Method, system and equipment for decomposing green electricity transaction volume in high-altitude area and medium

Abstract

The invention relates to the technical field of electricity market transaction and new energy scheduling, and discloses a high altitude area green electricity transaction volume decomposition method, system, device and medium, and the method comprises the steps: constructing a probability distribution model of historical electricity prices, generating an initial electricity price path reflecting spot electricity price fluctuation characteristics through a stratified sampling method, k representative scenes and corresponding probabilities are screened through a scene similarity calculation method to form a representative scene set; based on a multi-dimensional power transmission constraint condition, constructing a dynamic constraint system for sensing the power transmission capability in real time; the multi-dimensional power transmission constraint conditions comprise wind and light output prediction, power transmission line icing risk, spot electricity price fluctuation, green power total amount and time period performance constraint; and based on the representative scene set and the dynamic constraint system, a green power distribution scheme per hour is solved through a random optimization method, and green power transaction volume decomposition is realized. According to the invention, safe performance and economic benefit maximization of the green electricity contract can be realized.

Description

Technical Field

[0001] This invention relates to the fields of electricity market trading and new energy dispatch technology, and in particular to a method, system, equipment and medium for decomposing green electricity trading volume in high-altitude areas. Background Technology

[0002] Driven by the "dual carbon" goals, medium- and long-term green electricity contracts have become the main source of revenue for new energy power plants. Green electricity trading refers to the buying and selling of renewable energy electricity (such as wind power and photovoltaic power) through market mechanisms to optimize clean energy allocation, promote energy consumption, and support low-carbon transformation. my country's green electricity market mainly includes two stages: medium- and long-term contract trading and spot trading.

[0003] The medium- and long-term green electricity trading market, through multi-year contracts, offers relatively fixed prices and provides long-term revenue guarantees for wind and solar power plants, but it cannot flexibly respond to short-term electricity price fluctuations. The electricity spot market includes the day-ahead market (pricing one day in advance) and the real-time market (pricing before immediate use, with volatile prices). Its price formation mechanism reflects the temporal and spatial scarcity of electricity, and can be expressed as:

[0004] in, The spot electricity price for time period t; The variable cost of power generation for the marginal units of the system; Regional price differences caused by power transmission network congestion; Cost of power transmission losses; Price fluctuations caused by deviations in new energy output and load forecasting.

[0005] However, high-altitude regions (such as the Qinghai-Tibet Plateau and the western Sichuan Plateau) face fundamentally different technical challenges in implementing green electricity trading compared to plains areas due to their high altitude, cold climate, and weak power grid infrastructure. (1) The power transmission network is weak and susceptible to weather conditions. Transmission lines in high-altitude areas are long and difficult to maintain, making them highly susceptible to severe weather conditions such as icing and snowstorms. Icing on transmission lines can reduce their current-carrying capacity and even cause line outages, posing a significant potential risk of default on green electricity contracts. Unlike in plains areas, icing in high-altitude areas tends to occur during the nighttime low-temperature period, which coincides with peak wind power output, creating a spatiotemporal coupling of high output and high risk.

[0006] (2) Strong fluctuations in wind and solar power output and negative correlation with time series High-altitude regions exhibit unique temporal characteristics in their wind and solar power resources. Solar power output shows an extreme pattern of peaking at midday and zero output in the morning and evening; while the valley wind effect causes wind power output to concentrate at night and in the early morning. These two major green energy sources show a significant negative correlation during the day. More importantly, the peak wind power generation period at night coincides with the period of lowest temperatures and highest risk of line icing. This coupling relationship poses a challenge to traditional independent power source forecasting and dispatching methods.

[0007] (3) The electricity market mechanism is imperfect and lacks a flexible performance mechanism. Medium- and long-term green electricity trading contracts are usually signed in advance, lacking a flexible dispatch mechanism in the face of future electricity price fluctuations. Existing green electricity trading plans often allocate electricity based on static average forecasts or subjective experience, which cannot effectively cope with the risk of transmission icing in high-altitude areas, the strong volatility of wind and solar power output, and the temporal and spatial differences in spot electricity prices. This results in problems such as weak response to electricity price signals, low trading returns, and high default risks in the performance of green electricity contracts.

[0008] Currently, the time-series decomposition of green electricity contract volume is typically based on the following two technical approaches: The first type is the static equal allocation method. This method distributes the total contract volume evenly across time periods or proportionally based on historical load curves. However, it does not consider fluctuations in spot electricity prices and cannot respond to market price signals. It is particularly unsuitable in high-altitude areas, where the day-night price difference can be 3-5 times, leading to significant lost arbitrage opportunities due to static allocation.

[0009] The second category is scenario optimization methods based on historical statistics. This method fits a probability distribution to historical electricity price data to generate possible future electricity price scenarios, and then decomposes the electricity consumption with the goal of maximizing expected revenue. The core assumption of this method is that the statistical characteristics of future electricity prices are consistent with historical data. However, this assumption does not hold true in high-altitude areas because: (1) Electricity prices in high-altitude areas are significantly affected by icing on transmission lines. When nighttime temperatures drop below freezing, the current-carrying capacity of the lines may decrease by 30%-50%. At this time, the supply and demand of the system are tight, and the electricity price will trigger the congestion pricing mechanism, which may result in prices several times higher than normal operating conditions. Existing methods use a unified model for all historical data, which cannot identify this hierarchical structure, and the generated scenarios are distorted under extreme weather conditions.

[0010] (2) Existing optimization models typically set the transmission capacity as a fixed parameter (e.g., a line capacity ≤ 500MW), which is generally reasonable in plain areas. However, in high-altitude areas, the transmission capacity is not an exogenous parameter but a function of meteorological conditions. For example, a capacity of 500MW during normal periods may drop sharply to 350MW during periods of icing. This dynamic change in the constraint boundary causes the feasible region of the optimization problem to evolve over time, which existing static constraint models cannot handle.

[0011] (3) The objective function of existing methods is usually a linear summation of the returns in each time period, implicitly assuming that the decisions in each time period are independent. However, in high-altitude scenarios, if the grid connection plan in a certain time period exceeds the transmission capacity after icing, not only will a negative bias penalty occur in that time period, but it will also trigger a grid safety event, which may lead to restrictions on trading eligibility in subsequent time periods. This cross-time period risk transmission makes the return function exhibit path-dependent characteristics, which cannot be captured by simple linear models.

[0012] In addition, high-altitude areas have the following unique characteristics in power operation: (1) The time sequence of wind and solar power output is reversed: photovoltaic power output is concentrated between 10:00 and 16:00 (when the temperature is high and there is no risk of icing); wind power output is concentrated between 00:00 and 06:00 (when the temperature is low and there is a high risk of icing).

[0013] (2) Asymmetry of electricity price fluctuations: Under normal weather conditions, electricity prices exhibit a typical peak-to-valley difference (1.5-2 times); however, under icy weather conditions, nighttime electricity prices may experience extreme high prices (3-5 times the daily average price) due to supply gaps, while daytime prices may experience extreme low prices due to large-scale photovoltaic power generation. This asymmetric fluctuation characteristic cannot be characterized by a normal distribution or conventional statistical models.

[0014] Currently, the relevant patents for the breakdown of green electricity trading volume are as follows: CN117540986A discloses a method and system for wind-solar-storage collaborative planning based on typical scenarios. The method includes: acquiring scenario data, generating typical daily scenarios, generating typical daily multi-random scenarios, generating typical uncertainty scenarios, constructing a collaborative planning model, and solving for the wind-solar-storage collaborative planning results. K... The Means clustering algorithm reduces the annual operating scenarios, and Latin hypercube sampling is used to characterize the uncertainties of wind power, photovoltaics, and load. A Kantorovich distance-based reduction method is used to obtain typical scenarios. A multi-objective programming model, aiming to minimize system investment and operating costs and the annual renewable energy generation cut-off, is simplified into a linear single-objective programming problem using the analytic hierarchy process (AHP). However, its application is limited to installed capacity planning and does not address the special environments of high-altitude regions; it uses uniform sampling and equal-weighted scenario compression under a single statistical population, lacks meteorological pre-classification and hierarchical modeling logic; and the scenario generation lacks the ability to dynamically adjust with weather forecasts, failing to adapt to the characteristics of tiered electricity pricing structures.

[0015] CN118864147A discloses a method for source-load coordinated optimization operation of high-energy-consuming enterprises considering wind and solar uncertainties and demand response. The method includes the following steps: acquiring historical wind and solar meteorological datasets and preprocessing them; constructing a source output characterization model for wind and solar uncertainties based on Weibull and Beta distributions, and generating historical wind and solar output datasets; using Latin hypercube sampling combined with Kantorovich scene reduction method to generate and reduce scenes from the historical wind and solar output data; constructing price elasticity coefficients and price elasticity coefficient matrices for electricity and heat prices; classifying enterprise loads and constructing energy consumption cost models; introducing objective functions and constraints for optimization operation to obtain an optimization operation model; and inputting wind and solar output and load parameters into the optimization operation model to obtain the actual results of source-load coordinated optimization operation of high-energy-consuming enterprises. However, the application scenario is enterprise energy consumption optimization, and it does not address the coupling problem of meteorology, transmission, and electricity prices in high-altitude areas; it also uses a uniform scene compression method, failing to consider the preservation of scene differences during high-risk periods; it lacks a joint modeling mechanism for meteorological conditions, electricity prices, and transmission constraints, and the scene generation lacks dynamic adaptability.

[0016] CN115545768A proposes a day-ahead stochastic bidding method for large hydropower projects across provinces and regions, considering contract decomposition. This method first proposes a price scenario analysis method considering time-period correlation from the perspective of time-period volatility; then, it constructs a stochastic expectation model with the objective function of maximizing the total expected revenue of both the medium- and long-term contract market and the day-ahead market; secondly, it proposes a medium- and long-term contract decomposition method based on peak-shaving demand gradation, and constructs a cross-provincial and cross-regional stochastic coordination optimization model that coordinates peak-shaving performance and economic benefits; finally, it uses multi-objective transformation and linearization methods to transform the model into a MILP model, and solves the model using optimization software. However, its transmission constraints are static exogenous fixed parameters, and the constraint boundaries do not change with external meteorological conditions; the application scenario focuses on day-ahead bidding for large hydropower projects across provinces and regions, the decision dimension is spatial power allocation, and it does not involve time-series risk avoidance in high-altitude areas; the objective function only pursues pure economic optimization, does not quantify physical default risk, and lacks a three-domain coordination mechanism of meteorology, physics, and economy.

[0017] In summary, existing methods for allocating green electricity trading volume have the following problems when applied to high-altitude areas: (1) The inability to establish a correlation mechanism between the probability distribution of electricity prices and meteorological conditions leads to the generation of electricity price scenarios losing their representativeness under extreme weather conditions; (2) The inability to dynamically adjust the power transmission constraints according to meteorological conditions makes the optimization results physically infeasible; (3) The inability to quantify the reduction effect of physical default risk on economic benefits leads to overly aggressive decision-making and high penalties when actually fulfilling the contract. Summary of the Invention

[0018] To address the aforementioned issues, this invention proposes a method, system, equipment, and medium for decomposing green electricity trading volume in high-altitude areas. It deeply couples and models weather forecasts, physical constraints, and market electricity prices, enabling the search for economically optimal green electricity trading strategies within dynamically changing safety boundaries. By establishing a weather-electricity price joint probabilistic modeling mechanism, a dynamic reconstruction mechanism for transmission constraints, and a robust optimization mechanism for risk perception, this invention solves core problems in existing technologies such as distorted electricity price scenarios, static physical constraints, and lack of risk assessment in this context, thereby maximizing the safe fulfillment of green electricity contracts and economic benefits.

[0019] The technical solution adopted in this invention is as follows: A method for decomposing green electricity trading volume in high-altitude areas includes: A probability distribution model of historical electricity prices is constructed. An initial electricity price path reflecting the fluctuation characteristics of spot electricity prices is generated through a hierarchical sampling method. K representative scenarios and their corresponding probabilities are selected through a scenario similarity calculation method to form a representative scenario set. Based on multidimensional power transmission constraints, a dynamic constraint system for real-time perception of power transmission capacity is constructed; the multidimensional power transmission constraints include wind and solar power output prediction, transmission line icing risk, spot electricity price fluctuations, and green electricity total amount and time period performance constraints. Based on the representative scenario set and dynamic constraint system, the green electricity allocation scheme for each hour is solved by a stochastic optimization method to achieve the decomposition of green electricity trading volume.

[0020] Furthermore, the construction of the historical electricity price probability distribution model includes: collecting historical electricity price data and meteorological data of the same period in the target area; dividing the historical electricity price sample into a normal operating condition electricity price set and an icing operating condition electricity price set according to the critical conditions of icing of transmission lines; and constructing the historical electricity price probability distribution model by using a joint probability modeling method of electricity price and meteorology.

[0021] Furthermore, the method of generating an initial electricity price path that reflects the fluctuation characteristics of spot electricity prices through hierarchical sampling includes: constructing non-parametric probability density functions for the normal operating condition electricity price set and the icing operating condition electricity price set respectively; using the method of accumulating historical sample kernel weights, taking each historical price point as a probability mass center; and using a Gaussian kernel function to perform local diffusion to form a continuous probability density curve.

[0022] Furthermore, the step of selecting K representative scenarios and their corresponding probabilities through a scenario similarity calculation method to form a representative scenario set includes: setting time period importance weights, including risk indicator functions and risk weighting coefficients; calculating the weighted distance between scenarios; and then determining K representative scenarios through an iterative clustering compression process.

[0023] Furthermore, the dynamic constraint system for real-time perception of transmission capacity based on multi-dimensional transmission constraints includes: fitting a capacity derating function based on historical operating data of the target area power grid, establishing a mapping relationship between temperature forecast and transmission capacity, and generating a capacity derating model triggered by icing; the capacity derating function adopts a piecewise linear or exponential form.

[0024] Furthermore, the dynamic constraint system for real-time perception of transmission capacity based on multi-dimensional transmission constraints also includes: transforming the icing state judgment into a continuously differentiable constraint through the penalty factor method, introducing auxiliary variables to represent the icing state during a time period, and enabling the upper limit of transmission capacity to be automatically calculated and adjusted within the model based on weather forecasts.

[0025] Furthermore, the step of solving the hourly green electricity allocation scheme using a stochastic optimization method includes: using the maximization of expected revenue across multiple scenarios as the objective function, the objective function including the green electricity contract price, the scenario spot electricity price, and a negative deviation penalty cost term, the negative deviation penalty cost term including the penalty coefficient and the actual available power generation during the time period; and optimizing using multiple constraint types as constraint conditions, the multiple constraint types including energy balance constraints, total contract amount constraints, decomposition boundary constraints, dynamic transmission constraints, and icing state logic constraints.

[0026] A system for allocating green electricity trading volume in high-altitude areas includes: The representative scenario set construction module is configured to build a probability distribution model of historical electricity prices. It generates an initial electricity price path that reflects the fluctuation characteristics of spot electricity prices through a hierarchical sampling method, and selects K representative scenarios and their corresponding probabilities through a scenario similarity calculation method to form a representative scenario set. The dynamic constraint system construction module is configured to build a dynamic constraint system for real-time perception of power transmission capacity based on multi-dimensional power transmission constraint conditions; the multi-dimensional power transmission constraint conditions include wind and solar power output prediction, transmission line icing risk, spot electricity price fluctuations, and green electricity total amount and time period performance constraints. The green electricity trading volume decomposition module is configured to solve the hourly green electricity allocation scheme through a stochastic optimization method based on the representative scenario set and dynamic constraint system, thereby realizing the decomposition of green electricity trading volume.

[0027] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method for decomposing green electricity trading volume in high-altitude areas.

[0028] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for decomposing green electricity trading volume in high-altitude areas.

[0029] The beneficial effects of this invention are as follows: (1) Improved meteorological responsiveness of electricity price scenarios. Existing methods generate electricity price scenarios based on historical full-sample statistics. When future meteorological conditions deviate from the historical average (such as continuous low temperature and icing), the representativeness of the scenario drops sharply. This invention enables the electricity price scenario to respond to weather forecasts through meteorological stratification modeling—automatically generating the electricity price distribution under icing conditions when low temperature is forecast, ensuring the scenario fidelity under extreme conditions.

[0030] (2) Proactive avoidance of physical constraint violation risks. Existing methods use exogenous fixed parameters for power transmission constraints, and the optimization results may be theoretically optimal but physically infeasible. This invention introduces weather forecasting into the constraint generation process to achieve dynamic perception of power transmission capacity.

[0031] (3) Enhanced robustness of overall returns. Existing methods pursue the optimality under a single scenario or deterministic prediction, and the returns drop significantly when the actual situation deviates from the prediction. This invention adopts multi-scenario probability weighted optimization to achieve robust optimality in a statistical sense.

[0032] (4) Targeted adaptation to high-altitude scenarios. Existing general methods, when applied to high-altitude areas, do not consider the triple coupling characteristics of "meteorological extremes, power transmission vulnerability, and electricity price asymmetry" in this region. This invention designs a complete technical chain for this specific scenario: from meteorological-electricity price joint modeling to dynamic constraint construction and risk perception optimization, each link reflects the adaptability to high-altitude characteristics, filling the gap of existing technologies in this scenario.

[0033] (5) Compared with CN117540986A, this invention is adapted to the triple coupling characteristics of high altitude and can solve the problem of electricity price layer structure; meteorological layer modeling + dynamic scene generation, rather than single uniform sampling; risk-oriented non-uniform compression, giving priority to retaining the differences in high-risk periods.

[0034] (6) Compared with CN118864147A, this invention constructs a meteorological-electricity price joint model to adapt to the uncertainty of electricity prices at high altitudes; it weights and amplifies the differences in high-risk periods to avoid the limitations of uniform compression; it supports the time-series decomposition and risk quantification of green electricity contracts, making it more accurate in adapting to different scenarios.

[0035] (7) Compared with CN115545768A, the transmission constraints of this invention are dynamically endogenous and are adjusted in real time according to weather forecasts; the objective function includes a risk penalty term, which can achieve coordinated optimization of the three domains; it focuses on risk avoidance in the time dimension and meets the needs of green electricity trading at high altitudes. Attached Figure Description

[0036] Figure 1 This is a flowchart of a method for decomposing green electricity trading volume in high-altitude areas according to Embodiment 1 of the present invention.

[0037] Figure 2This is a flowchart of the electricity price scenario reduction in Embodiment 1 of the present invention.

[0038] Figure 3 This is a flowchart of the model solution process in Embodiment 1 of the present invention.

[0039] Figure 4 This is the wind and solar power output prediction diagram of Embodiment 3 of the present invention.

[0040] Figure 5 This is the green power load diagram of Embodiment 3 of the present invention.

[0041] Figure 6 This is a temperature map of a certain region according to Embodiment 3 of the present invention.

[0042] Figure 7 This is a simulation result diagram of the electricity price scenario in Embodiment 3 of the present invention.

[0043] Figure 8 This is a diagram showing the results of electricity price reduction in Embodiment 3 of the present invention.

[0044] Figure 9 This is a diagram showing the green electricity decomposition results of Embodiment 3 of the present invention.

[0045] Figure 10 This is a comparison chart of the green electricity decomposition results and green electricity load of Embodiment 3 of the present invention.

[0046] Figure 11 This is a comparison diagram of the green electricity decomposition results in Embodiment 3 of the present invention. Detailed Implementation

[0047] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments are now described. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0048] Example 1 like Figure 1 As shown, this embodiment provides a method for decomposing green electricity trading volume in high-altitude areas, including: A probability distribution model of historical electricity prices is constructed. An initial electricity price path reflecting the fluctuation characteristics of spot electricity prices is generated through a hierarchical sampling method. K representative scenarios and their corresponding probabilities are selected through a scenario similarity calculation method to form a representative scenario set. Based on multidimensional transmission constraints, a dynamic constraint system for real-time perception of transmission capacity is constructed. The multidimensional transmission constraints include wind and solar power output forecasting, transmission line icing risk, spot electricity price fluctuations, and green electricity total amount and time period performance constraints. Based on a representative set of scenarios and a dynamic constraint system, a stochastic optimization method is used to solve the green electricity allocation scheme for each hour, thereby decomposing the green electricity trading volume.

[0049] It should be noted that this method comprehensively considers market electricity prices, renewable energy output, and the unique line icing risk in high-altitude areas. Through an optimized decomposition process, it formulates a time-of-use (TOU) fulfillment plan for green electricity contracts that maximizes economic benefits while ensuring safety. Specifically, to address electricity price fluctuations, this method first uses stratified sampling to generate multiple sets of electricity price scenarios and then compresses them into representative scenarios through weighted distance to reduce computational complexity. Next, the total amount of green electricity contracts signed by users, combined with the uncertainty of spot market electricity prices and the dynamic changes in transmission capacity, is decomposed into various time periods to form a time-of-use green electricity supply plan.

[0050] Preferably, such as Figure 2 As shown, the electricity price scenario reduction process in this embodiment is as follows: First, K initial scenarios are randomly selected as cluster centers. Then, through an iterative optimization process, the weighted distance matrix between all scenarios is calculated, and the original scenarios are assigned to the nearest cluster center to form clusters. The cluster center positions are then updated based on the weighted average of the scenarios within each cluster. This iterative optimization process is repeated until the changes in the cluster center positions tend to stabilize (reaching a preset threshold or the maximum number of iterations). Finally, a representative set of reduced scenarios and their probability weights are output. This algorithm significantly reduces computational complexity while preserving key statistical features of electricity prices by minimizing the transmission cost between the original and reduced distributions.

[0051] Preferably, such as Figure 3 The diagram illustrates the model solution process for this embodiment, which optimizes the solution for green electricity trading based on wind and solar power output forecasts, electricity market information, and key weather forecasts. This embodiment maximizes the overall benefits of green electricity in both the green electricity trading market and the spot market through an integrated optimization decision-making process that incorporates icing risk assessment.

[0052] Specifically, the model solving process in this embodiment includes: model establishment and multi-dimensional information input, integrating market economic information, new energy output forecasts, and weather forecasts; quantification of market uncertainty, using hierarchical sampling and weighted clustering to generate and reduce scenarios; dynamic optimization solution, inputting typical electricity price scenarios, output forecasts, and weather forecasts into an optimization model with embedded icing risk assessment logic, and calling the solver for time-segmented optimization; finally, outputting a set of optimal decisions, including 24-hour time-segmented green electricity trading curves, future physical risk assessment results for each time period, and expected return analysis.

[0053] Example 2 This embodiment is based on embodiment 1: This embodiment provides a method for decomposing green electricity trading volume in high-altitude areas. It dynamically decomposes green electricity trading volume by combining the characteristics of wind and solar power output in high-altitude areas, the risk of transmission line icing, and the uncertainty of spot electricity prices. This method achieves optimal performance of green electricity contracts through the organic combination of electricity price scenario generation, probability compression, and stochastic optimization solution mechanisms. Specific details are as follows.

[0054] 1. The Basic Logic of Electricity Market Mechanisms and Green Electricity Trading Green electricity trading involves the coordinated operation of the medium- and long-term contract market and the spot market. Medium- and long-term contracts guarantee long-term returns with fixed prices, while the spot market reflects short-term supply and demand relationships through day-ahead and real-time pricing.

[0055] The market currently refers to time-of-use electricity (TOU) pricing, which is based on forecasted load and generation capacity, and is determined through centralized bidding to form time-of-use prices (mostly nodal or regional prices in China). Its main applications include: thermal power plants scheduling unit start-up and shutdown in advance; renewable energy power plants submitting their next day's generation capacity declarations; and electricity sales companies purchasing base electricity to hedge risks. While its price fluctuations are relatively mild, it incurs penalties for forecasting errors.

[0056] The real-time market operates from 1 hour to 15 minutes before the actual operating hours (mostly 15 minutes in China). It is primarily used to correct forecasting biases in the day-ahead market and to handle unforeseen factors such as sudden failures and weather changes. Its prices fluctuate wildly (the highest / lowest price in the domestic real-time market can be up to 5 times the day-ahead electricity price), reflecting the real-time congestion costs and the value of reserve capacity.

[0057] Green electricity contracts are typically settled using fixed electricity prices. However, in actual power supply, the amount of electricity delivered must be reasonably allocated at different times based on wind and solar power output capacity, transmission capacity, and fluctuations in spot electricity prices. Otherwise, significant deviations in performance or revenue losses may occur.

[0058] The essence of the spot market pricing mechanism is to generate dynamic price signals that reflect the temporal and spatial scarcity of electricity commodities through short-term supply and demand balance and marginal cost pricing principles. Price components include: Energy component refers to the fuel cost of the last unit dispatched in the system (marginal unit). Among them, coal-fired units cost approximately RMB 0.25~0.40 / kWh, gas-fired units cost RMB 0.50~0.80 / kWh, and wind / solar power has zero marginal cost, and priority clearing will lower the benchmark price.

[0059] The congestion component is mainly caused by insufficient transmission path capacity leading to regional supply and demand imbalance.

[0060]

[0061] In the formula, To block costs, Let be the power transmission distribution factor of node i to line k; Let n be the shadow price of line k, and n be the number of lines.

[0062] The network loss component is calculated using the node network loss factor.

[0063] In the formula, For network loss costs, The marginal electricity price at the reference node; This is the overall network loss rate from the sending end to the reference node (typically 2%~8%).

[0064] In high-altitude areas, wind power is mainly concentrated at night, while photovoltaic power output is concentrated at noon. Electricity price fluctuations are more easily affected by ice accumulation and transmission restrictions, exacerbating the phenomenon of high nighttime and low daytime power, or even sudden changes, and bringing uncertainty risks to the execution of green electricity contracts.

[0065] 2. Technical Approach Uncertainty Quantification and Representative Scenario Generation: A probability distribution model of historical electricity prices is constructed, and a large number of initial electricity price paths that comprehensively reflect the fluctuation characteristics of spot electricity prices are generated through a hierarchical sampling method. To reduce the computational complexity of subsequent optimization while ensuring model accuracy, this invention further employs a scenario similarity calculation method to select K of the most representative typical scenarios and their probabilities. The final output of this step is a simplified scenario set that retains key price fluctuation risks while maintaining a controllable computational scale, providing a data foundation for subsequent optimization decisions.

[0066] Dynamic decomposition and optimization modeling: Under multiple electricity price scenarios, taking into account wind and solar power output forecasts, transmission line icing risks, spot electricity price fluctuations, total amount of green electricity and time period performance constraints, the hourly green electricity allocation scheme is solved by stochastic optimization method to maximize contract performance benefits and reduce performance risks.

[0067] This method not only demonstrates strong theoretical innovation in green electricity trading but also possesses significant practical application value in high-altitude power grid environments or renewable energy microgrids. By employing reasonable input data, reliable electricity price scenarios, and a well-defined optimization model, this method avoids complex algorithm design and can efficiently adapt to complex electricity market and power grid operating environments.

[0068] 3. Technical Details 3.1 Dynamic Modeling of Electricity Price Probability Structure Based on Meteorological Conditions In view of the fact that the electricity price formation mechanism in high-altitude areas is significantly affected by meteorological conditions, this embodiment proposes a joint probabilistic modeling method of electricity price and meteorology.

[0069] 3.1.1 Labeling and stratification of meteorological conditions in historical data Historical electricity price data and concurrent meteorological data (temperature, humidity, wind speed, etc.) for the target area were collected, and the historical samples were stratified according to the critical conditions for icing of transmission lines. Specifically: Identify historical periods of icing (criterion: temperature below a threshold). And the relative humidity is higher than the threshold. ); The historical electricity price sample is divided into two subsets: the normal operating condition electricity price set. Electricity price collection under icing conditions .

[0070] 3.1.2 Construction of Stratified Probability Density Function Probability density models are constructed for the two subsets separately. To avoid distortion of parameter distribution assumptions (such as the multi-peak and heavy-tailed characteristics often observed in high-altitude electricity prices), this embodiment adopts a non-parametric modeling approach: For normal operating conditions, establish the probability density function. By accumulating historical sample kernel weights, each historical price point is treated as a probability mass center, and a Gaussian kernel function is used for local diffusion to form a continuous probability density curve. (1) in, This represents the sample size under normal operating conditions. For Gaussian kernel function, For smoothing parameters (bandwidth).

[0071] For the icing condition, establish the probability density function. The same kernel weighting method is used, but the samples are from the ice-covering period: (2) 3.1.3 Conditional Scenario Generation Based on the weather forecast for the next 24 hours, a corresponding probability density function is selected for each time period to generate conditional electricity pricing scenarios. The specific steps are as follows: (1) For each time period t, based on the predicted temperature Determine the type of operating condition: like Then choose ; like Then choose .

[0072] (2) For the selected probability density function, calculate its cumulative distribution function F(λ): (3) (3) Divide the cumulative probability interval [0,1] into B sub-intervals (B is the initial number of scenes), and generate a uniform random number in each sub-interval: (4) (4) Generate electricity price samples through inverse function mapping: (5) Repeat the above process to generate the electricity price time series curves for Group B. Each set of curves represents a possible future electricity price evolution path.

[0073] The hierarchical modeling mechanism in this embodiment can identify and retain the impact of meteorological conditions on electricity price distribution. For example, when the weather forecast indicates that low temperatures will occur at night, the generated nighttime electricity price scenario will automatically include more possibilities of high prices, reflecting the expected supply and demand tension caused by icing.

[0074] 3.2 Selection of representative scenarios that retain extreme risk characteristics Using the initially generated B group of scenarios (typically B=100~200) directly for subsequent optimization would lead to excessive computation. However, simple random sampling may result in the loss of crucial information (such as scenarios with extremely high or low prices). This embodiment proposes a scenario compression method oriented towards risk preservation.

[0075] 3.2.1 Setting the Importance Weight of Time Periods Not all time periods have the same impact on decision-making. In high-altitude scenarios, the high-risk periods of icing (usually during the nighttime low-temperature period) have a stronger constraint on decision-making, therefore, electricity pricing scenarios during these periods require higher fidelity.

[0076] Define time period weights : (6) in, For time period Risk indication function: (7) in, This is the risk weighting coefficient (usually taken as 1-2).

[0077] 3.2.2 Measurement of differences between scenarios Define two scenarios and Weighted distance between: (8) The characteristic of this distance metric is that electricity price differences are amplified during high-risk periods, while during normal periods, the contribution of electricity price differences to the total distance is relatively small.

[0078] 3.2.3 Iterative Clustering Compression The following iterative process is used to select K representative scenarios (typically K=8~15): (1) Initialization: Randomly select K scenes as cluster centers ; (2) Scene allocation: For each original scene Calculate the distance from each cluster center. ,Will Assigned to the nearest center to form a cluster ; (3) Central update: For each cluster Calculate the probability-weighted average of the scenes within the cluster as the new center: (9) Simultaneously update the probability weights of the cluster: ; (4) Convergence criterion: If the position change of all cluster centers is less than the threshold (like If the iteration stops, then stop; otherwise, return to step (2).

[0079] By introducing time period weights This compression method prioritizes retaining scenarios that differ during high-risk periods, i.e., icing periods. Even if some scenarios have similar electricity prices during normal periods, they will not be merged if their electricity prices differ significantly during icing periods. This ensures that the compressed scenario set maintains sufficient diversity during critical risk periods.

[0080] 3.3 Construction of a Constraint System for Dynamic Sensing of Transmission Capacity Existing optimization models typically use transmission capacity as a fixed constraint. This embodiment proposes a mechanism to dynamically adjust the constraint boundary based on weather forecasts.

[0081] 3.3.1 Icing-triggered capacity derating model Establish a mapping relationship between temperature forecasts and transmission capacity. Based on historical power grid operation data for the region, fit a capacity derating function: (10) in, The depreciation rate function can be expressed as a piecewise linear or exponential form. A typical piecewise linear model is as follows: (11) in, , This is the capacity reduction factor, calibrated based on line parameters and historical data.

[0082] 3.3.2 Continuous processing of logical constraints This embodiment transforms the icing state determination into a continuously differentiable constraint form, introducing auxiliary variables. To indicate whether icing occurred during time period t, a correlation was established using the Big M method: (12) Where M is a sufficiently large positive number. It is a small positive number (such as 0.01).

[0083] Transmission constraints are expressed as: (13) in, Let t be the total power used for internet access during time period t.

[0084] The key to this mechanism is that the upper limit of transmission capacity is no longer a fixed parameter input from outside the model, but a variable automatically calculated within the model based on weather forecasts. When the temperature forecast for a future period is updated, the upper limit of capacity for that period will be automatically adjusted, and the constraint boundary will change accordingly.

[0085] 3.3.3 Temporal decoupling characteristics of the feasible region Unlike traditional methods that apply uniform transmission constraints across all time periods, the constraints in this embodiment are time-independent: (1) The constraint boundary of each time period t is determined by the weather forecast for that time period. The decision is made independently of other time periods; (2) The constraints automatically tighten during the low-temperature period at night. Reduce, and keep restrictions loose during normal daytime hours. constant; (3) This segmented dynamic characteristic enables the optimizer to make refined decision adjustments in the time dimension.

[0086] Compared to the global unified constraints of existing technologies, the dynamic constraints of this embodiment can adjust the security boundary according to the actual physical conditions. During safe periods, it allows for greater internet power to increase revenue, and during risky periods, it automatically reduces the internet plan to avoid default, thus achieving a dynamic balance between security and economy.

[0087] 3.4 Robust Optimization Decision Based on Probability Weighting in Multiple Scenarios Based on the aforementioned representative scenario set and dynamic constraint system, an optimization model for the decomposition of green electricity trading volume is constructed.

[0088] 3.4.1 Definition of Decision Variables The main decision variables include: : Electricity (unit: MW) used to fulfill green electricity contracts during time period t; : Spot market transaction volume in time period t under scenario k (unit: MW, positive value indicates electricity sales, negative value indicates electricity purchase); : An indicator variable for the icing status during time period t (0 or 1).

[0089] 3.4.2 Objective Function The goal is to maximize expected returns across multiple scenarios: (14) in, Let k be the probability weight for scenario k. The price of green electricity contracts; The spot electricity price for time period t under scenario k; The cost is a penalty for negative deviation.

[0090] Modeling of penalty cost term: (15) in, This is the penalty coefficient (usually taken as 1.5-2.0). This represents the actual available power generation during time period t.

[0091] 3.4.3 Constraints (1) Energy balance constraint: (16) in, and These represent the predicted output power of photovoltaic and wind power at time t, respectively.

[0092] (2) Total Contract Amount Constraints: (17) in, Δt represents the total green power output planned and allocated to the scheduling cycle T; Δt is the time period length.

[0093] (3) Boundary constraints of decomposition quantities: (18) (4) Dynamic transmission constraints: (19) (5) Icing state logic constraints: (20) 3.4.4 Model Solving The above model is a mixed-integer linear programming problem (containing continuous variables). , and 0-1 integer variables The optimal green electricity decomposition scheme for each time period can be obtained by using the optimization solver CPLEX.

[0094] Risk-aware decision adjustment: When a certain period is judged to be at high risk of icing, the transmission limit for that period will be automatically reduced, and the optimizer will actively reduce the grid power for that period. Even if the electricity price is high during that period, some revenue will be appropriately given up in order to avoid physical default risk.

[0095] Cross-time period resource reallocation: Since the total contract amount is fixed, if internet access is reduced during certain periods due to ice restrictions, the optimizer will automatically transfer this power to other safe periods, thereby achieving time-series optimized resource allocation.

[0096] Multi-scenario probability balance: The optimization result does not depend on a single electricity price forecast, but seeks the optimal in the expected sense under multiple possible scenarios, reducing the impact of forecast error on decision-making.

[0097] Example 3 This embodiment is based on embodiment 2: This embodiment provides a method for decomposing green electricity trading volume in high-altitude areas, taking a wind and solar energy base in a high-altitude area as an example for illustration, as detailed below.

[0098] 1. Case Background Contract Information: Wind farms and photovoltaic power plants have signed green electricity trading contracts with users, stipulating a total green electricity trading volume of 481 MWh / day. Based on the trading situation in the pilot areas of the green electricity market, the green electricity price is set at 506.6 yuan / MWh, and the environmental premium is set at 91.3 yuan / MWh.

[0099] Market information: The spot market consists of both day-ahead and real-time markets, with electricity prices fluctuating between 200-700 yuan / MWh.

[0100] Risk parameters: Based on the power grid operation experience in this high-altitude area, a critical temperature threshold for line icing is set. The temperature is 0°C. When the model determines that icing has occurred, the transmission capacity decreases by [amount missing]. It is set at 8MW.

[0101] Input information: The region's landscape forecasting output is as follows: Figure 4 As shown, the peak output of photovoltaic power is 25MW during the daytime (12 hours) and 0MW at night; the peak output of wind power is 20MW during the nighttime (3 hours) and an average of 5-10MW during the day.

[0102] The region's demand for green electricity is as follows Figure 5 As shown, the average daily load in this region is 20MW, with no obvious peaks or valleys.

[0103] Temperature forecast for the region as follows Figure 6 As shown in the figure, the curve represents the hourly temperature forecast for the next 24 hours. According to this forecast, the temperature will drop to -2°C between 2:00 AM and 4:00 AM, below the set icing threshold of 0°C, while the temperature will remain above the threshold during the rest of the night. This temperature data will serve as a key physical input to the embedded icing risk assessment model in this embodiment, used to dynamically determine the physical safety status of the line.

[0104] 2. Generation of meteorologically stratified electricity pricing scenarios (1) Historical data stratification: Identify icing periods (nighttime periods with temperatures <0°C and relative humidity >80%) from historical electricity price data; Normal operating condition sample size (Approximately 10 years of normal weather data); Number of samples under icing conditions (Approximately 10 years of icing weather data).

[0105] (2) Stratified probability density fitting: Under normal operating conditions, the fitting yielded a unimodal distribution of electricity prices concentrated between 280-450 yuan / MWh; For icing conditions, the fitted data shows that the electricity price is concentrated in a right-skewed distribution of 400-700 yuan / MWh, with a significant high-price tail.

[0106] (3) Conditional scene generation: For the period from 2:00 to 4:00 AM (forecast temperature -2°C), the distribution of icing conditions... Sampling was performed in the middle period; for other time periods, the distribution was based on normal operating conditions. Mid-sampling; generating 100 initial electricity price scenarios such as Figure 7 As shown, the range of electricity price fluctuations covers various scenarios including normal, high-price, and low-price.

[0107] 3. Reduction of risk-oriented scenarios (1) Time period weighting: Set weights between 2 AM and 4 AM (High-risk period); Weights are set for other time periods. (Normal time period).

[0108] (2) Iterative clustering compression: The initial 100 scenarios were compressed into 10 representative scenarios, and converged after 5 iterations.

[0109] (3) Compression effect verification: The electricity price distribution of the 10 compressed scenarios between 2 and 4 am maintained the key characteristics of the original 100 scenarios (mean deviation <3%, standard deviation deviation <5%). During normal daytime hours, the compressed scene deviates slightly from the original scene (mean deviation <8%), but has little impact on decision-making because these periods are not bottlenecks of physical constraints.

[0110] After reducing the electricity pricing scenarios based on scenario compression, 10 typical day-ahead settlement price scenarios are as follows: Figure 8 As shown.

[0111] Typical scenarios include: high daytime prices (electricity price > 500 yuan / MWh for 8-11 hours) and stable nighttime prices (electricity price 280-420 yuan / MWh). Furthermore, the probability distribution of the 10 scenarios differs from the original set by less than 5%, while reducing computational load by 90%.

[0112] 4. Optimization Results of Green Electricity Decomposition Based on wind and solar power forecasts and day-ahead market settlement prices, the green electricity demand is decomposed, and the resulting green electricity decomposition results are as follows: Figure 9 As shown.

[0113] As shown in the figure, the wind and solar energy base will allocate green electricity to off-peak electricity price periods within a limited allocation area based on the day-ahead electricity price forecast, resulting in the final green electricity allocation curve being negatively correlated with the day-ahead electricity price forecast.

[0114] Table 1 - Optimization Results

[0115] During periods when the power grid is physically safe and there is no risk of icing, the model demonstrates a keen ability for economic arbitrage. For example, during the 12-16 hour period when spot prices are low, the model prioritizes using the large amount of photovoltaic power generated to fulfill higher-priced green electricity contracts. Conversely, during peak price periods such as the 8-11 hour period, the model appropriately reduces the allocation of green electricity contracts, releasing some of the electricity into the spot market to capture higher marginal returns.

[0116] However, the most fundamental difference between this embodiment and existing technologies lies in its ability to proactively mitigate risks based on input physical conditions. As shown in Table 1, during the 2-3 hour nighttime period, although wind power output is at its peak, the model intrinsically determines a high risk of icing based on the input low-temperature forecast and proactively limits grid-connected power to a safe range. This decision to sacrifice short-term power generation for grid physical security demonstrates the ability of this embodiment to coordinate physical feasibility with economic optimization.

[0117] 5. Result Comparison and Verification (1) Comparison of green electricity decomposition and green electricity load After considering the uncertainty of spot electricity prices and decomposing green electricity, the comparison with the original green electricity load is as follows: Figure 10 As shown, this diagram illustrates a comparison between the green electricity decomposition curve obtained after considering the uncertainty of spot electricity prices, with the objective of maximizing the returns of wind and solar power participating in the green electricity market and the electricity spot market, and the original green electricity load.

[0118] (2) Comparison of deviations in green electricity trading The deviations in green electricity after decomposition and before decomposition are as follows: like Figure 11 As shown, the green electricity deviation based on the green electricity decomposition curve and the green electricity deviation based on basic green electricity demand are displayed. The deviation after decomposition is 0 to +10MWh (surplus electricity is sold on the spot, with no negative deviation); the deviation before decomposition is -5 to +5MWh (fixed allocation leads to supply shortages in some periods).

[0119] It can be seen that the predicted output of wind and solar power can fully meet the green electricity allocation, and the surplus can participate in the day-ahead spot market to obtain spot market revenue. The green electricity deviation obtained from the electricity allocation curve after taking into account the overall benefits brought by the fluctuation of spot electricity prices only has positive deviations, and the positive deviation can be obtained by participating in the spot market; while the green electricity deviation brought by the original green electricity load that is not allocated has more negative deviations. Negative deviations cannot obtain revenue and may even lead to failure to meet the prescribed green electricity demand and incur penalty fees. (3) Comparison of green electricity trading revenue Case 1: Decomposing green electricity by considering only the uncertainty of spot electricity prices; Case 2: Green electricity decomposition considering electricity price uncertainty and icing risk.

[0120] Table 2 - Profit Comparison Table

[0121] The negative deviation penalty is calculated based on the 8 MWh physical negative deviation generated during the high icing risk period of Case 1, and estimated at the high market price.

[0122] As shown in Table 2, Case 1 did not consider the risk of icing on transmission lines, making the optimization result physically infeasible. During the low-temperature period at night, its grid-connected power arrangement failed to avoid the transmission bottleneck caused by the reduction in line capacity, ultimately leading to a large negative deviation and incurring high penalty fees, resulting in a significant decrease in overall actual benefits.

[0123] In contrast, Case 2 employs the optimization method proposed in this embodiment, relying on the embedded icing risk assessment model to proactively reduce grid connection plans during periods of higher risk at night. Although the green electricity trading volume and spot electricity price revenue are slightly adjusted, negative deviation penalties are effectively avoided. Ultimately, Case 2's total revenue is RMB 309,881.94, an increase of RMB 51,008.98 compared to Case 1, representing a growth of 19.7%. This demonstrates its advantages under the comprehensive consideration of economic efficiency and physical feasibility. The results prove that the solution proposed in this embodiment can achieve better overall benefits in complex high-altitude environments, combining economic efficiency and reliability.

[0124] 6. Summary This embodiment demonstrates the practical application effect of the method of the present invention in green electricity trading in high-altitude areas, verifying its comprehensive ability to cope with electricity price fluctuations and transmission risks. By constructing an optimization model based on electricity price scenarios and introducing a dynamic assessment mechanism for transmission icing risk, the present invention can flexibly adjust grid connection strategies under different meteorological conditions. During periods of significant electricity price fluctuations, the model can accurately capture spot trading opportunities; while during periods of low temperatures with severe meteorological conditions, it can proactively adjust grid connection plans to avoid transmission bottleneck risks, significantly improving the security and robustness of electricity trading performance.

[0125] Meanwhile, this method constructs a mixed-integer linear programming model and incorporates scenario compression techniques to control solution complexity, demonstrating good adaptability to engineering deployment and feasibility for practical applications. Overall, this invention provides an optimization strategy for green electricity trading in high-altitude areas that combines economy, security, and implementability, and has broad prospects for engineering promotion.

[0126] Example 4 This embodiment provides a green electricity trading volume allocation system for high-altitude areas, including: The representative scenario set construction module is configured to build a probability distribution model of historical electricity prices. It generates an initial electricity price path that reflects the fluctuation characteristics of spot electricity prices through a hierarchical sampling method, and selects K representative scenarios and their corresponding probabilities through a scenario similarity calculation method to form a representative scenario set. The dynamic constraint system construction module is configured to build a dynamic constraint system for real-time perception of power transmission capacity based on multi-dimensional power transmission constraint conditions; the multi-dimensional power transmission constraint conditions include wind and solar power output prediction, transmission line icing risk, spot electricity price fluctuations, and green electricity total amount and time period performance constraints. The green electricity trading volume decomposition module is configured to solve the hourly green electricity allocation scheme through a stochastic optimization method based on the representative scenario set and dynamic constraint system, thereby realizing the decomposition of green electricity trading volume.

[0127] Example 5 This embodiment is based on embodiment 1: This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method for decomposing green electricity trading volume in high-altitude areas as described in Embodiment 1. The computer program can be in the form of source code, object code, executable file, or some intermediate form.

[0128] Example 6 This embodiment is based on embodiment 1: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for decomposing green electricity trading volume in high-altitude areas as described in Embodiment 1. The computer program can be in the form of source code, object code, executable file, or some intermediate form. The storage medium includes any entity or device capable of carrying computer program code, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0129] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

[0130] It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. A method for decomposing green electricity trading volume in high-altitude areas, characterized in that, include: A probability distribution model of historical electricity prices is constructed. An initial electricity price path reflecting the fluctuation characteristics of spot electricity prices is generated through a hierarchical sampling method. K representative scenarios and their corresponding probabilities are selected through a scenario similarity calculation method to form a representative scenario set. Based on multidimensional power transmission constraints, a dynamic constraint system for real-time perception of power transmission capacity is constructed; the multidimensional power transmission constraints include wind and solar power output prediction, transmission line icing risk, spot electricity price fluctuations, and green electricity total amount and time period performance constraints. Based on the representative scenario set and dynamic constraint system, the green electricity allocation scheme for each hour is solved by a stochastic optimization method to achieve the decomposition of green electricity trading volume.

2. The method for decomposing green electricity trading volume in high-altitude areas according to claim 1, characterized in that, The construction of the historical electricity price probability distribution model includes: collecting historical electricity price data and meteorological data of the same period in the target area; dividing the historical electricity price sample into a normal operating condition electricity price set and an icing operating condition electricity price set according to the critical conditions of icing of transmission lines; and constructing the historical electricity price probability distribution model by using a joint probability modeling method of electricity price and meteorology.

3. The method for decomposing green electricity trading volume in high-altitude areas according to claim 2, characterized in that, The method of generating an initial electricity price path that reflects the fluctuation characteristics of spot electricity prices through hierarchical sampling includes: constructing non-parametric probability density functions for the normal operating condition electricity price set and the icing operating condition electricity price set respectively; using the method of accumulating the kernel weights of historical samples, taking each historical price point as the probability mass center; and using a Gaussian kernel function to perform local diffusion to form a continuous probability density curve.

4. The method for decomposing green electricity trading volume in high-altitude areas according to claim 1, characterized in that, The process of selecting K representative scenarios and their corresponding probabilities through scenario similarity calculation to form a representative scenario set includes: setting time period importance weights, including risk indicator functions and risk weighting coefficients; calculating the weighted distance between scenarios; and then determining K representative scenarios through an iterative clustering compression process.

5. The method for decomposing green electricity trading volume in high-altitude areas according to claim 1, characterized in that, The dynamic constraint system for real-time perception of transmission capacity based on multi-dimensional transmission constraints includes: fitting a capacity derating function based on historical operating data of the target area power grid, establishing a mapping relationship between temperature forecast and transmission capacity, and generating a capacity derating model triggered by icing; the capacity derating function adopts a piecewise linear or exponential form.

6. The method for decomposing green electricity trading volume in high-altitude areas according to claim 1, characterized in that, The dynamic constraint system for real-time perception of transmission capacity based on multidimensional transmission constraints also includes: transforming the icing state judgment into a continuously differentiable constraint through the penalty factor method, introducing auxiliary variables to represent the icing state during a time period, and enabling the upper limit of transmission capacity to be automatically calculated and adjusted within the model based on weather forecasts.

7. The method for decomposing green electricity trading volume in high-altitude areas according to claim 1, characterized in that, The method of solving the hourly green electricity allocation scheme using a stochastic optimization approach includes: The objective function is to maximize the expected return in multiple scenarios. The objective function includes the green electricity contract price, the scenario spot electricity price, and the negative deviation penalty cost term. The negative deviation penalty cost term includes the penalty coefficient and the actual available power generation during the time period. The optimization is performed using multiple constraint types, including energy balance constraints, total contract amount constraints, decomposition boundary constraints, dynamic transmission constraints, and icing state logic constraints.

8. A green electricity trading volume decomposition system for high-altitude areas, characterized in that, include: The representative scenario set construction module is configured to build a probability distribution model of historical electricity prices. It generates an initial electricity price path that reflects the fluctuation characteristics of spot electricity prices through a hierarchical sampling method, and selects K representative scenarios and their corresponding probabilities through a scenario similarity calculation method to form a representative scenario set. The dynamic constraint system construction module is configured to build a dynamic constraint system for real-time perception of power transmission capacity based on multi-dimensional power transmission constraint conditions; the multi-dimensional power transmission constraint conditions include wind and solar power output prediction, transmission line icing risk, spot electricity price fluctuations, and green electricity total amount and time period performance constraints. The green electricity trading volume decomposition module is configured to solve the hourly green electricity allocation scheme through a stochastic optimization method based on the representative scenario set and dynamic constraint system, thereby realizing the decomposition of green electricity trading volume.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method for decomposing green electricity trading volume in high-altitude areas as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for decomposing green electricity trading volume in high-altitude areas as described in any one of claims 1-7.

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