Cascade hydropower group optimization scheduling method adaptive to stepped carbon emission

Abstract

The invention discloses a cascade hydropower group optimization scheduling method adapted to cascade carbon emission, belongs to the field of hydropower group optimization scheduling, and aims to solve the problem of how to carry out optimization scheduling on the basis of comprehensively considering carbon emission and total power generation income of a cascade hydropower group. The method comprises the following steps: calculating the carbon emission of the cascade hydropower group; establishing a stepped carbon transaction mechanism model; establishing an optimal scheduling model comprehensively considering carbon emission and total power generation income; solving a scheduling model based on a variable-stage step-by-step optimization algorithm; based on the life cycle evaluation theory, a process analysis and input-output hybrid analysis method is adopted, the carbon emission of the cascade hydropower group is calculated, a cascade carbon transaction mechanism model is established, the carbon emission is converted into an economical function, the carbon emission and the total power generation income are comprehensively considered, and an optimal scheduling model is established. And carrying out model solving by using a variable-phase step-by-step optimization algorithm.

Description

Technical Field

[0001] The present invention relates to the field of hydropower group optimization scheduling, and in particular to a cascade hydropower group optimization scheduling method adapted to stepped carbon emissions. Background Art

[0002] Hydropower generation technology is a way of utilizing energy by converting water energy into electrical energy. Because it can coordinate with various new energy sources to complement each other, it has become one of the main development directions of clean energy in the future. my country has attached great importance to the construction of cascade hydropower stations, so the relevant construction technology is also constantly improving. The comprehensive planning of the river basin also provides room for the development of cascade hydropower stations. From the perspective of adapting to the cascade carbon trading, optimizing the scheduling of cascade hydropower stations in the basin can make full use of water resources, which is of great significance to building a low-carbon society and achieving green development.

[0003] At present, the algorithms for solving the optimization model of cascade hydropower stations can be divided into two categories. One category is the algorithms developed using the principles of bionics, such as catfish effect multi-objective particle swarm algorithm, initial population generation method, etc. The other category is dynamic programming algorithms, such as differential evolution algorithm, linear programming method, etc. A large number of scholars have conducted extensive research on the above two categories of algorithms and achieved fruitful results, but the following problems still exist:

[0004] 1. The current optimization and scheduling of cascade hydropower groups mainly focuses on construction costs and power generation, and rarely takes carbon emissions into consideration.

[0005] 2. Currently, the most widely used optimization scheduling algorithm for cascade hydropower groups is the dynamic programming method. The traditional dynamic programming algorithm has obvious defects. As the number of cascade hydropower stations increases, the optimization model will face the "curse of dimensionality", and the difficulty and time of solving the problem will increase exponentially. Researching and improving it has important practical significance.

[0006] Based on the above-mentioned background technology problems, researchers have proposed an optimal scheduling method for cascade hydropower groups that is adapted to stepped carbon emissions. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes a cascade hydropower group optimization scheduling method adapted to step-by-step carbon emissions, comprising the following steps:

[0008] S1. For the carbon emissions of cascade hydropower groups, based on the life cycle assessment theory, the process analysis method is used to carefully calculate and evaluate the carbon emissions, the input-output method is used to evaluate the carbon emissions, and then the hybrid analysis method is used to calculate the carbon emissions of the cascade hydropower groups.

[0009] S2. When establishing an optimal dispatch model, it is necessary to comprehensively consider carbon emissions and total power generation revenue for optimal dispatch. However, carbon emissions and total power generation revenue belong to different categories of functions and cannot be directly calculated. Therefore, it is necessary to convert carbon emissions into economic functions. The carbon emission model and carbon trading model are as follows:

[0010] Carbon emission model: In cascade hydropower groups, the sources of carbon emissions are mainly carbon emissions from electricity purchases from upstream sources and carbon emissions from hydropower generating units. A carbon emission model is established based on the carbon emissions from electricity purchases from upstream sources and carbon emissions from hydropower generating units.

[0011]

[0012] Where: E h,a is the actual carbon emissions of the cascade hydropower group; E e,buy,a The actual carbon emissions of electricity purchased from the superior; E water,a is the actual carbon emissions from hydroelectric power generation; 1 、b 1 、c 1 Respectively represent the carbon emission calculation parameters of the upper-level electricity purchase; a 2 、b 2 、c 2 They represent the carbon emission calculation parameters of the hydroelectric generating units respectively.

[0013] Carbon trading model: The tiered carbon trading mechanism divides the purchase of carbon emission rights into multiple intervals. As the tiered hydropower group needs to purchase more carbon emission quotas, the purchase price of the corresponding interval will be higher. A carbon trading model is established based on this mechanism.

[0014]

[0015] Where: f CO2 is the carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; α is the price increase.

[0016] S3. To ensure that the stepped hydropower group can obtain higher total power generation revenue while controlling carbon emissions, an optimization scheduling model that comprehensively considers carbon emissions and total power generation revenue is established, including the objective function level and the constraint condition level.

[0017] At the objective function level, the total power generation revenue f of the cascade hydropower group is comprehensively considered from the perspectives of low carbon and economy. water and carbon trading costs CO2 , taking the global benefit F as the maximum, establish the objective function;

[0018] At the constraint level, while controlling carbon emissions and increasing total power generation revenue, it is necessary to ensure that the water volume of hydropower generating units is balanced, the water storage level is reasonable, and the power generation water flow, power generation water consumption, and hydropower station output meet the upper and lower limits;

[0019] S4. Based on the optimal scheduling model established in S3 that comprehensively considers carbon emissions and total power generation revenue, the variable-stage stepwise optimization algorithm is used to solve the model.

[0020] (1) Initialize the water level. According to the current water level and the predicted water inflow, try to maintain the reservoir at a high water level. Since different initial values ​​take different optimization times, the constrained search method is used to select the initial water level value.

[0021] (2) Set the number of optimization stages to n, fix the water level at time t and time t+n, discretize the water level at n times between the tth and t+nth nodes into k groups of water levels, each group has n discrete values, then the optimization path is k n-1 strip;

[0022] (3) Traverse k n-1 The optimal path is used to determine the water level of the mth hydropower station when the power generation is optimal in the t+1 period, which is recorded as Z' m，t+1 ;

[0023] (4) Similarly, the water level of the t+2 period is optimized by following steps (2)-(3), traversing all reservoirs and all periods to obtain the water level trajectory Z' of each hydropower station in each period. 1,1 , …Z' 1,n , Z' 2，1 …Z' 2,n …Z' m,n ; Then use this new trajectory as the initial value of a new round of iteration, repeat steps (1) and (2), and compare the water level trajectory obtained in the new round of iteration with the trajectory obtained in the previous round. If the error meets the accuracy requirement, the calculation ends and the optimization is completed.

[0024] Furthermore, the specific form of the objective function in S3 is as follows:

[0025] max F=f water -f CO2 (7)

[0026] in,

[0027]

[0028] Where: n is the number of hydropower stations in the cascade; t is the time period divided into the planned dispatch period; λ n is the water energy utilization rate of the nth hydropower station, that is, the capacity coefficient of converting water energy into electrical energy through the generator set; p n,t is the on-grid electricity price of n hydropower stations in the tth period; q n,t is the average power generation water flow of the nth hydropower station in the tth period; h n,tis the average fall of the nth hydropower station in the tth period; m t is the duration of the tth period.

[0029] Furthermore, the specific form of the constraints in S3 is as follows:

[0030] (1) Water balance constraints:

[0031] v n,t+1 =v n,t +z n,t ×m t =v n,t +(r n,t -q n,t -s n,t )×m t (9)

[0032] Where: v n,t and v n,t+1 is the reservoir storage capacity of the nth hydropower station, the tth period and the beginning of the t+1th period.

[0033] (2) Reservoir water level constraints:

[0034] h n,min ≤h n,t ≤h n,max (10)

[0035] Where: h n,min and h n,max The minimum and maximum water storage levels that the reservoir of the nth hydropower station must meet during the planned scheduling period.

[0036] (3) Power generation water flow constraints:

[0037] q n,min ≤q n,t ≤q n,max (11)

[0038] Where: q n,min ,q n,max They represent the minimum and maximum power generation water flow of the nth hydropower station respectively.

[0039] (4) Constraints on water consumption for power generation:

[0040] w n,min ≤w n,t ≤w n,max (12)

[0041] Where: w n,min ,w n,max They represent the minimum and maximum water consumption for power generation of the nth hydropower station respectively.

[0042] (5) Hydropower station output constraints:

[0043] N n,min ≤N n,t ≤N n,max (13)

[0044] Where: N n,min ,N n,max They represent the minimum and maximum output of the nth hydropower station unit respectively.

[0045] The beneficial effects of adopting the above technical solution are:

[0046] Based on the life cycle assessment theory, the present invention adopts process analysis and input-output hybrid analysis to calculate the carbon emissions of cascade hydropower groups, establish a step-by-step carbon trading mechanism model, convert carbon emissions into economic functions, comprehensively consider carbon emissions and total power generation revenue to establish an optimization scheduling model, and use a variable-stage step-by-step optimization algorithm to solve the model. Through practical verification, it can be proved that the present invention can be put into practical engineering use to better realize the optimization scheduling of cascade hydropower groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is the structural diagram of the cascade hydropower group;

[0048] Figure 2 This is a diagram of the traditional step-by-step optimization algorithm structure;

[0049] Figure 3 The algorithm structure diagram is gradually optimized for the changing stage;

[0050] Figure 4 The location relationship of the cascade hydropower stations in Longnan area;

[0051] Figure 5 To optimize the scheduling results;

[0052] Figure 6 The change of total revenue from power generation of Hydropower Station A in May, June and July;

[0053] Figure 7 The change in total revenue from power generation of Hydropower Station B in May, June and July;

[0054] Figure 8 This is the change in total power generation revenue of Hydropower Station C in May, June and July. DETAILED DESCRIPTION

[0055] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples.

[0056] It illustrates the present invention but is not intended to limit the scope of the present invention.

[0057] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] 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 representative of selected embodiments of the present invention.

[0059] A method for optimizing the scheduling of cascade hydropower plants adapted to stepped carbon emissions is provided. The technology is divided into the following steps:

[0060] S1. Calculation of carbon emissions of cascade hydropower stations;

[0061] When calculating the carbon emissions of cascade hydropower groups, the present invention determines the main sources of carbon emissions based on the structure of the cascade hydropower groups, and uses process analysis and input-output hybrid analysis based on life cycle assessment theory to calculate the carbon emissions of cascade hydropower groups. Figure 1 shown.

[0062] S1.1 Process analysis method;

[0063] Process analysis method refers to the process of generating more effective and efficient transaction processing systems and business control information systems in an evolutionary manner. It obtains a carbon emission calculation list by analyzing the process, and then calculates the carbon emissions based on the list. The calculation results of this method are accurate and reliable, but for large-scale water conservancy and hydropower projects with complex system boundaries and numerous sub-processes, the upstream processes of some sub-processes are easily ignored during the calculation process, resulting in deviations in the calculation results and "truncation errors".

[0064] The process analysis method calculation formula is as follows:

[0065]

[0066] Where: Q i Indicates the intensity of an activity; R i Represents the carbon footprint per unit activity.

[0067] S1.2 Input-output method;

[0068] The input-output method establishes a mathematical model based on the "input" and "output" of the economic sector, and calculates the carbon emissions through the calculated carbon emission factor. This method is simple to calculate, but it does not distinguish between detailed processes, and the calculation is too rough to accurately evaluate the details of each sub-process in the process, but it can avoid "truncation errors."

[0069] The specific calculation process is as follows:

[0070]

[0071]

[0072]

[0073] Where P n represents static investment (base year); CPI n represents the consumer price index (in year n); PPP represents the purchasing power parity index of China to the United States (2002); F n represents the carbon footprint parameter per unit of economic activity; In represents China’s carbon emission intensity (in year n); I 2002 represents the carbon emission intensity of the United States (2002).

[0074] S1.3 Hybrid analysis method;

[0075] The process analysis method and the input-output method each have their own advantages and disadvantages, so the hybrid analysis method that combines the two is more applicable to the measurement and evaluation of carbon emissions of cascade hydropower groups.

[0076] S2. Establishment of a ladder-type carbon trading mechanism model;

[0077] The present invention comprehensively considers carbon emissions and total power generation revenue for optimal scheduling. Since carbon emissions and total power generation revenue belong to different categories of functions and cannot be directly integrated and optimized, it is necessary to establish a suitable carbon emission model and carbon trading model to convert carbon emissions into economic functions.

[0078] S2.1, Carbon emission model;

[0079] In the cascade hydropower group, according to Figure 1 It can be seen from the cascade hydropower structure diagram that the sources of carbon emissions are mainly carbon emissions from electricity purchases from the upper level and carbon emissions from hydroelectric generators. Therefore, the present invention establishes a carbon emission model based on the carbon emissions from electricity purchases from the upper level and the carbon emission data from hydroelectric generators. The specific carbon emission model is as follows:

[0080]

[0081] Where: E h,ais the actual carbon emissions of the cascade hydropower group; E e,buy,a The actual carbon emissions of electricity purchased from the superior; E water,a is the actual carbon emissions from hydroelectric power generation; 1 、b 1 、c 1 It is the calculation parameter of carbon emission of electricity purchased by the superior; a 2 、b 2 、c 2 Calculate the carbon emissions of hydroelectric generators.

[0082] S2.2, Carbon trading model;

[0083] The tiered carbon trading mechanism divides the purchase of carbon emission rights into multiple intervals. As the tiered hydropower group needs to purchase more carbon emission quotas, the purchase price of the corresponding interval will be higher. A carbon trading model is established based on this mechanism. The specific carbon trading model is as follows:

[0084]

[0085] Where: f CO2 is the carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; α is the price increase.

[0086] S3. Establishment of an optimal dispatching model that comprehensively considers carbon emissions and total power generation revenue;

[0087] In order to ensure that the stepped hydropower group can obtain higher total power generation revenue while controlling carbon emissions, an optimal scheduling model that comprehensively considers carbon emissions and total power generation revenue is established.

[0088] S3.1. Objective function

[0089] At the objective function level, the total power generation revenue f of the cascade hydropower group is comprehensively considered from the perspectives of low carbon and economy. water and carbon trading costs CO2 , taking the global benefit F as the maximum as the goal, establish the objective function. The specific objective function is as follows:

[0090] maxF=f water -f CO2 (7)

[0091] in,

[0092]

[0093] Where: n is the number of hydropower stations in the cascade; t is the time period divided into the planned dispatch period; λ n is the water energy utilization rate of the nth hydropower station, that is, the capacity coefficient of converting water energy into electrical energy through the generator set; p n,tis the on-grid electricity price of n hydropower stations in the tth period; q n,t is the average power generation water flow of the nth hydropower station in the tth period; h n,t is the average fall of the nth hydropower station in the tth period; m t is the duration of the tth period.

[0094] S3.2 Constraints

[0095] At the constraint level, while controlling carbon emissions and increasing total power generation revenue, it is necessary to ensure that the water volume of the hydropower generating units is balanced, the water storage level is reasonable, and the power generation water flow, power generation water consumption, and hydropower station output meet the upper and lower limits. The specific constraints are as follows:

[0096] (1) Water balance constraints:

[0097] v n,t+1 =v n,t +z n,t ×m t =v n,t +(r n,t -q n,t -s n,t )×m t (9)

[0098] Where: v n,t and v n,t+1 is the reservoir storage capacity of the nth hydropower station, the tth period and the beginning of the t+1th period.

[0099] (2) Reservoir water level constraints:

[0100] h n,min ≤h n,t ≤h n,max (10)

[0101] Where: h n,min and h n,max The minimum and maximum water storage levels that the reservoir of the nth hydropower station must meet during the planned scheduling period.

[0102] (3) Power generation water flow constraints:

[0103] q n,min ≤q n,t ≤q n,max (11)

[0104] Where: q n,min ,q n,max The minimum and maximum water flow for the nth hydropower station.

[0105] (4) Constraints on water consumption for power generation:

[0106] wn,min ≤w n,t ≤w n,max (12)

[0107] Where: w n,min ,w n,max The minimum and maximum water consumption for power generation of the nth hydropower station.

[0108] (5) Hydropower station output constraints:

[0109] N n,min ≤N n,t ≤N n,max (13)

[0110] Where: N n,min ,N n,max It is the minimum and maximum output of the nth hydropower station unit.

[0111] S4. Model solution based on variable stage stepwise optimization algorithm

[0112] Based on the optimization dispatch model established in S3 that comprehensively considers carbon emissions and total power generation revenue, the variable-stage stepwise optimization algorithm is used to solve the model. The variable-stage stepwise optimization algorithm proposed in the present invention improves the traditional stepwise optimization algorithm.

[0113] The strategy of the traditional stepwise optimization algorithm is to decompose the multi-stage problem into multiple two-stage problems, solve the two-stage problems in turn, and use the optimization results as the initial value of the next stage optimization, and repeat the cycle to finally reach the convergence requirement. The specific principle of the stepwise optimization algorithm to solve the optimal dispatch of cascade hydropower stations starting from the tth period of the i-th reservoir is as follows: Figure 2 shown.

[0114] The specific steps are:

[0115] (1) First, fix the water level Z in period t and period t+2 i，t , Z i，t+2 , by continuously adjusting the water level Z in period t+1 i , t+1 , so that the power generation from t to t+1 is the largest, and the water level in t+1 is Z' i，t+1 .

[0116] (2) Similarly, optimize the t+2 period and fix the water level Z' of the t+1 and t+3 periods. i，t+1 , Z i，t+3 , by continuously adjusting the water level Z in period t+2 i，t+2 , so that the power generation from t+1 to t+2 is the largest, and the water level in t+2 is Z' i，t+2 .

[0117] (3) Similarly, traverse all reservoirs and all time periods to obtain the water level trajectory Z' of each hydropower station in each time period 1 , 1 , …Z' 1，n , …, Z' 2，1 …Z' 2，n …Z' m，n ; Then use this new trajectory as the initial value of a new round of iteration, repeat steps (1) and (2), and compare the water level trajectory obtained in the new round of iteration with the trajectory obtained in the previous round. If the error meets the accuracy requirement, the calculation ends and the optimization is completed.

[0118] Traditional step-by-step optimization algorithms are prone to being trapped in local optimal solutions due to the small number of optimization paths. In order to enable the step-by-step optimization algorithm to escape from the local optimal solution, the present invention embeds a variable stage strategy in the two-stage step-by-step optimization algorithm to form a variable stage step-by-step optimization algorithm, so as to enhance the global search capability of the algorithm and ensure that the result converges to the global optimal solution.

[0119] The principle diagram of the variable stage stepwise optimization algorithm is as follows Figure 3 As shown in the figure, when the two-stage stepwise optimization method is used, when the water level of the i-th reservoir in the t+1 period is discretized into k points, there are k optimal paths. In order to enhance the ability of the stepwise optimization method to jump out of the local optimal solution, a variable stage strategy is proposed. Figure 3 As an example, the three-stage method shown in the figure fixes the water level at the time period t and t+3, and the water level at the time period t+1 and t+2 is discrete. The optimal path is k 2 Similarly, when the n-order stepwise optimization method is used, the optimal path is k n-1 The algorithm's optimization paths will increase, that is, the feasible domain of the algorithm will be expanded, making it easier to escape from the local optimal solution.

[0120] The specific steps are:

[0121] (1) Initialize the water level. According to the current water level and the predicted water inflow, try to maintain the reservoir at a high water level. Since different initial values ​​take different optimization times, the constrained search method is used to select the initial water level value.

[0122] (2) Set the number of optimization stages to n. Fix the water level at time t and time t+n, discretize the water level at n times between the tth and t+nth nodes into k groups of water levels, each with n discrete values, and the optimization path is k. n-1 strip.

[0123] (3) Traverse k n-1 The optimal path is used to determine the water level of the mth hydropower station when the power generation is optimal in the t+1 period, which is recorded as Z' m，t+1 .

[0124] (4) Similarly, the water level of the t+2 period is optimized by following steps (2)-(3). Traverse all reservoirs and all time periods to obtain the water level trajectory of each hydropower station in each time period.

[0125] Z' 1,1 , …Z' 1,n , Z' 2，1 …Z' 2,n …Z' m,n ; Then use this new trajectory as the initial value of a new round of iteration, repeat steps (1) and (2), and compare the water level trajectory obtained in the new round of iteration with the trajectory obtained in the previous round. If the error meets the accuracy requirement, the calculation ends and the optimization is completed.

[0126] Example:

[0127] In order to verify the practicality and effectiveness of the variable-stage stepwise optimization algorithm proposed in this invention in the optimal scheduling of cascade hydropower stations, the inflow data of the three cascade hydropower stations A, B, and C in Longnan City, Gansu Province from January 1, 2022 to December 31, 2022 were used as examples for calculation and analysis. The address location relationship and main parameters of each hydropower station are shown in Figure 2. Figure 4 And as shown in Table 1.

[0128] Table 1 Parameters of Longnan cascade hydropower station group

[0129]

[0130] Combined with the steps of the invention described above, the historical data is first analyzed, and the distribution structure of the cascade hydropower group in Longnan area of ​​Gansu Province is extracted. It is concluded that the cascade hydropower structure in the area conforms to the general cascade hydropower structure constructed by the present invention, and its carbon emissions mainly come from the carbon emissions of electricity purchases from the upper level and the carbon emissions of hydropower generating units. Subsequently, based on the life cycle assessment theory, process analysis and input-output hybrid analysis are used to calculate its carbon emissions. After the carbon emissions are calculated, the carbon emission model and carbon trading model constructed by the present invention are called to convert the carbon emissions into an economic function. After the conversion is completed, the optimal scheduling model that comprehensively considers carbon emissions and total power generation revenue constructed by the present invention is used to establish the corresponding objective function and constraints. Finally, the variable-stage step-by-step optimization algorithm constructed by the present invention is called to solve the problem, and the optimal solution for the scheduling of the cascade hydropower group in Longnan area of ​​Gansu Province is obtained. The optimal solution is as follows: Figure 5 shown.

[0131] from Figure 5It can be seen that the variable-stage stepwise optimization algorithm can obtain more global benefits than the traditional stepwise optimization method and has a faster convergence speed, which means that the traditional stepwise optimization algorithm may fall into the local optimal solution when solving the optimal dispatch of cascade hydropower stations, while the variable-stage stepwise optimization method can jump out of the local optimal solution during the iteration process and obtain the global optimal solution, thereby improving the global benefits of the cascade hydropower station within the optimization period.

[0132] In order to more intuitively explore the impact of the variable-stage stepwise optimization algorithm on the optimization results, the global benefits during the iterations in May, June and July are selected for analysis, such as Figure 6 , Figure 7 , Figure 8 The difference between the traditional stepwise optimization algorithm and the variable-stage stepwise optimization algorithm is that the variable-stage stepwise optimization algorithm can keep carbon emissions at a low level while making the total power generation revenue greater than the traditional stepwise optimization algorithm. The traditional stepwise optimization algorithm cannot optimize the total power generation revenue in May, June and July and falls into a local optimal solution, so the calculated global revenue is lower than that of the variable-stage stepwise optimization algorithm.

[0133] Through analysis and calculation, it is proved that the present invention can better realize the optimal dispatch of cascade hydropower stations after improving the traditional stepwise optimization method. In addition, in order to compare the calculation efficiency of the improved stepwise optimization algorithm, the present invention also selects the calculation results of the linear programming method and the new intelligent algorithm such as ICA for comparison, and the results are shown in Table 2.

[0134] Table 2 Comparison results of different optimization algorithms

[0135]

[0136] The difference between the linearization method (1) and the linearization method (2) in Table 2 is that the method (1) only discretizes the linearization of the water level-storage capacity constraint, while the method (2) simultaneously discretizes the water level-storage capacity constraint and the head-output constraint. According to the calculation results in Table 2, the linearization method (2) fails to solve. After analysis, the constraint conditions of the method (2) reach 32957, which contain a large number of 0, 1 variables. Therefore, the solver shows that the solution fails after a long period of calculation, and the model cannot be solved. However, the method (1) only discretizes the water level-storage capacity constraint, and the relationship between the unit head-output is expressed by a linear expression, thereby reducing a large number of 0, 1 variables, and the model can be solved successfully. It can be seen from Table 2 that each type of algorithm has its own advantages. The variable stage stepwise optimization method takes the least time, but the total power generation is lower than that of the imperialist competition algorithm. Although the linear programming method also takes less time to solve, in order to be able to solve successfully, some constraints need to be approximated by linear expressions, and the optimization results may deviate greatly from the actual operation. In general, the variable stage stepwise optimization algorithm proposed in the present invention has obvious advantages and can be put into practical engineering use.

[0137] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept, such as a technical solution formed by replacing the above-mentioned features with technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to).

Claims

1. A method for optimizing the scheduling of cascade hydropower groups adapted to step-by-step carbon emissions, characterized in that: The following steps are involved: S1. Based on the life cycle assessment theory, the carbon emissions of cascade hydropower stations are carefully calculated and evaluated using the process analysis method, the input-output method is used to evaluate the carbon emissions, and then the hybrid analysis method is used to calculate the carbon emissions of cascade hydropower stations; S2. When establishing an optimal dispatch model, it is necessary to comprehensively consider carbon emissions and total power generation revenue for optimal dispatch. However, carbon emissions and total power generation revenue belong to different categories of functions and cannot be directly calculated. Therefore, it is necessary to convert carbon emissions into economic functions. The carbon emission model and carbon trading model are as follows: Carbon emission model: In cascade hydropower groups, the sources of carbon emissions are mainly carbon emissions from electricity purchases from upstream and carbon emissions from hydropower generating units. A carbon emission model is established based on the carbon emissions from electricity purchases from upstream and carbon emissions from hydropower generating units. Where: E h,a is the actual carbon emissions of the cascade hydropower group; E e,buy,a The actual carbon emissions of electricity purchased from the superior; E water,a is the actual carbon emission of hydropower generation; a1, b1, c1 represent the carbon emission calculation parameters of the upper power purchase; a2, b2, c2 represent the carbon emission calculation parameters of the hydropower generating unit; Carbon trading model: The tiered carbon trading mechanism divides the purchase of carbon emission rights into multiple intervals. As the tiered hydropower group needs to purchase more carbon emission quotas, the purchase price of the corresponding interval will be higher. A carbon trading model is established based on this mechanism: Where: f CO2 is the carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; α is the price increase; S3. To ensure that the stepped hydropower group can obtain higher total power generation revenue while controlling carbon emissions, an optimization scheduling model that comprehensively considers carbon emissions and total power generation revenue is established, including the objective function level and the constraint condition level: At the objective function level, the total power generation revenue f of the cascade hydropower group is comprehensively considered from the perspectives of low carbon and economy. water and carbon trading costs CO2 , taking the global benefit F as the maximum, establish the objective function; At the constraint level, while controlling carbon emissions and increasing total power generation revenue, it is necessary to ensure that the water volume of hydropower generating units is balanced, the water storage level is reasonable, and the power generation water flow, power generation water consumption, and hydropower station output meet the upper and lower limits; S4, based on the optimal dispatch model established in S3 that comprehensively considers carbon emissions and total power generation revenue, the model is solved using a variable-stage stepwise optimization algorithm; (1) Initialize the water level. According to the current water level and the predicted water inflow, try to maintain the reservoir at a high water level. Since different initial values ​​take different optimization times, the constrained search method is used to select the initial water level value. (2) Set the number of optimization stages to n, fix the water level at time t and time t+n, discretize the water level at n times between the tth and t+nth nodes into k groups of water levels, each group has n discrete values, then the optimization path is k n-1 strip; (3) Traverse k n-1 The optimal path is used to determine the water level of the mth hydropower station when the power generation is optimal in the t+1 period, which is recorded as Z' m , t+1 ; (4) Similarly, the water level of the t+2 period is optimized by following steps (2)-(3), traversing all reservoirs and all periods to obtain the water level trajectory Z' of each hydropower station in each period. 1,1 , …Z' 1,n , Z' 2，1 …Z' 2,n …Z' m,n ; Then use this new trajectory as the initial value of a new round of iteration, repeat steps (1) and (2), and compare the water level trajectory obtained in the new round of iteration with the trajectory obtained in the previous round. If the error meets the accuracy requirement, the calculation ends and the optimization is completed.

2. The method for optimizing the scheduling of cascade hydropower plants adapted to step-by-step carbon emissions according to claim 1, characterized in that: The specific form of the objective function in S3 is as follows: maxF=f water -f CO2 (7) in, Where: n is the number of hydropower stations in the cascade; t is the time period divided into the planned dispatch period; λ n is the water energy utilization rate of the nth hydropower station, that is, the capacity coefficient of converting water energy into electrical energy through the generator set; p n,t is the on-grid electricity price of n hydropower stations in the tth period; q n,t is the average power generation water flow of the nth hydropower station in the tth period; h n,t is the average fall of the nth hydropower station in the tth period; m t is the duration of the tth period.

3. The method for optimizing the scheduling of cascade hydropower plants adapted to step-by-step carbon emissions according to claim 1, characterized in that: The specific form of the constraints in S3 is as follows: (1) Water balance constraints: v n,t+1 =v n,t +z n,t ×m t =v n,t +(r n,t -q n,t -s n,t )×m t (9) Where: v n,t and v n,t+1 is the reservoir storage capacity at the beginning of the nth hydropower station, the tth period and the t+1th period; (2) Reservoir water level constraints: h n,min ≤h n,t ≤h n,max (10) Where: h n,min and h n,max is the minimum and maximum water storage levels that the reservoir of the nth hydropower station must meet during the planned dispatch period; (3) Power generation water flow constraints: q n,min ≤q n,t ≤q n,max (11) Where: q n,min ,q n,max They represent the minimum and maximum power generation water flow of the nth hydropower station respectively; (4) Constraints on water consumption for power generation: In n,min ≤in n,t ≤in n,max (12) Where: w n,min ,w n,max They represent the minimum and maximum water consumption for power generation of the nth hydropower station respectively; (5) Hydropower station output constraints: N n,min ≤N n,t ≤N n,max (13) Where: N n,min ,N n,max They represent the minimum and maximum output of the nth hydropower station unit respectively.

Images