Medium-and-long-term multi-target random scheduling optimization method considering water-wind-light complementation

Abstract

The invention provides a medium-and-long-term multi-target random scheduling optimization method considering water-wind-light complementation, and the method comprises the steps: carrying out the time correlation analysis of a time sequence of the historical values of runoff inflow, wind power output and photovoltaic output resources through employing a Markov chain, and generating a related scene set; a reservoir group medium and long term scheduling multi-objective optimization model with the maximum power generation capacity expectation in the scheduling period and the maximum minimum output expectation of the multi-energy complementary system as objectives is constructed; and solving the medium-and-long-term scheduling multi-objective optimization model of the reservoir group to obtain the ex-reservoir flow of the hydropower station in the scheduling period in the comprehensive scene, thereby realizing water-wind-solar complementary coordinated scheduling. According to the method, time correlation of water, wind and light is comprehensively considered, a scene set is generated and used for depicting runoff and wind and light output characteristics, multi-target optimization scheduling and an optimization algorithm are constructed, a feasible strategy is provided for medium and long term multi-target optimization scheduling for solving water, wind and light complementarity, and theoretical and method support is provided for decision making of water, power and power operation scheduling.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems and renewable energy, and specifically relates to a medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power. Background Art

[0002] At present, there are few studies on medium- and long-term multi-objective random scheduling methods considering the multi-energy complementarity of water, wind and solar. Common multi-energy complementary systems include water-solar complementary systems, water-wind complementary systems, and water-wind-solar complementary systems; the operation and scheduling level of energy complementary systems mainly focuses on short-term and medium- and long-term research, such as multi-objective short-term complementary operation scheduling considering water-solar or wind-solar or water-wind, and single-objective or multi-objective medium- and long-term complementary operation scheduling with partial energy complementarity characteristics. The robustness of multi-objective optimization methods in different scheduling scenarios in the medium- and long-term multi-energy complementary scheduling process has not been well solved.

[0003] Existing dispatching methods, including short-term complementary operation dispatching considering partial energy complementarity (A), single-objective medium- and long-term complementary operation dispatching considering partial energy complementarity (B), and multi-objective medium- and long-term complementary operation dispatching considering partial energy complementarity (C), cannot solve the problem of how to consider intraday operation risks and peak-shaving capacity in the medium- and long-term scale, the huge computational burden brought by the nesting of medium- and long-term and short-term models, and the robustness of multi-objective optimization methods in different dispatching scenarios. The specific analysis is as follows:

[0004] A technology mainly includes short-term output prediction of wind, solar and other energy sources, characterization of output uncertainty, construction of short-term complementary scheduling optimization model and design of efficient solution algorithm. Among them, the prediction model includes linear / nonlinear regression, support vector machine, frequency domain decomposition, deep learning and a combination of various models. However, the accuracy of wind and solar energy prediction still cannot meet the requirements of practical applications, that is, it cannot be applied to the medium- and long-term multi-objective random scheduling method considering the complementarity of water, wind and solar.

[0005] Technology B establishes a medium- and long-term energy complementarity model by selecting one of the following as its sole goal: maximizing energy utilization efficiency, minimizing energy costs, maximizing renewable energy penetration, or minimizing carbon emissions. The final scheduling plan is obtained through medium- and long-term scheduling planning using relevant optimization methods. However, the scheduling plan obtained by the traditional single-objective optimization method usually cannot guarantee relative superiority in other objectives, especially when there is no obvious conversion relationship between different objectives or there is a competitive relationship between the objectives. In other words, it cannot be applied to the medium- and long-term multi-objective random scheduling method considering the complementarity of water, wind, and light.

[0006] C technology has established a multi-objective model for the medium- and long-term scheduling problem of multi-energy complementarity. Compared with short-term scheduling, the medium- and long-term scheduling problem involves a longer time span, and often needs to take into account seasonal changes, annual planning, and fluctuations in energy demand in different time periods. The characteristics of hydropower, wind power, and photovoltaic power generation make their output have significant uncertainty. Therefore, in the medium- and long-term hydro-wind-photovoltaic scheduling problem, multi-objective optimization and uncertainty analysis need to be closely combined, and the scheduling plan should be optimized using random optimization methods. However, the uncertainty analysis part only considers the correlation between some energy sources, such as hydro-photovoltaic, wind-photovoltaic, hydro-wind, etc., and the analysis level is relatively weak, which cannot be applied to the medium- and long-term multi-objective random scheduling method considering the complementarity of hydro-wind-photovoltaic. Summary of the invention

[0007] In view of the defects of the prior art, the present invention provides a medium- and long-term multi-objective random scheduling optimization method taking into account the complementarity of water, wind and solar power, so as to overcome the defects of the medium- and long-term multi-objective random scheduling method related to the complementarity of water, wind and solar power in the background technology, and solve the multi-objective random scheduling problem of the complementarity of water, wind and solar power resources.

[0008] The technical solution adopted by the present invention is as follows:

[0009] The present invention provides a medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power, comprising the following steps:

[0010] Step S1, determine resource variables E1, E2, E3, respectively, runoff inflow, wind power output, and photovoltaic output; for each resource variable E b , b=1,2,3, all get the time series of historical values ​​of resource variables in the target area in historical years, thus, we get the time series of historical values ​​of runoff, wind power output and photovoltaic output respectively;

[0011] Step S2: for each resource variable E b , determine the corresponding state space; then, apply the Markov chain to the resource variable E b The historical value time series is analyzed for time correlation to obtain the resource variable E b The cumulative probability state transfer matrix Q b ;

[0012] Determine the research time scale as the length of period t, Δt; set the number of simulation scenarios B and resource variables E that need to be initially generated b The initial value of the resource variable E b The cumulative probability state transfer matrix Q b , using random numbers to generate the resource variable E for each period t within the year b The simulation value time series forms a simulation scenario; the loop is executed B times to obtain the resource variable Eb B simulation scenarios form the resource variable E b The initial set of simulation scenarios;

[0013] Step S3, set the target number of simulation scenarios J; use the simulation scenario reduction algorithm to calculate the number of each resource variable E b The B simulation scenarios in the initial simulation scenario set are reduced to obtain a reduced simulation scenario set having J simulation scenarios;

[0014] Therefore, for the runoff, wind power output and photovoltaic output resource variables, a simulation scenario set with J simulation scenarios is obtained respectively, and each simulation scenario in the simulation scenario set of each resource variable is randomly arranged, thereby obtaining the final runoff simulation scenario set SCE1 = {S 11 ,S 12 ,...,S 1J}、Wind power output simulation scenario set SCE2 = {S 21 ,S 22 ,...,S 2J} and photovoltaic output simulation scenario set SCE3 = {S 31 ,S 32 ,...,S 3J};

[0015] The runoff simulation scenario set SCE1, the wind power output simulation scenario set SCE2 and the photovoltaic output simulation scenario set SCE3 are combined to form a comprehensive scenario set SC = {S1, S2, ..., S J}; Among them, for the comprehensive scene S in the comprehensive scene set SC j , j=1,2,...,J, with the following relationship: S j ={S 1j , S 2j , S 3j}, at the same time, the comprehensive scene S j The probability p j =(p 1j +p 2j +p 3j ) / 3; p 1j ,p 2j ,p 3j They are runoff simulation scenarios S 1j , Wind power output simulation scenario S 2j And photovoltaic output simulation scenario S 3j probability;

[0016] Step S4, constructing a medium- and long-term scheduling multi-objective optimization model for a reservoir group with the objectives of maximizing the expected power generation within the scheduling period and maximizing the expected minimum output of the multi-energy complementary system;

[0017] Step S5: for each comprehensive scenario S j , using runoff simulation scenario S 1j , Wind power output simulation scenario S 2j And photovoltaic output simulation scenario S 3j The simulated value time series of is taken as the input value, and the multi-objective optimization model of medium- and long-term operation of the reservoir group is solved to obtain the comprehensive scenario S j The outflow QT of the next hydropower station n in each time period t during the dispatch cycle n,j,t , to achieve coordinated and coordinated scheduling of the complementary effects of water, wind and solar power.

[0018] Preferably, step S2 specifically comprises:

[0019] Step S2.1, for each resource variable E b , determine the corresponding state space; the state space has β states, namely state 1, state 2, ..., state β;

[0020] Step S2.2, apply the Markov chain to the resource variable E b The historical value time series is analyzed for time correlation to obtain the resource variable E b The state transition probability matrix G b :

[0021]

[0022] Where: g u',v' Indicates resource variable E b The probability of transition from state u' to state v'; u' = 1, 2, ..., β; v' = 1, 2, ..., β;

[0023] Step S2.3, based on resource variable E b The state transition probability matrix G b , get the resource variable E b The cumulative probability state transfer matrix Q b :

[0024]

[0025] Where: q u',v' Indicates resource variable E b The cumulative probability of state transition from state u' to state v'; q u',v' =g u',1 +g u',2 +...+g u',v' ;

[0026] Step S2.4, generate resource variable E b A simulation scenario:

[0027] Step S2.4.1, set resource variable E b The initial value a0 of the input signal is obtained, and the initial state β0 is obtained according to the initial value a0; wherein β0=1,2,...,β;

[0028] Step S2.4.2, set time period t=1;

[0029] Step S2.4.3, generate a random number e1, e1∈[0,1]; in the cumulative probability state transfer matrix Q b In the state β t-1 The corresponding row vector is: t-1 Row vector of rows:

[0030] Step S2.4.4, traverse the row vector from front to back When the traversal reaches an element greater than or equal to the random number e1 When v'=1,2,...,β, the element The corresponding state v' after state transfer is the state of time period t, which is re-expressed as state β t ; State β t Corresponding resource variable E b The numerical range is: [a l ,a h ];

[0031] Step S2.4.5, generate a random number e2, e2∈[0,1]; by formula a t =a t-1 +e2×(a h -a l ), get the resource variable E b The simulated value a at time period t t ; Among them, a t-1 For resource variable E b The simulation value at time period t-1;

[0032] Step S2.4.6, determine whether the time period t reaches the preset total number of scheduling time periods T, if so, execute step S2.4.7; if not, set t = t + 1 and return to step S2.4.3;

[0033] Step S2.4.7, output the resource variable E obtained at this time b The simulation values ​​in time period 1, time period 2, ..., time period T form the simulation value time series a1, a2, ..., a T , forming a simulation scenario;

[0034] Step S2.4.8, determine whether the B simulation scene has been obtained. If not, return to step S2.4.2, and form another simulation scene by executing steps S2.4.2 to S2.4.7; repeat this process until the B simulation scene is obtained, forming the resource variable E. b The initial set of simulation scenarios.

[0035] Preferably, in step S3, a simulation scenario reduction algorithm is used to reduce the resource variables E b The B simulation scenes in the initial simulation scene set are reduced to obtain a reduced simulation scene set with J simulation scenes, specifically:

[0036] Step S3.1, resource variable E b The initial simulation scenario set includes B simulation scenarios, which can be expressed as: simulation scenario set RS = {ξ1,ξ2,...,ξ B};ξ1,ξ2,...,ξ B They represent B types of simulation scenarios respectively;

[0037] Step S3.2, set each simulation scenario ξ k The initial probability p k are equal, all are 1 / B; where x = 1, 2, ..., B; a set DS is established to store the scenes that need to be eliminated;

[0038] Step S3.3, set the set DS to be empty;

[0039] Step S3.4, traverse each simulation scenario ξ in the simulation scenario set RS k ;

[0040] For each traversed simulation scenario ξ k , determine the distance simulation scene ξ k Recent simulation scenariosξ k" , that is, it satisfies the relationship:

[0041] DT k (k″)=minDT k,s ,k∈RS,s∈RS,k≠s

[0042] Among them: DT k,s Represents the simulation scenario ξ k and simulation scenarios s The distance between k (k″) represents the simulation scenario ξ k and the most recent simulation scenarioξ k" The distance between

[0043] Using the formula PD k =p k*DT k (k″), k∈RS, calculate the simulation scenario ξ k The corresponding minimum distance probability PD k ;

[0044] Step S3.5, compare the simulation scenarios ξ in the simulation scenario set RS k The minimum distance probability PD k , using the formula PD d =minPD k , k∈S, and obtain the minimum distance probability PD in the simulation scene set RS k The minimum value of d ; The PD d Corresponding simulation scenarioξ d , distance simulation sceneξ d The most recent simulation scenario is ξ d* ;

[0045] Step S3.6, let the simulation scenario set RS = RS-{ξ d}, DS=DS+{ξ d}, and let p d* =p d* +p d ; Determine whether the number of simulation scenarios in the simulation scenario set RS is reduced to J, if yes, execute step S3.7; if not, return to step S3.4;

[0046] Step S3.7, obtaining a reduced simulation scenario set having J simulation scenarios, and each simulation scenario in the simulation scenario set has a probability.

[0047] Preferably, in step S4, in the multi-objective optimization model for medium- and long-term operation of the reservoir group, the hydropower station n in the period t in the comprehensive scenario S j Outbound flow QT n,j,t As the decision variable of the model; the objective function includes objective function F1 and objective function F2;

[0048] Objective function F1: The multi-energy complementary system maximizes the expected power generation, and the expression is:

[0049]

[0050] Where: P n,j,t Represents hydropower station n in comprehensive scenario S j output lower than the output in time period t; t=1,2,...,T; T is the total number of medium- and long-term dispatching time periods; n=1,2,...,N; N is the number of hydropower stations; j=1,2,...,J; J is the comprehensive scenario set SC={S1,S2,...,S J}The number of comprehensive scenes; p j Represents a comprehensive scenario S j The probability of; ΔT represents the length of the medium and long-term scheduling period;

[0051] Indicates the wind turbine in the comprehensive scenario S j The output is lower than the output in period t; Indicates that the photovoltaic unit is in the comprehensive scenario S j The output is lower than the output in period t;

[0052] Objective function F2: The expected minimum output of the multi-energy complementary system is maximized, and the expression is:

[0053]

[0054] Thus, the objective function F1 and the objective function F2 are established.

[0055] Preferably, the multi-objective optimization model for medium- and long-term operation of the reservoir group includes the following constraints:

[0056] (1) Water balance equation:

[0057]

[0058] Where: V n,j,t+1 、V n,j,t are the storage capacities of hydropower station n in time period t+1 and time period t respectively; I n,j,t , QT n,j,t They are respectively hydropower station n in time period t in comprehensive scenario S j The runoff inflow and outflow under the reservoir; Ω(n) is the set of hydropower stations upstream of hydropower station n; QT m,j,t The upstream hydropower station m of hydropower station n in the period t in the comprehensive scenario S j Runoff inflow under

[0059] (2) Outbound flow equation:

[0060]

[0061] Where: QP n,j,t , S n,j,t They are respectively hydropower station n in time period t in comprehensive scenario S j The power generation flow and abandoned water flow under

[0062] (3) Outbound flow restrictions:

[0063]

[0064] in: QT n,t , are the lower and upper bounds of the total outflow of hydropower station n in time period t, respectively;

[0065] (4) Power generation flow limit:

[0066]

[0067] in: QP n,t , are the lower and upper bounds of the power generation flow of hydropower station n in time period t, respectively;

[0068] (5) Output limit:

[0069]

[0070] in: P n,t , are the lower and upper bounds of the output of hydropower station n in time period t, respectively;

[0071] (6) Storage capacity limit:

[0072]

[0073] in: V n,t , are the lower and upper bounds of the reservoir capacity of hydropower station n in time period t, respectively;

[0074] (7) Water level constraints at the beginning and end of the dispatch period:

[0075]

[0076] Where: Z n,Ini , Z n,End are the initial water level and final water level of hydropower station n respectively; Z n,j,1 , Z n,j,T are the water levels of hydropower station n in period 1 and period T respectively;

[0077] (8) Water level-reservoir capacity constraints:

[0078]

[0079] in: is the nonlinear function between reservoir water level and water storage; Z n,j,t is the hydropower station n in the period t in the comprehensive scenario S j The water level below;

[0080] (9) Tailwater level-discharge constraint:

[0081]

[0082] in: is the nonlinear function between the tail water level and outflow flow of the hydropower station n; is the hydropower station n in the period t in the comprehensive scenario S j The tailwater level below;

[0083] (10) Hydropower output function:

[0084]

[0085] Where: H n,j,t is the hydropower station n in the period t in the comprehensive scenario S j The power generation head under K n is the output coefficient of hydropower station n; Z n,j,t+1 is the hydropower station n in the period t+1 in the comprehensive scenario S j The water level below;

[0086] (11) Transmission capacity limitation constraints:

[0087]

[0088] Where: W n represents the set of wind power stations associated with hydropower station n; P wd represents the output of the wind power station wd associated with the hydropower station n; B n represents the set of photovoltaic power stations associated with hydropower station n; P sol represents the output of the photovoltaic power station sol associated with the hydropower station n; P n represents the total output of hydropower station n; It is the maximum value of the integrated water, wind and solar power transmission capacity of hydropower station n.

[0089] Preferably, step S5 is specifically:

[0090] Step S5.1, generate individual code X:

[0091] X={QT 111 ,…,QT 11T ,QT 121 ,…,QT 12T ,…,QT NJ1 ,…,QT NJT}; Among them, QT n,j,t Meaning: Hydropower station n in time period t in comprehensive scenario S j The runoff inflow under is the decision variable; according to the individual code X, the corresponding number of individuals with initialization solutions are generated according to the set population size, thereby forming an initialization population;

[0092] Step S5.2, determine the population size NI and the ratio γ; wherein the ratio γ is the number of individuals in the dominant population / the number of individuals in the basic population;

[0093] Step S5.3, using the inter-individual dominance algorithm to determine the dominant population and the basic population;

[0094] Step S5.4, adopt the multi-recombination operator fusion strategy to perform crossover operation on the dominant population and the basic population to generate a new population; then return to step S5.3, re-adopt the inter-individual domination algorithm to divide the new population into the dominant population and the basic population, and repeat this cycle until the set number of cycles is reached, and then execute step S5.5;

[0095] Step S5.5, for each individual in the new population, a heuristic constraint processing method is used to perform heuristic constraint processing on the solution value of each individual, so as to obtain each individual with an updated solution value;

[0096] Step S5.6, based on the updated solution value of each individual obtained in step S5.5, calculate the objective function value of the objective function F1 and the objective function F2 corresponding to each individual;

[0097] Step S5.7, based on the objective function values ​​corresponding to each individual, determine whether the iteration termination condition is met through the restart mechanism. If so, execute step S5.8; if not, determine whether a restart mechanism is needed. If so, start the restart mechanism, update the basic population, and return to step S5.4; if not, return to step S5.3;

[0098] Step S5.8, output the solution value of each individual in the dominant population. The solution value of each individual is a scheduling plan, thereby obtaining a set of multi-objective optimization scheduling plans, thereby completing the medium- and long-term multi-objective optimization scheduling of the hydropower station group.

[0099] Preferably, step S5.3 is specifically:

[0100] Step S5.3.1, setting the value of parameter ∈;

[0101] Step S5.3.2, using the function value of the objective function F1 as the horizontal coordinate, the function value of the objective function F2 as the vertical coordinate, and the parameter ∈ as the grid side length to establish a gridded objective function space;

[0102] Step S5.3.3, for each current individual X c , c=1,2,…,NI, according to the function value of its objective function F1 and the function value of its objective function F2, determine the grid where it is located in the objective function space;

[0103] Step S5.3.4, traverse each individual and calculate each individual X c The dominance number NC is calculated as:

[0104] For individual X c, the initial value of its dominance number NC is 0; traverse the other NI-1 individuals in turn, and for the currently traversed individual X d , d=1,2,…,NI, and d≠c, if individual X d and individual X c The function values ​​of X are located in the same grid in the objective function space, then Algorithm 1 is used to determine the individual X c Whether it dominates individual X d , if it dominates, then the domination number NC increases by 1; if individual X d and individual X c If the function value of is located in different grids in the objective function space, then Algorithm 2 is used to determine the individual X c Whether it dominates individual X d If it dominates, the domination number NC increases by 1; thus, the traversal of the other NI-1 individuals is completed, and the value of the domination number NC is obtained, which is the value of individual X c The domination number NC represents the domination number of individual X. c The number of individuals controlled;

[0105] Among them: Algorithm 1 and Algorithm 2 are:

[0106] Individual X c = {QT 111 ,…,QT 11T ,QT 121 ,…,QT 12T ,…,QT NJ1 ,…,QT NJT}, individual X c The function value of the objective function F1 is F 1c ; Individual X c The function value of the objective function F2 is F 2c , individual X c The objective function value vector is expressed as: F c ={F 1c ,F 2c};

[0107] Individual X d The function value of the objective function F1 is F 1d ; Individual X d The function value of the objective function F2 is F 2d , individual X d The objective function value vector is expressed as: F d ={F 1d ,F 2d};

[0108] Algorithm 1 is: When conditions 1 and 2 are met, individual X c Dominant individual X d ;

[0109] Condition 1: F 1c ≥F 1d +∈, and F 2c ≥F 2d +∈;

[0110] Condition 2: There exists F xc ,x=1,2, satisfying the relationship: F xc >F 1d +∈, and F xc >F 2d +∈;

[0111] Algorithm 2 is: if and only if one of conditions 3 and 4 is met, individual X c Dominant individual X d :

[0112] Condition 3:

[0113] Among them: < represents and The relationship between conditions 1 and 2 is satisfied. represents round down;

[0114] Condition 4:

[0115] Among them: ∥∥ is the modulo operation; & is the logical AND;

[0116] Step S5.3.5, according to the population size NI and the ratio γ, obtain the number of individuals in the dominant population archiv esize and the number of individuals in the basic population population size;

[0117] Sort the individuals in descending order of dominance number. If two individuals have the same dominance number, they can be sorted arbitrarily. Among the sorted NI individuals, select the individuals with the archive size number that are ranked first to form the dominant population, and the other individuals form the basic population.

[0118] Preferably, step S5.4 is specifically:

[0119] Step S5.4.1, setting a crossover operator pool; the crossover operator pool has N_CO crossover operators;

[0120] Step S5.4.2, initially, each crossover operator CO is set to have the same selection probability PC;

[0121] Step S5.4.3, select a crossover operator to perform a crossover operation, and update the selection probability PC of the crossover operator:

[0122] ① Based on the selection probability PC of each current crossover operator, select the crossover operator A from the crossover operator pool; ② According to the number of parents N required by the crossover operator A A , randomly select a parent individual X from the dominant population A1 , select N from the basic population through a tournament A -1 parent individual, expressed as: parent individual X AB ,B=2,3,…,N A ;

[0123] ③Use the crossover operator A to perform crossover on the parent individual X A1 and parent individual X AB Perform crossover operation to generate N A offspring individuals, and N A The original parent individuals are deleted from the population;

[0124] ④According to N A The function value of the offspring individuals is used to determine the grid in the objective function space, and the offspring individuals within the dominant population range are added to the dominant population, and the other offspring individuals are added to the basic population; among which: the number of offspring individuals added to the dominant population generated by the crossover operator A this time is SN_A;

[0125] ⑤ Update the selection probability PC of the crossover operator A using the following formula: A :

[0126]

[0127] Where: SN_V represents the total number of offspring individuals added to the dominant population generated by the crossover operator V from the first cycle to the current cycle, V = 1, 2, ..., N_CO; is a constant,

[0128] ⑥When this cycle ends, return to step ① and continue with the next cycle until the set number of cycles is reached.

[0129] Preferably, step S5.5 is specifically:

[0130] For each individual X={QT 111 ,…,QT 11T ,QT 121 ,…,QT 12T ,…,QT NJ1 ,…,QT NJT}, both execute the dynamic balance constraint adjustment of power generation flow in step S5.4.1 and the reservoir capacity constraint adjustment in step S5.4.2:

[0131] Step S5.4.1, dynamic balance constraint adjustment of outbound flow:

[0132] ① According to the water balance equation and the outflow flow equation, the reservoir n in the entire dispatch period is obtained in the comprehensive scenario S j The water volume difference between the water consumption under the condition and the constrained water volume ΔQT n,j ;

[0133] ②Use the following formula to convert the water volume difference ΔQT n,j Adjust to reservoir n in comprehensive scenario S j The outbound flow QT of each period t under n,j,t The corrected outbound flow is obtained

[0134]

[0135] ③Use the following formula and apply the outbound flow restriction constraint to correct the outbound flow Further correction is made to obtain the corrected outbound flow

[0136]

[0137] ④ The corrected reservoir n is obtained in the comprehensive scenario S j The outflow rate of each period t under the condition of reservoir n in the comprehensive scenario S j Check whether the water balance equation is satisfied during the entire dispatch period; if so, complete the adjustment of the dynamic balance constraint of the outflow flow; otherwise, return to step ① and make corrections in a loop;

[0138] Step S5.4.2, reservoir capacity constraint adjustment:

[0139] ① According to the current individual X's reservoir n in the comprehensive scenario S j The outbound flow QT of each period t under n,j,t , the calculation results show that the hydropower station n in the comprehensive scenario S j The storage capacity V at time period t n,j,t ;

[0140] ② Determine the hydropower station n in the comprehensive scenario S j The storage capacity V at time period t n,j,t Whether the storage capacity constraint is met; if not, when the storage capacity V n,j,t Exceeds the upper limit of the storage capacity of hydropower station n in time period t Then execute ③; when the storage capacity V n,j,t Lower than the lower limit of the reservoir capacity of hydropower station n in time period t V n,t , then execute ④;

[0141] ③The excess Averaged over each period t of the scheduling period, that is, the mean value of the excess quantity is obtained

[0142] Then press the excess amount average Averaged over each period t of the dispatch period, the hydropower station n in the comprehensive scenario S j The storage capacity V at each time period t n,j,t amendments;

[0143] If the reservoir n is completed in the comprehensive scenario S j The storage capacity V of all time periods t under n,j,t After the correction, there is still a situation that exceeds the upper limit of storage capacity. The storage capacity V in time period t n,j,t When the excess amount is increased, the excess amount will be averaged into the outflow of the adjacent reservoirs;

[0144] ④The insufficient amount v n,t -v n,j,t Averaged over each period t of the scheduling period, we get the mean value of the shortage ΔV n,j,t =( V n,t -V n,j,t ) / T; then, the storage capacity V of other time periods t n,j,t Both reduce ΔV n,j,t , and replenish the storage capacity V of the current period t n,j,t ; Complete the hydropower station n in the comprehensive scenario S j The storage capacity V at time period t n,j,t amendments;

[0145] If the reservoir n is completed in the comprehensive scenario S j The storage capacity V of all time periods t under n,j,t After the correction, there is still insufficient storage capacity lower bound V n,t The storage capacity V in time period t n,j,t When the outflow from the adjacent reservoir is supplemented to the lower limit of insufficient storage capacity V n,t The storage capacity V in time period t n,j,t .

[0146] Preferably, in step S5.7, it is determined whether a restart mechanism is required. If necessary, the restart mechanism is started to update the basic population, specifically:

[0147] When the algorithm search stagnates, the restart mechanism is started: according to the current ratio γ and the current number of dominant population individuals archive size, the formula archive size / γ is used to obtain the number of basic population individuals;

[0148] Clear the current basic population, and fill the basic population with the corresponding number of individuals with the highest NC ranking in the current dominant population. The insufficient part is obtained through the mutation of individuals in the dominant population, thus obtaining the updated basic population.

[0149] The medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power provided by the present invention has the following advantages:

[0150] The present invention combines the correlation analysis of water, wind and solar with multi-objective random scheduling optimization, and constructs a decision-making process. This decision-making process is based on the obtained runoff and wind and solar output scenario set, and efficiently solves the multi-objective optimization scheduling of water, wind and solar in the medium and long term. Compared with the prior art, the present invention comprehensively considers the time correlation of water, wind and solar, and generates a scenario set on this basis to characterize the runoff and wind and solar output characteristics, and constructs multi-objective optimization scheduling and optimization algorithms to provide feasible strategies for solving the medium- and long-term multi-objective optimization scheduling of water, wind and solar complementarity, and provides theoretical and methodological support for the decision-making of hydropower operation scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0151] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0152] Figure 1 A flow chart of a medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power provided by the present invention. DETAILED DESCRIPTION

[0153] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0154] Driven by climate change and the "dual carbon" goals, renewable energy represented by wind and photovoltaics has developed rapidly in recent years. Making full use of the flexibility of hydropower and the natural complementarity between different resources to carry out complementary power generation is an effective way to promote the consumption of new energy. The present invention combines the correlation analysis of water, wind and solar with multi-objective random scheduling optimization, and constructs a set of decision-making processes. This set of decisions is based on the obtained runoff and wind and solar output scenario sets, and efficiently solves the multi-objective optimization scheduling of water, wind and solar in the medium and long term. Compared with the prior art, the present invention comprehensively considers the temporal correlation of water, wind and solar, and generates a scenario set on this basis to characterize the runoff and wind and solar output characteristics, and constructs multi-objective optimization scheduling and optimization algorithms to provide feasible strategies for solving the medium- and long-term multi-objective optimization scheduling of water, wind and solar complementarity, and provide theoretical and methodological support for the decision-making of hydropower operation scheduling.

[0155] The present invention designs a water-wind-solar time correlation model and a Borg algorithm for solving multi-objective scheduling for medium- and long-term multi-objective scheduling considering the complementarity of water, wind and solar. The Markov chain mainly describes the transformation of values ​​in the water-wind-solar time series between different states over time, and generates a set of related scenarios based on this. The Borg algorithm is an efficient multi-objective optimization framework, which combines dominance rules, random restart mechanisms and adaptive multi-operator recombination strategies. On the basis of the initialization population, the dominance rules are adopted to obtain the dominant population and the basic population, and then a multi-operator fusion strategy is adopted to perform a heuristic adjustment constraint processing method. According to the calculated objective function value, the stagnation in the process is judged to trigger a random restart. In the iterative process, different optimization problems dynamically adjust operators to improve the solution efficiency and the quality of the solution.

[0156] See also Figure 1 The present invention provides a medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power, comprising the following steps:

[0157] Step S1, determine resource variables E1, E2, E3, respectively, runoff inflow, wind power output, and photovoltaic output; for each resource variable E b , b=1,2,3, all get the time series of historical values ​​of resource variables in the target area in historical years, thus, we get the time series of historical values ​​of runoff, wind power output and photovoltaic output respectively;

[0158] Step S2: for each resource variable E b , determine the corresponding state space; then, apply the Markov chain to the resource variable E b The historical value time series is analyzed for time correlation to obtain the resource variable E b The cumulative probability state transfer matrix Q b ;

[0159] Determine the research time scale as the length of period t, Δt; set the number of simulation scenarios B and resource variables E that need to be initially generated b The initial value of the resource variable E b The cumulative probability state transfer matrix Q b , using random numbers to generate the resource variable E for each period t within the year b The simulation value time series forms a simulation scenario; the loop is executed B times to obtain the resource variable E b B simulation scenarios form the resource variable E b The initial set of simulation scenarios;

[0160] Step S2 is specifically as follows:

[0161] Step S2.1, for each resource variable E b , determine the corresponding state space; the state space has β states, namely state 1, state 2, ..., state β;

[0162] Step S2.2, apply the Markov chain to the resource variable E b The historical value time series is analyzed for time correlation to obtain the resource variable E b The state transition probability matrix G b :

[0163]

[0164] Where: g u',v' Indicates resource variable E b The probability of transition from state u' to state v'; u'=1,2,...,β; v'=1,2,...,β; g u',v' The expression is as follows:

[0165]

[0166] Where: α t+1 and α t It represents the resource variable E in time periods t and t+1 b The state of u',v' For resource variable E b The frequency of transition from state u' to state v', u', v'∈s, g(·|·) is the conditional transition probability; in the present invention, the time period t can be different time scales such as month, week, day, etc.

[0167] Step S2.3, based on resource variable E b The state transition probability matrix G b , get the resource variable E b The cumulative probability state transfer matrix Q b :

[0168]

[0169] Where: q u',v' Indicates resource variable E b The cumulative probability of state transition from state u' to state v'; q u',v' =g u',1 +g u',2 +...+g u',v' ;

[0170] Step S2.4, generate resource variable E b A simulation scenario:

[0171] Step S2.4.1, set resource variable E b The initial value a0 of the input signal is obtained, and the initial state β0 is obtained according to the initial value a0; wherein β0=1,2,...,β;

[0172] Step S2.4.2, set time period t=1;

[0173] Step S2.4.3, generate a random number e1, e1∈[0,1]; in the cumulative probability state transfer matrix Q b In the state β t-1 The corresponding row vector is: t-1 Row vector of rows:

[0174] Step S2.4.4, traverse the row vector from front to back When the traversal reaches an element greater than or equal to the random number e1 When v'=1,2,...,β, the element The corresponding state v' after state transfer is the state of time period t, which is re-expressed as state β t ; State β t Corresponding resource variable E b The numerical range is: [a l ,a h ];

[0175] Step S2.4.5, generate a random number e2, e2∈[0,1]; by formula a t =a t-1 +e2×(a h -a l ), get the resource variable E b The simulated value a at time period t t ; Among them, a t-1 For resource variable E b The simulation value at time period t-1;

[0176] Step S2.4.6, determine whether the time period t reaches the preset total number of scheduling time periods T, if so, execute step S2.4.7; if not, set t = t + 1 and return to step S2.4.3;

[0177] Step S2.4.7, output the resource variable E obtained at this time b The simulation values ​​in time period 1, time period 2, ..., time period T form the simulation value time series a1, a2, ..., a T , forming a simulation scenario;

[0178] Step S2.4.8, determine whether the B simulation scene has been obtained. If not, return to step S2.4.2, and form another simulation scene by executing steps S2.4.2 to S2.4.7; repeat this process until the B simulation scene is obtained, forming the resource variable E. b The initial set of simulation scenarios.

[0179] Step S3, set the target number of simulation scenarios J; use the simulation scenario reduction algorithm to calculate the number of each resource variable E b The B simulation scenarios in the initial simulation scenario set are reduced to obtain a reduced simulation scenario set having J simulation scenarios;

[0180] The simulation scenario reduction algorithm is as follows:

[0181] Step S3.1, resource variable E b The initial simulation scenario set includes B simulation scenarios, which can be expressed as: simulation scenario set RS = {ξ1,ξ2,...,ξ B};ξ1,ξ2,...,ξ B They represent B types of simulation scenarios respectively;

[0182] Step S3.2, set each simulation scenario ξ k The initial probability p k are equal, all are 1 / B; where x = 1, 2, ..., B; a set DS is established to store the scenes that need to be eliminated;

[0183] Step S3.3, set the set DS to be empty;

[0184] Step S3.4, traverse each simulation scenario ξ in the simulation scenario set RS k ;

[0185] For each traversed simulation scenario ξ k , determine the distance simulation scene ξ k Recent simulation scenariosξ k" , that is, it satisfies the relationship:

[0186] DT k (k″)=minDT k,s ,k∈RS,s∈RS,k≠s

[0187] Among them: DT k,s Represents the simulation scenario ξ k and simulation scenarios s The distance between K (k″) represents the simulation scenario ξ k and the most recent simulation scenarioξ k" The distance between

[0188] Using the formula PD k =p k *DT k (k″), k∈RS, calculate the simulation scenario ξ k The corresponding minimum distance probability PD k ;

[0189] Step S3.5, compare the simulation scenarios ξ in the simulation scenario set RS k The minimum distance probability PD k , using the formula PD d =minPD k , k∈S, and obtain the minimum distance probability PD in the simulation scene set RS k The minimum value of d ; The PD d Corresponding simulation scenarioξ d , distance simulation sceneξ d The most recent simulation scenario is ξ d* ;

[0190] Step S3.6, let the simulation scenario set RS = RS-{ξ d}, DS=DS+{ξ d}, and let p d* =p d* +p d ; Determine whether the number of simulation scenarios in the simulation scenario set RS is reduced to J, if yes, execute step S3.7; if not, return to step S3.4;

[0191] Step S3.7, obtaining a reduced simulation scenario set having J simulation scenarios, and each simulation scenario in the simulation scenario set has a probability.

[0192] Therefore, for the runoff, wind power output and photovoltaic output resource variables, a simulation scenario set with J simulation scenarios is obtained respectively, and each simulation scenario in the simulation scenario set of each resource variable is randomly arranged, thereby obtaining the final runoff simulation scenario set SCE1 = {S11 ,S 12 ,...,S 1J}、Wind power output simulation scenario set SCE2 = {S 21 ,S 22 ,...,S 2J} and photovoltaic output simulation scenario set SCE3 = {S 31 ,S 32 ,...,S 3J};

[0193] The runoff simulation scenario set SCE1, the wind power output simulation scenario set SCE2 and the photovoltaic output simulation scenario set SCE3 are combined to form a comprehensive scenario set SC = {S1, S2, ..., S J}; Among them, for the comprehensive scene S in the comprehensive scene set SC j , j=1,2,...,J, with the following relationship: S j ={S 1j , S 2j , S 3j}, at the same time, the comprehensive scene S j The probability p j =(p 1j +p 2j +p 3j ) / 3; p 1j ,p 2j ,p 3j They are runoff simulation scenarios S 1j , Wind power output simulation scenario S 2j And photovoltaic output simulation scenario S 3j probability;

[0194] Step S4, constructing a medium- and long-term scheduling multi-objective optimization model for a reservoir group with the objectives of maximizing the expected power generation within the scheduling period and maximizing the expected minimum output of the multi-energy complementary system;

[0195] Specifically, in the multi-objective optimization model for medium- and long-term operation of the reservoir group, the hydropower station n in the period t is used in the comprehensive scenario S j Outbound flow QT n,j,t As the decision variable of the model; the objective function includes objective function F1 and objective function F2;

[0196] Objective function F1: The multi-energy complementary system maximizes the expected power generation, and the expression is:

[0197]

[0198] Where: P n,j,t Represents hydropower station n in comprehensive scenario S joutput lower than the output in time period t; t=1,2,...,T; T is the total number of medium- and long-term dispatching time periods; n=1,2,...,N; N is the number of hydropower stations; j=1,2,...,J; J is the comprehensive scenario set SC={S1,S2,...,S J}The number of comprehensive scenes; p j Represents a comprehensive scenario S j The probability of; ΔT represents the length of the medium and long-term scheduling period;

[0199] Indicates the wind turbine in the comprehensive scenario S j The output is lower than the output in period t; Indicates that the photovoltaic unit is in the comprehensive scenario S j The output is lower than the output in period t;

[0200] Objective function F2: The expected minimum output of the multi-energy complementary system is maximized, and the expression is:

[0201]

[0202] Thus, the objective function F1 and the objective function F2 are established.

[0203] The multi-objective optimization model for medium- and long-term operation of the reservoir group includes the following constraints:

[0204] (1) Water balance equation:

[0205]

[0206] Where: V n,j,t+1 、V n,j,t are the storage capacities of hydropower station n in time period t+1 and time period t respectively; I n,j,t , QT n,j,t They are respectively hydropower station n in time period t in comprehensive scenario S j The runoff inflow and outflow under the reservoir; Ω(n) is the set of hydropower stations upstream of hydropower station n; QT m,j,t The upstream hydropower station m of hydropower station n in the period t in the comprehensive scenario S j Runoff inflow under

[0207] (2) Outbound flow equation:

[0208]

[0209] Where: QP n,j,t , S n,j,t They are respectively hydropower station n in time period t in comprehensive scenario S j The power generation flow and abandoned water flow under

[0210] (3) Outbound flow restrictions:

[0211]

[0212] in: QT n,t , are the lower and upper bounds of the total outflow of hydropower station n in time period t, respectively;

[0213] (4) Power generation flow limit:

[0214]

[0215] in: QP n,t , are the lower and upper bounds of the power generation flow of hydropower station n in time period t, respectively;

[0216] (5) Output limit:

[0217]

[0218] in: P n,t , are the lower and upper bounds of the output of hydropower station n in time period t, respectively;

[0219] (6) Storage capacity limit:

[0220]

[0221] in: V n,t , are the lower and upper bounds of the reservoir capacity of hydropower station n in time period t, respectively;

[0222] (7) Water level constraints at the beginning and end of the dispatch period:

[0223]

[0224]

[0225] Where: Z n,Ini , Z n,End are the initial water level and final water level of hydropower station n respectively; Z n,j,1 , Z n,j,T are the water levels of hydropower station n in period 1 and period T respectively;

[0226] (8) Water level-reservoir capacity constraints:

[0227]

[0228] in: is the nonlinear function between reservoir water level and water storage; Z n,j,t is the hydropower station n in the period t in the comprehensive scenario S j The water level below;

[0229] (9) Tailwater level-discharge constraint:

[0230]

[0231] in: is the nonlinear function between the tail water level and outflow flow of the hydropower station n; is the hydropower station n in the period t in the comprehensive scenario S j The tailwater level below;

[0232] (10) Hydropower output function:

[0233]

[0234] Where: H n,j,t is the hydropower station n in the period t in the comprehensive scenario S j The power generation head under K n is the output coefficient of hydropower station n; Z n,j,t+1 is the hydropower station n in the period t+1 in the comprehensive scenario S j The water level below;

[0235] (11) Transmission capacity limitation constraints:

[0236]

[0237] Where: W n represents the set of wind power stations associated with hydropower station n; P wd represents the output of the wind power station wd associated with the hydropower station n; B n represents the set of photovoltaic power stations associated with hydropower station n; P sol represents the output of the photovoltaic power station sol associated with the hydropower station n; P n represents the total output of hydropower station n; It is the maximum value of the integrated water, wind and solar power transmission capacity of hydropower station n.

[0238] Step S5: for each comprehensive scenario S j , using runoff simulation scenario S 1j , Wind power output simulation scenario S 2j And photovoltaic output simulation scenario S 3j The simulated value time series of is taken as the input value, and the multi-objective optimization model of medium- and long-term operation of the reservoir group is solved to obtain the comprehensive scenario S j The outflow QT of the next hydropower station n in each time period t during the dispatch cycle n,j,t , to achieve coordinated and coordinated scheduling of the complementary effects of water, wind and solar power.

[0239] Step S5 is specifically as follows:

[0240] Step S5.1, generate individual code X:

[0241] X={QT 111 ,…,QT 11T ,QT 121 ,…,QT 12T ,…,QT NJ1 ,…,QT NJT}; Among them, QT n,j,t Meaning: Hydropower station n in time period t in comprehensive scenario S j The runoff inflow under is the decision variable; according to the individual code X, the corresponding number of individuals with initialization solutions are generated according to the set population size, thereby forming an initialization population;

[0242] Step S5.2, determine the population size NI and the ratio γ; wherein the ratio γ is the number of individuals in the dominant population / the number of individuals in the basic population;

[0243] Step S5.3, using the inter-individual dominance algorithm to determine the dominant population and the basic population;

[0244] Step S5.3 is specifically as follows:

[0245] Step S5.3.1, setting the value of parameter ∈;

[0246] Step S5.3.2, using the function value of the objective function F1 as the horizontal coordinate, the function value of the objective function F2 as the vertical coordinate, and the parameter ∈ as the grid side length to establish a gridded objective function space;

[0247] Step S5.3.3, for each current individual X c , c=1,2,…,NI, determine the grid in the objective function space according to the function value of the objective function F1 and the function value of the objective function F2;

[0248] Step S5.3.4, traverse each individual and calculate each individual X c The dominance number NC is calculated as:

[0249] For individual X c , the initial value of its dominance number NC is 0; traverse the other NI-1 individuals in turn, and for the currently traversed individual X d , d=1,2,…,NI, and d≠c, if individual X d and individual X c The function values ​​of X are located in the same grid in the objective function space, then Algorithm 1 is used to determine the individual X c Whether it dominates individual X d , if it dominates, then the domination number NC increases by 1; if individual X d and individual Xc If the function value of is located in different grids in the objective function space, then Algorithm 2 is used to determine the individual X c Whether it dominates individual X d If it dominates, the domination number NC increases by 1; thus, the traversal of the other NI-1 individuals is completed, and the value of the domination number NC is obtained, which is the value of individual X c The domination number NC represents the domination number of individual X. c The number of individuals controlled;

[0250] Among them: Algorithm 1 and Algorithm 2 are:

[0251] Individual X c = {QT 111 ,…,QT 11T ,QT 121 ,…,QT 12T ,…,QT NJ1 ,…,QT NJT}, individual X c The function value of the objective function F1 is F 1c ; Individual X c The function value of the objective function F2 is F 2c , individual X c The objective function value vector is expressed as: F c ={F 1c ,F 2c};

[0252] Individual X d The function value of the objective function F1 is F 1d ; Individual X d The function value of the objective function F2 is F 2d , individual X d The objective function value vector is expressed as: F d ={F 1d ,F 2d};

[0253] Algorithm 1 is: When conditions 1 and 2 are met, individual X c Dominant individual X d ;

[0254] Condition 1: F 1c ≥F 1d +∈, and F 2c ≥F 2d +∈;

[0255] Condition 2: There exists F xc ,x=1,2, satisfying the relationship: F xc >F 1d +∈, and F xc >F 2d+∈;

[0256] Algorithm 2 is: if and only if one of conditions 3 and 4 is met, individual X c Dominant individual X d :

[0257] Condition 3:

[0258] in: represent and The relationship between conditions 1 and 2 is satisfied. represents round down;

[0259] Condition 4:

[0260] Among them: ∥∥ is the modulo operation; & is the logical AND;

[0261] Step S5.3.5, according to the population size NI and the ratio γ, obtain the archive size of the dominant population and the population size of the basic population;

[0262] Sort the individuals in descending order of dominance number. If two individuals have the same dominance number, they can be sorted arbitrarily. Among the sorted NI individuals, select the individuals with the archive size number that are ranked first to form the dominant population, and the other individuals form the basic population.

[0263] Step S5.4, adopt the multi-recombination operator fusion strategy to perform crossover operation on the dominant population and the basic population to generate a new population; then return to step S5.3, re-adopt the inter-individual domination algorithm to divide the new population into the dominant population and the basic population, and repeat this cycle until the set number of cycles is reached, and then execute step S5.5;

[0264] Step S5.4 is specifically as follows:

[0265] Step S5.4.1, setting a crossover operator pool; the crossover operator pool has N_CO crossover operators;

[0266] Step S5.4.2, initially, each crossover operator CO is set to have the same selection probability PC;

[0267] Step S5.4.3, select a crossover operator to perform a crossover operation, and update the selection probability PC of the crossover operator:

[0268] ① Based on the selection probability PC of each current crossover operator, select the crossover operator A from the crossover operator pool; ② According to the number of parents N required by the crossover operator A A , randomly select a parent individual X from the dominant populationA1 , select N from the basic population through a tournament A -1 parent individual, expressed as: parent individual X AB ,B=2,3,…,N A ;

[0269] ③Use the crossover operator A to perform crossover on the parent individual X A1 and parent individual X AB Perform crossover operation to generate N A offspring individuals, and N A The original parent individuals are deleted from the population;

[0270] ④According to N A The function value of the offspring individuals is used to determine the grid in the objective function space, and the offspring individuals within the dominant population range are added to the dominant population, and the other offspring individuals are added to the basic population; among which: the number of offspring individuals added to the dominant population generated by the crossover operator A this time is SN_A;

[0271] ⑤ Update the selection probability PC of the crossover operator A using the following formula: A :

[0272]

[0273] Where: SN_V represents the total number of offspring individuals added to the dominant population generated by the crossover operator V from the first cycle to the current cycle, V = 1, 2, ..., N_CO; is a constant,

[0274] The constant Prevents operator probabilities from reaching 0, thus ensuring that no operators are "lost" during algorithm execution.

[0275] ⑥When this cycle ends, return to step ① and continue with the next cycle until the set number of cycles is reached.

[0276] The multi-group operator fusion strategy of the present invention uses a pool including multiple crossover operators and establishes a feedback mechanism to compare the performance of operators during the search process. Operators with excellent performance will receive higher selection weights. Specifically, the selection weights of all operators are equal at the beginning, and their probabilities in the next selection are updated through the feedback mechanism, and the adjustment strategy is based on the number of solutions in the dominant population generated by the operator.

[0277] Step S5.5, for each individual in the new population, a heuristic constraint processing method is used to perform heuristic constraint processing on the solution value of each individual, so as to obtain each individual with an updated solution value;

[0278] Step S5.5 is specifically as follows:

[0279] For each individual X={QT 111 ,…,QT 11T ,QT 121 ,…,QT 12T ,…,QT NJ1 ,…,QT NJT}, both execute the dynamic balance constraint adjustment of power generation flow in step S5.4.1 and the reservoir capacity constraint adjustment in step S5.4.2:

[0280] Step S5.4.1, dynamic balance constraint adjustment of outbound flow:

[0281] ① According to the water balance equation and the outflow flow equation, the reservoir n in the entire dispatch period is obtained in the comprehensive scenario S j The water volume difference between the water consumption under the condition and the constrained water volume ΔQT n,j ;

[0282] ②Use the following formula to convert the water volume difference ΔQT n,j Adjust to reservoir n in comprehensive scenario S j The outbound flow QT of each period t under n,j,t The corrected outbound flow is obtained

[0283]

[0284] ③Use the following formula and apply the outbound flow restriction constraint to correct the outbound flow Further correction is made to obtain the corrected outbound flow

[0285]

[0286] ④ The corrected reservoir n is obtained in the comprehensive scenario S j The outflow rate of each period t under the condition of reservoir n in the comprehensive scenario S j Check whether the water balance equation is satisfied during the entire dispatch period; if so, complete the adjustment of the dynamic balance constraint of the outflow flow; otherwise, return to step ① and make corrections in a loop;

[0287] Step S5.4.2, reservoir capacity constraint adjustment:

[0288] ① According to the current individual X's reservoir n in the comprehensive scenario S j The outbound flow QT of each period t under n,j,t , the calculation results show that the hydropower station n in the comprehensive scenario S j The storage capacity V at time period t n,j,t ;

[0289] ② Determine the hydropower station n in the comprehensive scenario S j The storage capacity V at time period t n,j,t Whether the storage capacity constraint is met; if not, when the storage capacity V n,j,t Exceeds the upper limit of the storage capacity of hydropower station n in time period t Then execute ③; when the storage capacity V n,j,t Lower than the lower limit of the reservoir capacity of hydropower station n in time period t V n,t , then execute ④;

[0290] ③The excess Averaged over each period t of the scheduling period, that is, the mean value of the excess quantity is obtained

[0291] Then press the excess amount average Averaged over each period t of the dispatch period, the hydropower station n in the comprehensive scenario S j The storage capacity V at each time period t n,j,t amendments;

[0292] If the reservoir n is completed in the comprehensive scenario S j The storage capacity V of all time periods t under n,j,t After the correction, there is still a situation that exceeds the upper limit of storage capacity. The storage capacity V in time period t n,j,t When the excess amount is increased, the excess amount will be averaged into the outflow of the adjacent reservoirs;

[0293] ④The insufficient amount v n,t -v n,j,t Averaged over each period t of the scheduling period, we get the mean value of the shortage ΔV n,j,t =( V n,t -V n,j,t ) / T; then, the storage capacity V of other time periods t n,j,t Both reduce ΔV n,j,t , and replenish the storage capacity V of the current period t n,j,t ; Complete the hydropower station n in the comprehensive scenario S j The storage capacity V at time period t n,j,t amendments;

[0294] If the reservoir n is completed in the comprehensive scenario S j The storage capacity V of all time periods t under n,j,t After the correction, there is still insufficient storage capacity lower bound V n,t The storage capacity V in time period t n,j,t When the outflow from the adjacent reservoir is supplemented to the lower limit of insufficient storage capacity V n,t The storage capacity V in time period t n,j,t .

[0295] Step S5.6, based on the updated solution value of each individual obtained in step S5.5, calculate the objective function value of the objective function F1 and the objective function F2 corresponding to each individual;

[0296] Step S5.7, based on the objective function values ​​corresponding to each individual, determine whether the iteration termination condition is met through the restart mechanism. If so, execute step S5.8; if not, determine whether a restart mechanism is needed. If so, start the restart mechanism, update the basic population, and return to step S5.4; if not, return to step S5.3;

[0297] In this step, it is determined whether a restart mechanism is required. If necessary, the restart mechanism is started to update the basic population, specifically:

[0298] When the algorithm search stagnates, the restart mechanism is started: according to the current ratio γ and the current number of dominant population individuals archive size, the formula archive size / γ is used to obtain the number of basic population individuals;

[0299] Clear the current basic population, and fill the basic population with the corresponding number of individuals with the highest NC ranking in the current dominant population. The insufficient part is obtained through the mutation of individuals in the dominant population, thus obtaining the updated basic population.

[0300] Therefore, in the present invention, in order to prevent the algorithm from falling into a local optimum, a population restart mechanism is designed to detect the stagnation of the algorithm search. If the algorithm search is stagnant, the restart mechanism is used to regenerate the basic population. This mechanism updates the basic population individuals so that the algorithm can search again when it finds stagnation or falls into a local optimal solution to find a better solution.

[0301] Step S5.8, output the solution value of each individual in the dominant population. The solution value of each individual is a scheduling plan, thereby obtaining a set of multi-objective optimization scheduling plans, thereby completing the medium- and long-term multi-objective optimization scheduling of the hydropower station group.

[0302] The present invention provides a medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power. The main steps can be described as follows:

[0303] S1: Obtain the historical value time series of runoff, wind power output and photovoltaic output;

[0304] S2: Construct the state transition probability matrix and cumulative probability state transition matrix in the Markov chain;

[0305] S3: Use the Monte Carlo sampling method to obtain the initial water-landscape scene set, and use the synchronous back-substitution elimination method to reduce the initial scene set to obtain the final scene set;

[0306] S4: Construct a multi-objective optimization model for the medium- and long-term scheduling of a reservoir group with the goal of maximizing the expected power generation within the scheduling period and maximizing the expected minimum output of the multi-energy complementary system;

[0307] S5: Generate individual coding information; generate initialization solutions for each of them to obtain the initialization population; use box domination to obtain the dominant population and the basic population;

[0308] S6: The obtained population is fused through a multi-operator strategy and a heuristic constraint processing method is used; for specific water volume dynamic balance constraints, the amount of water that exceeds the constraint is first calculated, and then the outflow of the reservoir in this period is adjusted according to the violation amount, so that the reservoir capacity returns to the constraint range; for the reservoir capacity constraint, the amount of water that exceeds the constraint is first calculated, and then the outflow of the reservoir in this period is adjusted according to the violation amount, so that the reservoir capacity returns to the constraint range. At the same time, in order to maintain dynamic water volume balance, the strategy also makes an opposite and equal adjustment to the flow in the next period, so that the current storage capacity can meet the constraints without changing the storage capacity of other periods, and then calculate the objective function value;

[0309] S7: According to the obtained objective function value, the restart mechanism is used to determine whether the iteration termination condition is met. If it is terminated, the corresponding plan and Pareto frontier value are output. If there is no termination condition, it is necessary to determine whether a restart mechanism is needed. If necessary, the existing basic population is updated and step S6 is executed until the iteration termination condition is met, and the corresponding solution and the corresponding plan are output. Otherwise, steps S5 to S6 are executed, and the dominance rule is used again to obtain a new dominant population and basic population until the iteration termination condition is met, and the corresponding solution and the corresponding plan are output, thereby completing the medium- and long-term multi-objective optimization scheduling of the reservoir group.

[0310] The present invention proposes a medium- and long-term multi-objective random scheduling optimization method that takes into account the complementarity of water, wind and solar power, which has the following beneficial effects: driven by climate change and the "dual carbon" goals, renewable energy represented by wind and photovoltaic power has developed rapidly in recent years. Making full use of the flexibility of hydropower and the natural complementarity between different resources, and complementary power generation is an effective way to promote the consumption of new energy. The present invention combines the correlation analysis of water, wind and solar power with multi-objective optimization, and constructs a set of decision-making processes. The decision-making process is based on the obtained runoff and wind and solar power output scenario set, which efficiently solves the medium- and long-term multi-objective optimization scheduling of water, wind and solar power. Compared with the prior art, the present invention comprehensively considers the time correlation of water, wind and solar power, and generates a scenario set on this basis to characterize the runoff and wind and solar power output characteristics, and constructs multi-objective optimization scheduling and optimization algorithms to provide feasible strategies for solving the medium- and long-term multi-objective optimization scheduling of water, wind and solar power complementarity, and provides theoretical and methodological support for the decision-making of hydropower operation scheduling.

[0311] The embodiment of the present invention further provides a medium- and long-term multi-objective random scheduling optimization system considering the complementarity of water, wind and solar power, including:

[0312] Data acquisition module, used to obtain historical time series of runoff, wind power output and photovoltaic output;

[0313] The scenario generation module first uses the Markov chain to analyze the temporal correlation between water, wind and solar power, then uses the Monte Carlo sampling method to obtain the initial scenario set of runoff and wind and solar power output, and finally uses the scenario elimination method based on the probability distance to obtain the final scenario set that meets the requirements;

[0314] Model building module, used to build a multi-objective optimization model for medium- and long-term scheduling of a reservoir group with the goal of maximizing the expected power generation within the scheduling period and maximizing the expected minimum output of the multi-energy complementary system;

[0315] The initialization module is used to generate individual coding information. Each individual information corresponds to a scenario, and the initialization solution is generated for each of them to obtain the initialization population; the dominant population and the basic population are obtained by using the dominance algorithm;

[0316] The objective function value solving module uses a heuristic constraint processing method to combine the obtained population through a multi-operator fusion strategy. For specific water volume dynamic balance constraints, the amount of water that exceeds the constraint is first calculated, and then the outflow of the reservoir during this period is adjusted according to the violation amount, so that the reservoir capacity returns to the constraint range. For the reservoir capacity constraint, the amount of water that exceeds the constraint is first calculated, and then the outflow of the reservoir during this period is adjusted according to the violation amount, so that the reservoir capacity returns to the constraint range. At the same time, in order to maintain dynamic water volume balance, the strategy also makes an opposite and equal adjustment to the flow of the next period, so that the current capacity can meet the constraints without changing the capacity of other periods, and then calculate the objective function value.

[0317] The loop iteration module determines whether the iteration termination condition is met through the restart mechanism according to the obtained objective function value. If it is terminated, the corresponding solution and Pareto frontier value are output. If there is no termination condition, it is necessary to determine whether a restart mechanism is needed. If necessary, the existing basic population is updated and the objective function value solving module is executed until the iteration termination condition is met, and the corresponding solution and the corresponding solution are output. Otherwise, a new dominant population and basic population are obtained again, and the objective function value solving module is executed again until the iteration termination condition is met, and the corresponding solution and the corresponding solution are output.

[0318] The scheduling module is used to perform random scheduling of water, wind and solar power according to the corresponding solutions and schemes obtained.

[0319] It should be understood that the functional unit modules in various embodiments of the present invention may be concentrated in one processing unit, or each unit module may exist physically separately, or two or more unit modules may be integrated in one unit module, and may be implemented in the form of hardware or software.

[0320] An embodiment of the present invention further provides an electronic terminal, including:

[0321] one or more memories having computer programs or instructions stored thereon;

[0322] One or more processors are used to load and execute the computer program or instructions to implement the medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar as described above.

[0323] An embodiment of the present invention also provides a computer-readable storage medium on which a computer program or instruction is stored. When the computer program or instruction is called by a computer, the medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power as described above is implemented.

[0324] The present invention discloses a medium- and long-term multi-objective scheduling optimization method, system, terminal and medium considering the complementarity of water, wind and light; Markov chain is applied to characterize the time correlation of the three variables of water, wind and light through the state transfer matrix, Monte Carlo sampling method is used to generate the time correlation scene sequence of water, wind and light, and the synchronous back-substitution elimination method based on probability distance is used to reduce the initial scene set to achieve high-quality approximation of the initial scene with a small number of scenes; based on the obtained water, wind and light scene set, a multi-objective random scheduling optimization model is constructed in the medium- and long-term scale of the reservoir group with the maximum expected power generation and the maximum expected minimum output of water, wind and light; the Borg algorithm is used and the heuristic constraint processing strategy is designed for the model constraints to finally solve the problem. The present invention describes the time series characteristics of runoff, wind power and photovoltaic output. The established optimization model and the designed solution algorithm can efficiently solve the medium- and long-term multi-objective optimization problem of hydropower, and provide a feasible strategy for the coordinated operation of multiple energy sources to improve the renewable energy absorption capacity of the power system.

[0325] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0326] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0327] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0328] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0329] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0330] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar, characterized in that: The steps include: Step S1, determine resource variables E1, E2, E3, respectively, runoff inflow, wind power output, and photovoltaic output; for each resource variable E b , b=1,2,3, all get the time series of historical values ​​of resource variables in the target area in historical years, thus, we get the time series of historical values ​​of runoff, wind power output and photovoltaic output respectively; Step S2: for each resource variable E b , determine the corresponding state space; then, apply the Markov chain to the resource variable E b The historical value time series is analyzed for time correlation to obtain the resource variable E b The cumulative probability state transfer matrix Q b ; Determine the research time scale as the length of period t, Δt; set the number of simulation scenarios B and resource variables E that need to be initially generated b The initial value of Based on the resource variable E b The cumulative probability state transfer matrix Q b , using random numbers to generate the resource variable E for each period t within the year b The simulation value time series forms a simulation scenario; the loop is executed B times to obtain the resource variable E b B simulation scenarios form the resource variable E b The initial set of simulation scenarios; Step S3, set the target number of simulation scenarios J; use the simulation scenario reduction algorithm to calculate the number of each resource variable E b The B simulation scenarios in the initial simulation scenario set are reduced to obtain a reduced simulation scenario set having J simulation scenarios; Therefore, for the runoff, wind power output and photovoltaic output resource variables, a simulation scenario set with J simulation scenarios is obtained respectively, and each simulation scenario in the simulation scenario set of each resource variable is randomly arranged, thereby obtaining the final runoff simulation scenario set SCE1 = {S 11 ,S 12 ,...,S 1J }、Wind power output simulation scenario set SCE2 = {S 21 ,S 22 ,...,S 2J } and photovoltaic output simulation scenario set SCE3 = {S 31 ,S 32 ,...,S 3J }; The runoff simulation scenario set SCE1, the wind power output simulation scenario set SCE2 and the photovoltaic output simulation scenario set SCE3 are combined to form a comprehensive scenario set SC = {S1, S2, ..., S J }; Among them, for the comprehensive scene S in the comprehensive scene set SC j , j=1,2,...,J, with the following relationship: S j ={S 1j , S 2j , S 3j }, at the same time, the comprehensive scene S j The probability p j =(p 1j +p 2j +p 3j ) / 3; p 1j ,p 2j ,p 3j They are runoff simulation scenarios S 1j , Wind power output simulation scenario S 2j And photovoltaic output simulation scenario S 3j probability; Step S4, constructing a medium- and long-term scheduling multi-objective optimization model for a reservoir group with the objectives of maximizing the expected power generation within the scheduling period and maximizing the expected minimum output of the multi-energy complementary system; Step S5: for each comprehensive scenario S j , using runoff simulation scenario S 1j , Wind power output simulation scenario S 2j And photovoltaic output simulation scenario S 3j The simulated value time series of is taken as the input value, and the multi-objective optimization model of medium- and long-term operation of the reservoir group is solved to obtain the comprehensive scenario S j The outflow QT of the next hydropower station n in each time period t during the dispatch cycle n,j,t , to achieve coordinated and coordinated scheduling of the complementary effects of water, wind and solar power.

2. According to claim 1, a medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power is characterized in that: Step S2 is specifically as follows: Step S2.1, for each resource variable E b , determine the corresponding state space; the state space has β states, namely state 1, state 2, ..., state β; Step S2.2, apply the Markov chain to the resource variable E b The historical value time series is analyzed for time correlation to obtain the resource variable E b The state transition probability matrix G b : Where: g u',v' Indicates resource variable E b The probability of transition from state u' to state v'; u' = 1, 2, ..., β; v' = 1, 2, ..., β; Step S2.3, based on resource variable E b The state transition probability matrix G b , get the resource variable E b The cumulative probability state transfer matrix Q b : Where: q u',v' Indicates resource variable E b The cumulative probability of state transition from state u' to state v'; q u',v' =g u',1 +g u',2 +...+g u',v' ; Step S2.4, generate resource variable E b A simulation scenario: Step S2.4.1, set resource variable E b The initial value a0 of the input signal is obtained, and the initial state β0 is obtained according to the initial value a0; wherein β0=1,2,...,β; Step S2.4.2, set time period t=1; Step S2.4.3, generate a random number e1, e1∈[0,1]; in the cumulative probability state transfer matrix Q b In the state β t-1 The corresponding row vector is: t-1 Row vector of rows: Step S2.4.4, traverse the row vector from front to back When the traversal reaches an element greater than or equal to the random number e1 When v'=1,2,...,β, the element The corresponding state v' after state transfer is the state of time period t, which is re-expressed as state β t ; State β t Corresponding resource variable E b The numerical range is: [a l ,a h ]; Step S2.4.5, generate a random number e2, e2∈[0,1]; by formula a t =a t-1 +e2×(a h -a l ), get the resource variable E b The simulated value a at time period t t ; Among them, a t-1 For resource variable E b The simulation value at time period t-1; Step S2.4.6, determine whether the time period t reaches the preset total number of scheduling time periods T, if so, execute step S2.4.7; if not, set t = t + 1 and return to step S2.4.3; Step S2.4.7, output the resource variable E obtained at this time b The simulation values ​​in time period 1, time period 2, ..., time period T form the simulation value time series a1, a2, ..., a T , forming a simulation scenario; Step S2.4.8, determine whether the B simulation scene has been obtained. If not, return to step S2.4.2, and form another simulation scene by executing steps S2.4.2 to S2.4.7; repeat this process until the B simulation scene is obtained, forming the resource variable E. b The initial set of simulation scenarios.

3. According to claim 2, a medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power is characterized in that: In step S3, a simulation scenario reduction algorithm is used to reduce the resource variables E b The B simulation scenes in the initial simulation scene set are reduced to obtain a reduced simulation scene set with J simulation scenes, specifically: Step S3.1, resource variable E b The initial simulation scenario set includes B simulation scenarios, which can be expressed as: simulation scenario set RS = {ξ1,ξ2,...,ξ B };ξ1,ξ2,...,ξ B They represent B types of simulation scenarios respectively; Step S3.2, set each simulation scenario ξ k The initial probability p k are equal, all are 1 / B; where x = 1, 2, ..., B; a set DS is established to store the scenes that need to be eliminated; Step S3.3, set the set DS to be empty; Step S3.4, traverse each simulation scenario ξ in the simulation scenario set RS k ; For each traversed simulation scenario ξ k , determine the distance simulation scene ξ k Recent simulation scenariosξ k" , that is, it satisfies the relationship: DT k (k")=minDT k,s ,k∈RS,s∈RS,k≠s Among them: DT k,s Represents the simulation scenario ξ k and simulation scenarios s The distance between k (k") represents the simulation scenario ξ k and the most recent simulation scenarioξ k" The distance between Using the formula PD k =p k *DT k (k"), k∈RS, calculate the simulation scenario ξ k The corresponding minimum distance probability PD k ; Step S3.5, compare the simulation scenarios ξ in the simulation scenario set RS k The minimum distance probability PD k , using the formula PD d =minPD k , k∈S, and obtain the minimum distance probability PD in the simulation scene set RS k The minimum value of d ; The PD d Corresponding simulation sceneξ d , distance simulation sceneξ d The most recent simulation scenario is ξ d* ; Step S3.6, let the simulation scenario set RS = RS-{ξ d }, DS=DS+{ξ d }, and let p d* =p d* +p d ; Determine whether the number of simulation scenarios in the simulation scenario set RS is reduced to J, if yes, execute step S3.7; if not, return to step S3.4; Step S3.7, obtaining a reduced simulation scenario set having J simulation scenarios, and each simulation scenario in the simulation scenario set has a probability.

4. According to claim 1, a medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power is characterized in that: In step S4, in the multi-objective optimization model for medium- and long-term operation of the reservoir group, the hydropower station n in the period t is used in the comprehensive scenario S j Outbound flow QT n,j,t As decision variables of the model; The objective function includes an objective function F1 and an objective function F2; Objective function F1: The multi-energy complementary system maximizes the expected power generation, and the expression is: Where: P n,j,t Represents hydropower station n in comprehensive scenario S j output lower than the output in time period t; t=1,2,...,T; T is the total number of medium- and long-term dispatching time periods; n=1,2,...,N; N is the number of hydropower stations; j=1,2,...,J; J is the comprehensive scenario set SC={S1,S2,...,S J }The number of comprehensive scenes; p j Represents a comprehensive scenario S j The probability of; ΔT represents the length of the medium and long-term scheduling period; Indicates the wind turbine in the comprehensive scenario S j The output is lower than the output in period t; Indicates that the photovoltaic unit is in the comprehensive scenario S j The output is lower than the output in period t; Objective function F2: The expected minimum output of the multi-energy complementary system is maximized, and the expression is: Thus, the objective function F1 and the objective function F2 are established.

5. According to claim 4, a medium- and long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power is characterized in that: The multi-objective optimization model for medium- and long-term operation of the reservoir group includes the following constraints: (1) Water balance equation: Where: V n,j,t+1 、V n,j,t are the storage capacities of hydropower station n in time period t+1 and time period t respectively; I n,j,t , QT n,j,t They are respectively hydropower station n in time period t in comprehensive scenario S j The runoff inflow and outflow under the reservoir; Ω(n) is the set of hydropower stations upstream of hydropower station n; QT m,j,t The upstream hydropower station m of hydropower station n in the period t in the comprehensive scenario S j Runoff inflow under (2) Outbound flow equation: Where: QP n,j,t , S n,j,t They are respectively hydropower station n in time period t in comprehensive scenario S j The power generation flow and abandoned water flow under (3) Outbound flow restrictions: in: QT n,t , are the lower and upper bounds of the total outflow of hydropower station n in time period t, respectively; (4) Power generation flow limit: in: QP n,t , are the lower and upper bounds of the power generation flow of hydropower station n in time period t, respectively; (5) Output limit: in: P n,t , are the lower and upper bounds of the output of hydropower station n in time period t, respectively; (6) Storage capacity limit: in: V n,t , are the lower and upper bounds of the reservoir capacity of hydropower station n in time period t, respectively; (7) Water level constraints at the beginning and end of the dispatch period: Where: Z n,Ini , Z n,End are the initial water level and final water level of hydropower station n respectively; Z n,j,1 , Z n,j,T are the water levels of hydropower station n in period 1 and period T respectively; (8) Water level-reservoir capacity constraints: in: is the nonlinear function between reservoir water level and water storage; Z n,j,t is the hydropower station n in the period t in the comprehensive scenario S j The water level below; (9) Tailwater level-discharge constraint: in: is the nonlinear function between the tail water level and outflow flow of the hydropower station n; is the hydropower station n in the period t in the comprehensive scenario S j The tailwater level below; (10) Hydropower output function: Where: H n,j,t is the hydropower station n in the period t in the comprehensive scenario S j The power generation head under K n is the output coefficient of hydropower station n; Z n,j,t+1 is the hydropower station n in the period t+1 in the comprehensive scenario S j The water level below; (11) Transmission capacity limitation constraints: Where: W n represents the set of wind power stations associated with hydropower station n; P wd represents the output of the wind power station wd associated with the hydropower station n; B n represents the set of photovoltaic power stations associated with hydropower station n; P sol represents the output of the photovoltaic power station sol associated with the hydropower station n; P n represents the total output of hydropower station n; It is the maximum value of the integrated water, wind and solar power transmission capacity of hydropower station n.

6. The mid- to long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power according to claim 5 is characterized in that: Step S5 is specifically as follows: Step S5.1, generate individual code X: X={QT 111 ,…,QT 11T ,QT 121 ,…,QT 12T ,…,QT NJ1 ,…,QT NJT }; Among them, QT n,j,t Meaning: Hydropower station n in time period t in comprehensive scenario S j The runoff inflow under is the decision variable; according to the individual code X, the corresponding number of individuals with initialization solutions are generated according to the set population size, thereby forming an initialization population; Step S5.2, determine the population size NI and the ratio γ; wherein the ratio γ is the number of individuals in the dominant population / the number of individuals in the basic population; Step S5.3, using the inter-individual dominance algorithm to determine the dominant population and the basic population; Step S5.4, adopt the multi-recombination operator fusion strategy to perform crossover operation on the dominant population and the basic population to generate a new population; then return to step S5.3, re-adopt the inter-individual domination algorithm to divide the new population into the dominant population and the basic population, and repeat this cycle until the set number of cycles is reached, and then execute step S5.5; Step S5.5, for each individual in the new population, a heuristic constraint processing method is used to perform heuristic constraint processing on the solution value of each individual, so as to obtain each individual with an updated solution value; Step S5.6, based on the updated solution value of each individual obtained in step S5.5, calculate the objective function value of the objective function F1 and the objective function F2 corresponding to each individual; Step S5.7, based on the objective function values ​​corresponding to each individual, determine whether the iteration termination condition is met through the restart mechanism. If so, execute step S5.8; if not, determine whether a restart mechanism is needed. If so, start the restart mechanism, update the basic population, and return to step S5.4; if not, return to step S5.3; Step S5.8, output the solution value of each individual in the dominant population. The solution value of each individual is a scheduling plan, thereby obtaining a set of multi-objective optimization scheduling plans, thereby completing the medium- and long-term multi-objective optimization scheduling of the hydropower station group.

7. The mid- to long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power according to claim 6 is characterized in that: Step S5.3 is specifically as follows: Step S5.3.1, setting the value of parameter ∈; Step S5.3.2, using the function value of the objective function F1 as the horizontal coordinate, the function value of the objective function F2 as the vertical coordinate, and the parameter ∈ as the grid side length to establish a gridded objective function space; Step S5.3.3, for each current individual X c , c=1,2,…,NI, determine the grid where it is located in the objective function space according to the function value of its objective function F1 and the function value of its objective function F2; Step S5.3.4, traverse each individual and calculate each individual X c The dominance number NC is calculated as: For individual X c , the initial value of its dominance number NC is 0; traverse the other NI-1 individuals in turn, and for the currently traversed individual X d , d=1,2,…,NI, and d≠c, if individual X d and individual X c The function values ​​of X are located in the same grid in the objective function space, then Algorithm 1 is used to determine the individual X c Whether it dominates individual X d , if it dominates, then the domination number NC increases by 1; if individual X d and individual X c If the function value of is located in different grids in the objective function space, then Algorithm 2 is used to determine the individual X c Whether it dominates individual X d If it dominates, the domination number NC increases by 1; thus, the traversal of the other NI-1 individuals is completed, and the value of the domination number NC is obtained, which is the value of individual X c The domination number NC represents the domination number of individual X. c The number of individuals controlled; Among them: Algorithm 1 and Algorithm 2 are: Individual X c = {QT 111 ,…,QT 11T ,QT 121 ,…,QT 12T ,…,QT NJ1 ,…,QT NJT }, individual X c The function value of the objective function F1 is F 1c ; Individual X c The function value of the objective function F2 is F 2c , individual X c The objective function value vector is expressed as: c ={F 1c ,F 2c }; Individual X d The function value of the objective function F1 is F 1d ; Individual X d The function value of the objective function F2 is F 2d , individual X d The objective function value vector is expressed as: d ={F 1d ,F 2d }; Algorithm 1 is: When conditions 1 and 2 are met, individual X c Dominant individual X d ; Condition 1: F 1c ≥F 1d +∈, and F 2c ≥F 2d +∈; Condition 2: There exists F xc ,x=1,2, satisfying the relationship: F xc >F 1d +∈, and F xc >F 2d +∈; Algorithm 2 is: if and only if one of conditions 3 and 4 is met, individual X c Dominant individual X d : Condition 3: Among them: < represents and The relationship between conditions 1 and 2 is satisfied. represents round down; Condition 4: Among them: ∥∥ is the modulo operation; & is the logical AND; Step S5.3.5, according to the population size NI and the ratio γ, obtain the archive size of the dominant population and the population size of the basic population; Sort the individuals in descending order of dominance number. If two individuals have the same dominance number, they can be sorted arbitrarily. Among the sorted NI individuals, select the individuals with the archive size number that are ranked first to form the dominant population, and the other individuals form the basic population.

8. The mid- to long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power according to claim 7 is characterized in that: Step S5.4 is specifically as follows: Step S5.4.1, setting a crossover operator pool; the crossover operator pool has N_CO crossover operators; Step S5.4.2, initially, each crossover operator CO is set to have the same selection probability PC; Step S5.4.3, select a crossover operator to perform a crossover operation, and update the selection probability PC of the crossover operator: ① Based on the selection probability PC of each current crossover operator, select the crossover operator A from the crossover operator pool; ②According to the number of parents N required by the crossover operator A A , randomly select a parent individual X from the dominant population A1 , select N from the basic population through a tournament A -1 parent individual, expressed as: parent individual X AB ,B=2,3,…,N A ; ③Use the crossover operator A to perform crossover on the parent individual X A1 and parent individual X AB Perform crossover operation to generate N A offspring individuals, and N A The original parent individuals are deleted from the population; ④According to N A The function value of the offspring individuals is used to determine the grid in the objective function space, and the offspring individuals within the dominant population range are added to the dominant population, and the other offspring individuals are added to the basic population; among which: the number of offspring individuals added to the dominant population generated by the crossover operator A this time is SN_A; ⑤ Update the selection probability PC of the crossover operator A using the following formula: A : Where: SN_V represents the total number of offspring individuals added to the dominant population generated by the crossover operator V from the first cycle to the current cycle, V = 1, 2, ..., N_CO; is a constant, ⑥When this cycle ends, return to step ① and continue with the next cycle until the set number of cycles is reached.

9. The mid- to long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power according to claim 6 is characterized in that: Step S5.5 is specifically as follows: For each individual X={QT 111 ,…,QT 11T ,QT 121 ,…,QT 12T ,…,QT NJ1 ,…,QT NJT }, both execute the dynamic balance constraint adjustment of power generation flow in step S5.4.1 and the reservoir capacity constraint adjustment in step S5.4.2: Step S5.4.1, dynamic balance constraint adjustment of outbound flow: ① According to the water balance equation and the outflow flow equation, the reservoir n in the entire dispatch period is obtained in the comprehensive scenario S j The water volume difference between the water consumption under the condition and the constrained water volume ΔQT n,j ; ②Use the following formula to convert the water volume difference ΔQT n,j Adjust to reservoir n in comprehensive scenario S j The outbound flow QT of each period t under n,j,t The corrected outbound flow is obtained ③Use the following formula and apply the outbound flow restriction constraint to correct the outbound flow Further correction is made to obtain the corrected outbound flow ④ The corrected reservoir n is obtained in the comprehensive scenario S j The outflow rate of each period t under the condition of reservoir n in the comprehensive scenario S j Check whether the water balance equation is satisfied during the entire dispatch period; if satisfied, the dynamic balance constraint adjustment of the outflow flow is completed; Otherwise, return to step ① and make corrections in a loop; Step S5.4.2, reservoir capacity constraint adjustment: ① According to the current individual X's reservoir n in the comprehensive scenario S j The outbound flow QT of each period t under n,j,t , the calculation results show that the hydropower station n in the comprehensive scenario S j The storage capacity V at time period t n,j,t ; ② Determine the hydropower station n in the comprehensive scenario S j The storage capacity V at time period t n,j,t Whether the storage capacity constraint is met; if not, when the storage capacity V n,j,t Exceeds the upper limit of the storage capacity of hydropower station n in time period t Then execute ③; when the storage capacity V n,j,t Lower than the lower limit of the reservoir capacity of hydropower station n in time period t V n,t , then execute ④; ③The excess Averaged over each period t of the scheduling period, that is, the mean value of the excess quantity is obtained Then press the excess amount average Averaged over each period t of the dispatch period, the hydropower station n in the comprehensive scenario S j The storage capacity V at each time period t n,j,t amendments; If the reservoir n is completed in the comprehensive scenario S j The storage capacity V of all time periods t under n,j,t After the correction, there is still a situation that exceeds the upper limit of storage capacity. The storage capacity V in time period t n,j,t When the excess amount is increased, the excess amount will be averaged into the outflow of the adjacent reservoirs; ④The insufficient amount V n,t -V n,j,t Averaged over each period t of the scheduling period, we get the mean value of the shortage ΔV n,j,t =( V n,t -V n,j,t ) / T; then, the storage capacity V of other time periods t n,j,t Both reduce ΔV n,j,t , and replenish the storage capacity V of the current period t n,j,t ; Complete the hydropower station n in the comprehensive scenario S j The storage capacity V at time period t n,j,t amendments; If the reservoir n is completed in the comprehensive scenario S j The storage capacity V of all time periods t under n,j,t After the correction, there is still insufficient storage capacity lower bound V n,t The storage capacity V in time period t n,j,t When the outflow from the adjacent reservoir is supplemented to the lower limit of insufficient storage capacity V n,t The storage capacity V in time period t n,j,t .

10. The mid- to long-term multi-objective random scheduling optimization method considering the complementarity of water, wind and solar power according to claim 6 is characterized in that: In step S5.7, it is determined whether a restart mechanism is required. If necessary, the restart mechanism is started to update the basic population, specifically: When the algorithm search stagnates, the restart mechanism is started: according to the current ratio γ and the current number of dominant population individuals archive size, the formula archive size / γ is used to obtain the number of basic population individuals; Clear the current basic population, and fill the basic population with the corresponding number of individuals with the highest NC ranking in the current dominant population. The insufficient part is obtained through the mutation of individuals in the dominant population, thus obtaining the updated basic population.

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