Cascade hydropower station economic dispatching strategy considering power generation water consumption rate and minimum abandoned water amount

Abstract

The invention relates to a cascade hydropower station economic dispatching strategy considering the power generation water consumption rate and the minimum abandoned water amount. Firstly, a mathematical model of the cascade hydropower station is established, and the mathematical model comprises factors such as a working water head, a water balance relation, an upstream water level, a downstream water level and water head loss. Secondly, a target function is formulated, targets such as the maximum power generation capacity, the minimum power generation water consumption rate and the minimum abandoned water amount are covered, and economic dispatching of the cascade hydropower stations is achieved through target optimization. In the third step, constraint conditions of the economic dispatching strategy are formulated, and the constraint conditions comprise system net load constraint, power transmission channel constraint, water balance constraint and the like. And finally, providing a scheduling solving method based on a grey wolf optimization algorithm, and optimizing and solving the economic scheduling problem of the cascade hydropower stations in combination with Levy flight and a greedy search mechanism. According to the scheduling strategy, the economic benefits of the cascade hydropower stations can be effectively improved, sustainable development of clean energy is promoted, and a new solution is provided for operation optimization of a power system.

Description

Technical Field

[0001] The present invention is to design an economic dispatching method for cascade hydropower stations, which mainly involves an economic optimization dispatching strategy with the lowest water consumption rate for power generation and the lowest water abandonment. Background Art

[0002] With the continuous growth of global energy demand and the emphasis on renewable energy, cascade hydropower stations, as an important source of clean and renewable energy, are becoming increasingly important in the power system. Cascade hydropower stations can not only efficiently convert electrical energy through water resources, but also regulate the frequency and peak load of the power grid, thus improving the stability and reliability of the power system.

[0003] However, there are some challenges in the economic dispatch of traditional cascade hydropower stations. Traditional dispatch strategies mainly focus on maximizing power generation, while ignoring the impact of power generation water consumption rate and water abandonment. In actual operation, due to the volatility of power demand and seasonal changes in water flow, traditional dispatch strategies fail to fully utilize water resources, resulting in unnecessary waste of water resources and environmental impact.

[0004] Therefore, in order to solve the problems existing in the traditional cascade hydropower station dispatching strategy, the present invention proposes a new economic dispatching strategy. This strategy not only takes into account the requirement of maximizing power generation, but also fully considers the minimization of power generation water consumption rate and abandoned water. By introducing advanced power system optimization algorithms and water resources management technologies, this strategy can significantly reduce the power generation water consumption rate of cascade hydropower stations without affecting power generation, while minimizing the amount of abandoned water, thereby improving the overall economic benefits and environmental friendliness of the hydropower station. Summary of the invention

[0005] The innovation of this invention lies in that it comprehensively considers the complexity of power system operation optimization and water resource management. By accurately calculating the operating parameters of each hydropower station, such as water flow scheduling, water level management, and unit combination optimization, this strategy can achieve the best utilization of water resources. In addition, by using advanced prediction models and real-time data monitoring systems, it can effectively respond to changes in power market demand and environmental conditions, and ensure the efficient operation of cascade hydropower stations in different operating scenarios.

[0006] The above technical problems of the present invention are mainly achieved through the following technical solutions:

[0007] An economic dispatch strategy for cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water, characterized in that it comprises the following steps:

[0008] Obtain real-time data of cascade hydropower stations, including the following data:

[0009] q1(t), q2(t), q3(t) are the water inflow of the first, second and third hydropower stations at time t respectively; Q1(t), Q2(t), Q3(t) are the power generation flow of the first, second and third hydropower stations at time t respectively; S1(t), S2(t), S3(t) are the water abandonment of the first, second and third hydropower stations at time t respectively;

[0010] Z iu (t), Z id (t) are the upstream and downstream water levels of the i-th hydropower station at time t; S i (t) is the head loss of the i-th hydropower station at time t;

[0011] V i (t) is the reservoir water volume of the i-th hydropower station during period t;

[0012] q i (t) is the water inflow of the i-th hydropower station in period t;

[0013] Based on the established objective function and constraints, the dispatching strategy is solved. The objective function is to maximize the power generation of the cascade hydropower station, minimize the water consumption rate of power generation, and minimize the amount of abandoned water.

[0014] As a preferred method, the overall objective function is a linear combination of the maximum power generation, the minimum water consumption rate for power generation and the minimum amount of abandoned water in the cascade hydropower station:

[0015] MaxE=λ1F1-λ2F2-λ3F3

[0016] The objective function E is to maximize the economic benefits of the Qingjiang cascade hydropower station, where λ1~λ3 are the weight coefficients of the objective function. Different weight coefficients are set by evaluating the importance of the three objective functions.

[0017] λ1+λ2+λ3=1.

[0018] As a preferred option, cascade hydropower stations have the largest power generation capacity.

[0019]

[0020] H i,t =Z iis (t)-Z id (t)

[0021] Where: F1 is the power generation index of cascade hydropower stations; A i is the output coefficient of the i-th power station: Q i,t is the power generation flow of the i-th power station in the t-th period (m 3 / s); H i,tis the average net water head of the ith power station in the tth period (m); N is the total number of cascade power stations; T is the total number of calculation periods in a year; N is the total number of cascade power stations: M t is the number of hours in the tth period.

[0022] As a preferred option, the objective function is to minimize the unit's water consumption rate E.

[0023]

[0024] Where: k is the unit number; Q n,k,t is the power generation flow of unit k in hydropower station n during period t.

[0025] As a preference, the weight coefficients λ of the importance of water abandonment of hydropower stations at different levels are set. s,i , where i = 1, 2…N, N is the number of cascade hydropower stations;

[0026] The mathematical model of the total amount of water abandoned at the last stage of cascade hydropower stations can be expressed as follows:

[0027]

[0028] Preferably, the constraints include

[0029] (1) System net load constraints:

[0030]

[0031] Where P ref,t , P pv,i,t , P i,t They represent the reference load of the hydro-photovoltaic complementary system in time period t, the photovoltaic output of hydropower station i, and the hydropower output respectively;

[0032] (2) Transmission channel constraints:

[0033]

[0034] Where D G and P D,max They represent the subordinate power stations of the parallel line D and their maximum transmission capacity respectively;

[0035] Electricity market constraints:

[0036] (3) Constraints on hydropower output decomposition:

[0037] P i,t =P ri,i,t +P qx,i,t +R d1,i,t

[0038] Where P qx,i,t , R d1,i,tIt represents the medium- and long-term power curve contract output and the power contract output of the hydropower station in period t i;

[0039] (4) Constraints on the decomposition of medium- and long-term contract electricity:

[0040]

[0041] Where E d1,i,t Indicates the contractual electricity volume decomposed to today in the medium and long term;

[0042] (4) Water balance constraints:

[0043] Where V i,t I represents the final storage capacity of the hydropower station in period t; i,t , Q out,i,t and τi represent the interval runoff, outflow flow and the delay from the outflow flow of the upstream power station to the downstream at period t of the hydropower station i;

[0044] (5) Constraints on upper and lower limits of storage capacity and flow rate:

[0045]

[0046] Where V i,min 、V i,max , Q out,i,min , Q out,i,max represents the minimum and maximum storage capacity, minimum ecological outflow and maximum flow of hydropower station i;

[0047] (6) Water level and head constraints:

[0048] Z up,i,t =f i,zv (V i,t )

[0049] Z end,i,t =f i,zp (Q out,i,t )

[0050]

[0051] In the formula, Z up,i,t , Z end,i,t , H i,t , H loss,i They represent the water level, tailwater level, head and head loss of the hydropower station at time period t; f i,zv (·),f i,zp (·) represent the nonlinear relationship between the water level and the storage capacity of the hydropower station i and the nonlinear relationship between the tailwater level and the storage capacity respectively;

[0052] (7) Power generation flow limit:

[0053]

[0054] Where Q f,i,t , Q f,i,min , Q f,i,max ,q f,i,n,t 、N g,i It represents the power generation flow of the hydropower station at time period t, the lower and upper limits of the power generation flow, the power generation flow of the i unit of the hydropower station at time period t, and the number of units in the station.

[0055] As a preferred method, the scheduling strategy based on the Grey Wolf Optimization Algorithm is solved; LGWO is used to calculate the following levels of GWO:

[0056] In the wolf pack, other wolves are responsible for the task of β wolf. The LGWO algorithm only has α, β and ω wolves in the process of solving the model in this paper.

[0057] Improve the GWO algorithm using Levy flight;

[0058] The greedy search mechanism is combined with the modified hunting phase and applied to the GWO algorithm.

[0059] As a preference, the position of the gray wolf in LGWO is updated according to the α and β types. Levy flights can help LGWO simulate the hunting mode of gray wolves more realistically and accurately than the original GWO. Using Levy flights as a replacement is an effective way to alleviate the stagnation problem of GWO. The new position is determined by the following formula:

[0060]

[0061] Where: l represents the weight of controlling the step length; represents point-to-point multiplication; Levy(λ) is the path that follows the Levy distribution;

[0062]

[0063] Where u and v follow a normal distribution:

[0064]

[0065] Where: parameter λ is a random number between 0 and 2; σ v is 1.

[0066] As a preferred method, after retaining the previous generation of solutions, a greedy search mechanism is executed to recalculate the fitness; the greedy selection (GS) strategy adopts the theory of evolution, and sets the probability p to represent the survival probability of a wolf in the wolf pack during the algorithm calculation process, that is, survival of the fittest; and the addition of the GS strategy will make it very likely that a high-quality wolf body closer to the prey position will appear in each iterative calculation, so that the number of high-quality wolves in the wolf pack will increase and replace the wolf body with the worst position, so that the LGWO algorithm has a better optimization path and increases the probability of a better solution, so that the LGWO algorithm has a better search ability; Formula 27 is used to determine whether a high-quality wolf body appears and whether it can replace the position of the worst wolf body, and finally the t+1th iterative wolf body is calculated to refresh the position of the wolf pack again:

[0067]

[0068] Where: r new and p are random numbers between 0 and 1.

[0069] The scheduling strategy of the present invention can effectively improve the economic benefits of cascade hydropower stations, promote the sustainable development of clean energy, and provide a new solution for the operation optimization of power systems. At the same time, the scheduling solution method based on the gray wolf optimization algorithm has high accuracy and efficiency and is suitable for practical engineering applications.

[0070] Therefore, the economic dispatching strategy of cascade hydropower stations and its solution method of the present invention will have broad application prospects and economic benefits in the field of clean energy. The present invention aims to design a dispatching strategy to reduce the water consumption rate and water abandonment of cascade hydropower stations under the premise of meeting the maximum power generation, so as to maximize the economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The invention relates to a schematic diagram of a cascade hydropower station. DETAILED DESCRIPTION

[0072] The technical solution of the present invention is further described below through embodiments and in conjunction with the accompanying drawings.

[0073] The present invention relates to an economic dispatching method for cascade hydropower stations, mainly involving an economic optimization dispatching strategy with the lowest water consumption rate and water abandonment for power generation. As the importance of renewable clean energy in the power system becomes increasingly prominent, cascade hydropower stations, as an important form of energy supply, play a key role in the power system, including

[0074] Step 1: q1(t), q2(t), q3(t) are the water inflow of the first, second and third hydropower stations at time t; Q1(t), Q2(t), Q3(t) are the power generation flow of the first, second and third hydropower stations at time t; S1(t), S2(t), S3(t) are the water abandonment of the first, second and third hydropower stations at time t. The relationship between the various quantities of the cascade hydropower stations is as follows.

[0075] (1) Working head

[0076] The water level difference between the upstream and downstream of each power station is called the water head of the hydropower station. The working water head of the hydropower station can be calculated according to formula 1:

[0077]

[0078] Where: H i (t) is the working water head of the i-th hydropower station at time t; Z iu (t)Z id (t) are the upstream and downstream water levels of the i-th hydropower station at time t; S i (t) is the head loss of the i-th hydropower station at time t.

[0079] (2) Water balance relationship

[0080] Water balance means that in any area and any period of time, the difference between the amount of water collected and spent must be equal to the change in water storage in that period and in that area. The water balance relationship between each level of cascade hydropower station can be expressed as:

[0081] V i (t+1)=V i (t)+q i (t)×Δt+Q i-1 (t)×Δt Formula 2

[0082] Where: V i (t) is the reservoir water volume of the i-th hydropower station in period t; Δt is the duration of the period. For a three-level cascade hydropower station, Formula 2 can be expressed as:

[0083] V1(t+1)=V1(t)+q1(t)×Δt-Q1(t)×Δt

[0084] V2(t+1)=V2(t)+q2(t)×Δt-Q2(t)+Q1(t)×ΔtV3(t+1)=

[0085] V3(t)+q3(t)×Δt-Q2(t)×Δt Formula 3

[0086] (3) Upstream water level

[0087] When calculating the upstream water level, we can first use the water balance relationship to obtain the reservoir water volume of the hydropower station during period t, that is, use the above formula, and then use the relationship between the upstream water level and the reservoir water volume, that is, Formula 3, to calculate the upstream water level.

[0088] Z iis (t)=u1×[V i (t)] 2 +u2×V i (t)+u3 Formula 4

[0089] Where: V i (t) is the reservoir water volume of the i-th hydropower station in period t; u1, u2, u3 are the characteristic coefficients of the relationship between the upstream water level and the reservoir water volume.

[0090] (4) Downstream water level

[0091] The downstream water level of a hydropower station is closely related to the reservoir water volume and power generation flow of each lower hydropower station at the current moment. For a three-level cascade hydropower station, the downstream water level Z of the first hydropower station is id (t) is related to Q1(t), V2(t), and V3(t); the downstream water level Z of the second-stage hydropower station 21 (t) is related to Q2(t) and V3(t); the downstream water level Z1(t) of the third-level hydropower station is related to Q3(t).

[0092] The downstream water level can be calculated by formula 5:

[0093] Z id (t) = d 1i ×[S i (t)+Q i (t)] 2 +d 2i ×[S i (t)+Q i (t)]+m 1i ×[V i (t)] 2 +m2×[V i (t)]+l i Formula 5

[0094] Where: S i (t) is the amount of water abandoned by the i-th hydropower station at time t; Q i (t) is the power generation flow of the i-th hydropower station at time t; d 1i ,d 2i ,m 1i ,m 2i ,I i is the characteristic coefficient of the downstream water level.

[0095] (5) Amount of water abandoned by hydropower stations

[0096] The general expression of the amount of water abandoned by a hydropower station is:

[0097]

[0098] Where: k is the proportionality coefficient.

[0099] In actual calculations, it is relatively difficult to calculate head loss, and the head loss value is relatively small, and the impact on optimal scheduling is also relatively small, so in actual calculations, head loss is often ignored.

[0100] Step 2: Formulate the dispatch strategy objective function that takes into account the minimum water consumption rate and water abandonment of power generation.

[0101] In a power system containing cascade hydropower stations, the economic benefits of the cascade hydropower stations are maximized. The present invention maximizes the transmission capacity and economic benefits of the cascade hydropower stations from the perspectives of reducing the water consumption rate for power generation and reducing the water abandonment rate. Therefore, the present invention sets three objective functions, namely, maximum power generation, minimum water consumption rate for power generation, and minimum water abandonment. The maximum economic benefits of the cascade hydropower stations are ensured by optimizing the objective functions.

[0102] Goal 1: Maximum power generation from cascade hydropower stations

[0103]

[0104] H i,t =Z iis (t)-Z id (t) Formula 7

[0105] Where: F1 is the power generation index of cascade hydropower stations; A i is the output coefficient of the i-th power station: Q i,t is the power generation flow of the i-th power station in the t-th period (m 3 / s); H i,t is the average net water head of the ith power station in the tth period (m); N is the total number of cascade power stations; T is the total number of calculation periods in a year (the calculation period is month, T = 12); N is the total number of cascade power stations: M t is the number of hours in the tth period.

[0106] Objective 2: Taking the minimum water consumption rate E of the unit as the objective function

[0107]

[0108] Where: k is the unit number; Q n,k,t is the power generation flow of unit k in hydropower station n during period t.

[0109] Goal 3: Minimize the amount of water abandoned by hydropower stations

[0110] The abandonment of water in cascade hydropower stations is a key concern in the field of power system optimization and dispatching. According to the operating characteristics and abandonment characteristics of cascade hydropower stations, the abandonment of water from the upper hydropower station can be used as power generation water for the lower hydropower station. The abandonment of water within the cascade hydropower station does not necessarily mean a loss for the entire cascade hydropower station, that is, the abandonment of water can be reused within the cascade hydropower station. Only when the last stage of the cascade hydropower station produces abandonment, it is considered that the cascade hydropower station has abandoned water. The Qingjiang cascade hydropower station has multiple levels (3 levels), and the importance of abandonment of water at each level is different. The loss value of abandonment at the upstream is the largest, and the loss value of abandonment at the downstream is the smallest. The weight coefficient λ of the importance of abandonment of water at different levels of hydropower stations is set s,i (where i=1,2…N, N is the number of cascade hydropower stations).

[0111] The mathematical model of the total amount of water abandoned at the last stage of cascade hydropower stations can be expressed as follows:

[0112]

[0113] The cost of hydropower generation is negligible here. The overall goal is expressed as a linear combination of three goals:

[0114] MaxE=λ1F1-λ2F2-λ3F3 Formula 10

[0115] The objective function E is to maximize the economic benefits of the Qingjiang cascade hydropower stations, where λ1:λ3 are the weight coefficients of the objective function. Different weight coefficients are set by evaluating the importance of the three objective functions.

[0116] λ1+λ2+λ3=1 Formula 11

[0117] Step 3: Formulate economic dispatch strategy constraints that take into account the minimum water consumption rate of power generation and the amount of water abandoned.

[0118] (1) System net load constraints:

[0119]

[0120] Where P ref,t , P pv,i,t , P i,t They represent the reference load of the hydro-solar complementary system in time period t, the photovoltaic output of hydropower station i, and the hydropower output, respectively.

[0121] (2) Transmission channel constraints:

[0122]

[0123] Where D G and P D,max They represent the subordinate power station set of the parallel line D and the maximum transmission capacity respectively.

[0124] Electricity market constraints:

[0125] (3) Constraints on hydropower output decomposition:

[0126] P i,t =P ri,i,t +P qx,i,t +R d1,i,t Formula 14

[0127] Where P qx,i,t , R d1,i,t It represents the medium- and long-term power curve contract output and electricity contract output of the hydropower station in period t.

[0128] (4) Constraints on the decomposition of medium- and long-term contract electricity:

[0129]

[0130] Where E d1,i,t Indicates the contractual electricity volume decomposed to today in the medium and long term.

[0131] (4) Water balance constraints:

[0132]

[0133] Where V i,t I represents the final storage capacity of the hydropower station in period t; i,t , Q out,i,t and τi represent the interval runoff, outflow flow of the hydropower station i in period t, and the delay from the outflow flow of the upstream power station to the downstream.

[0134] (5) Constraints on upper and lower limits of storage capacity and flow rate:

[0135]

[0136] Where V i,min 、V i,max , Q out,i,min , Q out,i,max It represents the minimum and maximum storage capacity, minimum ecological outflow and maximum flow of hydropower station i.

[0137] (6) Water level and head constraints:

[0138] Z up,i,t =f i,zv (V i,t ) Formula 18

[0139] Z end,i,t =f i,zp (Q out,i,t ) Formula 19

[0140]

[0141] Where Z up,i,t , Z end,i,t , H i,t , H loss,i They represent the water level, tailwater level, head and head loss of the hydropower station at time period t; f i,zv (·),f i,zp (·) represent the nonlinear relationship between the water level and reservoir capacity of hydropower station i and the nonlinear relationship between the tailwater level and reservoir capacity, respectively.

[0142] (7) Power generation flow limit:

[0143]

[0144] Where Q f,i,t , Q f,i,min , Q f,i,max ,q f,i,n,t 、N g,i It represents the power generation flow of the hydropower station at time period t, the lower and upper limits of the power generation flow, the power generation flow of the i unit of the hydropower station at time period t, and the number of units in the station.

[0145] Step 4: Solve the scheduling strategy based on the Grey Wolf Optimization Algorithm. Calculate the following levels of GWO using LGWO:

[0146] (1) In the wolf pack, other wolves are responsible for the task of β wolf. The LGWO algorithm only has α, β and ω wolves in the process of solving the model in this paper;

[0147] (2) Improve the GWO algorithm using Levy flight;

[0148] (3) The greedy search mechanism is combined with the modified hunting phase and applied to the GWO algorithm.

[0149] The position of the gray wolf in LGWO is updated according to the α and β types. Levy flights can help LGWO simulate the hunting mode of gray wolves more realistically and accurately than the original GWO. Using Levy flights as a substitute is an effective way to alleviate the stagnation problem of GWO. The new position is determined by equations 22 and 23:

[0150]

[0151] Where: l represents the weight of controlling the step length; represents point-to-point multiplication; Levy(λ) is the path that follows the Levy distribution.

[0152]

[0153] Where u and v follow a normal distribution:

[0154]

[0155] Where: parameter λ is a random number between 0 and 2; σ υ is 1.

[0156] After retaining the previous generation of solutions, the greedy search mechanism is executed to recalculate the fitness; the greedy selection (GS) strategy adopts the theory of evolution, and sets the probability p to represent the survival probability of a wolf in the wolf pack during the algorithm calculation process, that is, survival of the fittest; and the addition of the GS strategy will make it very likely that a high-quality wolf closer to the prey position will appear in each iterative calculation, so that the number of high-quality wolves in the wolf pack will increase and replace the wolf with the worst position, so that the LGWO algorithm has a better search path and increases the probability of a better solution, so that the LGWO algorithm has better search capabilities. Formula 27 is used to determine whether a high-quality wolf appears and whether it can replace the position of the worst wolf. Finally, the t+1th iteration of the wolf is calculated to refresh the position of the wolf pack again:

[0157]

[0158] Where: r new and p are random numbers between 0 and 1.

[0159] It is easy for those skilled in the art to understand that the above-described working conditions are only preferred embodiments of the present invention and are not intended to limit the present invention. Any example analysis, equivalent substitution, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An economic dispatch strategy for cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water, characterized by: The following steps are included: Obtain real-time data of cascade hydropower stations, including the following data: q1(t), q2(t), q3(t) are the water inflows at the first, second, and third hydropower stations at time t, respectively; Q1(t), Q2(t), and Q3(t) are the power generation flows of the first, second, and third hydropower stations at time t, respectively; S1(t), S2(t), and S3(t) are the water abandonment amounts of the first, second, and third hydropower stations at time t, respectively; Z iu (t), Z id (t) are the upstream and downstream water levels of the i-th hydropower station at time t; S i (t) is the head loss of the i-th hydropower station at time t; V i (t) is the reservoir water volume of the i-th hydropower station during period t; q i (t) is the water inflow of the i-th hydropower station in period t; Based on the established objective function and constraints, the dispatching strategy is solved. The objective function is to maximize the power generation of the cascade hydropower station, minimize the water consumption rate of power generation, and minimize the amount of abandoned water.

2. The economic dispatch strategy of cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water according to claim 1, characterized in that: The overall objective function is a linear combination of the maximum power generation, the lowest water consumption rate, and the minimum amount of abandoned water of the cascade hydropower station: MaxE=λ1F1-λ2F2-λ3F3 The objective function E is to maximize the economic benefits of the Qingjiang cascade hydropower station, where λ1~λ3 are the weight coefficients of the objective function. Different weight coefficients are set by evaluating the importance of the three objective functions. λ1+λ2+λ3=1.

3. The economic dispatch strategy of cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water according to claim 1, characterized in that: Cascade hydropower stations have the largest power generation H i,t =Z iis (t)-Z id (t) Where: F1 is the power generation index of cascade hydropower stations; A i is the output coefficient of the i-th power station: Q i,t is the power generation flow of the i-th power station in the t-th period (m 3 / s); H i,t is the average net water head of the ith power station in the tth period (m); N is the total number of cascade power stations; T is the total number of calculation periods in a year; N is the total number of cascade power stations: M t is the number of hours in the tth period.

4. The economic dispatch strategy for cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water according to claim 1, characterized in that: The objective function is to minimize the water consumption rate E of the unit. Where: k is the unit number; Q n,k,t is the power generation flow of unit k in hydropower station n during period t.

5. The economic dispatch strategy for cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water according to claim 1, characterized in that: Set the weight coefficient λ of the importance of water abandonment of hydropower stations of different levels s,i , where i = 1, 2…N, N is the number of cascade hydropower stations; The mathematical model of the total amount of water abandoned at the last stage of cascade hydropower stations can be expressed as follows:

6. The economic dispatch strategy of cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water according to claim 1, characterized in that: Constraints include (1) System net load constraints: Where P ref,t , P pv,i,t , P i,t They represent the reference load of the hydro-solar hybrid system in time period t, the photovoltaic output of hydropower station i, and the hydropower output, respectively; (2) Transmission channel constraints: Where D G and P D,max They represent the subordinate power stations of the parallel line D and their maximum transmission capacity respectively; Electricity market constraints: (3) Constraints on hydropower output decomposition: P i,t =P ri,i,t +P qx,i,t +R d1,i,t Where P qx,i,t , R d1,i,t It represents the medium- and long-term power curve contract output and the power contract output of the hydropower station in period t i; (4) Constraints on the decomposition of medium- and long-term contract electricity: Where E d1,i,t Indicates the contractual electricity volume decomposed to today in the medium and long term; (4) Water balance constraints: Where V i,t I represents the final storage capacity of the hydropower station in period t; i,t , Q out,i,t and τi represent the interval runoff, outflow flow and the delay from the outflow flow of the upstream power station to the downstream at period t of the hydropower station i; (5) Constraints on upper and lower limits of storage capacity and flow rate: Where V i,min 、V i,max , Q out,i,min , Q out,i,max represents the minimum and maximum storage capacity, minimum ecological outflow and maximum flow of hydropower station i; (6) Water level and head constraints: With up,i,t =f i,zv (In i,t ) Z end,i,t =f i,zp (Q out,i,t ) In the formula, Z up,i,t , Z end,i,t , H i,t , H loss,i They represent the water level, tailwater level, head and head loss of the hydropower station at time period t; f i,zv (·),f i,zp (·) respectively represent the nonlinear relationship between the water level and the storage capacity of the hydropower station i and the nonlinear relationship between the tailwater level and the storage capacity; (7) Power generation flow limit: Where Q f,i,t , Q f,i,min , Q f,i,max ,q f,i,n,t 、N g,i It represents the power generation flow of the hydropower station at time period t, the lower and upper limits of the power generation flow, the power generation flow of the i unit of the hydropower station at time period t, and the number of units in the station.

7. The economic dispatch strategy of cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water according to claim 1, characterized in that: Scheduling strategy solution based on the Grey Wolf Optimization Algorithm; LGWO is used to calculate the following levels of GWO: In the wolf pack, other wolves are responsible for the task of β wolf. The LGWO algorithm only has α, β and ω wolves in the process of solving the model in this paper. Improve the GWO algorithm using Levy flight; The greedy search mechanism is combined with the modified hunting phase and applied to the GWO algorithm.

8. The economic dispatch strategy of cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water according to claim 7, characterized in that: The position of the gray wolf in LGWO is updated according to the α and β types. Levy flights can help LGWO simulate the hunting mode of gray wolves more realistically and accurately than the original GWO; using Levy flights as a replacement is an effective way to alleviate the stagnation problem of GWO. The new position is determined by the following formula: Where: l represents the weight of controlling the step length; represents point-to-point multiplication; Levy(λ) is the path that follows the Levy distribution; Where u and υ follow a normal distribution: Where: parameter λ is a random number between 0 and 2; σ v is 1.

9. The economic dispatch strategy of cascade hydropower stations taking into account the lowest water consumption rate for power generation and the lowest amount of abandoned water according to claim 8, characterized in that: After retaining the previous generation of solutions, the greedy search mechanism is executed to recalculate the fitness; the greedy selection (GS) strategy adopts the theory of evolution, and sets the probability p to represent the survival probability of a wolf in the wolf pack during the algorithm calculation process, that is, survival of the fittest; and the addition of the GS strategy will make it very likely that a high-quality wolf closer to the prey position will appear in each iterative calculation, so that the number of high-quality wolves in the wolf pack will increase and replace the wolf with the worst position, so that the LGWO algorithm has a better optimization path and increases the probability of a better solution, so that the LGWO algorithm has better search capabilities; Formula 27 is used to determine whether a high-quality wolf appears and whether it can replace the position of the worst wolf, and finally the t+1th iteration of the wolf is calculated to refresh the position of the wolf pack again: Where: r new and p are random numbers between 0 and 1.

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