A method and device for multi-objective optimization decision of hydroelectric planning

Abstract

The application provides a hydropower development multi-objective optimization decision method and device, and belongs to the technical field of hydropower development planning. The method comprises the following steps: constructing a hydropower development multi-objective optimization decision model for a river network to be developed; calculating the objective function value corresponding to each sub-model of the model of a current dam set as an original objective function value; adding each dam combination to be developed at a current development position to the current dam set to obtain an updated dam set corresponding to each combination, and calculating the objective function value corresponding to each sub-model of the model of the updated dam set as an updated objective function value; and screening an optimal combination at the current development position from all combinations by calculating the marginal increment of the updated objective function value of each sub-model under each dam combination relative to the original objective function value. The application can effectively reduce the calculation cost and provide an efficient and accurate hydropower development strategy for a large-scale basin.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hydropower development planning, and particularly relates to a hydropower planning multi-objective optimization decision method and device. BACKGROUND

[0002] With the imminent conflicts between global climate action, socio-economic development and the protection of river systems, the strategic planning of future hydropower development is more urgent than ever. Traditional dam development decisions are usually driven by the engineering factors of individual projects, and only consider the suitability of individual dam construction according to a limited range of local impacts. However, studies have shown that expanding the spatial scale of planning can provide more options for improving the negative impacts of hydropower development. Therefore, "strategic hydropower planning" has emerged. In contrast to individual project assessment, strategic hydropower planning enables decision makers to assess the impacts of dam combinations in entire river basins (or larger areas). The efficiency gain of such planning increases with the number of potential dam candidate sites. At the same time, in the past few years, most large-scale academic studies on strategic hydropower planning have only optimized for a single environmental standard, such as fish diversity, greenhouse gas emissions, sediment transport, and river network connectivity. However, the final development combination chosen often needs to consider balancing as many conflicting factors as possible, including environmental factors and cost factors. This represents a typical multi-objective optimization decision problem, and its computational complexity grows rapidly with the number of potential dams considered and the number of decision criteria. Precise calculation of the complete Pareto front of such a multi-objective optimization problem will be an NP-hard problem. For example, for a river basin with 253 potential dams, there are 253! combinations of dam development combinations in sequence, and 25! is already 1.6*10 25 , 253! will be an astronomical number.

[0003] In the field of hydropower development planning, existing methods for solving multi-objective optimization problems of hydropower development mainly fall into the following two categories: (1) using genetic algorithms to find near-optimal dam development combinations without evaluating all possible combinations; (2) using dynamic programming algorithms to obtain an approximately guaranteed range of Pareto optimal solutions in polynomial time. After obtaining the optimization results, combine decision-making methods, such as classical multi-objective decision-making techniques (TOPSIS, AHP, etc.) or exponential methods (entropy weight method), to determine the optimal hydropower development decision scheme.

[0004] However, in the prior art, there are the following problems: (1) as the number of considered targets increases, the calculation time of the optimization algorithm will increase at least exponentially; (2) the optimization algorithm is difficult to obtain a theoretical optimal solution, and can only approximate the Pareto frontier as much as possible, so that the result cannot achieve the optimal decision effect; (3) the existing decision method is limited to pure mathematical calculation, and the parameter setting and method principle lack physical meaning, resulting in that the decision result is disconnected with the engineering practice. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art, and to provide a hydropower planning multi-objective optimization decision method and device. The present application can reduce the calculation cost of obtaining hydropower development sequence decision results, make the decision results have strong explainability and operability, and realize the minimization of environmental damage caused by hydropower development, thereby providing an efficient and accurate hydropower development strategy for large-scale river basins.

[0006] The first aspect of the present application provides a hydropower planning multi-objective optimization decision method, comprising:

[0007] A hydropower planning multi-objective optimization decision model is constructed for a river network to be developed for hydropower, and the optimization decision model includes a power generation cost sub-model, a sediment interception sub-model, a carbon emission sub-model, and a river connectivity sub-model;

[0008] The target function values of the current planned dam set in each sub-model of the optimization decision model are calculated and recorded as original target function values;

[0009] Under the current development position of the hydropower planning, each kind of to-be-planned dam combination in the current to-be-planned dam set is obtained;

[0010] Each to-be-planned dam combination is added to the current planned dam set respectively, and the target function values of the updated planned dam set corresponding to each to-be-planned dam combination in each sub-model of the optimization decision model are calculated and recorded as updated target function values;

[0011] By calculating the marginal incremental value of each sub-model under each to-be-planned dam combination with respect to the original target function value, the optimal to-be-planned dam combination under the current development position is selected from the current to-be-planned dam set, so as to realize the hydropower development planning of the river network.

[0012] In one specific embodiment of the present application, the method further comprises:

[0013] Before the construction of the hydropower planning multi-objective optimization decision model, the hydropower planning information of the river network is obtained, comprising:

[0014] The number of each to-be-planned dam in the river network and the upstream and downstream relationship between the to-be-planned dams, the number of the river section where each to-be-planned dam is located, the distance from the uppermost dam of the river section to the intersection node of the present river section and the previous river section, the distance from the lowermost dam of the river section to the intersection node of the present river section and the next river section, the distance from the last river section to the river network outlet, other attribute information of each to-be-planned dam, including: position, annual power generation, power generation cost, installed capacity, power generation water head, dam height, submerged area, reservoir capacity information, and annual average temperature and annual precipitation information of the position where each to-be-planned dam is located.

[0015] In one specific implementation of the present application, the method further comprises:

[0016] The objective function of the power generation cost sub-model is to calculate the annual power generation cost of all planned dams, and the expression is as follows:

[0017]

[0018] In the formula, d is any planned dam; D is the total number of the current planned dams in the river basin; y is any year in the planning period; Y is the total number of years in the planning period; Gen d,y is the power generation of dam d in the yth year, Cost d is the power generation cost of dam d;

[0019] The objective function of the sediment interception sub-model is to calculate the annual sediment interception amount of all planned dams;

[0020] In the formula, the sediment interception rate of any planned dam is calculated according to the following expression:

[0021]

[0022] In the formula, TE d is the sediment interception rate of dam d; V d is the reservoir capacity of dam d; Q d is the multi-year average runoff of dam d; and the sediment inflow amount of dam d under natural conditions is calculated according to the following expression:

[0023]

[0024] In the formula, r(d) is all river sections upstream of dam d; U T is the river network containing all river sections in the river basin; ESPh r and ESPl r are the sediment yield agents of the areas with an elevation higher than 500 m and lower than 500 m, respectively, upstream of the r river section; W represents the sediment deposition agent; l r is the total length of the upstream river section of dam d; λ1, λ2, and λ3 are sub-model parameters, and all have values greater than 0;

[0025] The annual sediment interception amount of all planned dams is calculated according to the following expression:

[0026]

[0027] In the formula, con d is a 0-1 variable representing the construction state of the dam d, 0 representing non-construction and 1 representing construction; the objective function of the carbon emission sub-model is to calculate the annual net greenhouse gas emission of the river basin, and the expression is as follows:

[0028] CO2=-169.73+241.86*ATER+120.34*ln(S) (5)

[0029] ln(CH4)=-9.81-0.75*ln(A)+1.18*ln(ATER)+4.5*ln(T max ) (6)

[0030]

[0031] In the formula, S is the reservoir area formed by dam interception; ATER is the ratio of reservoir area and annual power generation, and when the number of constructed dams is 0, ATER is 0; A is the dam age; T max is the annual maximum temperature at the dam location;

[0032] The objective function of the river connectivity sub-model is to calculate the river connectivity, and the expression is as follows:

[0033]

[0034] In the formula, m is the river section number; M is the number of river sections; l m is the length of the mth river section; and L is the length of all river sections in the river basin.

[0035] In one specific embodiment of the present application, the marginal increment of the updated objective function value of each sub-model relative to the original objective function value under the combination of each to-be-planned dam is calculated according to the following expression:

[0036]

[0037] In the formula, n represents the total number of planned dams before updating; i is the number of planned dams at the current development position; n+i represents the total number of planned dams after updating; MC o is the marginal increment of the objective function of any sub-model after adding the combination of to-be-planned dams to the current set of planned dams; Obj n and Obj n+irespectively are the value of any sub-model objective function under the current planned dam set before updating and the value of any sub-model objective function under the current planned dam set after updating; Inst n and Inst n+i respectively are the total installed capacity of the basin corresponding to the current planned dam set before updating and the total installed capacity of the basin corresponding to the current planned dam set after updating.

[0038] In one specific implementation of the present application, the method further comprises:

[0039] The marginal increments of each sub-model objective function value under each to-be-planned dam combination are normalized respectively, and the weight of each sub-model in the hydropower planning is considered to obtain the normalized marginal increments MC o ’ of each sub-model objective function value under each to-be-planned dam combination.

[0040]

[0041] In the formula, p o is the preset weight of any sub-model; MC o,max and MC o,min respectively are the maximum value and the minimum value in the marginal increments of any sub-model objective function value under the current development position.

[0042] In one specific implementation of the present application, the method further comprises:

[0043] Based on the normalized marginal increments, the optimal combination under the current development position is screened from all to-be-planned dam combinations of the current to-be-planned dam set; the specific steps are as follows:

[0044] 1) From the current to-be-planned dam set, a to-be-planned dam combination that makes the sum of MC o ’ corresponding to each sub-model minimum is selected as the current minimum dam combination, and the current minimum dam combination is added to the initial empty candidate dam combination set under the current development position.

[0045] Then, the remaining to-be-planned dam combinations in the current to-be-planned dam set are traversed, if any combination in the remaining to-be-planned dam combinations has MC o ’ under each sub-model greater than MC o ’ corresponding to the current minimum dam combination, the combination is not added to the candidate dam combination set; otherwise, the remaining to-be-planned dam combination is added to the candidate dam combination set; after all the remaining to-be-planned dam combinations are traversed, the final candidate dam combination set under the current development position is obtained.

[0046] 2) Based on the result of step 1), MC oEuclidean distance between MC and MC

[0047] The calculation expression of the Euclidean distance is as follows:

[0048]

[0049] In the formula, d is the MC corresponding to each sub-model under any to-be-planned dam combination o Euclidean distance between MC and MC i MC corresponding to the i-th sub-model o MC, i = 1, 2, 3, 4.

[0050] In one specific implementation of the present application, the method further comprises:

[0051] determining:

[0052] If the planning scheme of the optimal combination has met the demand of the annual hydropower newly-installed capacity, the hydropower planning is completed; otherwise, the to-be-planned dam corresponding to the optimal combination is added to the current planned dam set to obtain an updated current planned dam set, and the to-be-planned dam corresponding to the optimal combination is deleted from the current to-be-planned dam set to obtain an updated current to-be-planned dam set, the next rank of hydropower development is taken as a new current development rank, and then a new round of hydropower development planning is performed.

[0053] The second aspect embodiment of the present application provides a hydropower planning multi-objective optimization decision device, which comprises:

[0054] A model construction module is configured to construct a hydropower planning multi-objective optimization decision model for a river network to be developed for hydropower, wherein the optimization decision model comprises a power generation cost sub-model, a sediment interception sub-model, a carbon emission sub-model and a river connectivity sub-model.

[0055] An original objective function value calculation module is configured to calculate the objective function value corresponding to the current planned dam set in each sub-model of the optimization decision model and record the value as an original objective function value.

[0056] A hydropower development combination acquisition module is configured to acquire each to-be-planned dam combination in a current to-be-planned dam set under a current development rank of hydropower planning.

[0057] An objective function value updating module is configured to add each to-be-planned dam combination into the current planned dam set respectively to obtain the objective function value corresponding to the updated planned dam set in each sub-model of the optimization decision model corresponding to each to-be-planned dam combination and record the value as an updated objective function value.

[0058] A planning module is configured to filter out an optimal dam combination at a current development site from the current set of dams to be planned by calculating a marginal increment of an updated objective function value of each sub-model with respect to an original objective function value of each sub-model under each dam combination to be planned, so as to achieve water and electricity development planning of the river network.

[0059] The third aspect of the present application provides an electronic device, comprising:

[0060] at least one processor; and a memory connected to the at least one processor in communication;

[0061] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the water and electricity planning multi-objective optimization decision method.

[0062] The fourth aspect of the present application provides a computer readable storage medium, which stores computer instructions for executing the water and electricity planning multi-objective optimization decision method.

[0063] The characteristics and advantages of the present application are:

[0064] 1) The water and electricity planning optimal decision scheme considering multiple objectives can be obtained by using the present application, and compared with the method of optimizing first and then deciding in the traditional method, the calculation time for obtaining the water and electricity development decision scheme can be reduced from the month scale to the second level.

[0065] 2) The importance of each target is adjusted by setting the weight, so that the decision maker can customize the water and electricity development sequence scheme which keeps the relative balance of each target.

[0066] 3) The present application can obtain a water and electricity planning scheme set closer to the theoretical Pareto frontier than the genetic algorithm and dynamic programming method, and the negative environmental impact caused by water and electricity development can be reduced to the maximum extent. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 is the overall flowchart of the water and electricity planning multi-objective optimization decision method of the embodiment of the present application;

[0068] Figure 2 is the optimization and decision result diagram under the fair consideration of each target calculated by the method and the genetic algorithm in a specific implementation of the present application. DETAILED DESCRIPTION

[0069] The water and electricity planning multi-objective optimization decision method and device proposed by the present application are described in further detail below in combination with the drawings and specific embodiments.

[0070] An embodiment of the first aspect of the present application provides a method for multi-objective optimization decision of water and electricity planning, the overall process is as shown in Figure 1 , which comprises:

[0071] A multi-objective optimization decision model for water and electricity planning is constructed for the river network to be developed for water and electricity, and the optimization decision model comprises a power generation cost sub-model, a sediment interception sub-model, a carbon emission sub-model, and a river connectivity sub-model.

[0072] The target function value of the current planned dam set in each sub-model of the optimization decision model is calculated and recorded as the original target function value.

[0073] Under the current development position of the water and electricity planning, each kind of to-be-planned dam combination in the current to-be-planned dam set is obtained.

[0074] Each to-be-planned dam combination is added to the current planned dam set respectively, and the target function value of the updated planned dam set corresponding to each to-be-planned dam combination in each sub-model of the optimization decision model is obtained and recorded as the updated target function value.

[0075] By calculating the marginal increment of the updated target function value of each sub-model under each to-be-planned dam combination relative to the original target function value, the optimal to-be-planned dam combination under the current development position is screened out from the current to-be-planned dam set, so as to realize the water and electricity development planning of the river network.

[0076] In one specific embodiment of the present application, the method for multi-objective optimization decision of water and electricity planning comprises the following steps:

[0077] 1) Obtain the water and electricity planning information of the river network.

[0078] In this embodiment, the information includes: the number of each to-be-planned dam in the river network and the upstream and downstream relationship between the to-be-planned dams, the river section number where each to-be-planned dam is located, the distance from the most upstream dam of the river section to the intersection node with the previous river section, the distance from the most downstream dam of the river section to the intersection node with the next river section, and the distance from the last river section to the river network outlet (this attribute is added to the to-be-planned dam located at the most downstream of the river section). In this embodiment, other attribute information of each to-be-planned dam is also required, including: location, annual power generation, power generation cost, installed capacity, power generation head, dam height, inundation area, and reservoir capacity information. In this embodiment, the annual average temperature and annual precipitation information at the location of each to-be-planned dam is also required, and in one specific embodiment of the present application, the annual average temperature and annual precipitation information is downloaded from the ERA5 dataset.

[0079] 2) Constructing a multi-objective optimization decision model for water and hydropower planning, which contains a power generation cost sub-model, a sediment interception sub-model, a carbon emission sub-model and a river connectivity sub-model.

[0080] The objective function of the power generation cost sub-model is to calculate the annual power generation cost (yuan / year) of all planned dams, which needs to be minimized in the optimization decision. The inputs of this sub-model are the annual power generation of each planned dam and the cost of power generation, and the calculation expression of the annual power generation cost required for building all planned dams is:

[0081]

[0082] In the formula, d is any planned dam; D is the total number of currently planned dams in the basin; y is any year in the planning period; Y is the total number of years in the planning period; Gen d,y is the power generation of dam d in year y (kWh / year), Cost d is the cost of power generation of dam d (yuan / kWh).

[0083] The objective function of the sediment interception sub-model is to calculate the annual sediment interception (Mt / year) of all planned dams, which needs to be minimized in the optimization decision. The inputs of this sub-model are the sediment interception rate of each planned dam and the natural sediment inflow. The sediment interception rate of each dam is calculated using Brune’s empirical curve:

[0084]

[0085] In the formula, TE d is the sediment interception rate of dam d (%); V d is the storage capacity of dam d (m 3 ); Q d is the multi-year average runoff of dam d (m 3 / year). The natural sediment inflow of each dam is affected by the sediment production and deposition processes of all upstream river networks and the interception of upstream reservoirs, and the calculation expression is:

[0086]

[0087] In the formula, r(d) is all upstream river sections of dam d; U T is the river network containing all river sections in the basin; T r(d) is the sediment inflow of dam d under natural conditions (Mt / year); ESPh r and ESPl rrespectively, are the sediment yield proxies for the areas with elevation higher than 500 m and lower than 500 m in the upstream of river reach r, which are calculated by the normalized average elevation, average slope and annual precipitation of the areas; W represents the sediment deposition proxy, which is determined by the wetland area, vegetation type and double-season flood state of the floodplain; l r is the total length of the upstream river reach of dam d (km); λ1, λ2, λ3 are the parameters of the sub-model, which are calibrated according to the sediment monitoring values of each sediment monitoring station in the basin, and the values of which all need to be greater than 0.

[0088] is the total length of the upstream river reach of dam d (km); λ1, λ2, λ3 are the parameters of the sub-model, which are calibrated according to the sediment monitoring values of each sediment monitoring station in the basin, and the values of which all need to be greater than 0.

[0089]

[0090] con d is a 0-1 variable representing the construction state of dam d, 0 representing not constructed and 1 representing constructed. The objective function of the carbon emission sub-model is to calculate the annual net greenhouse gas emissions (Tg CO2 eq / year) of the basin, which needs to be minimized in the optimization decision. In this embodiment, the annual net greenhouse gas emissions are the sum of the annual net greenhouse gas emissions of the constructed dams, mainly considering CO2 (kg CO2 / MWh) and CH4 (kg CH4 / MWh). The calculation expression of the average annual net greenhouse gas emissions of the constructed dams is as follows:

[0091] CO2 = -169.73 + 241.86 x ATER + 120.34 x ln(S) (5)

[0092] ln(CH4) = -9.81 - 0.75 x ln(A) + 1.18 x ln(ATER) + 4.5 x ln(T max ) (6)

[0093]

[0094] S is the reservoir area formed by dam interception (km 2 ); ATER is the ratio of reservoir area and annual power generation (km 2 / GWh), and ATER takes the value of 0 when the number of constructed dams is 0; A is the dam age (years); T max is the annual maximum temperature (℃) at the location of the dam.

[0095] The objective function of the river connectivity sub-model is to calculate the river connectivity (%), which needs to be maximized in the optimization decision. In order to represent the influence of the dam on the local connectivity, the calculation expression of the river connectivity in this embodiment is as follows:

[0096]

[0097] wherein m is the river reach number; M is the number of river reaches; l m is the length of the mth river reach (km); L is the length of all river reaches in the basin (km).

[0098] 3) Based on the model established in step 2), the value of the objective function corresponding to each sub-model of the hydropower planning multi-objective optimization decision model in the current set of planned dams is calculated; wherein, if the basin has not been developed for hydropower, the initial value of the total number of planned dams D is 0; when D is 0, the value of the objective function of each sub-model is 0 except that the river connectivity is 100%. If the basin has been developed for hydropower, the initial value of D is the number of actual constructed dams in the river network.

[0099] 4) Let the number of dams planned under the current development order be i, i is an integer greater than or equal to 1; obtain all combinations of i different dams in the current set of dams to be planned; initially, the current set of dams to be planned contains all dams to be planned in the river network.

[0100] In this embodiment, the value of i under different development orders can be different depending on the granularity required for planning. When i of all orders is equal to 1, the planning granularity is the finest, and the planning result is the optimal value of each objective.

[0101] 5) Based on the results of step 4), each planned dam combination in the current set of planned dams is added to the current set of planned dams to obtain the updated set of planned dams and the total number of planned dams D, and the value of the objective function of each sub-model of the hydropower planning multi-objective optimization decision model corresponding to the updated set of planned dams is calculated.

[0102] 6) Based on the results of steps 3) and 5), the marginal increment of the updated objective function value of each sub-model under each planned dam combination relative to the objective function value before updating is calculated, which is expressed as follows:

[0103]

[0104] wherein n represents the total number of planned dams before updating (i.e. D before updating); n+i represents the total number of planned dams after updating (i.e. D after updating); MC o is the marginal increment of the objective function of any sub-model produced after adding the planned dam combination to the current set of planned dams; Obj n and Obj n+i are the values of the objective function of any sub-model under the current set of planned dams before updating and under the updated set of planned dams, respectively; Inst n and Inst n+iTotal installed capacity of the basin corresponding to the current planned dam set before updating and total installed capacity of the basin corresponding to the current planned dam set after updating, respectively.

[0105] 7) Marginal increments of each sub-model objective function value under each planned dam combination obtained in step 6) are normalized respectively, and each sub-model weight in the hydropower planning is considered to obtain normalized marginal increments MC of each sub-model objective function value under each planned dam combination. o

[0106]

[0107] In the formula, p o is a preset weight of any sub-model, which can be set according to its relative importance; in one specific embodiment of the present application, p o of each sub-model can be 1, indicating that the importance of each sub-model is the same.

[0108] MC o,max and MC o,min are the maximum value and the minimum value in the marginal increments of the sub-model objective function value calculated in step 6), respectively.

[0109] 8) Based on the result of step 7), the optimal combination under the current development position is screened from all planned dam combinations in the current planned dam set; the specific steps are as follows:

[0110] 8-1) All planned dam combinations in the current planned dam set are screened.

[0111] In the embodiment, for the current development position, the objective function values of the four sub-models corresponding to the addition of each planned dam combination in the current planned dam set to the planned dam set are calculated, and the normalized marginal increments MC of each objective function value are calculated. o The planned dam combination that can make the sum of MC o of the four objective functions minimum is selected as the current minimum dam combination, MC o of each sub-model of the combination is taken as the current minimum normalized marginal increment of the sub-model, and the current minimum dam combination is added to the initial empty candidate dam combination set under the current development position.

[0112] Then, the remaining planned dam combinations in the current planned dam set are traversed, and if there is any combination in the remaining planned dam combinations whose MC o under each sub-model is greater than the MC o corresponding to the current minimum dam combination, the combination is added to the candidate dam combination set under the current development position.If not, the combination is not added into the candidate dam combination set; otherwise, the remaining dam combination to be planned is added into the candidate dam combination set. After all the remaining dam combinations to be planned are traversed, the final candidate dam combination set under the current development bit is obtained.

[0113] 8-2) Based on the result of step 8-1), determine the optimal dam combination to be planned under the current development bit;

[0114] In the embodiment, in the current development bit, the Euclidean distance between each sub-model corresponding MC o of each dam combination to be planned in the candidate dam combination set is calculated; the combination with the minimum Euclidean distance is selected as the optimal combination under the current development bit. The calculation expression of the Euclidean distance is as follows:

[0115]

[0116] In the formula, d is the Euclidean distance between each sub-model corresponding MC o ; MC i represents the MC o corresponding to the i-th sub-model, i = 1, 2, 3, 4.

[0117] 9) Determine the result of step 8):

[0118] If the planning scheme of the optimal combination obtained by executing step 8) has met the demand of the annual hydropower added capacity, the hydropower planning is completed; otherwise, the dam to be planned corresponding to the optimal combination obtained by step 8) is added into the current planned dam set to obtain an updated current planned dam set, and the dam to be planned corresponding to the optimal combination obtained by step 8) is deleted from the original current dam set to be planned to obtain an updated current dam set to be planned, the next bit of the hydropower development is taken as the new current development bit, and then step 3) is returned.

[0119] After the optimal dam selection of all development bits is completed, the optimal hydropower development sequence considering each target scenario is obtained.

[0120] Further, the method described in the embodiment is further illustrated as follows in combination with a specific implementation.

[0121] In this embodiment, the time complexity of planning decision of the method is compared with that of dynamic programming method and genetic algorithm respectively. In combination with Table 1, the method has the advantages of reducing the calculation complexity, saving time and algorithm cost compared with the two existing methods. In Table 1, d represents the number of dams (usually 200-400 in a large river basin), s represents the target number (usually 4-6), p represents the population number of genetic algorithm (usually 1000, the larger the better the optimal solution), and q represents the number of generations of genetic algorithm (usually 100, the larger the better the optimal solution). O() represents the required calculation complexity and the value in the parentheses is directly proportional.

[0122] Table 1 Comparison table of calculation complexity between the method and genetic algorithm and dynamic programming method

[0123]

[0124] Further, in an embodiment of the present application, the boundary of the target value after optimization by the method and genetic algorithm (taking NSGA-III as an example) is compared, that is, the Pareto front. Figure 2 is an example of the results of optimization of the cost of hydropower development and the amount of sediment interception in the Brahmaputra River basin in this embodiment. The horizontal coordinate represents the average annual cost of hydropower development, and the vertical coordinate represents the amount of sediment intercepted by the dam each year. The circle represents the multi-objective optimization results of genetic algorithm, and the black dot is the Pareto front of genetic algorithm. The triangular point is the Pareto front obtained by the method. Figure 2 It is shown that the optimization scheme obtained by the method is closer to the real Pareto front compared with the commonly used genetic algorithm. The hydropower development sequence obtained by the method is helpful to greatly reduce the negative environmental impact of hydropower development in the whole river basin.

[0125] To achieve the above embodiment, the second aspect embodiment of the present application proposes a hydropower planning multi-objective optimization decision device, comprising:

[0126] A model construction module is configured to construct a hydropower planning multi-objective optimization decision model for a river network to be developed for hydropower. The optimization decision model includes a power generation cost sub-model, a sediment interception sub-model, a carbon emission sub-model and a river connectivity sub-model.

[0127] An original objective function value calculation module is configured to calculate the corresponding objective function value of the current planned dam set in each sub-model of the optimization decision model and record it as the original objective function value.

[0128] The water power development combination obtaining module is configured to obtain each kind of to-be-planned dam combination in a current to-be-planned dam set under a current development level of water power planning.

[0129] The objective function value updating module is configured to add each kind of to-be-planned dam combination into the current planned dam set respectively to obtain an updated objective function value of each kind of to-be-planned dam combination corresponding to the planned dam set in each sub-model of the optimization decision model.

[0130] The planning module is configured to filter out an optimal to-be-planned dam combination under the current development level from the current to-be-planned dam set by calculating a marginal increment of the updated objective function value of each sub-model under each to-be-planned dam combination relative to the original objective function value, so as to realize water power development planning of the river network.

[0131] It should be noted that the foregoing embodiment of the method for multi-objective optimization decision of water power planning is also applicable to the method for multi-objective optimization decision of water power planning in this embodiment, and will not be described herein again. According to the method for multi-objective optimization decision of water power planning in the embodiment of the present application, a multi-objective optimization decision model of water power planning is constructed for a river network to be developed by water power, the optimization decision model includes a power generation cost sub-model, a sediment interception sub-model, a carbon emission sub-model and a river connectivity sub-model; an objective function value of a current planned dam set in each sub-model of the optimization decision model is calculated and recorded as an original objective function value; each kind of to-be-planned dam combination in a current to-be-planned dam set is obtained under a current development level of water power planning; each kind of to-be-planned dam combination is added into the current planned dam set respectively to obtain an updated objective function value of each kind of to-be-planned dam combination corresponding to the planned dam set in each sub-model of the optimization decision model; an optimal to-be-planned dam combination under the current development level is filtered out from the current to-be-planned dam set by calculating a marginal increment of the updated objective function value of each sub-model under each to-be-planned dam combination relative to the original objective function value, so as to realize water power development planning of the river network. Thus, the embodiment can reduce the calculation cost of obtaining a water power development sequence decision result, make the decision result have strong explainability and operability, and realize minimization of environmental damage caused by water power development, thereby providing an efficient and accurate water power development strategy for a large-scale watershed.

[0132] To achieve the above-mentioned embodiments, the third aspect of the present application provides an electronic device, comprising:

[0133] at least one processor; and a memory connected with the at least one processor in communication;

[0134] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the water and electricity planning multi-objective optimization decision method.

[0135] To achieve the above-mentioned embodiments, the fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the water and electricity planning multi-objective optimization decision method.

[0136] It should be noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the above two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer readable storage medium can include, but are not limited to, electrical connections with one or more conductive wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component. In the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or component. The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0137] The above-mentioned computer readable medium can be contained in the above-mentioned electronic device; it can also exist separately and not be assembled into the electronic device. The above-mentioned computer readable medium carries one or more programs, which make the electronic device execute the water and electricity planning multi-objective optimization decision method of the above-mentioned embodiments when the one or more programs are executed by the electronic device.

[0138] Computer program code for carrying out operations of the present disclosure can be written in any one or more programming languages or combinations of languages including object or visual programming languages such as Java, Smalltalk, C++ or conventional procedural programming languages such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0139] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples, without contradiction.

[0140] In addition, the terms "first", "second", etc. are used only for the purpose of description and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise explicitly specified.

[0141] Any process or method descriptions or descriptions of the flow diagrams in the specification or otherwise described herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions (or steps) in the process, and the various embodiments of the application can include additional or fewer functions. The various embodiments of the application can also modify other processes or methods described or otherwise suggested to perform the functions of the various embodiments of the application, and these processes or methods need not be mutually exclusive, and the various embodiments of the application should not be construed as being limited to the processes or methods described in the specification or otherwise suggested by the specification.

[0142] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of instructions to implement logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a computer- readable storage medium or a computer-readable signal medium. The computer- readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires (electrical connections), a portable computer diskette (a magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and stored in a computer memory.

[0143] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the various steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0144] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiments can be completed by programs instructing related hardware, and the programs can be stored in a computer-readable storage medium. When the programs are executed, they include one or a combination of the steps of the method embodiments.

[0145] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0146] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A multi-objective optimization decision-making method for hydropower planning, characterized in that, include: A multi-objective optimization decision-making model for hydropower planning in river network construction for hydropower development is proposed. The optimization decision-making model includes a power generation cost sub-model, a sediment interception sub-model, a carbon emission quantum model, and a river connectivity sub-model. Calculate the objective function value corresponding to the current set of planned dams in each sub-model of the optimization decision model and record it as the original objective function value; Under the current development priority of hydropower planning, obtain each combination of dams to be planned in the current set of dams to be planned; Each combination of dams to be planned is added to the current set of planned dams to obtain the updated objective function value of the set of planned dams corresponding to each combination of dams to be planned in each sub-model of the optimization decision model, and recorded as the updated objective function value. By calculating the marginal increase of the updated objective function value of each sub-model under each combination of dams to be planned relative to the original objective function value, the optimal combination of dams to be planned under the current development position is selected from the current set of dams to be planned, so as to realize the hydropower development planning of the river network. The marginal increase of the updated objective function value relative to the original objective function value for each sub-model under each combination of dams to be planned is calculated as follows: In the formula, n represents the total number of dams planned before the update; i is the number of dams planned under the current development ranking; n+i represents the total number of dams planned after the update; MC o To explain the marginal increase in the objective function of any sub-model after adding the combination of dams to be planned to the current set of planned dams; Obj n and Obj n+i Let Inst be the value of the objective function of any sub-model under the current set of planned dams before the update and the value under the current set of planned dams after the update, respectively; n and Inst n+i These are the total installed capacity of the basin corresponding to the currently planned dam set before the update and the total installed capacity of the basin corresponding to the currently planned dam set after the update, respectively. The marginal increase in the objective function value of each sub-model under each dam combination to be planned is normalized, and the weight of each sub-model in hydropower planning is considered to obtain the normalized marginal increase MC of the objective function value of each sub-model under each dam combination to be planned. o ': In the formula, p o Preset weights for any sub-model; MC o,max and MC o,min These are the maximum and minimum values, respectively, in the marginal increase of the objective function value of any sub-model under the current development position; Based on the normalized marginal increase, the optimal combination for the current development position is selected from all combinations of dams to be planned in the current set of dams to be planned; the specific steps are as follows: 1) Select from the current set of dams to be planned the MC that corresponds to each sub-model. o The dam combination with the smallest sum is taken as the current minimum dam combination, and this current minimum dam combination is added to the initially empty set of candidate dam combinations under the current development position. Then iterate through the remaining combinations of dams to be planned in the current set of dams to be planned. If any combination of the remaining combinations of dams to be planned exists in each sub-model, then... o 'All are greater than the MC corresponding to the current minimum dam combination' o If the remaining dam combination is not included in the candidate dam combination set, then the remaining dam combination to be planned is not added to the candidate dam combination set; otherwise, the remaining dam combination to be planned is added to the candidate dam combination set; after all remaining dam combinations to be planned have been traversed, the final candidate dam combination set under the current development position is obtained. 2) Based on the results of step 1), calculate the MC corresponding to each sub-model under each dam combination to be planned in the candidate dam combination set. o The Euclidean distance between the two pairs is used to determine the optimal combination for the current development position. The Euclidean distance is calculated as follows: In the formula, d is the MC corresponding to each sub-model under any combination of dams to be planned. o Euclidean distance between them; MC i Represents the MC corresponding to the i-th sub-model o ',i=1,2,3,4.

2. The method according to claim 1, characterized in that, The method further includes: Before constructing the multi-objective optimization decision model for hydropower planning, hydropower planning information of the river network is obtained, including: The data includes the number of each planned dam in the river network and the upstream and downstream relationships between them; the river segment number where each planned dam is located; the distance from the upstreammost dam of the river segment to the confluence of the current and previous river segments; the distance from the downstreammost dam of the river segment to the confluence of the current and next river segments; and the distance from the last river segment to the river network outlet. Other attribute information for each planned dam includes: location, annual power generation, cost per kilowatt-hour, installed capacity, hydropower head, dam height, inundated area, and reservoir capacity. It also includes the average annual temperature and annual precipitation at the location of each planned dam.

3. The method according to claim 1, characterized in that, The method further includes: The objective function of the power generation cost sub-model is to calculate the annual power generation cost of all planned dams, as expressed below: In the formula, d represents any planned dam; D represents the total number of currently planned dams within the basin; y represents any year within the planning period; Y represents the total number of years in the planning period; Gen d,y Cost is the amount of electricity generated by dam d in year y. d The cost per kilowatt-hour of electricity generated by the dam (d); The objective function of the sediment interception sub-model is to calculate the annual sediment interception volume of all planned dams. The formula for calculating the sand interception rate of any planned dam is as follows: In the formula, TE d V represents the sediment interception rate of the dam (d). d The reservoir capacity of the dam is d; Q d Let d be the multi-year average runoff of the dam; the expression for calculating the sediment inflow of the dam under natural conditions is: In the formula, r(d) represents all river sections upstream of the dam d; U T A river network encompassing all river sections within the basin; ESPh r and ESPl r These represent sediment yield proxies for all areas above and below 500m elevation in the upper reaches of the r-section; W represents sediment deposition proxies; l r λ1, λ2, and λ3 are the total length of the river section upstream of the dam d; λ1, λ2, and λ3 are sub-model parameters, all of which are greater than 0. The formula for calculating the annual sediment interception capacity of all planned dams is as follows: In the formula, con d The 0-1 variable represents the construction status of dam d, where 0 indicates no construction and 1 indicates construction. The objective function of the carbon emission quantum model is to calculate the annual net greenhouse gas emissions of the watershed, expressed as follows: CO2=-169.73+241.86×ATER+120.34×ln(S) (5) ln(CH4)=-9.81-0.75×ln(A)+1.18×ln(ATER)+4.5×ln(T max ) (6) In the formula, S is the reservoir area formed by the dam; ATER is the ratio of reservoir area to annual power generation, which is 0 when the number of dams built is 0; A is the age of the dam; T max The highest annual temperature at the location of the dam; The objective function of the river connectivity sub-model is to calculate river connectivity, as expressed below: In the formula, m is the river segment number; M is the number of river segments; l m Let m be the length of the m-th river segment; L is the length of all river segments within the basin.

4. The method according to claim 1, characterized in that, The method further includes: determination: If the planned scheme of the optimal combination has met the demand for new hydropower installed capacity in that year, then the hydropower planning is completed; otherwise, the planned dams corresponding to the optimal combination are added to the current set of planned dams to obtain an updated set of current planned dams, and the planned dams corresponding to the optimal combination are deleted from the current set of planned dams to obtain an updated set of current planned dams. The next position of hydropower development is taken as the new current development position, and then a new round of hydropower development planning is carried out.

5. A multi-objective optimization decision-making device for hydropower planning based on the method described in claim 1, characterized in that, include: The model building module is used to construct a multi-objective optimization decision model for hydropower planning in river networks to be developed for hydropower. The optimization decision model includes a power generation cost sub-model, a sediment interception sub-model, a carbon emission quantum model, and a river connectivity sub-model. The original objective function value calculation module is used to calculate the objective function value corresponding to the current set of planned dams in each sub-model of the optimization decision model and record it as the original objective function value; The hydropower development combination acquisition module is used to acquire each combination of dams to be planned in the current set of dams to be planned, given the current development position of the hydropower plan. The objective function value update module is used to add each combination of dams to be planned to the current set of planned dams, and obtain the updated objective function value of the set of planned dams corresponding to each combination of dams to be planned in each sub-model of the optimization decision model, and record it as the updated objective function value; The planning module is used to select the optimal combination of dams to be planned under the current development position from the current set of dams to be planned by calculating the marginal increase of the updated objective function value of each sub-model under each combination of dams to be planned relative to the original objective function value, so as to realize the hydropower development planning of the river network.

6. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-5.

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