Method for evaluating influence of small hydropower transformation pumped storage unit on power generation of water-light complementary system

Abstract

The invention relates to the technical field of water-light-storage complementary operation, and discloses a method for evaluating the power generation influence on a water-light-storage complementary system from a small hydropower transformation pumped storage unit, which comprises the following steps: constructing a cascade small hydropower-oriented water-light-storage complementary system joint optimization scheduling model; converting the multi-target optimization scheduling model into a single-target optimization scheduling model; solving and calculating the processed cascade small hydropower-oriented water-light-storage complementary system joint optimization scheduling model by adopting a bat algorithm; constructing a cascade small hydropower station-oriented water-light complementary system joint optimization scheduling model, and performing solving calculation of the model by adopting the bat algorithm; and analyzing the power generation influence of the small hydropower transformation hybrid pumped storage unit on the water-light complementary system by comparing the instance calculation results of the two joint optimization scheduling models in typical days of different weather scenes. The method has the advantages that power generation benefit comprehensive analysis of the pumped storage unit transformed from the small hydropower station is realized, and important technical support can be provided for new energy development and utilization for the cascade small hydropower station.

Description

Technical Field

[0001] The invention relates to the technical field of water-photovoltaic-storage complementary operation, and in particular to a method for evaluating the impact of small hydropower transformation of pumped storage units on power generation of a water-photovoltaic complementary system. Background Art

[0002] Clean, low-carbon and renewable energy sources such as wind power and photovoltaics have developed rapidly in recent years. However, due to the influence of meteorological factors, the output of wind and solar power generation has obvious randomness, volatility and intermittency, which restricts its large-scale grid-connected operation. Small hydropower is a globally recognized clean energy with advantages such as mature development technology and low cost. In addition, small hydropower and photovoltaic power generation have natural resource complementarity and high spatial overlap, which creates favorable conditions for the complementary operation of small hydropower and photovoltaics. However, due to the limitation of project scale, small hydropower has relatively weak regulation capacity. By transforming small hydropower into hybrid pumped storage units and carrying out joint optimization scheduling of water-photovoltaic-storage complementary systems for cascaded small hydropower, the regulation potential of cascaded small hydropower can be fully tapped and the development and utilization of photovoltaic resources around small hydropower can be promoted. However, the transformation of small hydropower into pumped storage units is bound to increase the construction cost. Therefore, the study of its role and influence on the power generation and operation of the water-photovoltaic complementary system is of great practical significance for the production decision-making of the complementary development and utilization of water-photovoltaic-storage based on the transformation of small hydropower into pumped storage units. Therefore, there is an urgent need to study a method for evaluating the impact of small hydropower transformation into hybrid pumped-storage units on the power generation of water-photovoltaic complementary systems, to achieve a comprehensive analysis of the power generation benefits of small hydropower transformation into pumped-storage units, and to provide important technical support for the development and utilization of new energy for cascade small hydropower. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for evaluating the power generation impact of small hydropower transformation pumped storage units on the water-photovoltaic complementary system, aiming at the production decision-making problem of the water-photovoltaic-storage complementary development and utilization based on small hydropower transformation pumped storage units.

[0004] The object of the present invention is achieved by the following technical solution: a method for evaluating the impact of small hydropower transformation pumped storage units on power generation of a water-photovoltaic complementary system, the method comprising:

[0005] Step S1, taking the maximum system power generation within the calculation period and the minimum system total output fluctuation within the calculation period as the objective function, constructing a joint optimization scheduling model for the water-solar-storage complementary system for cascade small hydropower;

[0006] Step S2, respectively standardize the two objective functions and convert them into dimensionless indicators ranging from 0 to 1, and use the weight coefficient method to convert the multi-objective optimization scheduling model into a single-objective optimization scheduling model;

[0007] Step S3, using the bat algorithm to solve and calculate the processed joint optimization dispatching model of the water-solar-storage complementary system for cascade small hydropower;

[0008] Step S4, using the same objective function as the joint optimization dispatch model of the water-photovoltaic-storage complementary system for cascade small hydropower and all constraints except the operation constraints of the hybrid pumped storage unit, construct a joint optimization dispatch model of the water-photovoltaic-storage complementary system for cascade small hydropower, and also perform standardization processing and solve the calculation using the bat algorithm;

[0009] Step S5, selecting relevant data such as hydropower, photovoltaic power generation and power load on typical days of different weather scenarios such as sunny days, cloudy days and rainy days, and carrying out example calculations of joint optimization scheduling model for water-photovoltaic-storage complementary system for cascade small hydropower and example calculations of joint optimization scheduling model for water-photovoltaic complementary system for cascade small hydropower respectively;

[0010] Step S6: By comparing the example calculation results of two joint optimization scheduling models under typical days in different weather scenarios, the impact of small hydropower transformation hybrid pumped storage units on the power generation of the water-solar complementary system is analyzed.

[0011] Specifically, the specific steps of step S1 include:

[0012] Step S11, the objective function for calculating the maximum power generation of the system within the cycle is as follows:

[0013]

[0014] In the formula, E sum is the total power generation of the system during the calculation period, MWh; N sum,t is the total system output during period t, MW; N hi,t is the average output of the i-th hydropower station in the system during period t, MW; N pi,t is the average output of the pumped storage power station in the system during period t, MW; N pv,t is the average output of the photovoltaic power station in the system during period t, MW; Δt is the period length, h; T is the number of calculation periods in the scheduling period; N is the number of cascade hydropower stations;

[0015] Step S12: The objective function for minimizing the fluctuation of the total system output within the calculation period is as follows:

[0016]

[0017] Where D t is the system output variance during period t; D v is the total system output variance within the calculation period; is the average output of the system, MW; T is the number of calculation periods in the dispatch period; N sum,t is the total system output during period t, MW;

[0018] Step S13, setting the boundary values ​​of each constraint condition in the short-term multi-objective scheduling model of water, wind, solar and storage, and setting the iterative termination conditions of the solution algorithm. The constraint conditions of the joint optimization scheduling model of the water, wind, solar and storage complementary system for cascade small hydropower include reservoir water balance constraints, reservoir water level constraints, outflow constraints, power generation flow constraints, cascade upstream and downstream flow balance constraints, hydropower station output constraints, photovoltaic power station output constraints and hybrid pumped storage unit operation constraints.

[0019] Specifically, the specific steps of step S2 include:

[0020] Step S21: Through standardization, the objective function of the maximum power generation of the system within the calculation period is converted into the ratio of actual power generation to the maximum rated power generation of the system. The specific calculation formula is as follows:

[0021]

[0022] Where, e is the standardized index of the maximum power generation target of the system, and its value ranges from 0 to 1; E max is the maximum power generation of the system under rated power operation, MWh; E sum is the total power generation of the system during the calculation period, MWh; is the total rated power of the system, MW; is the rated power of the i-th hydropower station in the system, MW; is the rated power of the pumped storage power station in the system, MW; is the rated power of the PV power station in the system, MW; T is the number of calculation periods in the dispatch period; N is the number of cascade hydropower stations; Δt is the time period length, h;

[0023] Step S22: Through standardization, the objective function of minimizing the fluctuation of the total system output within the calculation period is converted into 1 minus the total system output variance / (the maximum rated power of the system) 2 The specific calculation formula is as follows:

[0024]

[0025] Where f is the standardized index of the minimum fluctuation target of the total output of the system, and its value ranges from 0 to 1; D v is the total system output variance within the calculation period; is the total rated power of the system, MW;

[0026] Step S23: Use the weight coefficient method to transform the multi-objective optimization scheduling model into a single-objective optimization scheduling model. The calculation formula is as follows:

[0027] cg=α×e+β×f (9)

[0028] Where cg represents the comprehensive objective function of the model; α and β represent the weights of maximizing the system power generation and minimizing the system total output fluctuation in the comprehensive objective function within the calculation period, respectively, with values ​​ranging from 0 to 1, and α+β=1; e is the standardized index of the maximum system power generation target, with values ​​ranging from 0 to 1; f is the standardized index of the minimum system total output fluctuation target, with values ​​ranging from 0 to 1.

[0029] Specifically, the specific steps of step S3 are:

[0030] Step S31, initializing characteristic parameters of the bat algorithm, including bat population number n, minimum frequency fmin, maximum frequency fmax, loudness attenuation coefficient α, pulse increase coefficient γ and maximum number of iterations max_generation;

[0031] Step S32: Initialize all bat individuals and randomly generate the initial position of particle i within the water level constraints of each reservoir at each time period. speed Initial frequency f(i), pulse emission rate r i 0 and impulse loudness Where N is the total number of cascade small hydropower stations, and T is the total number of calculation periods;

[0032] Step S33, find the initial optimal position of the bats, and calculate the comprehensive objective function of the joint optimization scheduling model of the water-solar-storage complementary system as the initial fitness value of the bats 0 (i), the calculation formula is as follows: find the bat with the largest fitness value and assign the corresponding fitness value to the initial global optimal value, gbest 0 = max{fitness 0 (i)}i∈[1,n], the spatial position of the individual bat is the optimal position X of the bat population. * ;

[0033]

[0034] Where f(z) is the objective function, M·K is the penalty term, M is a positive number, and K is the number of calculation periods that do not meet the constraints; Step S34, global update of bat positions, using the following formula to update the bat positions and speed V i t Update and check whether the bat position exceeds all constraint boundaries of the model. For the bat position and speed that exceed the constraint boundary, replace them with the corresponding boundary values ​​and calculate the updated fitness function value fitness t (i);

[0035]

[0036] f i =f min +(f max -f min )β (13)

[0037] In the formula, Indicates the position of bat i in the tth iteration process, which is used to represent the parameters to be solved; represents the flying speed of bat i during the tth iteration; x* represents the best position of all bats before this iteration; f i represents the frequency of the ultrasonic pulse emitted by the i-th bat; f min 、f max Respectively represent the minimum and maximum values ​​of the frequency; β is a random number that satisfies uniform distribution, β∈[0,1];

[0038] Step S35: Local population update, randomly generate a uniformly distributed random number if Greater than bat i's current pulse emission rate r i t , then the current best position X * Generate new positions by random perturbation according to the following formula Calculate the fitness function value fitnew at the new position t (i);

[0039] x new =x old +θA t (14)

[0040] In the formula, x new represents the new position; x old represents a solution randomly selected from the current solution set; θ∈[-1,1] is a random number that satisfies uniform distribution; A t is the average loudness of all bats in the tth iteration;

[0041] Step S36: Determine whether to accept the locally updated solution. For the solution that meets the condition fitnew t (i)>fitness t (i) and Bat, accept the local update solution: fitness t (i) = fitnew t (i); otherwise, keep the original position unchanged;

[0042] Step S37: determine the current global optimal solution, and compare the fitness value of bat i after updating for the population that has completed a global update and a local update. t (i) and the group's historical global optimal value gbest t-1 , if fitness t (i)≥gbest t-1 , then the fitness value of the corresponding bat is assigned to the historical global optimal value: gbest t-1 =fitness t (i) Update each bat until all bats are compared, and update the historical optimal fitness value to the current optimal value: gbest t =gbest t-1 、gbest_X t =gbest_X t-1 ;

[0043] Step S38: Update bat loudness and pulse emission rate. Update the loudness of bat i according to the following formula: and the pulse emission rate r i t ;

[0044]

[0045] In the formula, is the loudness of the sound wave emitted by bat i at the tth iteration; α is the loudness attenuation coefficient, which is a constant and its value range is 0<α<1; r i t+1 is the pulse emission rate of bat i in the t+1th iteration; r i 0 is the maximum pulse emission rate of bat i, usually 1; γ is the pulse increase coefficient, which is a constant and its value range is γ>0;

[0046] Step S39, determine whether the termination condition is met, if not, go to step S34; otherwise exit the loop, and the best bat obtained is the joint optimization scheduling calculation result of the water-solar-storage complementary system for cascade small hydropower.

[0047] Specifically, in step S6, the calculation results of the two joint optimization scheduling models on typical days in different weather scenarios are compared to analyze the impact of the small hydropower transformation hybrid pumped storage unit on the power generation of the water-photovoltaic complementary system. The results of the joint optimization scheduling of the water-photovoltaic storage complementary system and the joint optimization scheduling of the water-photovoltaic complementary system on typical days in sunny, cloudy and rainy scenarios are mainly compared from the aspects of the total power generation of cascade small hydropower and photovoltaic power stations, the maximum fluctuation amplitude of the total power generation output process, and the reservoir water level change amplitude of each cascade hydropower station, and the changes in the power generation process of the cascade small hydropower and photovoltaic power stations with and without pumped storage units are analyzed.

[0048] The present invention has the following advantages:

[0049] In view of the production decision-making difficulties in the complementary development and utilization of water, photovoltaic and storage based on the transformation of small hydropower pumped-storage units, a joint optimization scheduling model of water, photovoltaic and storage complementary systems for cascade small hydropower was constructed, and the bat algorithm for solving the model was studied. The calculation results were compared with those of the joint optimization scheduling model of the water-photovoltaic complementary system without the participation of pumped-storage units, so as to realize the evaluation of the impact of small hydropower transformation hybrid pumped-storage units on the power generation of the water-photovoltaic complementary system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the evaluation method flow of the present invention; DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0052] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0053] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0054] The present invention is further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description. Figure 1 As shown, a method for evaluating the power generation impact of pumped storage units transformed from small hydropower stations on a water-photovoltaic complementary system comprises: step S1, taking the maximum system power generation within a calculation period and the minimum system total output fluctuation within a calculation period as objective functions, constructing a joint optimization scheduling model for a water-photovoltaic complementary system for cascaded small hydropower stations;

[0055] Step S2, respectively standardize the two objective functions and convert them into dimensionless indicators ranging from 0 to 1, and use the weight coefficient method to convert the multi-objective optimization scheduling model into a single-objective optimization scheduling model;

[0056] Step S3, using the bat algorithm to solve and calculate the processed joint optimization dispatching model of the water-solar-storage complementary system for cascade small hydropower;

[0057] Step S4, using the same objective function as the joint optimization dispatch model of the water-photovoltaic-storage complementary system for cascade small hydropower and all constraints except the operation constraints of the hybrid pumped storage unit, construct a joint optimization dispatch model of the water-photovoltaic-storage complementary system for cascade small hydropower, and also perform standardization processing and solve the calculation using the bat algorithm;

[0058] Step S5, selecting relevant data such as hydropower, photovoltaic power generation and power load on typical days of different weather scenarios such as sunny days, cloudy days and rainy days, and carrying out example calculations of joint optimization scheduling model for water-photovoltaic-storage complementary system for cascade small hydropower and example calculations of joint optimization scheduling model for water-photovoltaic complementary system for cascade small hydropower respectively;

[0059] Step S6: By comparing the example calculation results of two joint optimization scheduling models under typical days in different weather scenarios, the impact of small hydropower transformation hybrid pumped storage units on the power generation of the water-solar complementary system is analyzed.

[0060] Furthermore, the specific steps of step S1 include:

[0061] Step S11, the objective function for calculating the maximum power generation of the system within the cycle is as follows:

[0062]

[0063] In the formula, E sum is the total power generation of the system during the calculation period, MWh; N sum,t is the total system output during period t, MW; N hi,t is the average output of the i-th hydropower station in the system during period t, MW; N pi,t is the average output of the pumped storage power station in the system during period t, MW; N pv,t is the average output of the photovoltaic power station in the system during period t, MW; Δt is the period length, h; T is the number of calculation periods in the scheduling period; N is the number of cascade hydropower stations;

[0064] Step S12: The objective function for minimizing the fluctuation of the total system output within the calculation period is as follows:

[0065]

[0066] Where D t is the system output variance during period t; D v is the total system output variance within the calculation period; is the average output of the system, MW; T is the number of calculation periods in the dispatch period; N sum,t is the total system output during period t, MW;

[0067] Step S13, setting the boundary values ​​of each constraint condition in the short-term multi-objective scheduling model of water, wind, solar and storage, and setting the iterative termination conditions of the solution algorithm. The constraint conditions of the joint optimization scheduling model of the water, wind, solar and storage complementary system for cascade small hydropower include reservoir water balance constraints, reservoir water level constraints, outflow constraints, power generation flow constraints, cascade upstream and downstream flow balance constraints, hydropower station output constraints, photovoltaic power station output constraints and hybrid pumped storage unit operation constraints.

[0068] Furthermore, the specific steps of step S2 include:

[0069] Step S21: Through standardization, the objective function of the maximum power generation of the system within the calculation period is converted into the ratio of actual power generation to the maximum rated power generation of the system. The specific calculation formula is as follows:

[0070]

[0071] Where, e is the standardized index of the maximum power generation target of the system, and its value ranges from 0 to 1; E maxis the maximum power generation of the system under rated power operation, MWh; E sum is the total power generation of the system during the calculation period, MWh; is the total rated power of the system, MW; is the rated power of the i-th hydropower station in the system, MW; is the rated power of the pumped storage power station in the system, MW; is the rated power of the PV power station in the system, MW; T is the number of calculation periods in the dispatch period; N is the number of cascade hydropower stations; Δt is the time period length, h;

[0072] Step S22: Through standardization, the objective function of minimizing the fluctuation of the total system output within the calculation period is converted into 1 minus the total system output variance / (the maximum rated power of the system) 2 The specific calculation formula is as follows:

[0073]

[0074] Where f is the standardized index of the minimum fluctuation target of the total output of the system, and its value ranges from 0 to 1; D v is the total system output variance within the calculation period; is the total rated power of the system, MW;

[0075] Step S23: Use the weight coefficient method to transform the multi-objective optimization scheduling model into a single-objective optimization scheduling model. The calculation formula is as follows:

[0076] cg=α×e+β×f (9)

[0077] Where cg represents the comprehensive objective function of the model; α and β represent the weights of maximizing the system power generation and minimizing the system total output fluctuation in the comprehensive objective function within the calculation period, respectively, with values ​​ranging from 0 to 1, and α+β=1; e is the standardized index of the maximum system power generation target, with values ​​ranging from 0 to 1; f is the standardized index of the minimum system total output fluctuation target, with values ​​ranging from 0 to 1.

[0078] Furthermore, the specific steps of step S3 are:

[0079] Step S31, initializing characteristic parameters of the bat algorithm, including bat population number n, minimum frequency fmin, maximum frequency fmax, loudness attenuation coefficient α, pulse increase coefficient γ and maximum number of iterations max_generation;

[0080] Step S32: Initialize all bat individuals and randomly generate the initial position of particle i within the water level constraints of each reservoir at each time period. speed Initial frequency f(i), pulse emission rate r i 0 and impulse loudness Where N is the total number of cascade small hydropower stations, and T is the total number of calculation periods;

[0081] Step S33, find the initial optimal position of the bats, and calculate the comprehensive objective function of the joint optimization scheduling model of the water-solar-storage complementary system as the initial fitness value of the bats 0 (i), the calculation formula is as follows: find the bat with the largest fitness value and assign the corresponding fitness value to the initial global optimal value, gbest 0 = max{fitness 0 (i)}i∈[1,n], the spatial position of the individual bat is the optimal position X of the bat population. * ;

[0082]

[0083] Where f(z) is the objective function, M·K is the penalty term, M is a positive number, and K is the number of calculation periods that do not meet the constraints; Step S34, global update of bat positions, using the following formula to update the bat positions and speed V i t Update and check whether the bat position exceeds all constraint boundaries of the model. For the bat position and speed that exceed the constraint boundary, replace them with the corresponding boundary values ​​and calculate the updated fitness function value fitness t (i);

[0084]

[0085] f i =f min +(f max -f min )β (13)

[0086] In the formula, Indicates the position of bat i in the tth iteration process, which is used to represent the parameters to be solved; represents the flying speed of bat i during the tth iteration; x* represents the best position of all bats before this iteration; f i represents the frequency of the ultrasonic pulse emitted by the i-th bat; f min 、f max Respectively represent the minimum and maximum values ​​of the frequency; β is a random number that satisfies uniform distribution, β∈[0,1];

[0087] Step S35: Local population update, randomly generate a uniformly distributed random number if Greater than bat i's current pulse emission rate r i t , then the current best position X * Generate new positions by random perturbation according to the following formula Calculate the fitness function value fitnew at the new position t (i);

[0088] x new =x old +θA t (14)

[0089] In the formula, x new represents the new position; x old represents a solution randomly selected from the current solution set; θ∈[-1,1] is a random number that satisfies uniform distribution; A t is the average loudness of all bats in the tth iteration;

[0090] Step S36: Determine whether to accept the locally updated solution. For the solution that meets the condition fitnew t (i)>fitness t (i) and Bat, accept the local update solution: fitness t (i) = fitnew t (i); otherwise, keep the original position unchanged;

[0091] Step S37: determine the current global optimal solution, and compare the fitness value of bat i after updating for the population that has completed a global update and a local update. t (i) and the group's historical global optimal value gbest t-1 , if fitness t (i)≥gbest t-1 , then the fitness value of the corresponding bat is assigned to the historical global optimal value: gbest t-1 =fitness t (i) Update each bat until all bats are compared, and update the historical optimal fitness value to the current optimal value: gbest t =gbest t-1 、gbest_X t =gbest_X t-1 ;

[0092] Step S38: Update bat loudness and pulse emission rate. Update the loudness of bat i according to the following formula: and the pulse emission rate r it ;

[0093]

[0094] In the formula, is the loudness of the sound wave emitted by bat i at the tth iteration; α is the loudness attenuation coefficient, which is a constant and its value range is 0<α<1; r i t+1 is the pulse emission rate of bat i in the t+1th iteration; r i 0 is the maximum pulse emission rate of bat i, usually 1; γ is the pulse increase coefficient, which is a constant and its value range is γ>0;

[0095] Step S39, determine whether the termination condition is met, if not, go to step S34; otherwise exit the loop, and the best bat obtained is the joint optimization scheduling calculation result of the water-solar-storage complementary system for cascade small hydropower.

[0096] Furthermore, in step S6, by comparing the example calculation results of the two joint optimization scheduling models on typical days in different weather scenarios, the impact of the small hydropower transformation hybrid pumped storage unit on the power generation of the water-photovoltaic complementary system is analyzed. Mainly based on the joint optimization scheduling results of the water-photovoltaic-storage complementary system on typical days in sunny, cloudy and rainy scenarios and the joint optimization scheduling results of the water-photovoltaic complementary system, a comparison is made from aspects such as the total power generation of cascade small hydropower and photovoltaic power stations, the maximum fluctuation amplitude of the total power generation output process, and the reservoir water level change amplitude of each cascade hydropower station, and the changes in the power generation process of the cascade small hydropower and photovoltaic power stations with and without pumped storage units are analyzed.

[0097] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the above-mentioned technical content without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, any changes, modifications, equivalent changes and modifications made to the above embodiments based on the technology of the present invention without departing from the content of the technical solution of the present invention belong to the protection scope of the present technical solution.

Claims

1. A method for assessing the impact of small hydropower transformation of pumped storage units on the power generation of a water-photovoltaic complementary system, characterized by: The method includes: Step S1, taking the maximum system power generation within the calculation period and the minimum system total output fluctuation within the calculation period as the objective function, constructing a joint optimization scheduling model of a water-photovoltaic-storage complementary system for cascade small hydropower, wherein the constraints of the joint optimization scheduling model of a water-photovoltaic-storage complementary system for cascade small hydropower include reservoir water balance constraints, reservoir water level constraints, outflow constraints, power generation flow constraints, cascade upstream and downstream flow balance constraints, hydropower station output constraints, photovoltaic power station output constraints and hybrid pumped storage unit operation constraints; Step S2, respectively standardize the two objective functions and convert them into dimensionless indicators ranging from 0 to 1, and use the weight coefficient method to convert the multi-objective optimization scheduling model into a single-objective optimization scheduling model; Step S3, using the bat algorithm to solve and calculate the processed joint optimization dispatching model of the water-solar-storage complementary system for cascade small hydropower; Step S4, using the same objective function as the joint optimization dispatch model of the water-photovoltaic-storage complementary system for cascade small hydropower and all constraints except the operation constraints of the hybrid pumped storage unit, construct a joint optimization dispatch model of the water-photovoltaic-storage complementary system for cascade small hydropower, and also perform standardization processing and solve the calculation using the bat algorithm; Step S5, selecting relevant data such as hydropower, photovoltaic power generation and power load on typical days of different weather scenarios such as sunny days, cloudy days and rainy days, and carrying out example calculations of joint optimization scheduling model for water-photovoltaic-storage complementary system for cascade small hydropower and example calculations of joint optimization scheduling model for water-photovoltaic complementary system for cascade small hydropower respectively; Step S6: By comparing the example calculation results of two joint optimization scheduling models under typical days in different weather scenarios, the impact of small hydropower transformation hybrid pumped storage units on the power generation of the water-solar complementary system is analyzed.

2. The method for evaluating the impact of small hydropower transformation pumped storage units on the power generation of water-photovoltaic complementary systems according to claim 1 is characterized by: The specific steps of step S1 include: Step S11, the objective function for calculating the maximum power generation of the system within the cycle is as follows: In the formula, E sum is the total power generation of the system during the calculation period, MWh; N sum,t is the total system output during period t, MW; N hi,t is the average output of the i-th hydropower station in the system during period t, MW; N pi,t is the average output of the pumped storage power station in the system during period t, MW; N pv,t is the average output of the photovoltaic power station in the system during period t, MW; Δt is the period length, h; T is the number of calculation periods in the scheduling period; N is the number of cascade hydropower stations; Step S12: The objective function for minimizing the fluctuation of the total system output within the calculation period is as follows: Where D t is the system output variance during period t; D v is the total system output variance within the calculation period; is the average output of the system, MW; T is the number of calculation periods in the dispatch period; N sum,t is the total system output during period t, MW; Step S13, setting the boundary values ​​of various constraints in the short-term multi-objective scheduling model of hydropower, wind power, solar power and storage, and setting the iterative termination conditions of the solution algorithm.

3. The method for evaluating the impact of small hydropower transformation pumped storage units on the power generation of water-photovoltaic complementary systems according to claim 2 is characterized by: The specific steps of step S2 include: Step S21: Through standardization, the objective function of the maximum power generation of the system within the calculation period is converted into the ratio of actual power generation to the maximum rated power generation of the system. The specific calculation formula is as follows: Where, e is the standardized index of the maximum power generation target of the system, and its value ranges from 0 to 1; E max is the maximum power generation of the system under rated power operation, MWh; E sum is the total power generation of the system during the calculation period, MWh; is the total rated power of the system, MW; is the rated power of the i-th hydropower station in the system, MW; is the rated power of the pumped storage power station in the system, MW; is the rated power of the PV power station in the system, MW; T is the number of calculation periods in the dispatch period; N is the number of cascade hydropower stations; Δt is the time period length, h; Step S22: Through standardization, the objective function of minimizing the fluctuation of the total system output within the calculation period is converted into 1 minus the total system output variance / (the maximum rated power of the system) 2 The specific calculation formula is as follows: Where f is the standardized index of the minimum fluctuation target of the total output of the system, and its value ranges from 0 to 1; D v is the total system output variance within the calculation period; is the total rated power of the system, MW; Step S23: Use the weight coefficient method to transform the multi-objective optimization scheduling model into a single-objective optimization scheduling model. The calculation formula is as follows: cg=α×e+β×f (9) Where cg represents the comprehensive objective function of the model; α and β represent the weights of maximizing the system power generation and minimizing the system total output fluctuation in the comprehensive objective function within the calculation period, respectively, with values ​​ranging from 0 to 1, and α+β=1; e is the standardized index of the maximum system power generation target, with values ​​ranging from 0 to 1; f is the standardized index of the minimum system total output fluctuation target, with values ​​ranging from 0 to 1.

4. The method for evaluating the impact of small hydropower transformation pumped storage units on power generation of water-photovoltaic complementary systems according to claim 3 is characterized by: The specific steps of step S3 are: Step S31, initializing characteristic parameters of the bat algorithm, including bat population number n, minimum frequency fmin, maximum frequency fmax, loudness attenuation coefficient α, pulse increase coefficient γ and maximum number of iterations max_generation; Step S32: Initialize all bat individuals and randomly generate the initial position of particle i within the water level constraints of each reservoir at each time period. speed Initial frequency f(i), pulse emission rate r i 0 and impulse loudness Where N is the total number of cascade small hydropower stations, and T is the total number of calculation periods; Step S33, find the initial optimal position of the bats, and calculate the comprehensive objective function of the joint optimization scheduling model of the water-solar-storage complementary system as the initial fitness value of the bats 0 (i), the calculation formula is as follows, find the bat with the largest fitness value, and assign the corresponding fitness value to the initial global optimal value, gbest 0 = max{fitness 0 (i)}i∈[1,n], the spatial position of the individual bat is the optimal position X of the bat population. * ; Where f(z) is the objective function, M·K is the penalty term, M is a positive number, and K is the number of calculation periods that do not meet the constraints; Step S34: Global update of bat positions: and speed V i t Update and check whether the bat position exceeds all constraint boundaries of the model. For the bat position and speed that exceed the constraint boundary, replace them with the corresponding boundary values ​​and calculate the updated fitness function value fitness t (i); f i =f min +(f max -f min )β (13) In the formula, Indicates the position of bat i in the tth iteration process, which is used to represent the parameters to be solved; represents the flying speed of bat i during the tth iteration; x* represents the best position of all bats before this iteration; f i represents the frequency of the ultrasonic pulse emitted by the i-th bat; f min 、f max Respectively represent the minimum and maximum values ​​of the frequency; β is a random number that satisfies uniform distribution, β∈[0,1]; Step S35: Local population update, randomly generate a uniformly distributed random number if Greater than the current pulse emission rate of bat i The current best position X * Generate new positions by random perturbation according to the following formula Calculate the fitness function value fitnew at the new position t (i); x new =x old +θA t (14) In the formula, x new represents the new position; x old represents a solution randomly selected from the current solution set; θ∈[-1,1] is a random number that satisfies uniform distribution; A t is the average loudness of all bats in the tth iteration; Step S36: Determine whether to accept the locally updated solution. For the solution that meets the condition fitnew t (i)>fitness t (i) and Bat, accept the local update solution: fitness t (i) = fitnew t (i); otherwise, keep the original position unchanged; Step S37: determine the current global optimal solution, and compare the fitness value of bat i after updating for the population that has completed a global update and a local update. t (i) and the group's historical global optimal value gbest t-1 , if fitness t (i)≥gbest t-1 , then the fitness value of the corresponding bat is assigned to the historical global optimal value: gbest t-1 =fitness t (i) Update each bat until all bats are compared, and update the historical optimal fitness value to the current optimal value: gbest t =gbest t-1 、gbest_X t =gbest_X t-1 ; Step S38: Update bat loudness and pulse emission rate. Update the loudness of bat i according to the following formula: and pulse emission rate In the formula, is the loudness of the sound wave emitted by bat i at the tth iteration; α is the loudness attenuation coefficient, which is a constant and its value range is 0<α<1; is the pulse emission rate of bat i in the t+1th iteration; r i 0 is the maximum pulse emission rate of bat i, usually 1; γ is the pulse increase coefficient, which is a constant and its value range is γ>0; Step S39, determine whether the termination condition is met, if not, go to step S34; otherwise exit the loop, and the best bat obtained is the joint optimization scheduling calculation result of the water-solar-storage complementary system for cascade small hydropower.

5. The method for evaluating the impact of small hydropower transformation pumped storage units on power generation of water-photovoltaic complementary systems according to claim 1 is characterized by: In step S6, by comparing the example calculation results of the two joint optimization scheduling models on typical days in different weather scenarios, the impact of the small hydropower transformation hybrid pumped storage unit on the power generation of the water-photovoltaic complementary system is analyzed. Mainly based on the joint optimization scheduling results of the water-photovoltaic storage complementary system on typical days in sunny, cloudy and rainy scenarios and the joint optimization scheduling results of the water-photovoltaic complementary system, a comparison is made from aspects such as the total power generation of cascade small hydropower and photovoltaic power stations, the maximum fluctuation amplitude of the total power generation output process, and the reservoir water level change amplitude of each cascade hydropower station, and the changes in the power generation process of the cascade small hydropower and photovoltaic power stations with and without pumped storage units are analyzed.

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