Cascade water-wind-light long-term complementary scheduling rule optimization method under multiple uncertainties

Abstract

The invention discloses a cascade water-wind-light long-term complementary scheduling rule optimization method under multiple uncertainties. The method comprises the following steps: S1, quantifying water-wind-light multiple uncertainties; s2, constructing a cascade water-wind-light long-term complementary scheduling model; and S3, SDDP-based cascade water, wind and light optimization scheduling solution is carried out. According to the cascade water-wind-light long-term complementary scheduling rule optimization method under multiple uncertainty, a multiple uncertainty quantification method based on a Markov chain and an ARMA model is constructed to represent a long-term runoff and wind-light output random process, and meanwhile, a cascade water-wind-light long-term complementary scheduling model is provided. A nonlinear stage model is converted into a linear stage recursive model in combination with disjunction planning, finally, a cascade water-wind-light optimal scheduling algorithm based on SDDP is put forward for efficient solving, a water-wind-light long-term complementary scheduling rule is deduced, and the problem of cascade water-wind-light long-term regulation and control is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-energy complementary optimization scheduling, and specifically relates to a method for optimizing long-term complementary scheduling rules of cascade hydropower, wind power and solar power under multiple uncertainties. Background Art

[0002] Over the past decade, my country has seen rapid growth in installed renewable energy capacity. However, this large-scale expansion, coupled with the intermittent, random, seasonal, and poorly resistant nature of its generation, poses a serious security threat to power system operations and significantly increases the difficulty of accommodating renewable energy. New energy storage technologies offer a reliable solution for the sustainable development and effective absorption of renewable energy. However, currently, installed capacity is only 40 GW, primarily focused on short-term storage, far smaller than the scale of grid-connected wind and solar power. Furthermore, further breakthroughs are needed in terms of safety, affordability, and reliability, making it difficult to fill the gaps in the demand for long-term energy storage and the large-scale temporal and spatial transfer of electricity generated by the large-scale integration of renewable energy.

[0003] Compared to new energy storage, cascade hydropower in river basins offers advantages in both installed capacity and flexible regulation timescales. In particular, controlled reservoir power stations with annual or multi-year regulation capabilities possess vast natural storage capacity, effectively supporting the grid's integration of a high proportion of renewable energy, long-term energy storage, and flexible regulation. Furthermore, the seasonal volatility and randomness of renewable energy further exacerbate the complexity of cascade hydropower optimization and scheduling, particularly long-term scheduling and operation. This significantly increases the difficulty of long-term water level regulation and power generation control, making it a common challenge faced by clean energy bases such as hydropower, wind power, and solar power. These challenges are primarily manifested in the following ways: First, long-term runoff and wind and solar power output are difficult to accurately predict and exhibit strong randomness. Faced with multiple uncertainties in runoff, wind power, and photovoltaic power generation, long-term regulation becomes increasingly difficult. Second, the seasonal volatility of wind and solar power generation alters the original optimal operating trajectory of cascade hydropower, placing higher demands on long-term water level and power generation control.

[0004] Therefore, how to comprehensively consider the above factors in the long-term complementary scheduling of cascade hydro-wind-solar systems is directly related to whether the complementary scheduling of cascade hydro-wind-solar systems can fully exert the long-term complementary benefits and the relevant requirements for clean energy consumption. Summary of the Invention

[0005] The present invention is proposed to solve the above-mentioned shortcomings, and its purpose is to provide a method for optimizing the long-term complementary scheduling rules of cascade water, wind and solar power under multiple uncertainties. The method constructs a multiple uncertainty quantification method based on Markov chain and ARMA model to characterize the long-term runoff and wind and solar power output random processes. At the same time, a long-term complementary scheduling model of cascade water, wind and solar power is proposed, and the nonlinear stage model is converted into a linear stage recursive model by combining disjunctive programming. Finally, a cascade water, wind and solar power optimization scheduling algorithm based on SDDP is proposed to efficiently solve and derive the long-term complementary scheduling rules of water, wind and solar power, solving the problem of long-term regulation of cascade water, wind and solar power.

[0006] In order to achieve the above purpose, the present invention adopts the following scheme:

[0007] A method for optimizing the long-term complementary scheduling rules of cascade hydropower, wind power and solar power under multiple uncertainties includes the following steps:

[0008] S1: Quantification of multiple uncertainties in water, wind, and solar power

[0009] Collect historical monthly data on runoff and wind and solar power output;

[0010] The deviation rate is used to divide the Markov chain state and form a multi-stage and multi-state state transition probability matrix for runoff.

[0011] The stationarity test of the aggregated wind and solar power station output series is conducted, and its random process is fitted based on the ARMA model;

[0012] The white noise of runoff and wind and solar power output is coupled through Cartesian product to obtain a random scenario that can describe the uncertainty of the joint effects of water, wind and solar power.

[0013] S2: Construction of a long-term complementary scheduling model for cascaded hydropower, wind power, and solar power

[0014] On the basis of satisfying the operating constraints of cascade hydropower stations, a long-term complementary optimization model is constructed with the maximum long-term power generation of cascade hydropower, wind power and solar power as the objective function, and the model is reconstructed.

[0015] S3: Optimal Scheduling Solution for Cascaded Hydropower, Wind-Solar Systems Based on SDDP

[0016] Based on the dynamic programming Bellman equation, the multi-stage stochastic programming problem is decomposed into stage recursive sub-problems;

[0017] Disjunctive programming is used to transform it into linear constraints by introducing binary auxiliary variables;

[0018] Use linear relaxation to construct Benders cuts and convert them into a staged recursive linear model;

[0019] By combining forward simulation with backward recursion, the water-wind-solar scheduling scheme is continuously iterated and updated until convergence is met or the preset number of iterations is reached, thus obtaining a long-term complementary scheduling rule for cascaded water-wind-solar that takes into account multiple uncertainties.

[0020] As a preferred embodiment, in step S1, the quantification of multiple uncertainties of water, wind and solar power specifically includes the following steps:

[0021] S101: Historical data collection and preprocessing

[0022] Collect monthly historical data on runoff and wind and solar power output; based on the historical data, calculate the deviation between the historical runoff and the historical runoff mean, and calculate the deviation rate of the historical data for each month;

[0023] Aggregate wind and solar power stations into new energy power stations and calculate the historical output of new energy after aggregation;

[0024] S102: Constructing a Runoff Markov Chain

[0025] Based on the runoff deviation rate, several Markov states are divided, and the state transitions of adjacent stages are statistically analyzed to form a state transition probability matrix;

[0026] The hydrological random disturbance is divided into intervals and the frequency is calculated in each Markov state;

[0027] S103: New energy output modeling based on ARMA:

[0028] The aggregated new energy output series is tested for stationarity and processed for detrending and deseasonalizing.

[0029] Determine the order of AR and MA based on the autocorrelation function and partial autocorrelation function, fit the ARMA model, and extract the random characteristic parameters of renewable energy output;

[0030] S104: Coupling of runoff and renewable energy output

[0031] Cartesian product operation is performed on runoff white noise and renewable energy output deviation white noise to couple runoff and renewable energy output uncertainties.

[0032] As a preferred embodiment, in step S102, the specific method for constructing the runoff Markov chain is as follows: for the runoff, the deviation rate is used as the basis for dividing the Markov state, each state is represented by an integer, the deviation rate in the range of 0-25% is represented as state 1, the deviation rate in the range of 25%-50% is represented as state 2, the deviation rate in the range of 50%-75% is represented as state 3, and the deviation rate in the range of 75%-100% is represented as state 4, for a total of 12 stages; except for the first stage, each of the other stages contains 4 Markov states, for a total of 45 state nodes;

[0033] Each Markov state is divided into three groups of intervals using the equal-frequency binning method, and the white noise and the corresponding frequency in each group of intervals are calculated; based on the Markov state of each stage, the probability matrix of Markov state transition between stages is calculated.

[0034] As a preferred embodiment, in step S103, the specific method of modeling the new energy output based on ARMA is as follows: for the new energy output, ADF is used to test whether its historical data is stable; the data stability is improved by detrending and deseasonalizing; after the data stability test is passed, the autocorrelation function and partial autocorrelation function are used to determine the orders p and q of AR and MA, and based on p and q, the relevant parameters of the ARMA model are deduced again. The ARMA model can be specifically expressed as the following formula (1):

[0035] (1);

[0036] Where, represents the new energy output generated in period t; represents the AR term coefficient matrix; represents the new energy output generated in period t-1; represents the MA term; represents the constant term of the ARMA model; Indicates the time period. Formula (1) represents the formula for calculating the output of new energy using the ARMA model.

[0037] As a preferred embodiment, in step S2, a long-term complementary optimization model is constructed with the maximum long-term power generation of the cascade hydropower, wind power and solar power as the objective function, wherein the objective function is:

[0038] (2);

[0039] Where: are the indexes of time period and reservoir respectively; are the sets of time periods and reservoirs respectively; represents the output of reservoir i in period t; represents the power curtailment reservoir of reservoir i in period t; represents the output variable of renewable energy in period t; Indicates the time corresponding to time period t; represents the power curtailment penalty coefficient; Formula (2) represents the model optimization objective function.

[0040] As a preferred embodiment, in step S2, the constraints include:

[0041] Water balance constraints:

[0042] (3);

[0043] Where: , They represent the storage capacity of reservoir i in period t and period t+1 respectively, represents the inflow of reservoir i in period t, represents the outflow of reservoir i in period t, represents the evaporation of reservoir i in period t; represents the time corresponding to period t; formula (3) represents the water balance equation;

[0044] Outbound flow constraints:

[0045] (4);

[0046] Where: represents the power generation flow of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the outflow of reservoir i in period t; Formula (4) represents the outflow constraint;

[0047] Inbound flow constraints:

[0048] (5);

[0049] Where: represents the inflow of reservoir i in period t; represents the outflow of reservoir i-1 in period t; represents the natural runoff of reservoir i in period t; formula (5) represents the inflow constraint;

[0050] Hydropower output constraints:

[0051] (6);

[0052] Where: represents the output of reservoir i in period t; represents the power generation flow of reservoir i in period t; represents the water consumption rate of reservoir i in period t; Formula (6) represents the hydropower output constraint;

[0053] Power curtailment constraints:

[0054] (7);

[0055] Where: represents the power curtailment of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the water consumption rate of reservoir i in period t; Formula (7) represents the power curtailment constraint;

[0056] New energy output constraints:

[0057] (8);

[0058] Where: represents the output variable of renewable energy in period t; represents the new energy output generated in the tth period; Formula (8) represents the new energy output constraint;

[0059] Water abandonment constraints:

[0060] (9);

[0061] Where: represents the upper limit of storage capacity of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the storage capacity of reservoir i in period t; Formula (9) represents the water abandonment constraint;

[0062] Tie line channel constraints:

[0063] (10);

[0064] Where: Indicates the lower limit of the total output of water, wind and solar power, which is the minimum guaranteed output; It represents the upper limit of the total output of water, wind and solar power, which is the capacity of the interconnection line channel; represents the output variable of renewable energy in period t; represents the output of reservoir i in period t; formula (10) represents the tie line channel constraint;

[0065] Power generation flow limit:

[0066] (11);

[0067] Where: 、 represents the upper and lower limits of the power generation flow of reservoir i in period t; represents the power generation flow of reservoir i in period t; formula (11) represents the power generation flow limit;

[0068] Outbound traffic restrictions:

[0069] (12);

[0070] Where: 、 、 They represent the shipping demand, irrigation demand and drinking water demand of reservoir i in period t respectively; represents the upper limit of the outflow of reservoir i in period t; represents the outflow of reservoir i in period t; formula (12) represents the outflow limit;

[0071] Hydropower output limits:

[0072] (13);

[0073] Where: 、 represents the upper and lower limits of hydropower output of reservoir i in period t; represents the output of reservoir i in period t; formula (13) represents the hydropower output limit;

[0074] Storage capacity limit:

[0075] (14);

[0076] Where: 、 represents the upper and lower limits of the storage capacity of reservoir i in period t; represents the storage capacity of reservoir i in period t; formula (14) represents the storage capacity limit;

[0077] Initial and final storage capacity limits:

[0078] (15);

[0079] Where: is the initial storage capacity of reservoir i during the scheduling period; is the initial storage capacity constraint of reservoir i during the operation period; Formula (15) represents the initial storage capacity constraint;

[0080] (16);

[0081] Where: is the storage capacity of reservoir i in time period T; is the storage capacity of reservoir i at the end of the scheduling period; formula (16) represents the final storage capacity limit.

[0082] As a preferred embodiment, in step S2, the model is reconstructed and defined as follows:

[0083] (17);

[0084] (18);

[0085] (19);

[0086] (20);

[0087] (twenty one);

[0088] Where: and represent the state variables and control variables corresponding to time period t respectively; represents the expected function; represents the value function; represents the end-of-period energy storage control function; 、 、 and Represents the matrix related to the corresponding state variables and control variables; represents the feasible solution set of stage t; represents the random variable associated with the white noise of natural runoff and wind and solar power output, represents random disturbance; formula (17) represents the objective function after model reconstruction; formula (18) represents the unified expression of constraints related to state variables and control variables; formula (19) represents the unified expression of constraints related to control variables; formula (20) represents the feasible set; formula (21) represents the time index set.

[0089] As a preferred embodiment, in step S3, solving the optimal scheduling of cascade hydropower, wind power and solar power based on SDDP specifically includes the following steps:

[0090] S301: Based on the dynamic programming Bellman equation, the multi-stage stochastic programming problem is decomposed into stage recursive sub-problems:

[0091] (twenty two);

[0092] (twenty three);

[0093] Where: represents the state transition function corresponding to time period t-1; represents the value function; represents the expected function; represents the state transition function corresponding to time period t+1; and represent the state variables and control variables corresponding to time period t respectively; represents the feasible solution set for period t; represents the state variable corresponding to time period t-1; represents random disturbance; formula (22) represents the stage recursive equation; formula (23) represents the feasible set of state variables and decision variables.

[0094] The specific form is as follows: (twenty four);

[0095] (25);

[0096] (26);

[0097] (27);

[0098] (28);

[0099] (29);

[0100] Where: represents the state transition function; represents the storage capacity in period t; represents the new energy output in period t; represents the random disturbance of renewable energy output in period t, which is also the MA term; represents the power abandonment penalty coefficient; Indicates the number of states in the t+1 period; Represent the Markov states of adjacent stages respectively; represents the output of reservoir i in period t+1; represents the power curtailment reservoir of reservoir i in period t+1; represents the output variable of renewable energy in period t+1; Indicates the time corresponding to time period t; represents the state transition probability; represents the state transition function in time period t+1; represents the constant term in the ARMA model. The goal of formula (24) is to maximize the combined benefits of hydropower, wind power, and solar power, including both current-stage revenue and future expected combined revenue. Current-stage revenue comes from hydropower, wind power, and solar power generation, while any excess over the power generation cap is considered a penalty. Future expected costs are represented by the weighted sum of the Markov states in the next stage, which is gradually constrained by Benders cuts. Indicates the slave state Transfer to state The probability of . Formula (25) represents the formula for calculating the output of new energy by the ARMA model. Formula (26) represents the MA term formula. The ARMA model fitted in this invention is a three-order model, that is, the output of new energy in the current stage is related to the previous three stages, that is, and , and Represents the coefficient matrix of AR terms and MA terms; and They represent the coefficients in the AR term coefficient matrix and the MA term coefficient matrix respectively. Formulas (27) and (28) represent and The coefficient matrix of . , represents the dual variable of the corresponding constraint; Represents the dual variable of the water balance equation; , They represent the storage capacity of reservoir i in period t and period t+1 respectively, represents the inflow of reservoir i in period t, represents the outflow of reservoir i in period t, represents the evaporation of reservoir i in period t; represents the time corresponding to period t. Formula (29) represents the water balance equation.

[0101] S302: Using disjunctive programming by introducing binary auxiliary variables Convert it into a linear constraint as follows:

[0102] (30);

[0103] (31);

[0104] (32);

[0105] (33);

[0106] Where: represents the upper limit of the water discharge of reservoir i in period t; 、 represents the upper and lower limits of the storage capacity of reservoir i in period t; represents the storage capacity of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the auxiliary variable of water abandonment of reservoir i in period t; formula (30) indicates that water abandonment occurs when the storage capacity is greater than the upper limit of storage capacity; formula (31) indicates that the storage capacity is less than the upper limit of storage capacity; formula (32) indicates that the water abandonment is less than the upper limit of water abandonment; formula (33) indicates that the auxiliary variable of water abandonment is a binary variable.

[0107] S303: Linear relaxation is used to construct Benders cuts, which are divided into two categories based on the time scale. The first 11 stages are represented by formula (35), which includes the storage capacity term. , New energy output items , New energy output white noise item and the constant term ; The 12th stage is formula (36), which includes the end-of-period energy storage term , as follows:

[0108] (34);

[0109] (35);

[0110] (36);

[0111] (37);

[0112] (38);

[0113] Where, represents the state transition function in time period t+1; represents the storage capacity at the t+1 period; represents the new energy output in the t+1 period; represents the random disturbance of renewable energy output in the t+1 period; represents the comprehensive benefits in time period t; represents the storage capacity of the i-th reservoir in time period t+1; represents the random disturbance of renewable energy output in the cth iteration at time period t+1; represents the penalty coefficient; Reservoir, Indicates the time period, and represent the Markov states of adjacent stages, represents the number of iterations, represents the state transition probability, Indicates the number of states, represents the dual variable of the water balance equation, represents the dual variable of the new energy output constraint, The dual variable representing the random disturbance constraint of renewable energy output; is the constant term for comprehensive benefit calculation; represents the comprehensive benefits in time period T; represents the auxiliary variable of storage capacity at the end of the period; represents the water consumption rate of reservoir i in period t; is the storage capacity of reservoir i in time period T; represents the storage capacity of reservoir i at the end of the operation period; formula (34) represents the recursive formula after the conversion; formula (35) represents the storage capacity of reservoir i at the end of the operation period; formula (35) represents the recursive formula after the conversion ... Calculation method; Formula (36) represents the 12th stage The calculation method of storage capacity auxiliary variables is shown in formula (37); formula (38) represents the definition of storage capacity auxiliary variables.

[0114] S304: Using a combination of forward simulation and reverse recursion, the water-wind-solar scheduling scheme is continuously updated iteratively until convergence is achieved or the preset number of iterations is reached, thereby obtaining a long-term complementary scheduling rule for cascaded water-wind-solar that takes into account multiple uncertainties.

[0115] As a preferred implementation method, in step S304, the specific process of the forward simulation is: randomly sampling multiple groups of uncertainty scenarios based on the constructed Markov chain and ARMA model, inputting each group of randomly sampled scenarios forward stage by stage, optimizing and deducing the state variables of each stage, and forming a state trajectory.

[0116] As a preferred embodiment, the specific process of the reverse recursion in step S304 is as follows: based on the state variables of each stage in each group of randomly sampled random scenarios obtained by the forward simulation, Benders cuts related to storage capacity, new energy output, new energy output white noise, and end-of-period energy storage are constructed. The Benders cuts in the Tth stage can be calculated by formula (36), and those from the 2nd to T-1st stage can be calculated by formula (35). The above forward simulation and reverse recursive process are continuously iterated until the stopping criterion is met. Here, the maximum number of iterations is selected as 6000 as the stopping criterion.

[0117] Compared with the prior art, the present invention has the following beneficial effects:

[0118] First, the optimization method of the long-term complementary scheduling rules of cascade water, wind and solar power under multiple uncertainties of the present invention constructs a water, wind and solar power multiple uncertainty quantification method based on Markov chain and ARMA, fully considers the multiple uncertainties of water and wind power in the long-term complementary scheduling model of cascade water, wind and solar power, and uses the SDDP-based cascade water, wind and solar power optimization scheduling algorithm for efficient solution, and derives the long-term scheduling rules of cascade water, wind and solar power considering the uncertainties of water and wind power, providing an efficient and reliable control and solution method for the long-term coordinated operation of cascade water, wind and solar power.

[0119] Secondly, the present invention divides the monthly runoff state through Markov chain and adopts ARMA model to describe the random process of wind and solar power output, so that the long-term randomness of runoff and wind and solar power output can be quantified in detail. The Markov state transition probability and ARMA white noise are introduced into the subsequent scheduling model, so as to more realistically simulate the fluctuation of water, wind and solar power output at various stages and improve the accuracy of the characterization of randomness.

[0120] Third, the present invention establishes a long-term complementary scheduling model, which integrates the storage capacity evolution of cascade reservoirs, outflow flow, and the upper and lower limits of wind and solar power station output into the objective function and constraints; it takes into account the complementarity of hydropower and wind and solar power output, and sets penalties for abandoning water and power, so as to maximize the use of clean energy and reduce resource waste.

[0121] Fourthly, the present invention uses the SDDP (Stochastic Dual Dynamic Programming) algorithm to split large-scale multi-stage stochastic programming problems into recursively solvable sub-problems; through forward simulation, runoff and wind and solar power output are input sequentially under different random scenarios, and Benders cuts are constructed based on dual information during backward recursion, which greatly improves the solution efficiency and continuously approaches the optimal solution; it solves the problem of difficulty in directly solving large-scale nonlinear models, making it possible to optimize scheduling on a monthly or longer time scale.

[0122] Fifth, the present invention takes into account both operational safety and economy. While maximizing the total power generation of hydropower, wind power and solar power, the objective function incorporates a penalty item for power abandonment to avoid excessive concentration or waste of system output, thereby improving the economic benefits of system operation.

[0123] Sixth, through dynamic control of abandoned water and power, the present invention can more reasonably carry out scheduling in scenarios with abundant water inflow or abundant wind and solar resources, thereby reducing unnecessary flow or power losses; when there is a large deviation in random water inflow or wind and solar output, the multi-stage dynamic planning strategy can also timely adjust the scheduling plan of each cascade reservoir to better deal with uncertainty and maintain smooth operation of the system.

[0124] In summary, the method of the present invention forms a close connection between the quantification of multiple uncertainties in hydropower, wind and solar power, the construction of long-term scheduling models and the efficient solution of SDDP. It can also make optimal decisions based on scheduling rules on a monthly or annual scale, thereby achieving coordination and complementarity between cascaded hydropower and wind and solar power generation. It has the significant beneficial effects of improving the utilization rate of renewable energy, taking into account various needs and achieving efficient solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0125] Figure 1 This is a flow chart of the solution of the cascade hydropower, wind-solar power optimization scheduling algorithm based on SDDP of the present invention;

[0126] Figure 2 is a diagram of a runoff process that obeys a Markov chain;

[0127] Figure 3 This is the new energy output process diagram simulated by ARMA;

[0128] Figure 4 This is the COM water, wind and light monthly output process diagram;

[0129] Figure 5 This is the IOM water, wind and solar monthly output process diagram;

[0130] Figure 6 It is the water level map of reservoir A for long-term complementary operation of water, wind and solar power;

[0131] Figure 7It is the water level map of reservoir B for long-term complementary operation of water, wind and solar power;

[0132] Figure 8 This is a monthly average utilization rate diagram of 1,000 simulated scheduling channels based on water, wind and solar scheduling rules;

[0133] Figure 9 It is a high utilization rate diagram of 1000 sets of simulated scheduling channels based on water, wind and solar scheduling rules;

[0134] Figure 10 It is a monthly total power curtailment graph of 1,000 simulated dispatches based on the water, wind, and solar dispatch rules;

[0135] Figure 11 It is a graph of power curtailment of each power source in each month based on 1000 sets of simulated scheduling rules for water, wind and solar power. DETAILED DESCRIPTION

[0136] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0137] It should be understood that the drawings are for illustrative purposes only and are not to be construed as limiting the present invention. To better illustrate the present embodiment, some components in the drawings may be omitted, enlarged, or reduced in size, and do not represent actual product dimensions. Those skilled in the art will understand that some well-known structures and their descriptions may be omitted from the drawings. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present invention.

[0138] A method for optimizing the long-term complementary scheduling rules of cascade hydropower, wind-solar power plants under multiple uncertainties includes quantifying the multiple uncertainties of hydropower, wind-solar power plants, constructing a long-term complementary scheduling model for cascade hydropower, wind-solar power plants, and solving the optimal scheduling of cascade hydropower, wind-solar power plants based on SDDP. The specific steps are as follows:

[0139] S1: Quantification method of multiple uncertainties of water, wind and solar power

[0140] To describe the long-term random processes of runoff and wind and solar power output, a multi-uncertainty quantification method for water, wind and solar power based on Markov chains and ARMA is constructed. The specific steps are as follows:

[0141] S101: Collect monthly historical data on runoff and wind and solar power output. Based on the historical data, first, calculate the deviation between the historical runoff and the historical runoff mean, and calculate the deviation rate of the historical data for each month. Second, aggregate the wind and solar power stations into new energy power stations and calculate the historical output of the aggregated new energy.

[0142] S102: For runoff, the deviation rate is used as the basis for dividing the Markov state. Each state is represented by an integer. Specifically, the deviation rate in the range of 0-25% is represented as state 1, the deviation rate in the range of 25%-50% is represented as state 2, the deviation rate in the range of 50%-75% is represented as state 3, and the deviation rate in the range of 75%-100% is represented as state 4. That is, there are 12 stages in total. Except for the first stage, each stage contains 4 Markov states, with a total of 45 state nodes; for each Markov state, the equal frequency binning method is used to divide it into 3 groups of intervals, and the white noise and the corresponding frequency in each group of intervals are calculated; based on the Markov states of each stage, the probability matrix of the Markov state transition between stages is calculated;

[0143] S103: For renewable energy output, ADF is used to test whether its historical data is stable. Regardless of whether it is stable or not, the data stability can be improved by removing trends and seasonality. After the data stability test is passed, the autocorrelation function and partial autocorrelation function are used to determine the AR and MA orders p and q. Based on p and q, the relevant parameters of the ARMA model are deduced again. Therefore, the ARMA model can be specifically expressed as formula (1).

[0144] (1);

[0145] Where, represents the new energy output generated in period t; represents the AR term coefficient matrix; represents the new energy output generated in period t-1; represents the MA term; represents the constant term of the ARMA model; Indicates the time period. Formula (1) represents the formula for calculating the output of new energy using the ARMA model.

[0146] S104: Performing a Cartesian product operation on the runoff white noise and the renewable energy output deviation white noise to couple the runoff and renewable energy output uncertainties.

[0147] S2: Construction of a long-term complementary scheduling model for cascaded hydropower, wind power, and solar power

[0148] S201: Original model

[0149] On the basis of satisfying the operation constraints of cascade hydropower stations, a long-term complementary optimization model is constructed with the maximum long-term power generation of cascade hydropower, wind power and solar power as the objective function:

[0150] The objective function is as follows:

[0151] (2);

[0152] Where: are the indexes of time period and reservoir respectively; are the sets of time periods and reservoirs respectively; represents the output of reservoir i in period t; represents the power curtailment reservoir of reservoir i in period t; represents the output variable of renewable energy in period t; Indicates the time corresponding to time period t; represents the power curtailment penalty coefficient; Formula (2) represents the model optimization objective function.

[0153] Constraints include:

[0154] (3);

[0155] Where: , They represent the storage capacity of reservoir i in period t and period t+1 respectively, represents the inflow of reservoir i in period t, represents the outflow of reservoir i in period t, represents the evaporation of reservoir i in period t; represents the time corresponding to period t; formula (3) represents the water balance equation;

[0156] Outbound flow constraints:

[0157] (4);

[0158] Where: represents the power generation flow of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the outflow of reservoir i in period t; Formula (4) represents the outflow constraint;

[0159] Inbound flow constraints:

[0160] (5);

[0161] Where: represents the inflow of reservoir i in period t; represents the outflow of reservoir i-1 in period t; represents the natural runoff of reservoir i in period t; formula (5) represents the inflow constraint;

[0162] Hydropower output constraints:

[0163] (6);

[0164] Where: represents the output of reservoir i in period t; represents the power generation flow of reservoir i in period t; represents the water consumption rate of reservoir i in period t; Formula (6) represents the hydropower output constraint;

[0165] Power curtailment constraints:

[0166] (7);

[0167] Where: represents the power curtailment of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the water consumption rate of reservoir i in period t; Formula (7) represents the power curtailment constraint;

[0168] New energy output constraints:

[0169] (8);

[0170] Where: represents the output variable of renewable energy in period t; represents the new energy output generated in the tth period; Formula (8) represents the new energy output constraint;

[0171] Water abandonment constraints:

[0172] (9);

[0173] Where: represents the upper limit of storage capacity of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the storage capacity of reservoir i in period t; Formula (9) represents the water abandonment constraint;

[0174] Tie line channel constraints:

[0175] (10);

[0176] Where: Indicates the lower limit of the total output of water, wind and solar power, which is the minimum guaranteed output; It represents the upper limit of the total output of water, wind and solar power, which is the capacity of the interconnection line channel; represents the output variable of renewable energy in period t; represents the output of reservoir i in period t; formula (10) represents the tie line channel constraint;

[0177] Power generation flow limit:

[0178] (11);

[0179] Where: 、 represents the upper and lower limits of the power generation flow of reservoir i in period t; represents the power generation flow of reservoir i in period t; formula (11) represents the power generation flow limit;

[0180] Outbound traffic restrictions:

[0181] (12);

[0182] Where: 、 、 They represent the shipping demand, irrigation demand and drinking water demand of reservoir i in period t respectively; represents the upper limit of the outflow of reservoir i in period t; represents the outflow of reservoir i in period t; formula (12) represents the outflow limit;

[0183] Hydropower output limits:

[0184] (13);

[0185] Where: 、 represents the upper and lower limits of hydropower output of reservoir i in period t; represents the output of reservoir i in period t; formula (13) represents the hydropower output limit;

[0186] Storage capacity limit:

[0187] (14);

[0188] Where: 、 represents the upper and lower limits of the storage capacity of reservoir i in period t; represents the storage capacity of reservoir i in period t; formula (14) represents the storage capacity limit;

[0189] Initial and final storage capacity limits:

[0190] (15);

[0191] Where: is the initial storage capacity of reservoir i during the scheduling period; is the initial storage capacity constraint of reservoir i during the operation period; Formula (15) represents the initial storage capacity constraint;

[0192] (16);

[0193] Where: is the storage capacity of reservoir i in time period T; is the storage capacity of reservoir i at the end of the scheduling period; formula (16) represents the final storage capacity limit.

[0194] S202: Model reconstruction

[0195] The above model can be defined as formulas (17)-(21):

[0196] (17);

[0197] (18);

[0198] (19);

[0199] (20);

[0200] (twenty one);

[0201] Where: and represent the state variables and control variables corresponding to time period t respectively; represents the expected function; represents the value function; represents the end-of-period energy storage control function; 、 、 and Represents the matrix related to the corresponding state variables and control variables; represents the feasible solution set of stage t; represents the random variable associated with the white noise of natural runoff and wind and solar power output, represents random disturbance; formula (17) represents the objective function after model reconstruction; formula (18) represents the unified expression of constraints related to state variables and control variables; formula (19) represents the unified expression of constraints related to control variables; formula (20) represents the feasible set; formula (21) represents the time index set.

[0202] S3: Optimal Scheduling Solution for Cascaded Hydropower, Wind-Solar Systems Based on SDDP

[0203] The above problem is essentially a multi-stage stochastic programming problem. To solve it efficiently, this paper proposes a cascade hydropower, wind power and solar power optimization scheduling algorithm based on SDDP for efficient solution. The specific steps are as follows:

[0204] S301: Based on the dynamic programming Bellman equation, the multi-stage stochastic programming problem is decomposed into a stage recursive problem:

[0205] (twenty two);

[0206] (twenty three);

[0207] Where: represents the state transition function corresponding to time period t-1; represents the value function; represents the expected function; represents the state transition function corresponding to time period t+1; and represent the state variables and control variables corresponding to time period t respectively; represents the feasible solution set for period t; represents the state variable corresponding to time period t-1; represents random disturbance; formula (22) represents the stage recursive equation; formula (23) represents the feasible set of state variables and decision variables.

[0208] The specific form is as follows: (twenty four);

[0209] (25);

[0210] (26);

[0211] (27);

[0212] (28);

[0213] (29);

[0214] Where: represents the state transition function; represents the storage capacity in period t; represents the new energy output in period t; represents the random disturbance of renewable energy output in period t, which is also the MA term; represents the power abandonment penalty coefficient; Indicates the number of states in the t+1 period; Represent the Markov states of adjacent stages respectively; represents the output of reservoir i in period t+1; represents the power curtailment reservoir of reservoir i in period t+1; represents the output variable of renewable energy in period t+1; Indicates the time corresponding to time period t; represents the state transition probability; represents the state transition function in time period t+1; represents the constant term in the ARMA model. The goal of formula (24) is to maximize the combined benefits of hydropower, wind power, and solar power, including both current-stage revenue and future expected combined revenue. Current-stage revenue comes from hydropower, wind power, and solar power generation, while any excess over the power generation cap is considered a penalty. Future expected costs are represented by the weighted sum of the Markov states in the next stage, which is gradually constrained by Benders cuts. Indicates the slave state Transfer to state The probability of . Formula (25) represents the formula for calculating the output of new energy by the ARMA model. Formula (26) represents the MA term formula. The ARMA model fitted in this invention is a three-order model, that is, the output of new energy in the current stage is related to the previous three stages, that is, and , Respectively represent the new energy output of the previous period, the new energy output of the previous two periods, and the new energy output of the previous three periods. They represent the random disturbance of new energy output in the previous stage, the random disturbance of new energy output in the first two stages, and the random disturbance of new energy output in the first three stages respectively; and Represents the coefficient matrix of AR terms and MA terms; and They represent the coefficients in the AR term coefficient matrix and the MA term coefficient matrix respectively. Formulas (27) and (28) represent and The coefficient matrix of . , represents the dual variable of the corresponding constraint; Represents the dual variable of the water balance equation; , They represent the storage capacity of reservoir i in period t and period t+1 respectively, represents the inflow of reservoir i in period t, represents the outflow of reservoir i in period t, represents the evaporation of reservoir i in period t; represents the time corresponding to period t. Formula (29) represents the water balance equation.

[0215] S302: The recursive problem in the above stage contains nonlinear constraints related to water abandonment, which is difficult to solve directly. Therefore, disjunctive programming is used here to introduce binary auxiliary variables. Convert it into a linear constraint as follows:

[0216] (30);

[0217] (31);

[0218] (32);

[0219] (33);

[0220] Where: represents the upper limit of the water discharge of reservoir i in period t; 、 represents the upper and lower limits of the storage capacity of reservoir i in period t; represents the storage capacity of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the auxiliary variable of water abandonment of reservoir i in period t; formula (30) indicates that water abandonment occurs when the storage capacity is greater than the upper limit of storage capacity; formula (31) indicates that the storage capacity is less than the upper limit of storage capacity; formula (32) indicates that the water abandonment is less than the upper limit of water abandonment; formula (33) indicates that the auxiliary variable of water abandonment is a binary variable.

[0221] S303: Linear relaxation is used to construct Benders cuts, which are divided into two categories based on the time scale. The first 11 stages are represented by formula (35), which includes the storage capacity term. , New energy output items , New energy output white noise item and the constant term ; The 12th stage is formula (36), which includes the end-of-period energy storage term , as follows:

[0222] (34);

[0223] (35);

[0224] (36);

[0225] (37);

[0226] (38);

[0227] Where, represents the state transition function in time period t+1; represents the storage capacity at the t+1 period; represents the new energy output in the t+1 period; represents the random disturbance of renewable energy output in the t+1 period; represents the comprehensive benefits in time period t; represents the storage capacity of the i-th reservoir in time period t+1; represents the random disturbance of renewable energy output in the cth iteration at time period t+1; represents the penalty coefficient; Reservoir, Indicates the time period, and represent the Markov states of adjacent stages, represents the number of iterations, represents the state transition probability, Indicates the number of states, represents the dual variable of the water balance equation, represents the dual variable of the new energy output constraint, The dual variable representing the random disturbance constraint of renewable energy output; is the constant term for comprehensive benefit calculation; represents the comprehensive benefits in time period T; represents the auxiliary variable of storage capacity at the end of the period; represents the water consumption rate of reservoir i in period t; is the storage capacity of reservoir i in time period T; represents the storage capacity of reservoir i at the end of the operation period; formula (34) represents the recursive formula after the conversion; formula (35) represents the storage capacity of reservoir i at the end of the operation period; formula (35) represents the recursive formula after the conversion ... Calculation method; Formula (36) represents the 12th stage The calculation method of storage capacity auxiliary variables is shown in formula (37); formula (38) represents the definition of storage capacity auxiliary variables.

[0228] S304: After steps S301-303, the original model is converted into a stage recursive linear model, and then forward simulation and reverse recursive iterative optimization are used. The forward simulation process is: based on the constructed Markov chain and ARMA model, randomly sample multiple groups of uncertainty scenarios, input each group of randomly sampled scenarios forward stage by stage, optimize and deduce the state variables of each stage, and form a state trajectory; for reverse recursion, it constructs the Benders cuts related to storage capacity, new energy output, new energy output white noise and end-of-period energy storage based on the state variables of each stage under each group of randomly sampled random scenarios obtained by forward simulation. The Benders cuts in the Tth stage can be calculated by formula (36), and the 2nd to T-1 stages are calculated by formula (35); the above forward simulation and reverse recursive process are continuously iterated until the stopping criterion is met. Here, the maximum number of iterations is 6000 times as the stopping criterion. The above solution steps are as follows: Figure 1 shown.

[0229] Example:

[0230] (1) Project background and parameter setting

[0231] This example uses a cascade water, wind and solar integrated base in southwest my country as an example to verify the effectiveness of the proposed method. The water, wind and solar integrated base includes two hydropower stations, namely reservoir A and reservoir B, with a total installed capacity of 10,050 MW, nearly 4,000 MW of wind and solar power stations built, and an external transmission channel capacity of 10,000 MW. Based on the above engineering background, the method proposed in the present invention is applied to verify its effectiveness and feasibility. The runoff and new energy output simulated by the proposed water, wind and solar multiple uncertainty method are as follows: Figure 2 and Figure 3 As shown in the figure, it is used as the input of the long-term complementary scheduling model of cascade hydropower, wind power and solar power. The target end-of-period water levels of the two hydropower stations are set to 1230 m and 801 m, respectively. The SDDP-based cascade hydropower, wind power and solar power optimization scheduling algorithm is used for efficient solution. The algorithm is developed using JuMP in Julia and solved using Gurobi10.0. All programs are run on a personal computer with an Intel Corei7-9750H CPU equipped with a 2.6GHz CPU and 16.0RAM. The iteration is terminated when the number of iterations reaches 6000.

[0232] (2) Results analysis

[0233] In order to compare the effectiveness of the proposed water-wind-solar complementary scheduling model (COM), the water-wind-solar uncoordinated scheduling model (IOM) was set as a comparison model, and the scheduling rules derived from the two models were applied to the scheduling simulation to verify the advantages and disadvantages of the scheduling rules. The scheduling simulation scenarios were set to 1000 groups, that is, 1000 groups of runoff and renewable energy output scenarios were randomly generated as the scheduling rule input for scheduling simulation. Figure 4-Figure 7 The long-term output and water level processes of hydropower, wind power, and solar power are shown, which are typical scenarios in the 1,000 sets of scheduling simulation results. It can be seen from the figure that there is no power curtailment in the proposed model COM, while there is power curtailment in IOM in April, June, and September-November. The water level process corresponds to the output process, which shows that the proposed model can give full play to the temporal and spatial complementarity of hydropower, wind power, and solar power to reduce power curtailment and promote the consumption of new energy. Figure 8-Figure 9 and Figure 10-11 The channel utilization and overall power curtailment of 1000 groups of scheduling simulation results are shown respectively. Figure 8 It can be seen that the monthly outbound channel utilization of the proposed COM model is generally higher than that of the IOM model. For example, from May to July, the average channel utilization increased by 4.5%, 7.5%, and 4.9%, respectively; Figure 9 The results of 1000 scheduling simulations are shown in the scenario where the channel utilization rate in each month reaches 80%-100%. From the figure, we can see that COM always includes IOM, that is, it is always higher than the IOM model. Figure 10 and Figure 11It can be seen that in the 1000 sets of simulation scheduling results, the COM power curtailment is always lower than that of the IOM model.

[0234] Overall, the model can derive long-term complementary scheduling rules for water, wind and solar power that take into account the multiple uncertainties of water, wind and solar power, and can fully tap the temporal and spatial complementarity potential of water, wind and solar power, reduce the curtailment of new energy, promote the efficient consumption of new energy, and ensure the efficient operation of the water, wind and solar power integrated energy base.

[0235] The above embodiments are merely illustrative of the technical solutions of the present invention. The present invention is not limited to the contents described in the above embodiments, but is subject to the scope defined by the claims. Any modifications, supplements, or equivalent substitutions made by those skilled in the art based on these embodiments are within the scope of protection claimed in the claims of the present invention.

Claims

1. A method for optimizing the long-term complementary scheduling rules of cascade hydropower, wind power, and solar power under multiple uncertainties, characterized by: The steps include: S1: Quantification of multiple uncertainties in water, wind, and solar power Collect historical monthly data on runoff and wind and solar power output; The deviation rate is used to divide the Markov chain state and form a multi-stage and multi-state state transition probability matrix for runoff. The stationarity test of the aggregated wind and solar power station output series is conducted, and its random process is fitted based on the ARMA model; The white noise of runoff and wind and solar power output is coupled through Cartesian product to obtain a random scenario that can describe the uncertainty of the joint effects of water, wind and solar power. S2: Construction of a long-term complementary scheduling model for cascaded hydropower, wind power, and solar power On the basis of satisfying the operating constraints of cascade hydropower stations, a long-term complementary optimization model is constructed with the maximum long-term power generation of cascade hydropower, wind power and solar power as the objective function, and the model is reconstructed. S3: Optimal Scheduling Solution for Cascaded Hydropower, Wind-Solar Systems Based on SDDP Based on the dynamic programming Bellman equation, the multi-stage stochastic programming problem is decomposed into stage recursive sub-problems; Disjunctive programming is used to transform it into linear constraints by introducing binary auxiliary variables; Linear relaxation is used to construct the Bendes cutting plane and converted into a staged recursive linear model; By combining forward simulation with backward recursion, the water-wind-solar scheduling scheme is continuously iterated and updated until convergence is met or the preset number of iterations is reached, thus obtaining a long-term complementary scheduling rule for cascaded water-wind-solar that takes into account multiple uncertainties.

2. The method for optimizing long-term complementary scheduling rules for cascaded hydropower, wind power, and solar power under multiple uncertainties according to claim 1 is characterized by: In step S1, the quantification of multiple uncertainties of water, wind and solar power specifically includes the following steps: S101: Historical data collection and preprocessing Collect monthly historical data on runoff and wind and solar power output; based on the historical data, calculate the deviation between the historical runoff and the historical runoff mean, and calculate the deviation rate of the historical data for each month; Aggregate wind and solar power stations into new energy power stations and calculate the historical output of new energy after aggregation; S102: Constructing a Runoff Markov Chain Based on the runoff deviation rate, several Markov states are divided, and the state transitions of adjacent stages are statistically analyzed to form a state transition probability matrix; The hydrological random disturbance is divided into intervals and the frequency is calculated in each Markov state; S103: New energy output modeling based on ARMA: The aggregated new energy output series is tested for stationarity and processed for detrending and deseasonalizing. Determine the order of AR and MA based on the autocorrelation function and partial autocorrelation function, fit the ARMA model, and extract the random characteristic parameters of renewable energy output; S104: Coupling of runoff and renewable energy output Cartesian product operation is performed on runoff white noise and renewable energy output deviation white noise to couple runoff and renewable energy output uncertainties.

3. The method for optimizing long-term complementary scheduling rules for cascaded hydropower, wind power, and solar power under multiple uncertainties according to claim 2 is characterized by: In step S102, the specific method for constructing the runoff Markov chain is as follows: for the runoff, the deviation rate is used as the basis for dividing the Markov state, and each state is represented by an integer. The deviation rate in the range of 0-25% is represented as state 1, the deviation rate in the range of 25%-50% is represented as state 2, the deviation rate in the range of 50%-75% is represented as state 3, and the deviation rate in the range of 75%-100% is represented as state 4, for a total of 12 stages; Each Markov state is divided into three groups of intervals using the equal-frequency binning method, and the white noise and the corresponding frequency in each group of intervals are calculated; based on the Markov state of each stage, the probability matrix of Markov state transition between stages is calculated.

4. The method for optimizing long-term complementary scheduling rules for cascaded hydropower, wind power, and solar power under multiple uncertainties according to claim 3 is characterized by: In step S103, the specific method of the new energy output modeling based on ARMA is as follows: for the new energy output, the ADF is used to test whether its historical data is stable; the data stability is improved by removing the trend and seasonality; after the data stability test passes, the autocorrelation function and the partial autocorrelation function are used to determine the AR and MA orders p and q, and based on p and q, the relevant parameters of the ARMA model are deduced again. The ARMA model can be specifically expressed as the following formula: ； Where, represents the new energy output generated in period t; represents the AR term coefficient matrix; represents the new energy output generated in period t-1; represents the MA term; represents the constant term of the ARMA model; Indicates time period.

5. The method for optimizing long-term complementary scheduling rules for cascaded hydropower, wind power, and solar power under multiple uncertainties according to claim 4 is characterized by: In step S2, a long-term complementary optimization model is constructed with the maximum long-term power generation of the cascade hydropower, wind power and solar power as the objective function, wherein the objective function is: ； Where: are the indexes of time period and reservoir respectively; are the sets of time periods and reservoirs respectively; represents the output of reservoir i in period t; represents the power curtailment reservoir of reservoir i in period t; represents the output variable of renewable energy in period t; Indicates the time corresponding to time period t; Represents the power curtailment penalty coefficient.

6. The method for optimizing long-term complementary scheduling rules for cascaded hydropower, wind power, and solar power under multiple uncertainties according to claim 5 is characterized by: In step S2, the constraints include: Water balance constraints: ； Where: , They represent the storage capacity of reservoir i in period t and period t+1 respectively, represents the inflow of reservoir i in period t, represents the outflow of reservoir i in period t, represents the evaporation of reservoir i in period t; Indicates the time corresponding to time period t; Outbound flow constraints: ； Where: represents the power generation flow of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the outflow of reservoir i in period t; Inbound flow constraints: ； Where: represents the inflow of reservoir i in period t; represents the outflow of reservoir i-1 in period t; represents the natural runoff of reservoir i in period t; Hydropower output constraints: ； Where: represents the output of reservoir i in period t; represents the power generation flow of reservoir i in period t; represents the water consumption rate of reservoir i in period t; Power curtailment constraints: ； Where: represents the power curtailment of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the water consumption rate of reservoir i in period t; New energy output constraints: ； Where: represents the output variable of renewable energy in period t; represents the new energy output generated in period t; Water abandonment constraints: ； Where: represents the upper limit of storage capacity of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the storage capacity of reservoir i in period t; Tie line channel constraints: ； Where: Indicates the lower limit of the total output of water, wind and solar power, which is the minimum guaranteed output; It represents the upper limit of the total output of water, wind and solar power, which is the capacity of the interconnection line channel; represents the output variable of renewable energy in period t; represents the output of reservoir i in period t; Power generation flow limit: ； Where: 、 represents the upper and lower limits of the power generation flow of reservoir i in period t; represents the power generation flow of reservoir i in period t; Outbound traffic restrictions: ； Where: 、 、 They represent the shipping demand, irrigation demand and drinking water demand of reservoir i in period t respectively; represents the upper limit of the outflow of reservoir i in period t; represents the outflow of reservoir i in period t; Hydropower output limits: ； Where: 、 represents the upper and lower limits of hydropower output of reservoir i in period t; represents the output of reservoir i in period t; Storage capacity limit: ； Where: 、 represents the upper and lower limits of the storage capacity of reservoir i in period t; represents the storage capacity of reservoir i in period t; Initial and final storage capacity limits: ； Where: is the initial storage capacity of reservoir i during the scheduling period; is the initial storage capacity constraint of reservoir i during the operation period; ； Where: is the storage capacity of reservoir i in time period T; is the storage capacity of reservoir i at the end of the scheduling period.

7. The method for optimizing long-term complementary scheduling rules for cascaded hydropower, wind power, and solar power under multiple uncertainties according to claim 6 is characterized by: In step S2, the model is reconstructed and defined as follows: ； ； ； ； ； Where: and represent the state variables and control variables corresponding to time period t respectively; represents the expected function; represents the value function; represents the end-of-period energy storage control function; 、 、 and Represents the matrix related to the corresponding state variables and control variables; represents the feasible solution set of stage t; represents the random variable associated with the white noise of natural runoff and wind and solar power output, represents a random disturbance.

8. The method for optimizing long-term complementary scheduling rules for cascade hydropower, wind power, and solar power under multiple uncertainties according to any one of claims 1 to 7, characterized in that: In step S3, the SDDP-based optimal scheduling solution for cascaded hydropower, wind power, and solar power systems specifically includes the following steps: S301: Based on the dynamic programming Bellman equation, the multi-stage stochastic programming problem is decomposed into stage recursive sub-problems: ； ； Where: represents the state transition function corresponding to time period t-1; represents the value function; represents the expected function; represents the state transition function corresponding to time period t+1; and represent the state variables and control variables corresponding to time period t respectively; represents the feasible solution set for period t; represents the state variable corresponding to time period t-1; represents random disturbance; The specific form is as follows: ； ； ； ； ； ； Where: represents the state transition function; represents the storage capacity in period t; represents the new energy output in period t; represents the random disturbance of renewable energy output in period t; represents the power abandonment penalty coefficient; Indicates the number of states in the t+1 period; Represent the Markov states of adjacent stages respectively; represents the output of reservoir i in period t+1; represents the power curtailment reservoir of reservoir i in period t+1; represents the output variable of renewable energy in period t+1; Indicates the time corresponding to time period t; represents the state transition probability; represents the state transition function in time period t+1; represents the constant term of the ARMA model; Indicates the slave state Transfer to state probability; and Represents the coefficient matrix of AR terms and MA terms; and Represent the coefficients in the AR term coefficient matrix and the MA term coefficient matrix respectively; Represents the dual variable of the water balance equation; , They represent the storage capacity of reservoir i in period t and period t+1 respectively, represents the inflow of reservoir i in period t, represents the outflow of reservoir i in period t, represents the evaporation of reservoir i in period t; Indicates the time corresponding to time period t; S302: Using disjunctive programming by introducing binary auxiliary variables Convert it into a linear constraint as follows: ； ； ； ； Where: represents the upper limit of the water discharge of reservoir i in period t; 、 represents the upper and lower limits of the storage capacity of reservoir i in period t; represents the storage capacity of reservoir i in period t; represents the water discharge of reservoir i in period t; represents the auxiliary variable of water abandonment of reservoir i in period t; S303: Use linear relaxation to construct the Bendes cutting plane, which is divided into two categories based on the time scale. The first 11 stages include the storage capacity term. , New energy output items , New energy output white noise item and the constant term ; Stage 12 includes the final energy storage item , as follows: ； ； ； ； ； Where, represents the state transition function in time period t+1; represents the storage capacity at the t+1 period; represents the new energy output in the t+1 period; represents the random disturbance of renewable energy output in the t+1 period; represents the comprehensive benefits in time period t; represents the storage capacity of the i-th reservoir in time period t+1; represents the random disturbance of renewable energy output in the cth iteration at time period t+1; represents the penalty coefficient; Reservoir, Indicates the time period, and represent the Markov states of adjacent stages, represents the number of iterations, represents the state transition probability, Indicates the number of states, represents the dual variable of the water balance equation, represents the dual variable of the new energy output constraint, The dual variable representing the random disturbance constraint of renewable energy output; is the constant term for comprehensive benefit calculation; represents the comprehensive benefits in time period T; represents the auxiliary variable of storage capacity at the end of the period; represents the water consumption rate of reservoir i in period t; is the storage capacity of reservoir i in time period T; represents the storage capacity of reservoir i at the end of the scheduling period; S304: Using a combination of forward simulation and reverse recursion, the water-wind-solar scheduling scheme is continuously updated iteratively until convergence is achieved or the preset number of iterations is reached, thereby obtaining a long-term complementary scheduling rule for cascaded water-wind-solar that takes into account multiple uncertainties.

9. The method for optimizing long-term complementary scheduling rules for cascaded hydropower, wind power, and solar power under multiple uncertainties according to claim 8 is characterized by: In step S304, the specific process of the forward simulation is: randomly sampling multiple groups of uncertainty scenarios based on the constructed Markov chain and ARMA model, inputting each group of randomly sampled scenarios forward stage by stage, optimizing and deducing the state variables of each stage, and forming a state trajectory.

10. The method for optimizing long-term complementary scheduling rules for cascaded hydropower, wind power, and solar power under multiple uncertainties according to claim 9 is characterized by: In step S304, the specific process of reverse recursion is: constructing the Bendes cutting plane related to storage capacity, new energy output, new energy output white noise and end-of-period energy storage based on the state variables of each stage under each group of randomly sampled random scenarios obtained by forward simulation.

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