mismatch. (3) This invention analyzes the changes in the transmission power of each transmission channel based on the propagation chain of new energy power output fluctuations, determines the changes in the remaining available transmission capacity of the transmission channel, and uses the degree of change in transaction exposure, electricity price data and changes in the remaining available transmission capacity of the transmission channel together to determine the urgency of transaction risk, thereby realizing real-time correction of transaction feasibility and joint optimization of recommended transaction timing and transaction volume. Attached Figure Description

[0017] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of the strategy recommendation-based power market transaction decision support method of the present invention; Figure 2 This is a schematic diagram illustrating the construction of the propagation chain of weather system movement and new energy output fluctuations according to the present invention; Figure 3This is a schematic diagram of the process for constructing the propagation chain of new energy power output fluctuations and determining response mismatch in this invention. Figure 4 This is a schematic diagram illustrating the process for determining the degree of change in transaction exposure and the change in the remaining available transmission capacity of the power transmission channel according to the present invention. Figure 5 This is a schematic diagram of the structure of the strategy recommendation-based power market transaction decision support system of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0019] Example 1

[0020] Please see Figure 1 , Figure 2 , Figure 3 and Figure 4 , Figure 1 This is a schematic diagram of the overall process of the strategy recommendation-based power market transaction decision support method provided in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the construction of the propagation chain between weather system movement and new energy output fluctuations according to the present invention. Figure 3 This is a schematic diagram illustrating the process of constructing the propagation chain of new energy power output fluctuations and determining response mismatch according to the present invention. Figure 4 This is a schematic diagram illustrating the process for determining the degree of change in transaction exposure and the change in the remaining available transmission capacity of the transmission channel according to the present invention. The following is in conjunction with... Figure 1 , Figure 2 , Figure 3 and Figure 4 The steps of this embodiment will be described in detail.

[0021] S1. Obtain weather data, new energy output data, electricity load data, electricity price data, and power transmission channel data between different new energy power stations for each new energy power station; In this embodiment, each new energy power station is a wind power station and a photovoltaic power station within the same regional power grid that are affected by the same weather system. The new energy power stations are connected through power transmission channels to achieve power transmission.

[0022] The weather data are meteorological parameters continuously collected in the area where each new energy power station is located within the target trading cycle, including wind speed, wind direction, irradiance, cloud cover, temperature and air pressure. The weather data are acquired in real time through the meteorological monitoring system supporting each new energy power station or a regional meteorological observation station; the air pressure data and cloud cover data in the weather data are used in subsequent steps to identify the spatial position of the weather system. The specific identification method is to extract the air pressure field distribution corresponding to each sampling moment from the air pressure data, and take the position of the lowest air pressure value as the central position coordinate of the weather system at that moment. When there are multiple low-pressure centers in the air pressure field, the low-pressure center corresponding to the area with the maximum cloud cover in the cloud cover data is selected as the central position of the weather system, so as to ensure the uniqueness of the identification of the spatial position of the weather system; the reference value of the sampling interval is 15 minutes, which is determined jointly according to the minimum trading period in power market trading decision-making and the time resolution of the weather forecast system. The actual sampling interval is adjusted according to the moving speed of the weather system or the distance between power stations, wherein the moving speed of the weather system is obtained by calculation in subsequent steps, specifically by dividing the distance between the spatial positions of the weather system corresponding to adjacent sampling moments by the time interval between adjacent sampling moments: when the moving speed of the weather system exceeds a preset speed threshold, the sampling interval is adjusted to 50% of the reference value, otherwise the reference value is adopted. The preset speed threshold is the ratio of the distance between adjacent power stations to the duration of the weather system's impact, and this ratio characterizes the relationship between the distance the weather system can move within the impact duration and the distance between power stations; when the distance between power stations is less than the preset distance threshold, the sampling interval is adjusted to 50% of the reference value, otherwise the reference value is adopted. This 50% is determined according to the minimum effective ratio for maintaining sampling continuity during the movement of the weather system, so as to ensure that the sequence in which each power station is successively affected can still be captured when the moving speed of the weather system increases or the distance between power stations is small; the preset distance threshold is 30% of the radius of the weather system's impact range, the weather system's impact range is determined according to the continuous distribution range of the area with large cloud cover in the cloud cover data of each new energy power station, and this 30% is determined according to the coverage ratio of the power station layout density to the weather system's impact range, so as to reflect the magnitude relationship between the distance between power stations and the weather system's impact range.

[0023] The new energy output data are the actual generated power and predicted generated power corresponding to each sampling moment of each new energy power station within the target trading cycle, wherein the actual generated power is recorded in real time by the power collection system of each power station, and the predicted generated power is provided by the new energy output prediction model corresponding to each power station, and the new energy output prediction model is a conventional prediction model constructed based on numerical weather forecast and historical output data; the new energy output data are time-aligned with the weather data according to the sampling moments, and are used for subsequent analysis of the sequence and degree of the impact of the weather system movement on the output of each power station.

[0024] Electricity load data consists of the actual and predicted regional electricity load values ​​at each sampling time within the target trading period, provided by the power grid dispatch system. The electricity load data is time-aligned with the renewable energy output data according to the sampling time, which is used to subsequently determine the degree of response mismatch between renewable energy output and electricity load.

[0025] Electricity price data consists of the market clearing price and forecast price for each trading period within the target trading cycle. These prices are provided by the power trading center or a price forecasting model, which is a conventional forecasting model built based on historical electricity price data and market supply and demand factors. The electricity price data is matched with new energy output data and electricity load data according to the trading period to determine the urgency of trading risks and recommended trading volume for different trading periods.

[0026] The transmission channel data comprises the rated transmission capacity, current transmission power, and channel impedance parameters of the transmission channels between various renewable energy power plants within the target trading cycle. This data is obtained from the power grid dispatching system or the transmission channel operation monitoring system. The transmission channel data is used for subsequent analysis of changes in the transmission power and remaining available transmission capacity of each transmission channel during the propagation of renewable energy output fluctuations. The aforementioned weather data, renewable energy output data, electricity load data, electricity price data, and transmission channel data are time-aligned according to the sampling time to form a multi-dimensional data vector corresponding to each sampling time, and arranged in chronological order as the basic input data for subsequent steps. When data is missing at a certain sampling time, the linear interpolation result of the corresponding data from the two adjacent valid sampling times before and after that sampling time is taken as supplementary data. When the amount of continuously missing data... When the data loss exceeds a preset missing threshold, the time period is marked as invalid and skipped. The preset missing threshold is determined based on the weather system's movement speed and the distance between stations. Specifically, when the continuous missing duration exceeds 50% of the time required for the weather system to move from the adjacent station, the data is marked as invalid. This percentage is determined based on the effective coverage ratio of the weather system's influence duration to ensure that invalid data marking will not excessively remove valid data due to too short a missing duration, nor will it introduce unreliable interpolated data due to too long a missing duration. When there is a missing air pressure or cloud cover data at a certain sampling time, making it impossible to identify the center location of the weather system, the sampling time is marked as invalid for the spatial location identification of the weather system, and linear interpolation is performed using the spatial location of the weather system at adjacent valid sampling times to supplement it, in order to ensure the continuity of the weather system's movement path.

[0027] S2. Determine the direction and path of the weather system based on weather data, and determine the order in which each new energy power station is affected by the same weather system based on the direction and path of the weather system, and construct the propagation chain of new energy output fluctuations according to the order of the weather system. In this embodiment, the weather system moves continuously in space and affects the renewable energy power stations it passes through in sequence. To accurately describe this propagation process, it is necessary to first determine the direction and path of the weather system's movement, then determine the order in which each power station is affected based on the spatial relationship between the movement path and each renewable energy power station, and finally construct the renewable energy output fluctuation propagation chain by combining renewable energy output data.

[0028] Specifically, according to the time sequence of weather data collection, the spatial location of the weather system corresponding to each consecutive sampling time is determined. The spatial location of the weather system is the location of the weather system represented by the weather data at the corresponding consecutive sampling time, expressed using latitude and longitude coordinates. The coordinates of the center position of the weather system at each sampling time are extracted from the weather data as the spatial location of the weather system at that time. The distance between the spatial locations of the weather system corresponding to adjacent sampling times is divided by the time interval between adjacent sampling times to obtain the movement speed of the weather system. The spherical distance formula is used for distance calculation to eliminate the influence of the Earth's curvature on the distance calculation. Based on the movement speed of the weather system and the consecutive sampling times... The direction of change in the spatial location of the corresponding weather system is used to determine the direction and path of the weather system's movement. Specifically, the spatial locations of the weather system corresponding to each consecutive sampling time are connected sequentially in chronological order to form the weather system's movement path. This movement path is then used as the weather system's movement path. The direction of change in the spatial location of the weather system between adjacent sampling times is taken as the direction of movement of the weather system within that time period. When the distance between the spatial locations of the weather system corresponding to adjacent sampling times is 0, it indicates that the weather system has not moved spatially within that time period. In this case, the movement speed of the weather system within that time period is set to 0, the movement direction of the weather system is marked as no direction, and the data for the next time period is processed.

[0029] After determining the direction and path of the weather system, the arrival time of the weather system at each renewable energy power station is determined based on this direction and path. The order in which each power station is affected by the same weather system is then determined according to the order of arrival times, with adjacent power stations designated as adjacent propagation nodes. Specifically, the path point closest to each power station's spatial location is selected from the weather system's path, and the time corresponding to this path point is taken as the arrival time of the corresponding power station. When multiple equidistant path points exist between the weather system's path and the power station's location, the earliest time is taken as the arrival time, ensuring that the determined arrival time reflects the time when the weather system first affects the power station. The power stations are then sorted from earliest to latest arrival times to obtain the order in which they are affected by the same weather system.

[0030] After determining the order of sampling, the difference between the power output data of adjacent renewable energy plants at the same sampling time is calculated based on the renewable energy output data. The difference is used as the change in renewable energy output. Specifically, for any two adjacent renewable energy plants, the actual power generation data corresponding to the same sampling time is extracted. The absolute value of the difference between the actual power generation at the later sampling time and the actual power generation at the earlier sampling time is used as the change in renewable energy output at that sampling time. When there is missing data in the actual power generation data corresponding to adjacent sampling times, that sampling time is not included in the calculation of the change in renewable energy output to avoid distortion of the change calculation due to missing data.

[0031] Based on the order in which each renewable energy power station is affected by the same weather system, the power station affected first among adjacent stations is designated as the preceding station, and the power station affected later is designated as the following station. The change in renewable energy output corresponding to the following station is divided by the change in renewable energy output corresponding to the preceding station to obtain the output propagation change ratio. This ratio characterizes the degree of change in renewable energy output fluctuation as the weather system propagates from the preceding station to the following station. When the output propagation change ratio is greater than 1, it indicates... The output fluctuation of new energy sources is amplified during the propagation process. When the output propagation change ratio is less than 1 and greater than 0, it indicates that the output fluctuation of new energy sources is attenuated during the propagation process. When the output propagation change ratio is equal to 1, it indicates that the amplitude of the output fluctuation of new energy sources remains unchanged during the propagation process. When the output change of new energy sources corresponding to the preceding new energy power station is 0, the output propagation change ratio cannot be calculated by division. In this case, the output propagation change ratio is determined to be 1, indicating that the amplitude change of the output fluctuation of new energy sources cannot be effectively judged on this propagation path. The value of 1 is used as a neutral value to avoid calculation abnormalities caused by the denominator being 0.

[0032] Based on the order in which each renewable energy power station is affected by the same weather system, adjacent propagation nodes and their corresponding output propagation change ratios are connected to form a renewable energy output fluctuation propagation chain. Specifically, the renewable energy output fluctuation propagation chain consists of multiple adjacent propagation nodes and directed connections between adjacent nodes. Each adjacent propagation node corresponds to a renewable energy power station and its arrival time, and each directed connection corresponds to the output propagation change ratio between two adjacent power stations. The starting point of the renewable energy output fluctuation propagation chain is the renewable energy power station first affected by the weather system, and the ending point is the renewable energy power station last affected by the weather system. The constructed renewable energy output fluctuation propagation chain serves as the basis for subsequent analysis steps to determine the propagation process of renewable energy output fluctuations along the propagation chain and its impact on the transmission power of the transmission channel.

[0033] S3. Analyze the propagation process of new energy output fluctuations along the new energy output fluctuation propagation chain based on new energy output data, and determine the degree of response mismatch between new energy output and electricity load based on electricity load data. In this embodiment, after constructing the propagation chain of renewable energy output fluctuations, it is necessary to analyze the propagation process of renewable energy output fluctuations along the propagation chain to determine how the impact of weather system movement on the output of each renewable energy power station is transmitted sequentially along the propagation chain. Combined with electricity load data, the degree of response mismatch between renewable energy output and electricity load is determined, providing a basis for subsequently determining the degree of change in transaction exposure. Specifically, the degree of response mismatch is used to characterize the degree of asynchronous time between changes in renewable energy output and changes in electricity load. The larger the value, the more severe the time misalignment between changes in renewable energy output and changes in electricity load.

[0034] According to the connection order of adjacent propagation nodes in the new energy output fluctuation propagation chain, the new energy output changes corresponding to adjacent propagation nodes are sequentially correlated to obtain the new energy output fluctuation propagation process. Specifically, the new energy output changes corresponding to adjacent propagation nodes have been calculated in the previous steps. According to the order from the preceding station to the following station in the new energy output fluctuation propagation chain, the new energy output changes corresponding to each adjacent propagation node are arranged sequentially to form a new energy output change sequence along the propagation chain direction. This new energy output change sequence reflects the order and magnitude of the output changes of each station during the movement of the weather system, i.e., the new energy output fluctuation propagation process. When the new energy output change corresponding to a certain adjacent propagation node in the new energy output fluctuation propagation chain cannot be obtained, the node is skipped, and the previous available node is connected to the next available node to ensure the continuity of the propagation process.

[0035] Based on the electricity load data, the difference between the electricity load data corresponding to adjacent sampling times is calculated, and the difference is used as the change in electricity load. Specifically, the actual value of the regional electricity load corresponding to each sampling time is extracted from the electricity load data. The absolute value of the difference between the actual value of the electricity load at the later sampling time and the actual value of the electricity load at the previous sampling time is used as the change in electricity load at that sampling time. The change in electricity load is used to characterize the magnitude of the change in electricity load between adjacent sampling times. When there is missing data in the electricity load data at adjacent sampling times, the change in electricity load corresponding to that adjacent sampling time cannot be calculated. In this case, the sampling time is skipped, and the data at the next available sampling time is used to calculate the difference, so as to ensure the integrity of the electricity load change sequence.

[0036] The difference between the change in renewable energy output and the change in electricity load at the same sampling time is calculated, and this difference is used as the source-load change difference. Specifically, the change in renewable energy output at each propagation node during the propagation of renewable energy output fluctuations is matched with the change in electricity load at the corresponding sampling time. The change in renewable energy output is subtracted from the change in electricity load, and the resulting difference is used as the source-load change difference at that sampling time. The source-load change difference is used to characterize the degree of difference between the change in renewable energy output and the change in electricity load at the same sampling time. When there is missing data for the change in renewable energy output or the change in electricity load at a certain sampling time, the source-load change difference at that sampling time cannot be calculated. In this case, the sampling time is skipped to ensure the accuracy of subsequent calculations.

[0037] The sum of the absolute values ​​of the source-load variation differences at different sampling times is divided by the sum of the absolute values ​​of the changes in renewable energy output and electricity load at each sampling time, and the resulting ratio is used as the degree of response mismatch. Specifically, the degree of response mismatch is calculated as follows: the absolute values ​​of the source-load variation differences at all valid sampling times are summed to obtain the sum of the absolute values ​​of the source-load variation differences; the absolute values ​​of the changes in renewable energy output and electricity load at all valid sampling times are summed separately, and then the two are added together to obtain the change. The sum of absolute values ​​of the source-load variation; the ratio obtained by dividing the sum of the absolute values ​​of the source-load variation difference by the sum of the absolute values ​​of the variation is the response mismatch degree. The larger the ratio, the more severe the source-load mismatch under a unit change intensity; when the sum of the absolute values ​​of the change in new energy output and the absolute values ​​of the change in electricity load at each sampling time is 0, it means that neither the new energy output nor the electricity load has changed. At this time, the response mismatch degree is determined to be 0 to avoid the denominator being 0, which would lead to calculation abnormalities; the calculated response mismatch degree is used as the basis for determining the degree of change in transaction exposure in subsequent steps.

[0038] S4. Determine the degree of change in transaction exposure based on the degree of response mismatch, and analyze the changes in transmission power of each transmission channel based on the propagation chain of new energy output fluctuations to determine the changes in the remaining available transmission capacity of the transmission channel. In this embodiment, after determining the degree of response mismatch between renewable energy output and electricity load, it is necessary to convert the degree of response mismatch into the degree of change in trading exposure in order to quantify the impact of source-load mismatch caused by the movement of weather systems on the trading plan. At the same time, it is also necessary to analyze the changes in the transmission power of each transmission channel based on the propagation chain of renewable energy output fluctuations to determine the changes in the remaining available transmission capacity of the transmission channels, so as to assess whether the trading strategy is limited by the capacity of the transmission channels during actual execution.

[0039] Specifically, the degree of change in transaction exposure is determined based on the degree of response mismatch, including: obtaining the renewable energy output data and electricity load data corresponding to each transaction period according to the transaction period corresponding to the electricity price data, where the transaction period is the smallest time unit for electricity market transactions, the renewable energy output data corresponding to each transaction period is the average renewable energy output at each sampling time within that period, and the electricity load data is the average electricity load at each sampling time within that period; matching the renewable energy output data and electricity load data corresponding to each transaction period, and multiplying the absolute value of the difference between the renewable energy output data and the electricity load data by the length of the transaction period, and using the resulting product as the planned transaction volume for each transaction period, which represents the scale of electricity to be traded in that transaction period under the influence of passive load mismatch; when the difference between the renewable energy output data and the electricity load data is 0, the planned transaction volume for that transaction period is 0.

[0040] The product of the response mismatch degree and the planned transaction volume is taken as the transaction deviation volume. This transaction deviation volume represents the deviation between the planned transaction volume and the actual demand caused by the source-load mismatch. The greater the response mismatch degree or the larger the planned transaction volume, the larger the transaction deviation volume. When the response mismatch degree is 0, the transaction deviation volume is 0, indicating that there is no impact of source-load mismatch on the transaction volume. The difference between the transaction deviation volume of the current transaction period and the transaction deviation volume of the previous transaction period is taken as the transaction deviation volume change. This transaction deviation volume change represents the magnitude of the change in transaction deviation volume between adjacent transaction periods. A positive value indicates an increase in transaction deviation volume, and a negative value indicates an increase in transaction deviation volume. The value indicates a decrease in trading deviation volume. The ratio of the change in trading deviation volume to the trading deviation volume of the previous trading period is used as the degree of change in trading exposure. This degree of change in trading exposure is used to characterize the relative rate of change of trading deviation volume relative to the previous trading period. The larger the value, the more drastic the change in trading exposure. When the trading deviation volume of the previous trading period is 0, the ratio of the change in trading deviation volume to the trading deviation volume of the previous trading period cannot be calculated. In this case, the degree of change in trading exposure is determined to be 0, indicating that there was no trading deviation in the previous trading period. The rate of change of the current trading period relative to the previous trading period is undefined and is treated as a neutral value of 0.

[0041] Based on the analysis of the propagation chain of renewable energy output fluctuations, the changes in the transmission power of each transmission channel are determined to ascertain the changes in the remaining available transmission capacity of the transmission channels. This includes: determining the transmission channels corresponding to each renewable energy power station based on transmission channel data; and, according to the order in which each renewable energy power station is affected by the same weather system in the propagation chain of renewable energy output fluctuations, summing the changes in renewable energy output of each renewable energy power station corresponding to the same transmission channel as the power change of the transmission channel. This power change of the transmission channel represents the cumulative transmission power change generated by the output changes of each power station along the propagation chain direction; when the number of renewable energy power stations corresponding to the same transmission channel is 0, the power change of the transmission channel corresponding to that transmission channel is 0; obtaining the transmission capacity and current transmission power of each transmission channel from the transmission channel data, and using the difference between the transmission capacity and the current transmission power as the current remaining available transmission capacity. The remaining available transmission capacity represents the transmission capacity of a transmission channel that is not currently being used under its operating conditions. The difference between the current remaining available transmission capacity and the change in power output of the transmission channel is taken as the propagated remaining available transmission capacity. This propagated remaining available transmission capacity represents the transmission capacity that the transmission channel can still use after the propagation of the new energy output fluctuation. When the change in power output of the transmission channel is greater than the current remaining available transmission capacity, the propagated remaining available transmission capacity is negative, indicating that the transmission channel can no longer carry the additional transmission power based on the current transmission power. The difference between the propagated remaining available transmission capacity and the current remaining available transmission capacity is taken as the change in the remaining available transmission capacity of the transmission channel. This change represents the magnitude of the change in the remaining available transmission capacity of the transmission channel before and after the propagation of the new energy output fluctuation. A positive value indicates an increase in the remaining available transmission capacity, and a negative value indicates a decrease in the remaining available transmission capacity.

[0042] The changes in the degree of exposure to the aforementioned transactions and the changes in the remaining available transmission capacity of the transmission channels are used as inputs for subsequent steps to jointly determine the urgency of transaction risks corresponding to different transaction periods, together with electricity price data.

[0043] S5. Based on the degree of change in transaction exposure, electricity price data, and changes in the remaining available transmission capacity of transmission channels, determine the urgency of transaction risks for different transaction periods, and determine the recommended transaction timing and corresponding recommended transaction volume based on the urgency of transaction risks. In this embodiment, after determining the degree of change in transaction exposure and the change in the remaining available transmission capacity of the transmission channel, it is necessary to comprehensively consider the three dimensions of the degree of change in transaction exposure, electricity price data, and the change in the remaining available transmission capacity of the transmission channel to quantify the urgency of transaction risks corresponding to different transaction periods, and then determine the recommended transaction timing and corresponding recommended transaction volume based on the urgency of transaction risks.

[0044] Specifically, the urgency of transaction risks for different trading periods is determined based on the degree of change in transaction exposure, electricity price data, and changes in the remaining available transmission capacity of transmission channels.

[0045] The ratio of the change in trading exposure to the trading risk benchmark is used as the trading exposure risk ratio. This trading exposure risk ratio is used to characterize the relative magnitude of the current change in trading exposure relative to the benchmark level. The trading risk benchmark is determined based on the statistical average of the historical changes in trading exposure. Specifically, the changes in trading exposure corresponding to each trading period in several historical trading periods prior to the target trading period are obtained, and the arithmetic mean of the above changes in trading exposure is calculated as the trading risk benchmark. When the changes in trading exposure corresponding to each trading period in the historical trading period are all 0, the trading risk benchmark is set to a value of 1 to avoid the trading risk benchmark being 0, which would cause the ratio calculation to be abnormal.

[0046] The ratio of the absolute value of the change in the remaining available transmission capacity of a transmission channel to the current remaining available transmission capacity is used as the transmission channel capacity change ratio. This transmission channel capacity change ratio is used to characterize the relative magnitude of the change in the remaining available transmission capacity of the transmission channel with respect to the current remaining available transmission capacity. When the change in the remaining available transmission capacity of the transmission channel is 0, the transmission channel capacity change ratio is 0, indicating that the remaining available transmission capacity of the transmission channel has not changed.

[0047] The time urgency ratio is the reciprocal of the number of trading sessions between the current trading session and subsequent trading sessions. This ratio is used to characterize the relative urgency of the current trading session relative to subsequent trading sessions. The fewer subsequent trading sessions, the more urgent the time, and the larger the time urgency ratio. Subsequent trading sessions refer to all trading sessions after the current trading session that belong to the same target trading cycle. When the number of subsequent trading sessions is 0, the current trading session is regarded as the last trading session, and the time urgency ratio is 1, indicating that the time is most urgent.

[0048] The sum of the risk exposure ratio, the transmission channel capacity change ratio, and the time urgency ratio is used as the risk urgency of the transaction. This risk urgency comprehensively reflects the combined impact of the three dimensions of risk exposure change, transmission channel capacity change, and time urgency on the transaction risk. The higher the value, the higher the transaction risk in the current transaction period.

[0049] After determining the urgency of trading risks for each trading session, the recommended trading timing and corresponding recommended trading volume are determined based on the urgency of the trading risks.

[0050] The trading risk urgency of each trading session is compared according to the time sequence of the trading sessions. The trading session with the highest urgency of trading risk is selected as the recommended trading opportunity. When there are multiple trading sessions with the same urgency of trading risk and all of them are at the maximum value, the earliest trading session is selected as the recommended trading opportunity to ensure that trading adjustments are executed as early as possible when the risk reaches the highest level.

[0051] The smaller of the transaction deviation electricity and the product of the remaining available transmission capacity of the transmission channel corresponding to the recommended transaction timing and the length of the transaction period is used as the initial adjustable transaction electricity. This initial adjustable transaction electricity represents the upper limit of the actual adjustable transaction electricity in the current transaction period after considering the limitation of the remaining available transmission capacity of the transmission channel. When the remaining available transmission capacity of the transmission channel corresponding to the recommended transaction timing is 0 or negative, it means that the transmission channel has no available transmission capacity, and the initial adjustable transaction electricity is determined to be 0.

[0052] The trading period adjacent to the recommended trading opportunity is designated as the candidate trading period. The candidate trading period includes the trading period preceding and following the recommended trading opportunity. When the recommended trading opportunity is the first trading period, the candidate trading period is only the following trading period. When the recommended trading opportunity is the last trading period, the candidate trading period is only the preceding trading period. The product of the initial adjustable trading volume and the difference between the electricity price data corresponding to the recommended trading opportunity and the electricity price data corresponding to the candidate trading period is divided by the electricity price data corresponding to the candidate trading period to obtain the electricity price adjustment volume. This electricity price adjustment volume is used to characterize the amount of correction made to the initial adjustable trading volume due to the electricity price difference between the recommended trading opportunity and the candidate trading period. The electricity price difference is the electricity price data corresponding to the recommended trading opportunity minus the electricity price data corresponding to the candidate trading period. When the electricity price data corresponding to the candidate trading period is 0, the electricity price adjustment volume is determined to be 0.

[0053] The sum of the initial adjustable trading volume and the price-adjusted trading volume is used as the recommended trading volume. This recommended trading volume is the final output recommended trading volume and is used to generate a trading decision support strategy. When the initial adjustable trading volume is 0, the recommended trading volume is determined to be 0, indicating that there is currently no adjustable trading volume and no trading adjustment is required.

[0054] S6. Generate trading decision support strategies based on recommended trading timing and recommended trading volume; In this embodiment, after determining the recommended trading timing and the corresponding recommended trading volume, it is necessary to transform the recommended trading timing and recommended trading volume into a trading decision support strategy that can be directly used by market trading entities to guide them in trading operations within the target trading period.

[0055] Specifically, a trading decision support strategy is generated based on the recommended trading timing and volume. This includes: using the trading period corresponding to the recommended trading timing as the strategy execution time, the recommended trading volume as the strategy execution electricity, and the electricity price data corresponding to the recommended trading timing as the strategy reference electricity price. The above information is combined to generate the trading decision support strategy, which contains three core elements: strategy execution time, strategy execution electricity, and strategy reference electricity price. The strategy execution time indicates when the trading entity will execute the trade, the strategy execution electricity indicates how much trading volume the trading entity will execute at that time, and the strategy reference electricity price indicates the expected market price for the trading entity at that time. When the recommended trading volume is 0, the strategy execution electricity of the trading decision support strategy is 0, indicating that no trading operation needs to be performed. In this case, the strategy execution time and strategy reference electricity price are treated as invalid values.

[0056] The specific method for generating the trading decision support strategy is as follows: the trading period identifier corresponding to the recommended trading opportunity is used as the strategy execution time, which includes the trading period number and the start and end times of the trading period; the recommended trading volume is used as the strategy execution electricity, with the unit of strategy execution electricity being megawatt-hours; the electricity price data corresponding to the recommended trading opportunity is used as the strategy reference electricity price, with the unit of strategy reference electricity price being yuan per megawatt-hour; the strategy execution time, strategy execution electricity, and strategy reference electricity price are combined according to a preset format to form a structured text-based trading decision support strategy, which is used to directly output to the trading entity's trading decision system or trading operation interface.

[0057] When multiple trading periods correspond to the same level of trading risk urgency and are all at their maximum, the recommended trading period is the earliest among them. In this case, the execution time of the trading decision support strategy is the earliest trading period. When the remaining available transmission capacity of the transmission channel corresponding to the recommended trading period is 0 or negative, the initial adjustable trading volume is determined to be 0, the recommended trading volume is 0, the execution volume of the trading decision support strategy is 0, and a prompt message indicating insufficient transmission channel capacity is output to inform the trading entity of the reason why the current trading adjustment cannot be executed.

[0058] The generated trading decision support strategy is output to the trading entity's trading decision system or trading operation interface to assist the trading entity in executing trading operations according to the recommended trading timing and recommended trading volume within the target trading period. When the trading entity confirms the execution of the trading decision support strategy, it inputs the strategy execution time, strategy execution volume, and strategy reference electricity price as trading instructions into the electricity market trading system to complete the trading operation. When the trading entity needs to adjust the trading decision support strategy, it re-acquires the weather data, renewable energy output data, electricity load data, electricity price data, and transmission channel data corresponding to the target trading period, and executes the subsequent steps in sequence to obtain the updated recommended trading timing and recommended trading volume.

[0059] Example 2

[0060] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a strategy-recommendation-based power market transaction decision support system provided in an embodiment of the present invention. The system includes a data acquisition module, a propagation chain construction module, a response mismatch determination module, a transaction exposure and transmission capacity determination module, a transaction risk and recommendation strategy determination module, and a decision support strategy generation module.

[0061] The data acquisition module acquires weather data, renewable energy output data, electricity load data, electricity price data, and transmission channel data between different renewable energy power plants. The propagation chain construction module determines the direction and path of the weather system based on the weather data, and determines the order in which each renewable energy power plant is affected by the same weather system, constructing a propagation chain for renewable energy output fluctuations according to this order. The response mismatch determination module analyzes the propagation process of renewable energy output fluctuations along the propagation chain based on renewable energy output data, and determines the degree of response mismatch between renewable energy output and electricity load based on electricity load data. The transaction exposure and transmission capacity determination module... The module is used to determine the degree of change in trading exposure based on the degree of response mismatch, and to analyze the changes in the transmission power of each transmission channel based on the propagation chain of new energy output fluctuations, thereby determining the changes in the remaining available transmission capacity of the transmission channels; the module for determining trading risk and recommended strategy is used to determine the urgency of trading risk corresponding to different trading periods based on the degree of change in trading exposure, electricity price data, and changes in the remaining available transmission capacity of the transmission channels, and to determine the recommended trading timing and corresponding recommended trading volume based on the urgency of trading risk; the module for generating decision support strategy is used to generate trading decision support strategy based on the recommended trading timing and recommended trading volume; the specific implementation of the above modules corresponds to steps S1 to S6 in Example 1, and will not be repeated here.

[0062] Example 3

[0063] This embodiment provides an electronic device, including a processor and a memory. The memory stores a computer program, and when the processor executes the computer program, it implements the strategy recommendation-based power market transaction decision support method as described in Embodiment 1.

[0064] Specifically, the electronic device can be an industrial control computer or server with the following hardware configuration: Intel Xeon Gold 6248R CPU, 64GB DDR4 memory, Ubuntu 22.04 LTS operating system, and data processing algorithms implemented based on Python 3.10. This electronic device connects to the power trading center data interface, the power grid dispatching system data interface, the new energy power station data acquisition system, and the meteorological data service interface via industrial Ethernet or dedicated network. After receiving weather data, new energy output data, electricity load data, electricity price data, and transmission channel data, it performs the following actions: constructing the new energy output fluctuation propagation chain, determining the degree of response mismatch, determining the degree of change in trading exposure and the change in the remaining available transmission capacity of the transmission channel, determining the urgency of trading risks, and determining the recommended trading timing and volume. Finally, it outputs a trading decision support strategy.

[0065] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the aforementioned strategy-based recommendation-based electricity market transaction decision support method. The computer-readable storage medium can be any tangible medium containing or storing a program, such as a read-only memory, random access memory, disk, optical disk, USB flash drive, solid-state drive, etc.

Claims

1. A strategy-recommendation-based decision support method for electricity market transactions, characterized in that, include: Acquire weather data, renewable energy output data, electricity load data, electricity price data, and power transmission channel data between different renewable energy power plants for each renewable energy power plant; Based on weather data, determine the direction and path of the weather system, and based on the direction and path of the weather system, determine the order in which each new energy power station is affected by the same weather system, and construct the propagation chain of new energy output fluctuations according to the order of the weather system. Based on the analysis of renewable energy output data, the propagation process of renewable energy output fluctuations along the renewable energy output fluctuation propagation chain is analyzed, and the degree of response mismatch between renewable energy output and electricity load is determined in combination with electricity load data. The degree of change in transaction exposure is determined based on the degree of response mismatch, and the changes in the transmission power of each transmission channel are analyzed based on the propagation chain of new energy output fluctuations to determine the changes in the remaining available transmission capacity of the transmission channels. Based on the degree of change in trading exposure, electricity price data, and changes in the remaining available transmission capacity of transmission channels, the urgency of trading risks corresponding to different trading periods is determined, and the recommended trading timing and corresponding recommended trading volume are determined based on the urgency of trading risks. Trading decision support strategies are generated based on recommended trading timing and volume.

2. The power market transaction decision support method based on strategy recommendation according to claim 1, characterized in that, Determining the direction and path of a weather system's movement based on weather data includes: According to the order of weather data collection time, the spatial location of the weather system corresponding to each consecutive sampling time is determined. The spatial location of the weather system is the location of the weather system represented by the weather data at the corresponding consecutive sampling time. The speed of the weather system movement is obtained by dividing the distance between the spatial locations of the weather system corresponding to adjacent sampling times by the time interval between adjacent sampling times. The direction and path of the weather system's movement are determined based on the system's speed of movement and the direction of its spatial location change at each continuous sampling time.

3. The power market transaction decision support method based on strategy recommendation according to claim 1, characterized in that, The process of determining the direction and path of the weather system based on weather data, determining the order in which each new energy power station is affected by the same weather system based on the direction and path of the weather system, and constructing a propagation chain of new energy output fluctuations according to the order of the weather system's movement includes: Based on the direction and path of the weather system's movement, the time when the weather system arrives at each new energy power station is determined. Then, according to the order in which the weather system arrives at each new energy power station, the order in which different new energy power stations are affected by the same weather system is determined, and adjacent new energy power stations are regarded as adjacent propagation nodes. Based on the renewable energy output data, the difference between the renewable energy output data of adjacent renewable energy power plants at the same collection time is calculated, and the difference is used as the change in renewable energy output. According to the order in which each new energy power station is affected by the same weather system, the new energy power station that is affected by the same weather system first among adjacent new energy power stations is designated as the preceding new energy power station, and the new energy power station that is affected by the same weather system later is designated as the following new energy power station. Divide the change in renewable energy output corresponding to the subsequent renewable energy power station by the change in renewable energy output corresponding to the preceding renewable energy power station to obtain the power output propagation change ratio. According to the order in which each new energy power station is affected by the same weather system, adjacent propagation nodes and their corresponding output propagation change ratios are connected to form a new energy output fluctuation propagation chain.

4. The power market transaction decision support method based on strategy recommendation according to claim 1, characterized in that, The process of analyzing the propagation of renewable energy output fluctuations along the renewable energy output fluctuation propagation chain based on renewable energy output data, and determining the degree of response mismatch between renewable energy output and electricity load based on electricity load data, includes: According to the connection order of adjacent propagation nodes in the propagation chain of new energy power output fluctuations, the new energy power output changes corresponding to adjacent propagation nodes are sequentially correlated to obtain the propagation process of new energy power output fluctuations. Based on the electricity load data, calculate the difference between the electricity load data corresponding to adjacent sampling times, and use the difference as the change in electricity load. Calculate the difference between the change in new energy output and the change in electricity load corresponding to the same sampling time, and use the difference as the source-load change difference; The sum of the absolute values ​​of the source-load change differences at each sampling time is divided by the sum of the absolute values ​​of the new energy output change and the absolute values ​​of the electricity load change at each sampling time, and the resulting ratio is used as the degree of response mismatch.

5. The power market transaction decision support method based on strategy recommendation according to claim 1, characterized in that, The determination of the degree of change in transaction exposure based on the degree of response mismatch includes: According to the trading period corresponding to the electricity price data, obtain the new energy output data and electricity load data corresponding to each trading period; Match the new energy output data with the electricity load data corresponding to each trading period, and multiply the absolute value of the difference between the new energy output data and the electricity load data by the length of the trading period. Use the product as the planned trading electricity for each trading period. The product of the response mismatch degree and the planned transaction volume is taken as the transaction deviation volume; The difference between the trading deviation electricity volume in the current trading period and the trading deviation electricity volume in the previous trading period is taken as the change in trading deviation electricity volume. The ratio of the change in trading deviation electricity volume to the trading deviation electricity volume of the previous trading period is used as the degree of change in trading exposure.

6. The power market transaction decision support method based on strategy recommendation according to claim 1, characterized in that, The analysis of changes in the transmission power of each transmission channel based on the propagation chain of new energy power output fluctuations, to determine the changes in the remaining available transmission capacity of the transmission channels, includes: Based on the transmission channel data, the corresponding transmission channel for each new energy power station is determined. According to the order in which each new energy power station is affected by the same weather system in the propagation chain of new energy power output fluctuation, the sum of the new energy power output changes of each new energy power station corresponding to the same transmission channel is taken as the power change of the transmission channel. The transmission capacity and current transmission power of each transmission channel are obtained from the transmission channel data, and the difference between the transmission capacity and the current transmission power is taken as the current remaining available transmission capacity. The difference between the current remaining available transmission capacity and the change in power of the transmission channel is taken as the remaining available transmission capacity after propagation; The difference between the remaining available transmission capacity after propagation and the current remaining available transmission capacity is used as the change in the remaining available transmission capacity of the transmission channel.

7. The power market transaction decision support method based on strategy recommendation according to claim 1, characterized in that, The determination of the urgency of transaction risks for different trading periods, based on changes in transaction exposure, electricity price data, and changes in the remaining available transmission capacity of transmission channels, includes: The ratio of the degree of change in trading exposure to the benchmark value of trading risk is used as the trading exposure risk ratio; The ratio of the absolute value of the change in the remaining available transmission capacity of the transmission channel to the current remaining available transmission capacity is taken as the transmission channel capacity change ratio. The inverse of the number of trading sessions between the current trading session and subsequent trading sessions is used as the time urgency ratio; The sum of the risk exposure ratio, the transmission channel capacity change ratio, and the time urgency ratio is used as the degree of risk urgency of the transaction.

8. The power market transaction decision support method based on strategy recommendation according to claim 1, characterized in that, The process of determining the recommended trading timing and corresponding recommended trading volume based on the urgency of trading risks includes: Compare the urgency of trading risks in each trading session according to the time sequence of the trading sessions, and select the trading session with the highest urgency of trading risks as the recommended trading opportunity. Compare the trading deviation electricity volume with the product of the remaining available transmission capacity of the transmission channel corresponding to the recommended trading time and the length of the trading period, and take the smaller value between the trading deviation electricity volume and the product as the initial adjustable trading electricity volume; The trading period adjacent to the recommended trading opportunity will be selected as the candidate trading period; The product of the initial adjustable trading volume and the electricity price data corresponding to the recommended trading time and the electricity price data corresponding to the candidate trading period is divided by the electricity price data corresponding to the candidate trading period to obtain the electricity price adjustment volume. The sum of the initial adjustable trading volume and the price-adjusted trading volume is used as the recommended trading volume.

9. A power market trading decision support system based on strategy recommendation, characterized in that, The method for implementing the strategy-recommendation-based power market transaction decision support method according to any one of claims 1 to 8 includes: The data acquisition module is used to acquire weather data, new energy output data, electricity load data, electricity price data, and power transmission channel data between different new energy power stations. The propagation chain construction module is used to determine the direction and path of the weather system based on weather data, and to determine the order in which each new energy power station is affected by the same weather system based on the direction and path of the weather system, and to construct the propagation chain of new energy output fluctuations in the order of the weather system. The response mismatch determination module is used to analyze the propagation process of renewable energy output fluctuations along the renewable energy output fluctuation propagation chain based on renewable energy output data, and to determine the degree of response mismatch between renewable energy output and electricity load based on electricity load data. The transaction exposure and transmission capacity determination module is used to determine the degree of change in transaction exposure based on the degree of response mismatch, and to analyze the changes in the transmission power of each transmission channel based on the propagation chain of new energy output fluctuations, thereby determining the changes in the remaining available transmission capacity of the transmission channel. The transaction risk and recommendation strategy determination module is used to determine the urgency of transaction risks for different transaction periods based on the degree of change in transaction exposure, electricity price data, and changes in the remaining available transmission capacity of transmission channels, and to determine the recommended transaction timing and corresponding recommended transaction volume based on the urgency of transaction risks. The decision support strategy generation module is used to generate trading decision support strategies based on recommended trading timing and recommended trading volume.

10. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the power market transaction decision support method based on strategy recommendation as described in any one of claims 1 to 8.

Images