Cascade hydropower station group medium-and-long-term contract electric quantity optimal combination method, system and equipment considering income risk and medium

Abstract

The invention discloses a cascade hydropower station group medium-and-long-term contract electric quantity optimal combination method, system and device considering income risks, and a medium, and belongs to the technical field of cascade hydropower station contract electric quantity combination, and the method comprises the steps: generating incoming water scenes of different frequencies according to historical incoming water data; determining an electricity price simulation strategy according to the market share proportion and establishing a contract electricity price function; constructing an income risk minimization model by taking the maximum income under the given confidence coefficient as a target; optimization scheduling is carried out under the operation constraint condition; determining an annual and monthly contract electric quantity distribution proportion by adopting a contract electric quantity distribution strategy; calculating the total power generation benefit and evaluating the minimum benefit; and outputting an optimal scheme through iterative optimization. According to the method, the risk value theory and the differentiated electricity price strategy are introduced, and optimized scheduling and contract allocation are combined, so that risk quantitative evaluation under incoming water and electricity price dual uncertainty is realized, and scientific decision support is provided for the cascade hydropower station group to participate in medium and long-term market transaction.

Description

Technical Field

[0001] This invention relates to the field of contracted electricity combination technology for cascade hydropower stations, specifically to a method, system, equipment, and medium for optimal combination of long-term contracted electricity in a group of cascade hydropower stations, taking into account revenue risk. Background Technology

[0002] Currently, China's domestic power market, dominated by hydropower, primarily relies on medium- and long-term transactions, with hydropower companies deriving their main revenue from these contracts. However, due to factors such as the uncertainty of natural water inflows and significant electricity price fluctuations, cascade hydropower stations struggle to balance market share across different time scales when submitting medium- and long-term contract electricity declarations. Excessive annual contract electricity volume may result in revenue losses due to low electricity prices, while excessive monthly contract electricity volume may lead to penalties for contract non-fulfillment due to insufficient generation capacity. Existing research mainly focuses on optimizing deterministic objectives such as maximizing generation benefits and minimizing electricity volume allocation deviations. This approach is unsuitable for addressing the issue of formulating electricity declaration strategies for cascade hydropower stations facing multiple coupled uncertainties, and lacks effective methods for assessing revenue and risk, as well as a decision-making mechanism for the optimal combination of annual and monthly contract electricity volumes. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention provides a method, system, equipment and medium for the optimal combination of long-term contracted electricity volume in a cascade hydropower station group that takes into account revenue risk.

[0004] Therefore, the technical problem addressed by this invention is: how to formulate reasonable annual and monthly contracted electricity volume combinations for a cascade hydropower station group under the coupled influence of water inflow uncertainty and electricity price volatility, so as to minimize the risk of revenue. Existing methods mainly use deterministic models to solve a single optimization objective, which makes it difficult to comprehensively consider the randomness of water inflow, the uncertainty of electricity prices, and the trade-offs between contracted electricity volumes at different time scales, and cannot effectively assess the market risk of contract combination schemes.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a method for optimally combining long-term contracted electricity volumes in a cascade hydropower station group, considering both revenue and risk, comprising, Multiple inflow scenarios with different frequencies are generated based on historical inflow data of the cascade hydropower station group; Based on the market share of the cascade hydropower station group in the electricity market, a price simulation strategy is determined, and an annual contract price function and a monthly contract price function are established based on the price simulation strategy. A return-risk minimization model is constructed with the objective of maximizing the minimum return under a given confidence level. The return-risk is quantified by a return-risk characterization parameter, which is negatively correlated with the annual contract electricity ratio. Under the condition of meeting the operational constraints of the cascade hydropower station group, the operation status of each hydropower station at each time period is optimized and scheduled. A contract power allocation strategy is adopted to determine the allocation ratio of annual contract power and monthly contract power in the power generation of each hydropower station in each time period; The total power generation benefit is calculated based on the annual contract electricity price function, the monthly contract electricity price function, and the allocated annual and monthly contract electricity volumes. The minimum revenue is assessed based on the total power generation benefit, and the minimum revenue is maximized through iterative optimization. The annual contracted electricity share and monthly contracted electricity share of each hydropower station for each time period are then output.

[0006] As a preferred embodiment of the optimal combination method for long-term contracted electricity volume of a cascade hydropower station group considering revenue risk as described in this invention, the step of generating multiple inflow scenarios with different frequencies based on historical inflow data of the cascade hydropower station group includes obtaining statistical characteristic parameters of inflow of each hydropower station in the basin. Based on the aforementioned statistical characteristic parameters of incoming water, multiple different incoming water scenarios are generated using a random discrete method; The multiple water inflow scenarios are sorted according to the water inflow volume, the empirical frequency corresponding to each water inflow scenario is calculated, and the target water inflow scenario is obtained by equidistant sampling according to a preset frequency step size.

[0007] As a preferred embodiment of the optimal combination method for long-term contract electricity volume of a cascade hydropower station group considering revenue risk as described in this invention, the step of determining the electricity price simulation strategy based on the market share of the cascade hydropower station group in the electricity market, and establishing the annual contract electricity price function and the monthly contract electricity price function based on the electricity price simulation strategy, includes determining the market share range of the cascade hydropower station group in the electricity market. Based on the aforementioned market share range, select the corresponding electricity price simulation strategy from a variety of electricity price simulation strategies; Based on the selected electricity price simulation strategy, a correlation function between the monthly contract electricity price and the declared electricity volume or water inflow process is established as the monthly contract electricity price function; Based on the selected electricity price simulation strategy, a correlation function is established between the annual contract electricity price, the extreme value of the monthly contract electricity price, and the proportion of annual contract electricity volume as the annual contract electricity price function.

[0008] As a preferred embodiment of the optimal combination method for long-term contracted electricity volume of a cascade hydropower station group considering revenue risk as described in this invention, the method for constructing a revenue risk minimization model includes setting the revenue risk characterization parameter as a function of the annual contracted electricity volume ratio, wherein the annual contracted electricity volume ratio is calculated by the ratio of the annual contracted electricity volume of each hydropower station in each time period to the total power generation. Establish a total power generation benefit calculation function, which includes an annual contract benefit item, a monthly contract benefit item, and a water abandonment loss item; Based on the aforementioned revenue risk characterization parameters and total power generation benefit calculation function, a revenue probability density function is established. A return-risk minimization model is constructed with the goal of maximizing the minimum return corresponding to the probability density function of the return at a given confidence level.

[0009] The beneficial effects of this preferred technical solution are as follows: by setting the revenue risk characterization parameter as a function of the annual contracted electricity volume ratio, and establishing a total power generation benefit calculation function that includes annual contract benefits, monthly contract benefits, and water abandonment losses, and combining it with the revenue probability density function to construct a VaR model, it is possible to quantitatively assess the market risk of the contracted electricity volume combination under the coupled influence of water inflow uncertainty and electricity price fluctuations, and provide a scientific basis for formulating a contract application strategy that takes into account both revenue and risk for cascade hydropower station groups.

[0010] As a preferred embodiment of the optimal combination method for long-term contracted electricity volume of a cascade hydropower station group considering revenue risk as described in this invention, the step of optimizing the scheduling of the operating status of each hydropower station at each time period under the condition of satisfying the operating constraints of the cascade hydropower station group includes setting the initial reservoir capacity and target end-of-period reservoir capacity of each hydropower station at each time period. Under the condition of fixed water levels in adjacent time periods, the outflow of each hydropower station in the current time period is discretized to generate multiple discrete schemes; For each discrete scheme, the reservoir capacity change of each hydropower station is calculated based on the water balance constraint, and the inflow of the downstream hydropower station is calculated based on the upstream and downstream hydraulic connection constraint. Under the conditions of satisfying the constraints of outflow, power output, and reservoir water level, calculate the power output and water wastage of each hydropower station corresponding to each discrete scheme; According to the time period sequence and the order of hydropower stations, discrete optimization is performed on each hydropower station for each time period to obtain the operating status of all hydropower stations for the entire time period.

[0011] As a preferred embodiment of the optimal combination method for long-term contracted electricity volume in a cascade hydropower station group considering revenue risk as described in this invention, the method of adopting a contracted electricity volume allocation strategy to determine the allocation ratio of annual contracted electricity volume and monthly contracted electricity volume in the power generation of each hydropower station in each time period includes setting an upper limit and a lower limit for the proportion of annual contracted electricity volume, as well as an electricity volume allocation step size. While keeping the contracted electricity allocation ratio of other hydropower stations unchanged for other time periods, the electricity generation of the current hydropower station for the current time period is discretized within the range of the upper and lower limits according to the electricity allocation step size, to form multiple allocation schemes; For each allocation scheme, the corresponding power generation benefits are calculated based on the annual contract electricity price function and the monthly contract electricity price function; The allocation scheme that maximizes power generation benefits is selected as the optimal allocation scheme for the current hydropower station in the current time period; The contracted electricity allocation for each hydropower station for each time period was completed in the order of time periods and hydropower station sequence.

[0012] The beneficial effects of this preferred technical solution are as follows: by setting upper and lower limits for the proportion of annual contracted electricity and the step size of electricity allocation, while keeping the allocation ratio of other time periods unchanged, the power generation of the current time period is discretized according to the step size and the power generation benefits of each allocation scheme are calculated. The scheme with the greatest benefit is selected as the optimal configuration, and the contracted electricity allocation of each hydropower station for each time period is completed one by one. This can accurately balance the price stability of annual contracts and the flexibility of monthly contracts, and achieve the optimal combination configuration of contracted electricity at different time scales.

[0013] As a preferred embodiment of the method for optimal combination of long-term contracted electricity in a cascade hydropower station group considering revenue risk as described in this invention, the step of maximizing the minimum revenue through iterative optimization includes, for each water inflow scenario, sequentially optimizing the operation status and allocating contracted electricity for each time period according to the time period sequence, and calculating the total power generation benefit corresponding to the water inflow scenario. Based on the total power generation benefits of each water inflow scenario and the aforementioned revenue-risk characterization parameters, calculate the minimum revenue for the current iteration; Compare the minimum profit of the current iteration with the minimum profit of the previous iteration. If the two minimum profits are the same, the optimization is complete. Output the annual contracted electricity volume ratio and monthly contracted electricity volume ratio of each hydropower station for each time period. If the two minimum returns are different, the current optimization result is used as the new initial state, and the optimization continues until the minimum return is maximized.

[0014] This invention provides an optimal combination system for long-term contracted electricity volumes in a cascade hydropower station group, taking into account both revenue and risk.

[0015] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an optimal combination system for medium and long-term contracted electricity volume of a cascade hydropower station group considering revenue risk, comprising: a water inflow scenario generation module, used to generate multiple water inflow scenarios of different frequencies based on historical water inflow data of the cascade hydropower station group; The electricity price function establishment module is used to determine the electricity price simulation strategy based on the market share of the cascade hydropower station group in the electricity market, and to establish the annual contract electricity price function and the monthly contract electricity price function based on the electricity price simulation strategy. The risk model building module is used to construct a risk-return minimization model with the objective of maximizing the minimum return under a given confidence level. The optimized scheduling module is used to optimize the scheduling of the operation status of each hydropower station in each time period while meeting the operational constraints of the cascade hydropower station group. The contract allocation module is used to determine the allocation ratio of annual contract electricity and monthly contract electricity in the power generation of each hydropower station in each time period using a contract electricity allocation strategy. The benefit calculation module is used to calculate the total power generation benefit based on the annual contract electricity price function, the monthly contract electricity price function, and the allocated annual contract electricity volume and monthly contract electricity volume; The iterative optimization module is used to evaluate the minimum benefit based on the total power generation benefit, and maximize the minimum benefit through iterative optimization, and output the annual contract power ratio and monthly contract power ratio of each hydropower station for each time period.

[0016] The present invention 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 steps of the method for optimal combination of long-term contracted electricity volume in a cascade hydropower station group considering revenue and risk.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method for optimal combination of long-term contracted electricity volume in a cascade hydropower station group considering revenue and risk are implemented.

[0018] The beneficial effects of this invention are as follows: It introduces the Value at Risk (VaR) theory to construct an optimization model with the objective of maximizing minimum return under a given confidence level, and uses the annual contracted electricity volume ratio to characterize return risk, achieving a quantitative assessment of the dual uncertainties of water inflow and electricity price; it designs three electricity price simulation strategies for different proportions of cascade hydropower station groups in the electricity market, establishing monthly and annual contracted electricity price functions, thus improving the adaptability of electricity price forecasting; it employs a method combining contracted electricity volume allocation strategies with optimized scheduling, determining the contracted electricity volume combination of each hydropower station for each time period through step-size discretization and the maximization of benefit criterion, achieving precise allocation of contracted electricity volume at different time scales; and it uses an iterative optimization algorithm to repeatedly seek optimization until convergence, ensuring the reliability of the optimal solution and providing scientific decision support for cascade hydropower station groups to participate in medium- and long-term market transactions. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The following is a flowchart illustrating an overall method for optimizing the combination of long-term contracted electricity volumes in a cascade hydropower station group, taking into account revenue risk, as provided in one embodiment of the present invention.

[0021] Figure 2 The probability density function curve of a method for optimal combination of long-term contracted electricity volume in a cascade hydropower station group considering revenue risk, provided as an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram illustrating a simulation method for a method of optimally combining long-term contracted electricity volumes in a cascade hydropower station group, considering revenue risk, according to an embodiment of the present invention, with hydropower accounting for a large proportion of the market.

[0023] Figure 4 This is a schematic diagram illustrating a simulation method for a method of optimally combining long-term contracted electricity volumes in a cascade hydropower station group, considering revenue risk, according to an embodiment of the present invention, where hydropower accounts for a relatively small proportion of the market.

[0024] Figure 5 This is a schematic diagram illustrating a simulation method for determining the market share of hydropower in a cascade hydropower station group that considers revenue risk and is an embodiment of the present invention. Detailed Implementation

[0025] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue and risk, including: Step 1: Generate multiple inflow scenarios with different frequencies based on historical inflow data of the cascade hydropower station group; Step 2: Determine the electricity price simulation strategy based on the market share of the cascade hydropower station group in the electricity market, and establish the annual contract electricity price function and the monthly contract electricity price function based on the electricity price simulation strategy; Step 3: Construct a return-risk minimization model with the objective of maximizing the minimum return under a given confidence level. The return-risk is quantified by a return-risk characterization parameter, which is negatively correlated with the annual contract electricity volume ratio. Step 4: Under the condition of satisfying the operational constraints of the cascade hydropower station group, optimize the scheduling of the operation status of each hydropower station in each time period; Step 5: Adopt a contract power allocation strategy to determine the allocation ratio of annual contract power and monthly contract power in the power generation of each hydropower station in each time period; Step 6: Calculate the total power generation benefit based on the annual contract electricity price function, the monthly contract electricity price function, and the allocated annual and monthly contract electricity volumes; Step 7: Evaluate the minimum revenue based on the total power generation benefit, and maximize the minimum revenue through iterative optimization, outputting the annual contracted electricity share and monthly contracted electricity share of each hydropower station for each time period.

[0027] In the practical application of cascade hydropower station groups participating in medium- and long-term electricity market transactions, the core dilemma faced by hydropower enterprises lies in the significant uncertainty of power generation capacity caused by the seasonal and interannual variations in water inflow, while market electricity prices fluctuate dynamically with supply and demand. This coupling of factors makes contract power volume declaration decisions extremely complex. Specifically, if too much annual contract power volume is declared, although the contract can be fulfilled in a high-water year, revenue may be lost due to the annual electricity price being lower than the monthly electricity price; in a low-water year, insufficient power generation capacity may lead to penalties for default. If too much monthly contract power volume is declared, although adjustments can be made flexibly according to actual water inflow, monthly electricity prices fluctuate significantly, and there is a risk of declaration failure. Therefore, it is necessary to establish an optimization model that comprehensively considers the randomness of water inflow, the uncertainty of electricity prices, and the interconnectedness of cascade hydropower, to rationally allocate annual and monthly contract power volumes while ensuring basic revenue, thereby minimizing revenue and risk.

[0028] This embodiment solves the aforementioned technical problems through a systematic approach involving seven steps. Step 1 generates multi-frequency scenarios using historical water inflow data, simulating the random variations in water inflow and providing uncertainty input for subsequent optimization. Step 2 establishes a differentiated electricity price function based on market share, accurately depicting the electricity price fluctuation patterns under different market environments. Step 3 introduces VaR theory to construct a revenue-risk model, transforming the abstract concept of risk into a quantifiable optimization objective. Step 4 calculates the power generation capacity under each scenario through optimized scheduling, ensuring the feasibility of contract application schemes. Step 5 uses a step-size discretization method to accurately allocate monthly and annual contract electricity, achieving the optimal combination at different time scales. Step 6 calculates the total benefit including water wastage losses, comprehensively evaluating the economics of each scheme. Step 7 uses iterative optimization to ensure that the minimum benefit is maximized, outputting the optimal contract configuration scheme that balances benefit and risk. The entire process organically combines water inflow uncertainty handling, electricity price forecasting, risk assessment, and optimized scheduling, systematically solving the problem of optimal combination of long-term contract electricity in cascade hydropower station groups.

[0029] Example 2, an embodiment of the present invention, provides a method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue risk, based on the previous embodiment, including: In generating multiple inflow scenarios with different frequencies based on historical inflow data of a cascade hydropower station group, the following steps A1-A3 are included: A1: Obtain statistical characteristic parameters of water inflow for each hydropower station within the basin; A2: Based on the aforementioned statistical characteristic parameters of the incoming water, multiple different incoming water scenarios are generated using a random discrete method; A3: Sort the multiple water inflow scenarios according to the water volume, calculate the empirical frequency corresponding to each water inflow scenario, and perform equidistant sampling according to the preset frequency step size to obtain the target water inflow scenario.

[0030] In this embodiment of the application, in step A1, the statistical characteristic parameters of the inflow of each hydropower station in the basin are obtained by: obtaining historical inflow data of each hydropower station in the basin, calculating the multi-year average inflow of each hydropower station in each time period as the central trend parameter; calculating the standard deviation of the inflow of each hydropower station in each time period as the fluctuation degree parameter; and using the multi-year average inflow and standard deviation as the statistical characteristic parameters of the inflow.

[0031] In an optional implementation, in step A1, the statistical characteristic parameters of inflow for each hydropower station in the basin can be obtained by: acquiring historical inflow data for each hydropower station in the basin and calculating the multi-year average inflow for each hydropower station in each time period; calculating the coefficient of variation of the inflow for each hydropower station in each time period, wherein the coefficient of variation is the ratio of the standard deviation to the mean; and using the multi-year average inflow and the coefficient of variation as statistical characteristic parameters of inflow to characterize the relative fluctuation level of inflow in different time periods.

[0032] In another optional implementation, in step A1, obtaining the statistical characteristic parameters of water inflow for each hydropower station in the basin can also be achieved by: obtaining historical water inflow data for each hydropower station in the basin, calculating the smoothed average water inflow for each hydropower station in each time period using the moving average method; calculating the variance of the water inflow for each hydropower station in each time period as a dispersion parameter; and using the smoothed average water inflow and variance as statistical characteristic parameters of water inflow.

[0033] In this embodiment of the application, in step A2, multiple sets of different water inflow scenarios are generated using a random discrete method: The average inflow rate over many years for each hydropower station at each time period is used as the baseline inflow process. A Gaussian random number generation method is used to generate random discrete coefficients, which follow a standard normal distribution. For each inflow scenario, the product of the baseline inflow process and the random discrete coefficients and standard deviation is added to obtain the corrected inflow rate. The above process is repeated to generate multiple different inflow scenarios.

[0034] In an optional implementation, in step A2, generating multiple different water inflow scenarios using a random discrete method can be achieved by: The average inflow rate of each hydropower station at each time period is used as the benchmark inflow process. The Monte Carlo simulation method is used to randomly sample according to the historical probability distribution of the inflow rate to generate multiple inflow scenarios that conform to historical statistical characteristics. This ensures that the generated inflow scenarios are consistent with the historical inflow distribution in a statistical sense.

[0035] In another alternative implementation, in step A2, generating multiple different water inflow scenarios using a random discrete method can also be achieved by: The average inflow rate over many years for each hydropower station at each time period is used as the baseline inflow process; stratified random numbers are generated using the Latin hypercube sampling method to ensure that the random samples are uniformly distributed in the parameter space; the corrected flow rate is calculated based on the stratified random numbers and standard deviation to generate multiple sets of inflow scenarios covering different frequency ranges.

[0036] Step 2: Based on the market share of the cascade hydropower station group in the electricity market, determine the electricity price simulation strategy, and establish the annual contract electricity price function and monthly contract electricity price function based on the electricity price simulation strategy, including the following steps B1-B4: B1: Determine the market share range of the cascade hydropower station group in the electricity market; B2: Based on the aforementioned market share range, select the corresponding electricity price simulation strategy from a variety of electricity price simulation strategies; B3: Based on the selected electricity price simulation strategy, establish a correlation function between the monthly contract electricity price and the declared electricity volume or water inflow process as the monthly contract electricity price function; B4: Based on the selected electricity price simulation strategy, establish a correlation function between the annual contract electricity price, the extreme value of the monthly contract electricity price, and the proportion of annual contract electricity volume as the annual contract electricity price function.

[0037] In this embodiment, step 2 involves the following steps: When it is determined that the cascade hydropower station group has a large market share in the electricity market, a price simulation strategy based on the declared electricity volume is adopted; a linear negative correlation function is established between the monthly contract price and the ratio of the total declared output and the total installed capacity of each hydropower station as the monthly contract price function, and the monthly contract price decreases when the declared electricity volume increases; based on the maximum and minimum values ​​of the monthly contract price, combined with the average of the upper and lower limits of the annual contract electricity volume ratio, an annual contract price function is established, so that the annual contract price decreases as the annual contract electricity volume ratio increases, and the annual contract price is adjusted by an empirical constant to be between the maximum and minimum values ​​of the monthly contract price and slightly higher than the minimum value.

[0038] In an optional implementation, in step 2, the electricity price simulation strategy can be implemented by: adopting an electricity price simulation strategy based on the water inflow process when the market share of the cascade hydropower station group in the electricity market is relatively small; establishing a piecewise function of the monthly contract electricity price based on the historical monthly contract electricity price curve, setting a higher stable electricity price during the dry season and a lower stable electricity price during the flood season, and using linear decreasing and linear increasing functions to represent the electricity price change trend before and after the flood season, respectively; and establishing an annual contract electricity price function based on the difference between the stable electricity price during the dry season and the stable electricity price during the flood season, combined with the average of the upper and lower limits of the annual contract electricity volume ratio.

[0039] In another optional implementation, in step 2, the electricity price simulation strategy can also be: when it is determined that the market share of the cascade hydropower station group in the electricity market is moderate, a comprehensive electricity price simulation strategy is adopted; the monthly contract electricity price function based on the declared electricity volume and the monthly contract electricity price function based on the water inflow process are weighted and averaged to obtain a comprehensive monthly contract electricity price function; similarly, the annual contract electricity price functions corresponding to the two strategies are weighted and averaged to obtain a comprehensive annual contract electricity price function, and the weight coefficients can be flexibly adjusted according to the actual market characteristics.

[0040] Step 3: Constructing a return-risk minimization model with the objective of maximizing the minimum return at a given confidence level includes the following steps C1-C4: C1: Set the return risk representation parameter as a function of the annual contracted electricity ratio, which is calculated by the ratio of the annual contracted electricity to the total power generation of each hydropower station in each time period; C2: Establish a total power generation benefit calculation function, which includes an annual contract benefit item, a monthly contract benefit item, and a water abandonment loss item; C3: Based on the aforementioned revenue risk characterization parameters and total power generation benefit calculation function, establish a revenue probability density function; C4: Construct a return-risk minimization model with the goal of maximizing the minimum return corresponding to the return probability density function at a given confidence level.

[0041] Step 4: Optimizing the scheduling of the operating status of each hydropower station at different times, under the condition of satisfying the operational constraints of the cascade hydropower station group, includes the following steps D1-D5: D1: Set the initial reservoir capacity and target final reservoir capacity for each hydropower station at each time period; D2: Under the condition of fixed water levels in adjacent time periods, the outflow of each hydropower station in the current time period is discretized to generate multiple discrete schemes; D3: For each discrete scheme, calculate the reservoir capacity change of each hydropower station based on the water balance constraint, and calculate the inflow of the downstream hydropower station based on the upstream and downstream hydraulic connection constraint. D4: Under the conditions of satisfying the outflow constraint, power output constraint and reservoir water level constraint, calculate the power output and water wastage of each hydropower station corresponding to each discrete scheme; D5: According to the time period sequence and the order of hydropower stations, perform discrete optimization for each time period of each hydropower station to obtain the operating status of all hydropower stations for the entire time period.

[0042] Step 5: Adopting a contracted power allocation strategy to determine the allocation ratio of annual and monthly contracted power generation for each hydropower station in each time period includes the following steps E1-E5: E1: Set the upper and lower limits of the annual contract electricity volume ratio, as well as the electricity allocation step size; E2: While keeping the contracted power allocation ratio of other hydropower stations unchanged for other time periods, the power generation of the current hydropower station for the current time period is discretized within the range of the upper and lower limits according to the power allocation step size, to form multiple allocation schemes; E3: For each allocation scheme, calculate the corresponding power generation benefits based on the annual contract electricity price function and the monthly contract electricity price function; E4: Select the allocation scheme with the greatest power generation benefit as the optimal allocation scheme for the current hydropower station in the current time period; E5: The contracted electricity allocation for each hydropower station for each time period will be completed in the order of time periods and hydropower station sequence.

[0043] In this embodiment, step 5 involves the following steps: First, calculate the maximum allocation level based on the upper limit of the annual contracted electricity ratio and the allocation step size. Second, calculate the minimum allocation level based on the lower limit of the annual contracted electricity ratio and the allocation step size. Third, for the current power generation of the hydropower station in the current period, starting from the minimum allocation level, gradually increase the annual contracted electricity ratio according to the allocation step size, and correspondingly decrease the monthly contracted electricity ratio until the maximum allocation level is reached. Fourth, for each discrete allocation scheme, multiply the annual contracted electricity ratio by the annual contracted electricity price and the monthly contracted electricity ratio by the monthly contracted electricity price; the sum of these two is the power generation benefit of the scheme. Fifth, select the allocation scheme corresponding to the maximum power generation benefit to determine the annual contracted electricity ratio and the monthly contracted electricity ratio of the current hydropower station in the current period.

[0044] In an optional implementation, in step 5, the contract power allocation strategy can be achieved by: using an adaptive step size search method, initially setting a large power allocation step size for coarse search to quickly locate areas with high power generation benefits; within these areas, reducing the power allocation step size for fine search to improve the accuracy of the optimal allocation scheme; and by combining coarse and fine search, reducing the computational load while ensuring solution accuracy, thereby improving the efficiency of contract power allocation.

[0045] In another optional implementation, in step 5, the contract power allocation strategy can also be achieved by: using the golden ratio to determine the annual contract power ratio; selecting two trial points within the upper and lower limits according to the golden ratio; calculating the power generation benefit corresponding to each trial point; gradually narrowing the search interval according to the size of the power generation benefit; and quickly converging to the annual contract power ratio with the largest power generation benefit through an iterative approximation method, thereby determining the monthly contract power ratio accordingly.

[0046] Step 7: Based on the total power generation benefit, assess the minimum revenue, and maximize the minimum revenue through iterative optimization. Output the annual contracted electricity share and monthly contracted electricity share for each hydropower station in each time period, including the following steps F1-F4: F1: For each water inflow scenario, optimize the operation status and allocate contracted electricity in the order of time periods, and calculate the total power generation benefit corresponding to the water inflow scenario. F2: Based on the total power generation benefits of each water inflow scenario and the aforementioned revenue-risk characterization parameters, calculate the minimum revenue for the current iteration; F3: Compare the minimum profit of the current iteration with the minimum profit of the previous iteration. If the two minimum profits are the same, the optimization is completed, and the annual contracted electricity share and monthly contracted electricity share of each hydropower station for each time period are output. F4: If the two minimum returns are different, use the current optimization result as the new initial state, return to continue optimization, and continue until the minimum return reaches the maximum.

[0047] Example 3, referring to Figures 1-5 As an embodiment of the present invention, based on the previous embodiment, a method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue risk is provided, including: Currently, the objectives for formulating the optimal combination of medium- and long-term contracted electricity volumes for cascade hydropower station groups are to maximize power generation benefits, minimize monthly electricity volume decomposition deviations, and minimize electricity price uncertainty risks. However, these objectives are mainly used for deterministic model solutions and are not well-suited for formulating electricity volume declaration strategies for cascade hydropower station groups that consider multiple coupled uncertainty factors. Therefore, the method in this embodiment is based on Value at Risk (VaR). Theory, definition To achieve a given confidence level Minimum profit, such as Figure 5 As shown, For total revenue, The probability density function is , If the expected return is, then To minimize possible losses, For the level of return volatility, for a given... ,Depend on Caused No more than a certain level of return The probability is: (1) With the objective of minimizing the revenue risk of the entire cascade power generation system at a given confidence level, this study rationally weighs the annual / monthly bilateral contracted electricity volumes of a cascade hydropower station group. The advantage of this objective is that it can address the revenue risk assessment issue of annual / monthly contracted electricity volume declaration strategies under conditions of uncertainty in water inflow and electricity prices. Minimizing revenue risk is key. That is, satisfy The objective function formula is as follows: (2) In the formula, This represents the minimum return at a given confidence level, expressed in yuan. , These represent maximum power generation efficiency and revenue volatility, respectively. Since a higher percentage of annual contracted electricity volume leads to more stable trading, the percentage of annual contracted electricity volume is negatively correlated with revenue risk. Select Indicates the level of return volatility. This represents the percentage of total annual market electricity consumption, in units of... ; The confidence level is indicated by F1, F2, and F3, which represent the annual contract benefit, monthly contract benefit, and water abandonment loss (water abandonment penalty), respectively, in yuan; M represents the total number of hydropower stations; T represents the total number of months. , The power output and corresponding water discharge output of the i-th hydropower station in month t, in MW; , These represent the annual contracted electricity volume ratio and the monthly contracted electricity volume ratio of the i-th hydropower station in month t, respectively, in units of... ; Electricity price simulation method The monthly electricity price in month t, The monthly electricity price for each hydropower station in month t is the same, and the unit is yuan / kWh; Electricity price simulation method Annual contract electricity price, The annual contract price for electricity is the same for all power plants, and the unit is yuan / kWh; This represents the total number of seconds in the t-th month, i.e. The unit is seconds; This represents the number of days in the t-th month.

[0048] Cascade hydropower stations must meet the following constraints: (1) Water balance constraint: (3) In the formula, , Indicates the first The first hydropower station in Month and the The warehouse capacity for each month, in units of ; , , They represent the first The first hydropower station in Inflow, power generation, and water discharge for each month, in units of .

[0049] (2) Hydraulic constraints of upstream and downstream reservoirs: (4) In the formula, Indicates the first The first hydropower station in The interval flow for each month, in units of .

[0050] (3) Outbound flow constraints: (5) In the formula, Indicates the first The first hydropower station in Outbound volume for the month, in units of ; , They represent the first The first hydropower station in The minimum and maximum outflow rates for each month, in units of .

[0051] (4) Output constraints: (6) In the formula, Indicates the first The first hydropower station in Monthly output, in units of ; , They represent the first The first hydropower station in The minimum and maximum output for each month, in units of .

[0052] (5) Reservoir water level constraints: (7) In the formula, Indicates the first The first hydropower station in The upper reservoir water level for each month, in units of ; , Indicates the first The first hydropower station in The lowest and highest water levels in the upper reservoir for each month, in units of... .

[0053] (6) End-of-period water level control constraints: (8) In the formula, Indicates the first The target control water level at the end of the scheduling period for each hydropower station, in units of .

[0054] (7) Electricity price upper and lower limits constraints: (9) (10) To avoid the numerous negative impacts of large fluctuations in electricity prices on the market, it is necessary to regulate the range of electricity price fluctuations. This is of great practical significance for ensuring the safe and stable operation of the market. In the formula... , This indicates the maximum and minimum annual and monthly contract electricity prices, expressed in yuan / kWh.

[0055] (8) Annual contracted electricity volume range constraints: (11) In the formula, , Indicates the first The first hydropower station in The upper and lower limits of the annual contracted electricity volume ratio for each month, in units of Due to the uncertainties in natural water inflow and electricity prices, cascade hydropower stations generally do not sign too many annual contracted electricity volumes when participating in actual bilateral transactions, in order to avoid penalties for failing to meet contracted electricity volumes and losses due to excessively low electricity prices. Therefore, this project sets upper and lower limits on the proportion of annual contracted electricity volumes.

[0056] This embodiment focuses on the decision-making problem of combining annual / monthly contracted electricity volumes for a cascade hydropower station group under different water inflow frequencies and market models. It needs to solve three key sub-problems: first, how to design water inflow processes with different frequencies; second, how to fit electricity price curves that conform to different market models; and third, how to determine a reasonable allocation scheme for annual and monthly electricity volumes. To address these problems, the solution approach of this project is as follows: randomly generate water inflow processes with different frequencies based on historical water data over many years; select hydropower stations with different market proportions to fit market contracted electricity price curves; and then, with a given confidence level... Minimum Profit With the goal of maximizing power generation, a hybrid algorithm combining stepwise optimization and successive approximation is used to iteratively solve for the combination of power plant output and contracted power volume. These are elaborated upon below.

[0057] Specifically, determining the inflow processes at different frequencies includes selecting the multi-year average flow rate of natural inflow from each reservoir within the basin during the same period. As the initial predicted inflow process, multiple inflow processes are discretized based on this set of predicted inflow processes, using the standard deviation of the natural inflow flow of each reservoir in the basin during the same period as the initial predicted inflow process. To represent the degree of fluctuation in water inflow, and to ensure a consistent trend in water inflow across months, the `Random.nextGaussian()` method in Java is used to generate random numbers. As the coefficient of variation, the corrected flow rate is Different frequencies of water inflow processes are simulated based on different discrete coefficients.

[0058] To fully reflect the random variation characteristics of incoming water, multiple sets of incoming water frequencies are generated using the above method. However, some incoming water frequencies are similar, leading to similar scheduling processes under the minimum benefit-risk model. Therefore, to minimize the amount of incoming water at different frequencies while ensuring the characteristics of the incoming water frequency, this project arranges the incoming water in each scenario in descending order, numbers them sequentially (X=1,2,…,Y), and calculates the empirical frequency according to formula (12), with a step size of... Case studies were conducted using equidistant sampling of water inflow at specific frequencies.

[0059] (12) in, X represents the empirical frequency; X represents the different water inlet serial numbers, X=1,2,…,Y; Y represents the total number of generated water inlet.

[0060] The fitting function for annual / monthly contract electricity prices includes: When hydropower participates in medium- and long-term market transactions, the market price is related to factors such as the declared electricity volume, market demand, and water inflow. The main influencing factors differ under different market models. Common electricity price simulation methods include empirical fixed price methods, hypothetical fixed price methods, and regression analysis methods, which are highly valuable for price fitting. However, due to the complexity of the problem, the aforementioned literature does not consider the impact of different water inflows or different declared electricity volumes on the price. This project proposes three simulation methods for medium- and long-term transaction electricity prices under different market models: (1) Electricity Price Simulation Method 1: Transaction Situation Where Cascade Hydropower Stations Account for a Larger Market Share. When large-scale cascade hydropower station groups account for a large share of market transactions, their monthly electricity price is generally linearly negatively correlated with market demand, that is, the electricity price fitting function can reflect the trend of monthly electricity price decreasing as the electricity demand increases. The electricity price relationship function is as follows: Figure 2 As shown, based on actual production patterns, the annual electricity price is generally between the maximum and minimum monthly electricity price and slightly higher than the minimum monthly electricity price, and it is negatively correlated with the declared electricity volume. Therefore, the annual electricity price is fitted using the maximum and minimum monthly electricity price and the proportion of annual contracted electricity volume. The corresponding monthly contracted electricity price and annual contracted electricity price are shown in formulas (13) and (14): (13) (14) In the formula, , Representing the annual contract electricity price of electricity price simulation method 1, respectively, and the... The monthly contract electricity price for each month is expressed in yuan / kWh. Indicates the hydroelectric power station number; This indicates the total number of hydroelectric power stations; Indicates the first The first hydropower station in Installed capacity for the month, in units of ; It is the average of the upper and lower limits of the annual contracted electricity volume, i.e. The unit is ; This represents the difference between the maximum and minimum monthly contract electricity price. ; For empirical constants, increase the constant. To align with actual electricity price patterns; Equations (13) and (14) are constrained by equations (9) and (10), respectively.

[0061] (2) Electricity Price Simulation Method 2: Transaction Situation Where Cascade Hydropower Stations Have a Small Market Share. When the market share of cascade hydropower stations is relatively small, their monthly electricity price is greatly affected by natural water inflow, exhibiting a "stable-declining-stable-rising" pattern with different seasonal water inflow processes. Electricity prices are relatively stable during the dry and flood seasons, while prices before and after the flood season show declining and rising trends, respectively. For example... Figure 3 As shown, a new electricity price curve is generated based on the historical monthly bilateral transaction electricity price curve of the power grid, and a monthly electricity price function is fitted. The historical maximum / minimum electricity price is used as the stable electricity price during the dry / flood season. It is assumed that the rising and falling parts of the electricity price curve have a linear positive correlation and a linear negative correlation, respectively. This part is taken as the average of the electricity prices at both ends of the monthly electricity price curve. The annual electricity price function is fitted with the maximum and minimum monthly electricity price and the proportion of annual contracted electricity volume. The corresponding monthly and annual contracted electricity prices are shown in formulas (15) and (16): (15) (16) In the formula, , Representing the annual contract electricity price of electricity price simulation method 2, respectively, and the... The monthly contract electricity price for each month is expressed in yuan / kWh. , The stable electricity prices during the dry season and the flood season are respectively, with the unit being yuan / kWh; Equations (15) and (16) are constrained by equations (9) and (10) respectively.

[0062] (3) Electricity Price Simulation Method 3: Transaction Situation with a Moderate Market Proportion of Cascade Hydropower Stations. Electricity price simulation methods 1 and 2 are relatively idealized, while the actual market is more complex. It is also important to study medium- and long-term market transactions between the two electricity prices mentioned above. For ease of understanding, the average annual contract electricity price and the average monthly contract electricity price of the two methods are selected as the annual contract electricity price and the monthly contract electricity price of electricity price simulation method 3, respectively, to explore the medium- and long-term market transaction results under this electricity price. The electricity price curve is illustrated as follows. Figure 5 As shown, the corresponding monthly contract electricity price and annual contract electricity price are shown in formulas (17) and (18): (17) (18) In the formula, , Representing the annual contract electricity price of electricity price simulation method 3, respectively, and the... The monthly contract electricity price for each month, in yuan / kWh.

[0063] Developing the optimal combination strategy for annual / monthly contracted electricity volumes includes: When cascade hydropower stations participate in the medium- and long-term market, to avoid market risks and ensure basic returns, they typically select a portion of their electricity output for annual contracts. After deducting the contracted output from the total annual power generation, the remaining output is primarily used for monthly contracts. Therefore, the key issue is how to rationally balance the combination of annual and monthly contract outputs to effectively reduce market risks and maximize benefits.

[0064] This project employs a method combining optimized hydropower scheduling and medium-to-long-term trading to solve the minimum return-risk model. The main idea is to optimize the scheduling of the cascade hydropower station group and verify water balance, then allocate annual and monthly contracted electricity volumes. The generation benefits are calculated based on annual and monthly electricity prices. The maximum generation benefit is selected to assess the return-risk, and the minimum benefit is determined. Is it the maximum? Therefore, after the cascade hydropower stations determine the power generation for each time period through optimized scheduling, it is necessary to select the optimal combination of annual and monthly contracted power to obtain the maximum power generation benefit, assess the benefit risk to obtain the optimal decomposition scheme, and so on. The first hydropower station in The approach to finding the maximum power generation benefit for each month is as follows: The annual and monthly contracted electricity allocation ratios for each power station remain unchanged during other periods. As the step size for allocating electricity generation to the annual contracted electricity volume As a measure of the allocation of annual contracted electricity volume, the first The first hydropower station in In the monthly power generation The electricity volume is considered as the annual contracted volume. The electricity generated after deducting the annual contracted volume will be used as the monthly contracted volume. The amount of electricity exceeded the maximum annual contracted amount. Then, the maximum annual contracted electricity volume is used as the annual contracted electricity volume, and the electricity generated after deducting the maximum annual contracted electricity volume is used as the monthly contracted electricity volume. The power generation benefit is calculated for different allocation schemes, and the allocation scheme with the maximum power generation benefit is the [number]th allocation scheme. The first hydropower station in The optimal solution for each month.

[0065] The specific approach to adding upper and lower limits to the annual contracted electricity volume ratio is as follows: Annual contracted electricity ratio cap and lower limit value by To discretize the step size, the discretized ratio range is used as the annual contracted electricity ratio range. Different ratio ranges are selected to calculate the power generation benefits, and the ratio limit with the lowest risk is selected as the upper and lower limits of the annual contracted electricity ratio.

[0066] No. The first hydropower station in The proportion of annual contracted electricity volume in each month under different discrete combinations for: (19) In the formula, , These are the upper and lower limits of the annual contracted electricity volume, in kWh. , This indicates the maximum and minimum distribution levels. This indicates the selected degree of dispersion. .in (20) In the formula , They represent ratios , The smallest largest integer.

[0067] An improved version of the Progressive Optimization-Device Approximation Algorithm (POA-DPSA) is proposed to solve the optimal scheduling process of cascade hydropower stations. This version couples the optimal combination decision-making function for annual and monthly contracted electricity volumes. The scheduling period is one year, with a one-month time interval. The goal is to find the minimum benefit under a given confidence level. The detailed solution steps for the maximum time scheduling scheme are as follows: Step 1: Determine the inflow frequency for different water inflow frequencies. Based on historical inflow data, randomly generate multiple sets of inflow frequencies, and determine the empirical frequency using formula (12). The group selects a portion of the incoming water at different frequencies based on the step size. Step Two: Discrete Flow Rates. The state variables and decision variables of each power station remain unchanged during other time periods, and are fixed. and The water level of the month, discrete number The outflow from each power station in a given month can be used to generate discrete solutions. Discrete schemes; Step 3: Select the first item from Step 2 Given a discrete scheme, calculate the power generation and abandoned water volume of each power station in each time period under the discrete scheme; Step 4: Let , , No. The first hydropower station in The monthly power generation discrete value is used as the annual contract power volume, and the power generation after deducting the discrete value is used as the monthly contract power volume. The power generation benefits after each discrete value are calculated, the optimized value with the maximum benefit is selected, the optimized value of the power plant is saved and assigned to the initial value. Step 5: Let , for the The first hydropower station in The optimization process is conducted every month, using the same calculation method as in step four, until the conditions are met. until; Step Six: Let Select the first Each hydropower station proceeds through steps four and five until the conditions are met. Using the maximum power generation benefit as the expected value, calculate the minimum benefit of this discrete scheme at a given confidence level. Select Save the optimized value at its maximum. Step Seven: Let Select the first step in step two. For each discrete scheme, the calculation method is the same as steps three to six, until the conditions are met. ; Step 8: Order , for the Optimization is performed every month, using the same calculation method as steps two through seven, and optimized values ​​are saved until the last time period. All optimized decision variable and state variable values ​​are saved. Step Nine: Compared with the previous optimization The results are compared; if the two results differ, a new round of optimization calculations begins until the two results are the same. The overall flowchart is as follows: Figure 1 As shown.

[0068] Example 3 is an embodiment of the present invention, which provides an optimal combination system for long-term contracted electricity volumes in a cascade hydropower station group considering revenue and risk, including: The inflow scenario generation module is used to generate multiple inflow scenarios of different frequencies based on the historical inflow data of the cascade hydropower station group. The electricity price function establishment module is used to determine the electricity price simulation strategy based on the market share of the cascade hydropower station group in the electricity market, and to establish the annual contract electricity price function and the monthly contract electricity price function based on the electricity price simulation strategy. The risk model building module is used to construct a risk-return minimization model with the objective of maximizing the minimum return under a given confidence level. The optimized scheduling module is used to optimize the scheduling of the operation status of each hydropower station in each time period while meeting the operational constraints of the cascade hydropower station group. The contract allocation module is used to determine the allocation ratio of annual contract electricity and monthly contract electricity in the power generation of each hydropower station in each time period using a contract electricity allocation strategy. The benefit calculation module is used to calculate the total power generation benefit based on the annual contract electricity price function, the monthly contract electricity price function, and the allocated annual contract electricity volume and monthly contract electricity volume; The iterative optimization module is used to evaluate the minimum benefit based on the total power generation benefit, and maximize the minimum benefit through iterative optimization, and output the annual contract power ratio and monthly contract power ratio of each hydropower station for each time period.

[0069] This embodiment also provides an electronic device applicable to a method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue risk. The device includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue risk, as proposed in the above embodiment.

[0070] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group, taking into account revenue risk, as proposed in the above embodiment.

[0071] The storage medium proposed in this embodiment and the method for achieving the optimal combination of long-term contract electricity in a cascade hydropower station group considering revenue risk proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0072] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimally combining long-term contracted electricity volumes in a cascade hydropower station group, considering both revenue and risk, characterized in that: include, Multiple inflow scenarios with different frequencies are generated based on historical inflow data of the cascade hydropower station group; Based on the market share of the cascade hydropower station group in the electricity market, a price simulation strategy is determined, and an annual contract price function and a monthly contract price function are established based on the price simulation strategy. A return-risk minimization model is constructed with the objective of maximizing the minimum return under a given confidence level. The return-risk is quantified by a return-risk characterization parameter, which is negatively correlated with the annual contract electricity ratio. Under the condition of meeting the operational constraints of the cascade hydropower station group, the operation status of each hydropower station at each time period is optimized and scheduled. A contract power allocation strategy is adopted to determine the allocation ratio of annual contract power and monthly contract power in the power generation of each hydropower station in each time period; The total power generation benefit is calculated based on the annual contract electricity price function, the monthly contract electricity price function, and the allocated annual and monthly contract electricity volumes. The minimum revenue is assessed based on the total power generation benefit, and the minimum revenue is maximized through iterative optimization. The annual contracted electricity share and monthly contracted electricity share of each hydropower station for each time period are then output.

2. The method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue and risk, as described in claim 1, is characterized in that: The process of generating multiple inflow scenarios at different frequencies based on historical inflow data of the cascade hydropower station group includes obtaining statistical characteristic parameters of inflow for each hydropower station within the basin. Based on the aforementioned statistical characteristic parameters of incoming water, multiple different incoming water scenarios are generated using a random discrete method; The multiple water inflow scenarios are sorted according to the water inflow volume, the empirical frequency corresponding to each water inflow scenario is calculated, and the target water inflow scenario is obtained by equidistant sampling according to a preset frequency step size.

3. The method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue and risk, as described in claim 2, is characterized in that: The step of determining the electricity price simulation strategy based on the market share of the cascade hydropower station group in the electricity market, and establishing the annual contract electricity price function and monthly contract electricity price function based on the electricity price simulation strategy, includes determining the market share range of the cascade hydropower station group in the electricity market. Based on the aforementioned market share range, select the corresponding electricity price simulation strategy from a variety of electricity price simulation strategies; Based on the selected electricity price simulation strategy, a correlation function between the monthly contract electricity price and the declared electricity volume or water inflow process is established as the monthly contract electricity price function; Based on the selected electricity price simulation strategy, a correlation function is established between the annual contract electricity price, the extreme value of the monthly contract electricity price, and the proportion of annual contract electricity volume as the annual contract electricity price function.

4. The method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue and risk, as described in claim 3, is characterized in that: The construction of the revenue-risk minimization model includes setting the revenue-risk representation parameter as a function of the annual contracted electricity ratio, which is calculated by the ratio of the annual contracted electricity to the total power generation of each hydropower station in each time period. Establish a total power generation benefit calculation function, which includes an annual contract benefit item, a monthly contract benefit item, and a water abandonment loss item; Based on the aforementioned revenue risk characterization parameters and total power generation benefit calculation function, a revenue probability density function is established. A return-risk minimization model is constructed with the goal of maximizing the minimum return corresponding to the probability density function of the return at a given confidence level.

5. The method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue and risk, as described in claim 4, is characterized in that: The optimization scheduling of the operation status of each hydropower station at each time period under the condition of satisfying the operation constraints of the cascade hydropower station group includes setting the initial reservoir capacity and target end reservoir capacity of each hydropower station at each time period. Under the condition of fixed water levels in adjacent time periods, the outflow of each hydropower station in the current time period is discretized to generate multiple discrete schemes; For each discrete scheme, the reservoir capacity change of each hydropower station is calculated based on the water balance constraint, and the inflow of the downstream hydropower station is calculated based on the upstream and downstream hydraulic connection constraint. Under the conditions of satisfying the constraints of outflow, power output, and reservoir water level, calculate the power output and water wastage of each hydropower station corresponding to each discrete scheme; According to the time period sequence and the order of hydropower stations, discrete optimization is performed on each hydropower station for each time period to obtain the operating status of all hydropower stations for the entire time period.

6. The method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue and risk, as described in claim 5, is characterized in that: The adoption of the contract power allocation strategy to determine the allocation ratio of annual contract power and monthly contract power in the power generation of each hydropower station in each time period includes setting an upper limit and a lower limit for the proportion of annual contract power, as well as the power allocation step size. While keeping the contracted electricity allocation ratio of other hydropower stations unchanged for other time periods, the electricity generation of the current hydropower station for the current time period is discretized within the range of the upper and lower limits according to the electricity allocation step size, to form multiple allocation schemes; For each allocation scheme, the corresponding power generation benefits are calculated based on the annual contract electricity price function and the monthly contract electricity price function; The allocation scheme that maximizes power generation benefits is selected as the optimal allocation scheme for the current hydropower station in the current time period; The contracted electricity allocation for each hydropower station for each time period was completed in the order of time periods and hydropower station sequence.

7. The method for optimal combination of long-term contracted electricity volumes in a cascade hydropower station group considering revenue and risk, as described in claim 6, is characterized in that: The step of maximizing the minimum benefit through iterative optimization includes optimizing the operation status and allocating contracted electricity for each time period in sequence for each water inflow scenario, and calculating the total power generation benefit corresponding to the water inflow scenario. Based on the total power generation benefits of each water inflow scenario and the aforementioned revenue-risk characterization parameters, calculate the minimum revenue for the current iteration; Compare the minimum profit of the current iteration with the minimum profit of the previous iteration. If the two minimum profits are the same, the optimization is complete. Output the annual contracted electricity volume ratio and monthly contracted electricity volume ratio of each hydropower station for each time period. If the two minimum returns are different, the current optimization result is used as the new initial state, and the optimization continues until the minimum return is maximized.

8. A system for optimal combination of medium- and long-term contracted electricity volumes in a cascade hydropower station group considering revenue risk, comprising applying the method for optimal combination of medium- and long-term contracted electricity volumes in a cascade hydropower station group considering revenue risk as described in any one of claims 1 to 7, characterized in that, include: The inflow scenario generation module is used to generate multiple inflow scenarios of different frequencies based on the historical inflow data of the cascade hydropower station group. The electricity price function establishment module is used to determine the electricity price simulation strategy based on the market share of the cascade hydropower station group in the electricity market, and to establish the annual contract electricity price function and the monthly contract electricity price function based on the electricity price simulation strategy. The risk model building module is used to construct a risk-return minimization model with the objective of maximizing the minimum return under a given confidence level. The optimized scheduling module is used to optimize the scheduling of the operation status of each hydropower station in each time period while meeting the operational constraints of the cascade hydropower station group. The contract allocation module is used to determine the allocation ratio of annual contract electricity and monthly contract electricity in the power generation of each hydropower station in each time period using a contract electricity allocation strategy. The benefit calculation module is used to calculate the total power generation benefit based on the annual contract electricity price function, the monthly contract electricity price function, and the allocated annual contract electricity volume and monthly contract electricity volume; The iterative optimization module is used to evaluate the minimum benefit based on the total power generation benefit, and maximize the minimum benefit through iterative optimization, and output the annual contract power generation ratio and monthly contract power generation ratio of each hydropower station for each time period.

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 steps of the method for optimal combination of long-term contracted electricity volume in a cascade hydropower station group considering revenue risk, as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for optimal combination of long-term contracted electricity volume in a cascade hydropower station group considering revenue risk, as described in any one of claims 1 to 7.

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