Method for evaluating the influence of small hydropower transformation pumping and storage units on water-light complementary system

Abstract

The present application relates to water-light storage complementary operation technical field, disclose a kind of small hydropower reconstruction pumping storage unit's power generation influence evaluation method to water-light complementary system, including the construction facing cascade small hydropower water-light storage complementary system joint optimization scheduling model;Multiple objective optimization scheduling model is converted into single objective optimization scheduling model;Solving calculation is carried out to the water-light storage complementary system joint optimization scheduling model for cascade small hydropower facing by adopting bat algorithm after processing;Water-light complementary system joint optimization scheduling model facing cascade small hydropower is constructed, and similarly solving calculation of model is carried out using bat algorithm;By comparing the instance calculation results of two joint optimization scheduling models under different weather scenarios typical day, analyze the power generation influence of small hydropower reconstruction hybrid pumping storage unit to water-light complementary system.The present application has the advantages that the power generation benefit of small hydropower reconstruction pumping storage unit is comprehensively analyzed, and important technical support can be provided for new energy development and utilization facing cascade small hydropower.

Description

Technical Field

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

[0002] Clean, low-carbon renewable energy sources such as wind power and photovoltaics have developed rapidly in recent years. However, due to meteorological factors, wind and solar power generation exhibit significant randomness, volatility, and intermittency, hindering their large-scale grid integration. Small hydropower, on the other hand, is a globally recognized clean energy source, boasting advantages such as mature development technology and low construction costs. Furthermore, small hydropower and photovoltaic power generation share natural resource complementarity and a high degree of spatial overlap, creating favorable conditions for their complementary operation. However, due to project scale limitations, small hydropower has relatively weak regulation capabilities. By retrofitting small hydropower projects with hybrid pumped-storage units and developing joint optimized scheduling for hydropower, photovoltaic, and storage complementary systems for cascaded small hydropower projects, the regulatory potential of cascaded small hydropower projects can be fully tapped and the development and utilization of photovoltaic resources surrounding these projects can be promoted. However, retrofitting small hydropower projects with pumped-storage units inevitably increases construction costs. Therefore, studying the impact of pumped-storage conversion on the generation and operation of hydropower and photovoltaic complementary systems is of great practical significance for decision-making regarding the development and utilization of hydropower, photovoltaic, and storage complementary systems based on small hydropower retrofits with 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 existing technology and provide a method for evaluating the power generation impact of small hydropower transformation pumped storage units on the hydro-photovoltaic complementary system, targeting the production decision-making problem of the hydro-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 of pumped storage units on the power generation of a water-solar complementary system, the method comprising:

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

[0006] Step S2: normalize the two objective functions respectively and convert them into dimensionless indicators ranging from 0 to 1, and use the weight coefficient method to transform 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 scheduling model of the water-solar-storage complementary system for cascade small hydropower;

[0008] Step S4: Using the same objective function as the joint optimization scheduling model for the hydro-photovoltaic-storage complementary system for cascaded small hydropower and all constraints except the operation constraints of the hybrid pumped storage unit, a joint optimization scheduling model for the hydro-photovoltaic-storage complementary system for cascaded small hydropower is constructed, and similarly standardized processing and solution calculation are performed using the bat algorithm;

[0009] Step S5: Select relevant data such as hydropower, photovoltaic power generation, and electricity load for typical days of different weather scenarios, such as sunny, cloudy, and rainy days, and perform example calculations of a joint optimization scheduling model for a hydro-photovoltaic-storage complementary system for cascaded small hydropower stations and a joint optimization scheduling model for a hydro-photovoltaic complementary system for cascaded small hydropower stations;

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

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

[0012] Step S11: Calculate the objective function for maximizing system power generation within the cycle as follows:

[0013]

[0014] Where, 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 scheduling period; N sum,t is the total system output during period t, MW;

[0018] Step S13, set the boundary values ​​of each constraint condition in the short-term multi-objective scheduling model of water, wind, solar and storage, and set the iterative termination condition 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 processing, the objective function of maximizing the system power generation within the calculation period is converted into the ratio of actual power generation to the system's maximum rated power generation. The specific calculation formula is as follows:

[0021]

[0022] Where, e is the standardized index of the maximum power generation target of the system, with a value of 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 scheduling 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 / (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 system power generation and minimizing system total output fluctuation within the calculation period in the comprehensive objective function, respectively, with values ​​ranging from 0 to 1, and α + β = 1; e is the standardized index of the system maximum power generation target, with values ​​ranging from 0 to 1; f is the standardized index of the system minimum 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 constraint range 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, the bat position is globally updated, and the bat position is updated using the following formula and speed V i t Update and check whether the bat position exceeds all the constraint boundaries of the model. For the bat position and speed that exceed the constraint boundaries, 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] wherein, represents the position of the i-th bat in the t-th iteration, and is used to represent the parameter to be solved; represents the flight speed of the i-th bat in the t-th 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 satisfying uniform distribution, and β ∈ [0, 1];

[0038] Step S35, population local update, a random number satisfying uniform distribution is randomly generated If is greater than the current pulse emission rate r i t of the i-th bat, then a new position is randomly generated according to the following formula from the current best position X * : The fitness function value fitnew t (i) at the new position is calculated.

[0039] x new =x old +θA t (14)

[0040] wherein, 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 satisfying uniform distribution; A t is the average loudness of all bats in the t-th iteration.

[0041] Step S36, judging whether to accept the local updated solution, for the bat satisfying the condition fitnew t (i) > fitness t (i), and , the local updated solution is accepted: fitness t (i) = fitnew t (i); otherwise, the original position is kept unchanged.

[0042] Step S37: Determine the current global optimal solution, and compare the updated fitness value of bat i in turn 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 best fitness value to the current best value: gbest t =gbest t-1 、gbest_X t =gbest_X t-1 ;

[0043] Step S38: Update the 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] Where, 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, which is 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 obtained best bat 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 under typical days in different weather scenarios are compared to analyze the impact of the small hydropower transformation into a hybrid pumped storage unit on the power generation of the hydro-photovoltaic complementary system. Mainly based on the joint optimization scheduling results of the hydro-photovoltaic storage complementary system and the joint optimization scheduling results of the hydro-photovoltaic complementary system under typical days in sunny, cloudy and rainy scenarios, a comparison is made 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 development and utilization of hydropower, photovoltaic and storage complementary systems based on the transformation of small hydropower into pumped storage units, a joint optimization scheduling model of hydropower, photovoltaic and storage complementary systems for cascade small hydropower was constructed, and the bat algorithm for solving the model was studied. The results were compared with the calculation results of the joint optimization scheduling model of the hydropower and photovoltaic complementary systems without the participation of pumped storage units, so as to realize the evaluation of the power generation impact of small hydropower transformation into hybrid pumped storage units on the hydropower and photovoltaic complementary systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Schematic diagram of the evaluation method of the present invention; DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for the purpose of explaining the present invention and are not intended to limit the present invention. That is, the embodiments described herein are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and illustrated 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 as claimed, but is merely intended to represent selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without 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 actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

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

[0055] Step S2: normalize the two objective functions respectively and convert them into dimensionless indicators ranging from 0 to 1, and use the weight coefficient method to transform 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 scheduling model of the water-solar-storage complementary system for cascade small hydropower;

[0057] Step S4: Using the same objective function as the joint optimization scheduling model for the hydro-photovoltaic-storage complementary system for cascaded small hydropower and all constraints except the operation constraints of the hybrid pumped storage unit, a joint optimization scheduling model for the hydro-photovoltaic-storage complementary system for cascaded small hydropower is constructed, and similarly standardized processing and solution calculation are performed using the bat algorithm;

[0058] Step S5: Select relevant data such as hydropower, photovoltaic power generation, and electricity load for typical days of different weather scenarios, such as sunny, cloudy, and rainy days, and perform example calculations of a joint optimization scheduling model for a hydro-photovoltaic-storage complementary system for cascaded small hydropower stations and a joint optimization scheduling model for a hydro-photovoltaic complementary system for cascaded small hydropower stations;

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

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

[0061] Step S11: Calculate the objective function for maximizing system power generation within the cycle as follows:

[0062]

[0063] Where, 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 scheduling period; N sum,t is the total system output during period t, MW;

[0067] Step S13, set the boundary values ​​of each constraint condition in the short-term multi-objective scheduling model of water, wind, solar and storage, and set the iterative termination condition 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 processing, the objective function of maximizing the system power generation within the calculation period is converted into the ratio of actual power generation to the system's maximum rated power generation. The specific calculation formula is as follows:

[0070]

[0071] Where, e is the standardized index of the maximum power generation target of the system, with a value of 0 to 1; E maxMWh is the maximum power generation of the system under rated power operation, MWh; E sum MWh is the total power generation of the system in the calculation period, MWh; MWh is the total rated power of the system, MW; MWh is the rated power of the i-th hydroelectric power station in the system, MW; MWh is the rated power of the pumped storage power station in the system, MW; MWh is the rated power of the photovoltaic power station in the system, MW; T is the number of calculation periods in the dispatching period; N is the number of cascade hydroelectric power stations; Δt is the period length, h;

[0072] In step S22, the objective function of the minimum total output fluctuation of the system in the calculation period is converted into 1 minus the ratio of the total output variance of the system to the maximum rated power of the system through standardization processing, and the specific calculation formula is as follows: 2

[0073]

[0074] In the formula, f is the standardized index of the minimum total output fluctuation objective of the system, and the value is 0-1; D v MWh is the total output variance of the system in the calculation period; MWh is the total rated power of the system, MW;

[0075] In step S23, the multi-objective optimization scheduling model is converted into a single-objective optimization scheduling model by using the weight coefficient method, and the calculation formula is as follows:

[0076] cg=α×e+β×f (9)

[0077] In the formula, cg represents the comprehensive objective function of the model; α and β respectively represent the weights of the maximum power generation of the system in the calculation period and the minimum total output fluctuation of the system in the comprehensive objective function, and the values are 0-1, and α+β=1; e is the standardized index of the maximum power generation objective of the system, and the value is 0-1; f is the standardized index of the minimum total output fluctuation objective of the system, and the value is 0-1.

[0078] Further, the specific steps of step S3 are as follows:

[0079] In step S31, the characteristic parameters of the bat algorithm are initialized, including the number of bat populations n, the minimum frequency fmin, the maximum frequency fmax, the loudness decay coefficient α, the pulse increase coefficient γ, and the maximum iteration number max_generation;

[0080] In step S32, all bat individuals are initialized, and the initial position of particle i is randomly generated within the water level constraint range of each reservoir at each period speed ​Initial frequency f(i), pulse emission rate r i 0 And pulse loudness Where N is the total number of ladder small hydropower, T is the total number of calculation period;

[0081] Step S33, find the initial best position of the bat, calculate the comprehensive objective function of the water-light storage complementary system joint optimization scheduling model as the initial fitness value of the bat 0 (i), the calculation formula is shown in the following formula, find the bat with the maximum 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 bat individual is the best position X of the bat population * ;

[0082]

[0083] In the formula, 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 position, update the position and speed V i t of the bat by using the following formula, check whether the bat position exceeds all the constraint boundaries of the model, replace the bat position and speed that exceed the constraint boundaries 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, represents the position of bat i in the tth iteration process, and is used to represent the to-be-solved parameters; represents the flight speed of bat i in the tth iteration process; x* represents the best position of all bats before this iteration; f i represents the frequency of the ultrasonic pulse emitted by the ith 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, population local 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 a new position 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] Where 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. 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 updated fitness value of bat i in turn 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 best fitness value to the current best value: gbest t =gbest t-1 、gbest_X t =gbest_X t-1 ;

[0092] Step S38: Update the 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] Where, 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, which is 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 obtained best bat is the joint optimization scheduling calculation result of the water-solar-storage complementary system for cascade small hydropower.

[0096] Furthermore, 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 into a hybrid pumped storage unit on the power generation of the hydro-photovoltaic complementary system. Mainly based on the joint optimization scheduling results of the hydro-photovoltaic storage complementary system and the joint optimization scheduling results of the hydro-photovoltaic complementary system on typical days in sunny, cloudy and rainy scenarios, a comparison is made 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.

[0097] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make many possible changes and modifications to the technical solution of the present invention using the above technical content, or modify it into an equivalent embodiment with 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 shall fall within the scope of protection of the present technical solution.

Claims

1. A method for assessing the impact of small hydropower station retrofitting pumped storage units on the power generation of a hydro-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 for the water-photovoltaic-storage complementary system for cascade small hydropower, wherein the constraints of the joint optimization scheduling model for the water-photovoltaic-storage complementary system for cascade small hydropower include reservoir water balance constraint, reservoir water level constraint, outflow constraint, power generation flow constraint, cascade upstream and downstream flow balance constraint, hydropower station output constraint, photovoltaic power station output constraint and hybrid pumped storage unit operation constraint; Step S2: normalize the two objective functions respectively and convert them into dimensionless indicators ranging from 0 to 1, and use the weight coefficient method to transform 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 scheduling model of the water-solar-storage complementary system for cascade small hydropower; Step S4: Using the same objective function as the joint optimization scheduling model for the hydro-photovoltaic-storage complementary system for cascaded small hydropower and all constraints except the operation constraints of the hybrid pumped storage unit, a joint optimization scheduling model for the hydro-photovoltaic-storage complementary system for cascaded small hydropower is constructed, and similarly standardized processing and solution calculation are performed using the bat algorithm; Step S5: Select hydropower, photovoltaic power generation, and electricity load data for typical days under different weather scenarios, and perform example calculations of a joint optimization scheduling model for a hydro-photovoltaic-storage complementary system for cascaded small hydropower stations and a joint optimization scheduling model for a hydro-photovoltaic complementary system for cascaded small hydropower stations; the different weather scenarios include sunny, cloudy, and rainy days; Step S6: By comparing the example calculation results of the two joint optimization scheduling models under different weather scenarios on typical days, the impact of the small hydropower transformation hybrid pumped storage unit on the power generation of the hydro-photovoltaic complementary system is analyzed.

2. The method for evaluating the impact of small hydropower transformation of pumped storage units on the power generation of a hydro-photovoltaic complementary system according to claim 1 is characterized by: The specific steps of step S1 include: Step S11: Calculate the objective function for maximizing system power generation within the cycle as follows: （1） （2） Where, is the total power generation of the system during the calculation period, MWh; for Total system output during the period, MW; For the system Hydropower Station Average output during the period, MW; Pumped storage power station in the system Average output during the period, MW; For photovoltaic power stations in the system Average output during the period, MW; For a long period of time, ; Calculate the number of time slots in the scheduling period; 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: （3） （4） Where, for System output variance during time period; is the total system output variance within the calculation period; is the average output of the system, MW; Calculate the number of time slots in the scheduling period; for Total system output during the period, MW; Step S13: setting the boundary values ​​of each constraint condition in the short-term multi-objective scheduling model of hydropower, wind power, solar power and storage, and setting the iterative termination condition of the solution algorithm.

3. The method for evaluating the impact of small hydropower transformation of pumped storage units on the power generation of a hydro-photovoltaic complementary system according to claim 2 is characterized by: The specific steps of step S2 include: Step S21: Through standardization processing, the objective function of maximizing the system power generation within the calculation period is converted into the ratio of actual power generation to the system's maximum rated power generation. The specific calculation formula is as follows: （5） （6） （7） Where, It is a standardized indicator of the maximum power generation target of the system, with a value of 0~1; is the maximum power generation of the system under rated power operation, MWh; is the total power generation of the system during the calculation period, MWh; is the total rated power of the system, MW; For the system Rated power of the hydropower station, 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; Calculate the number of time slots in the scheduling period; is the number of cascade hydropower stations; For a long period of time, ; 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 / (maximum rated power of the system) 2 The specific calculation formula is as follows: （8） Where, It is a standardized indicator for the minimum fluctuation of the total system output, with a value of 0~1; 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: （9） Where, represents the comprehensive objective function of the model; and They represent the weights of maximizing system power generation and minimizing system total output fluctuation in the comprehensive objective function within the calculation period, and their values ​​range from 0 to 1, and ; e It is a standardized indicator of the maximum power generation target of the system, with a value of 0~1; It is a standardized indicator for the minimum fluctuation of the total system output, with a value of 0~1.

4. The method for evaluating the impact of small hydropower transformation of pumped storage units on the power generation of a hydro-photovoltaic complementary system according to claim 3 is characterized by: The specific steps of step S3 are: Step S31: Initialize the characteristic parameters of the bat algorithm, including the number of bat populations , minimum frequency fmin, maximum frequency fmax, loudness attenuation coefficient , pulse increase coefficient and the maximum number of iterations max_generation; Step S32: Initialize all bat individuals and randomly generate particles within the water level constraints of each reservoir at each time period. Initial position ,speed , initial frequency , pulse emission rate and impulse loudness ,in is the total number of cascade small hydropower stations, 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. , 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, , the spatial position of the individual bat is the optimal position of the bat population ; （10） Where, is the objective function, is the penalty item, is a positive number, is the number of calculation periods that do not meet the constraints; Step S34: Global update of bat positions. The bat positions are updated using the following formula: and speed Update and check whether the bat position exceeds all the constraint boundaries of the model. For the bat position and speed that exceed the constraint boundaries, replace them with the corresponding boundary values ​​and calculate the updated fitness function value ; （11） （12） （13） Where, Represents bats In the The position of the iteration process is used to represent the parameters to be solved; Represents bats In the The flight speed during the iteration; represents the best position of all bats before this iteration; Indicates the The frequency of ultrasonic pulses emitted by bats; 、 Represent the minimum and maximum values ​​of frequency respectively; is a random number that satisfies a uniform distribution. ; Step S35: Local population update, randomly generate a uniformly distributed random number ,if Bigger than a bat Current pulse emission rate , then the current best position Generate a new position by random perturbation according to the following formula ; Calculate the fitness function value at the new position ; （14） Where, Represents the new location; represents a solution randomly selected from the current solution set; , is a random number that satisfies uniform distribution; For the The average loudness of all bats in iterations; Step S36: Determine whether to accept the locally updated solution. ,and Bat, accept the local update solution: , ; Otherwise, keep the original position unchanged; Step S37: Determine the current global optimal solution, and compare the bat populations that have completed a global update and a local update. i Updated fitness value and the global optimal value of the group history ,like , then the fitness value of the corresponding bat is assigned to the historical global optimal value: 、 , update each bat until the comparison of all bats is completed, and update the historical optimal fitness value to the current optimal value: 、 ; Step S38: Update the bat loudness and pulse emission rate according to the following formula: Loudness and pulse emission rate ; （15） （16） Where, For bats In the The loudness of the sound wave emitted at the iteration; is the loudness attenuation coefficient, which is a constant and its value range is ; For bats In the The pulse firing rate in iterations; For bats The maximum pulse emission rate, usually 1; is the pulse increase coefficient, which is a constant and its value range is ; Step S39, determine whether the termination condition is met, if not, go to step S34; otherwise exit the loop, and the obtained best bat 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 of pumped storage units on the power generation of a hydro-photovoltaic complementary system according to claim 1 is characterized by: In step S6, the impact of the small hydropower transformation into a hybrid pumped storage unit on the power generation of the hydro-photovoltaic complementary system is analyzed by comparing the example calculation results of the two joint optimization scheduling models on typical days in different weather scenarios. Mainly based on the joint optimization scheduling results of the hydro-photovoltaic storage complementary system and the joint optimization scheduling results of the hydro-photovoltaic complementary system on typical days in sunny, cloudy and rainy scenarios, a comparison is made from the aspects of the total power generation of the 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.

Images