CN111612270B - Clean energy delivery planning and operation optimization method considering adaptability of leading reservoirs - Google Patents

Clean energy delivery planning and operation optimization method considering adaptability of leading reservoirs Download PDF

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CN111612270B
CN111612270B CN202010467940.7A CN202010467940A CN111612270B CN 111612270 B CN111612270 B CN 111612270B CN 202010467940 A CN202010467940 A CN 202010467940A CN 111612270 B CN111612270 B CN 111612270B
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江栗
路亮
魏明奎
周全
黄媛
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Abstract

The invention discloses a clean energy delivery planning and operation optimization method considering the adaptability of leading reservoirs, which is used for calculating the membership degree of an evaluation set to which each scheme corresponds and determining the sequencing of alternative schemes; optimizing the development scale and production time sequence of the hydropower cluster under the constraint boundary condition; optimizing partition balance and changing the power flow direction; forming a hydropower cluster sending scheme; according to the output characteristics and the annual energy production of the hydropower cluster after the faucet reservoir is put into operation, the monthly power generation capacity of the hydropower cluster is obtained according to the intra-provincial load characteristics, and the monthly power and electricity demand of the province is obtained; an annual electric power and quantity trading plan is formulated by taking the power generation capacity of the hydropower cluster, the intra-provincial electric power and quantity demand, the section conveying capacity and the channel conveying capacity as boundaries; arranging the operation mode of the thermal power generating unit; according to a trading plan, temporary trading is arranged by combining the water inflow condition and the faucet reservoir regulating capacity, and the operation mode of the hydropower cluster is optimized.

Description

Clean energy delivery planning and operation optimization method considering adaptability of leading reservoirs
Technical Field
The invention belongs to the field of power grids, and relates to a clean energy delivery planning and operation optimization method considering the adaptability of a faucet reservoir.
Background
At present, relevant researches on the step hydropower have been initially carried out in China, but researches mainly centered on various coordinated optimization scheduling methods in the operation process of the step hydropower are carried out.
The patent [1] provides a scheduling method of a cascade hydroelectric virtual pumped storage power station, which takes the minimum deviation of actual peak regulation power and the minimum water consumption of cascade hydroelectric as scheduling targets, constructs a scheduling objective function of the cascade hydroelectric virtual pumped storage power station to solve and optimize scheduling so as to realize a short-term scheduling plan of a power system;
patent [2] discloses a self-adaptive optimization method and system for power generation dispatching of a cascade hydroelectric system, which are used for improving the overall power generation benefit of the cascade hydroelectric system;
patent [3] provides a cascade hydropower station short-term peak regulation model based on electric quantity control and a solving method, which can make cascade hydropower station fully play the peak regulation function of a cascade hydropower station group while meeting the daily optimized electric quantity, output climbing and output fluctuation control requirements;
the patent [4] discloses a multi-target scheduling parallel dimension reduction method of a giant cascade hydroelectric system;
the patent [5] provides a multi-energy coordination optimization scheduling method considering peak-shaving frequency modulation requirements;
the patent [6] provides a cascade hydropower robust optimization scheduling method based on a random security domain, the method judges the robust feasibility of a pre-scheduling scheme, and the scheduling scheme with robustness is finally obtained through feedback correction coordination optimization;
the patent [7] discloses a multi-period power flow optimization method for the cascade hydropower station water level control based on real-time feedback, which constructs a multi-period optimal power flow control method for coordinating the reservoir water level and the power grid operation, realizes the effect of linear treatment of complex nonlinear conditions based on real-time feedback, and greatly improves the running efficiency of the cascade hydropower station;
the patent [8] provides a combined trading strategy optimization method relating to the stepped hydropower participation provincial and western-to-east power transmission market, which provides beneficial support for the dispatching operation management of large-scale stepped hydropower station groups in the southwest region of China in a new power environment;
patent [9] proposes a double-layer optimization method for medium-and-long-term scheduling and maintenance of a cascade hydropower station in a market environment, wherein a medium-and-long-term scheduling intermediate result is taken as a boundary condition, the minimum maintenance loss is taken as an optimization target, and a maintenance loss optimization result and medium-and-long-term power generation income are merged into total income, so that joint optimization is realized;
the patent [10] provides a method and a system for collaborative combination division of a water, wind and light power station group based on regulation performance, improves the precision of collaborative operation optimization of multiple power sources, is beneficial to scheduling optimization of a complex power system containing multiple power sources, has important significance for improving development and utilization of clean energy, and has important popularization and use values;
patent [11] provides a hydropower group scheduling method considering non-constant coupling constraints; the patent [12] provides a daily optimized scheduling method of a cascade hydropower station considering continuous change of water flow delay;
patent [13] proposes a long-term operation method of a cross-basin cascade hydropower station group under the dynamic production of a giant hydropower station;
patent [14] proposes a daily optimization scheduling method for a cascade hydropower station considering continuous change of water flow delay, and compared with the previous scheduling method, the method has the advantages of detailed description of water flow delay, accurate model, good convergence effect, strong practicability and the like;
patent [15] proposes a real-time optimization scheduling method for a cascade hydropower station group under complex constraint, which incorporates a day-ahead power generation plan into a real-time scheduling algorithm, takes the maximum total energy storage of a cascade hydropower system as an optimization target, and meets the requirements of safety, timeliness, practicability and economy of real-time scheduling.
Patent [16] provides a method for making a stepped hydropower station medium-term power generation plan under the condition of a multi-scale power market, comprehensively considers the upstream and downstream complex constraint problem of a stepped hydropower station under the traditional non-market condition and new problems of multi-market power price, performance coupling, market risk and the like brought by the multi-scale market, can better guide the stepped hydropower station power generation process to respond to market price change, improves the overall income through market optimization and avoids the market risk;
the patent [17] provides a method for optimizing a combined trading strategy of a cascade hydropower participation provincial and western-to-east power transmission market, which provides beneficial support for the dispatching operation management of a large-scale cascade hydropower station group in the southwest region of China in a new power environment;
patent [18] proposes a double-layer optimization method for medium and long-term scheduling and overhaul of a cascade hydropower station in a market environment;
patent [19] proposes a day-ahead market clearing mechanism based on the coupling relation of cascade hydropower stations, which realizes the combined clearing of upstream and downstream power stations and solves the problem of unbalance matching between the bid amount and the generating capacity amount in the downstream power stations.
The invention discloses a medium-voltage distribution network accurate planning method based on three-layer macroscopic networking constraint, and the operability, the scientificity and the accuracy of a planning scheme are improved through the target guidance and the old-fashioned principle of global overall planning in space and near-far coordination and reinforcement planning in time.
Patent [21] discloses a power corridor planning method based on GIS information data, which reduces the problems of large water abandonment of hydropower and serious economic benefit loss caused by the delay of planning and construction of an outgoing channel, ensures that the green and environment-friendly hydropower is smoothly sent out, and creates continuous and reliable economic benefit, ecological benefit and social benefit.
The above patent [1-15] basically focuses on the operation side of the cascade hydropower stations and focuses on the problem of coordination and scheduling among the cascade hydropower stations; patents [16-19] focus on the electricity market side, and focus on the problems of how hydropower stations in upstream and downstream participate in competition in the electricity market and determination of clearing price; although the patent [20-21] relates to the problem of grid planning, the patent [20-21] mainly aims at a planning method of a precise power distribution network, and the planning method does not relate to the large-area coordination planning problem of the partition electric quantity balance class, and does not aim at the long-time dynamic process development analysis of leading reservoir construction, and the establishment of a suitable evaluation scheme and an evaluation system.
Therefore, a clean energy delivery planning and operation optimization method considering the adaptability of the leading reservoirs needs to be researched urgently.
Disclosure of Invention
The invention aims to: the method comprises the steps of establishing a corresponding planning model for partition power and electric quantity balance aiming at the long-term problem of delivery network frame construction under the background of the faucet reservoir construction and development process, optimizing a mathematical model according to the construction and operation conditions of delivery channels, hydropower station cluster development planning and production time sequence, solving by adopting a greedy algorithm to obtain corresponding power transmission alternative schemes, finally evaluating each alternative scheme by adopting an evaluation method of an entropy weight method, and screening out the optimal scheme of hydropower cluster delivery and related network frame construction.
The technical scheme adopted by the invention is as follows:
the clean energy delivery planning and operation optimization method considering the adaptability of the leading reservoir presets a constraint hard index and an evaluation soft index, establishes an adaptability analysis index system and presets a power transmission scheme alternative library from a hydropower cluster to a load center;
the establishment of the adaptive analysis index system comprises the following steps (S1-S7):
s1: dividing all indexes into constraint hard indexes and evaluation soft indexes according to preset index characteristics;
s2: forming an adaptive analysis index system by the soft indexes;
s3: forming an adaptive analysis index system;
s4: quantizing an index system;
s5: solving each index weight by using an entropy weight method, and reducing the dimension of an index system;
s6: constructing a weighted normalization matrix;
s7: determining the membership degree in the comment set (turning to S14);
defining a constraint hard index and an evaluation soft index, establishing an adaptability analysis index system, and simultaneously carrying out primary processing on the index system, such as index system quantification, solving the weight of each index and determining a structural weighting standardization matrix, wherein the hard index comprises safety and stability constraint, serious fault check and channel constraint, and the soft index comprises economic adaptability, supply and demand accommodation adaptability, network source matching adaptability and the like of leading reservoir construction. The evaluation of the soft index is to complete the screening by the method of entropy weight method, and the hard index is to be used as a boundary condition to screen out the scheme which does not accord with the constraint. When the hard index cannot be met, a new alternative scheme can be obtained by changing the development scale and the production time sequence of the hydropower cluster, and evaluation and screening are carried out again until the optimal scheme meeting the hard index is obtained. The optimal scheme guides the construction of the network frame for the water and electricity clusters in the southwest leading reservoir construction.
The establishment of the power transmission scheme alternative library comprises the following steps (S8-S15):
s8, determining the horizontal year and the prospective year of the prospect of the future of the analysis and research target; the invention preliminarily determines the planned horizontal year taking 2018 as a research, and 2035 as a prospective prospect year of the research;
s9, analyzing and calculating the load demand and load characteristics of the load center;
in Sichuan province, the climate is humid, the summer is hot and stuffy, the winter is cold and humid, and the air conditioning load accounts for a large proportion. The annual load characteristic shows a double-peak characteristic, the load characteristic diagram is shown in figure 2, and 2011-2017 annual double peaks appear in 7-8 months in summer and 12-1 months in winter. The temperature in spring and autumn is proper, the load of the air conditioner is greatly reduced, and the load in the period is relatively low. In recent years, as the cooling load in summer continuously rises, the maximum load in summer is increased more obviously than that in winter, the ratio of double peaks in winter and summer is gradually reduced,
while a typical daily load curve is shown in fig. 3, the typical daily load curve has a difference in peak-valley appearance times in summer and winter. Summer: early peak occurs at 11:00-12:00, late peak occurs at 21:00-22:00, trough load occurs in the morning 7: 00-8: 00; in winter: the early peak appears at 11:00-12:00, the late peak appears at 18:00-20:00, the load curve between the early peak and the late peak is relatively flat, the load of the valley appears in the morning 4: 00-5: 00.
s10, analyzing and calculating the installed capacity and the output characteristic of the hydropower cluster governed by the leading reservoir, and providing data support for the related calculation of the next partition balance power flow plan; specifically, reference can be made to data of planning schemes of hydroelectric power groups in three watersheds of yamo river, jinshajiang river and great river.
S11, obtaining a partition balance power flow plan through partition balance analysis;
s12, obtaining a hydropower cluster sending scheme and sending channel requirements according to the partition balance power flow plan;
s13: is the hydroelectricity cluster delivery scheme optimized after the faucet reservoir is commissioned? If yes, go to the next step. If not, go to S27
S14: and (4) according to the channel conditions (the passing original forest, the natural protected area and the like), restricting and optimizing the sending scheme.
S15: forming a power transmission scheme alternative library from the hydropower cluster to the load center;
on the basis of a partition balance power flow planning theory of power and electricity, a power transmission scheme library is preliminarily formed, and the scheme is further optimized according to the channel condition, the hydropower cluster development scale and the production sequence to form an alternative library of the power transmission scheme, wherein 2018 is preliminarily determined as a planning horizontal year of research, and 2025 is taken as a prospective development year of research;
according to the power transmission scheme alternative library, the alternative library is evaluated in an entropy weight method evaluation mode to obtain a hydropower cluster power transmission net rack optimization scheme, and the method comprises the following steps (S16-S32):
s16: and calculating the membership degree of each scheme corresponding to the evaluation set to which the scheme belongs, and determining the alternative scheme ordering.
S17: and selecting the current optimal hydropower cluster sending scheme.
S18: is the system safety and stability constraints satisfied? If yes, go to the next step. If not, go to S21
S19: can a critical failure be checked? If yes, go to the next step. If not, go to S21
S20: is the channel constraint (the channel constraint mainly considers the influence of ecological red lines such as natural protected areas and original forests on the path and width of the power transmission corridor)? If not, the next step is carried out. If so, go to S26
S21: the current solution is deleted in the alternative library.
S22: is there an alternative in the alternative library? If not, the next step is carried out. If so, go to S17
S23: is the hydropower cluster development scale and commissioning timing optimizable? If yes, go to the next step. If not, go to S25.
S24: and optimizing the development scale and the production time sequence of the hydropower cluster under the constraint boundary condition. Go to S11.
S25: optimizing partition balance and changing power flow direction. Go to S12.
S26: forming a hydropower cluster sending scheme;
s27: acquiring the monthly power generation capacity of the hydropower cluster according to the output characteristics and the annual power generation capacity of the hydropower cluster after the faucet reservoir is put into operation;
s28: according to the intra-provincial load characteristics, the monthly power and electricity demand of the province is obtained;
s29: an annual electric power and quantity trading plan is formulated by taking the power generation capacity of the hydropower cluster, the intra-provincial electric power and quantity demand, the section conveying capacity and the channel conveying capacity as boundaries;
s30: arranging the operation mode of the thermal power generating unit;
s31: according to a trading plan, combining the water inflow condition and the tap reservoir adjusting capacity, arranging temporary trading and optimizing the operation mode of the hydropower cluster;
s32: and (6) ending.
Further: the load demand and load characteristic analysis and calculation are researched by adopting a gray system prediction method, the calculation amount of the gray system in the load analysis is small, and the load analysis has more uncertain factors, which is just the advantage of the gray prediction method, so that the accuracy is higher. The GM (1,1) model is the most commonly used effective grey prediction model, where the first 1 in (1,1) represents a 1 st order equation and the last 1 represents that the 1 st order differential equation contains only a single variable; the S9 adopts a gray prediction method of a GM (1,1) model to judge the meeting requirement and the meeting characteristic, and comprises the following three steps:
the method comprises the following steps: accumulating to generate a structure increasing number sequence, and listing a differential equation matrix;
the original data column and the data column formed by first-order accumulation are respectively shown as formulas (1) and (2);
x(0)={x(0)(1),x(0)(2),…,x(0)(k)},k=1,2,…,n(1)
x(1)={x(1)(1),x(1)(2),…,x(1)(k)},k=1,2,…,n(2)
in the formula (I), the compound is shown in the specification,
Figure BDA0002513308720000051
x(0)representing the raw data column, i.e. the historical data of the load in the investigation region. x is the number of(1)Data columns formed after the first-order accumulation are represented, namely accumulated data of loads in the research area;
the differential equation can thus be set forth as shown in equation (3):
Figure BDA0002513308720000052
in the formula, a and u are model parameters and respectively become development gray scale and endogenous gray scale parameters:
the differential equation (3) is rewritten into a matrix form as shown in equation (4):
Figure BDA0002513308720000061
step two: obtaining the time response function of the GM (1,1) model,
substituting the accumulated data values into a differential equation matrix to obtain the values of the model parameters a and u, and substituting the values into the original differential equation to obtain an iterative equation shown in the formula (5):
Figure BDA0002513308720000062
step three: finally, a grey prediction model of the original sequence is obtained,
the formula (5) is reduced to obtain a gray prediction model of the original number sequence X (0), k is attached with different values according to the model and the sequence number of the time to be predicted, the predicted value of the time point is calculated, and the prediction work is finished
Further: and S11 and S12 obtain a partition balance power flow plan through partition balance analysis, and obtain a hydropower cluster delivery scheme and delivery channel requirements according to the power flow plan, so that an external delivery scheme library is formed preliminarily.
When the electric quantity is checked, the value of the transmitted electric quantity can be used as an independent variable to check the electric quantity of different partitions. The objective function of the load balance of the whole power grid system is shown as the formula (6).
Figure BDA0002513308720000063
In the formula, NG、NdNumber of generator sets and number of balance divisions, Pi,jAnd (t) and L (t) are respectively the active power output of the ith unit and the total load of the power grid at the moment t in the partition j at the moment t.
In each subarea, the sum of the generated power of each unit and the transmission power of the transmission channel connected with the subarea is equal to the total load in the subarea, so that the objective function of each subarea is shown as a formula (7).
Figure BDA0002513308720000064
In the formula, EG,jAnd ED,jRespectively representing the total power generation amount and the total internal power consumption amount of the j sub-area, ET,kRepresenting the magnitude of the electric quantity transmitted on channel k, Bk,jIs a two-dimensional variable used to represent the direction of the transmitted power on channel k.
Generally, the constraint conditions of the mathematical model include a unit power constraint, a direct current power flow constraint, a unit minimum on/off time constraint, a transmission channel capacity constraint and the like.
The critical unit power constraint and the critical direct current power flow constraint are respectively expressed as formulas (8) and (9):
Figure BDA0002513308720000071
Figure BDA0002513308720000072
in the formula, Pi,tThe output of unit i at time t, and Pi,minAnd Pi,maxRespectively the minimum and maximum generating power of the unit i. PbAnd QbRespectively an active power column vector and a reactive power vector of each branch; b isbAdmittance diagonal matrix for each branch, theta is node phase angle column vector, and R is network node branch associationAnd (4) matrix.
Whereas problems with partitions in the grid are solved using greedy algorithms. In general, the greedy algorithm is solved as follows:
1) and inputting the profit and loss electric quantity of each subarea, and dividing the subareas into a transmission electric quantity class and a receiving electric quantity class according to the profit and loss conditions.
2) And respectively sequencing the subareas of each type from large to small according to the magnitude of the profit and loss electric quantity.
3) And finding out the partition i with the largest redundancy from the transmission electric quantity class each time, transmitting the partition electric quantity to the partition j with the largest electric quantity lack in the received electric quantity class, jumping to the 4 th step if the redundant electric quantity of the partition i is larger than the electric quantity lack of the partition j, and jumping to the 5 th step if the redundant electric quantity of the partition i is not larger than the electric quantity lack of the partition j.
4) After the redundant electric quantity of the partition i is subtracted by the lack electric quantity of the partition j, the partition i is inserted into the transmission electric quantity class in sequence, and meanwhile, the partition j is deleted from the received electric quantity class.
5) After the redundant electric quantity of the partition i is subtracted from the lack electric quantity of the partition j, the partition j is inserted into the received electric quantity class in sequence, and meanwhile, the partition i is deleted from the transmission electric quantity class.
6) And then processing the next subarea until the subareas in the power grid are balanced in electric quantity.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. the clean energy delivery planning and operation optimization method considering the adaptability of the tap reservoir considers the influence of characteristic changes such as output and annual energy generation capacity on the planning and operation of the power system after the tap reservoir is put into operation, can effectively improve the power electric quantity balance precision of the clean energy high-permeability power system, and optimizes the target grid structure.
2. The invention provides an optimal planning method for the delivery channel of the hydropower cluster containing the leading reservoir from the planning angle, and provides an optimal operation control method for the hydropower cluster containing the leading reservoir from the power system operation angle, so that the method has the effects of improving the utilization rate of clean energy and reducing water abandon.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other relevant drawings can be obtained according to the drawings without inventive effort, wherein:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of the load of the power grid in Szechwan in 2005-2016;
FIG. 3 is a typical daily load curve of the Sichuan power grid in summer and winter according to the present invention;
fig. 4 is a diagram of the network topology of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The features and properties of the present invention are described in further detail below with reference to examples.
Example one
In the method for planning and optimizing delivery of clean energy considering the adaptability of the faucet and the reservoir, which is provided by the preferred embodiment of the invention, a network topology structure diagram is shown in fig. 4, taking a test system of an IEEE39 node as an example. Data of an actual cascade hydropower station in a certain basin are respectively connected with the positions of a BUS-30, a BUS-33, a BUS-34, a BUS-35, a BUS-36, a BUS-37, a BUS-38 and a BUS-39 of the cascade hydropower station, and parameters such as monthly output and installed capacity of a certain open water year of the cascade hydropower station are shown in a table 1-installed capacity and output of the cascade hydropower station.
TABLE 1 (Unit: MW)
Figure BDA0002513308720000081
Figure BDA0002513308720000091
In addition, thermal power generating units with installed capacities of 1360MW and 1670MW are connected to the two nodes of BUS-31 and BUS-32 respectively, as shown in figure 4.
Monthly power and electricity demand P of the regionDAs shown in table 2-annual electricity usage in certain area:
TABLE 2 (Unit: MW)
Month of the year 1 2 3 4 5 6 7 8 9 10 11 12
Load(s) 5550 4971 5215 5032 5184 5337 5642 6099 5587 5032 5520 5916
Under the condition that the leading reservoir is accessed, an annual electric power and electric quantity demand plan is formulated by taking the power generation capacity, the electric power and electric quantity demand and the channel transmission requirement of the hydropower cluster as boundaries, and the thermal power generating unit is arranged to participate in operation aiming at the monthly electric power and electric quantity unbalance. The monthly power unbalance amounts calculated from tables 1 and 2 are shown in table 3-the monthly power unbalance amount in a certain area:
TABLE 3 (Unit: MW. h)
Figure BDA0002513308720000092
The unbalanced electric quantity of 0 in table 3 indicates that the generating capacity of the hydropower cluster in the month is enough to meet the demand of the load, and the thermal power generating unit does not need to be additionally arranged to participate in operation.
It can be known from table three that under the condition of the combined operation of the cascade power station, the time period in which the hydraulic power generation can not meet the load demand in the whole year mainly occurs in spring and winter, especially 12 months and 1 month. In spring and winter time periods, a thermal power generating unit needs to be additionally arranged to participate in operation so as to meet load requirements. And summer is a rich water period, a thermal power generating unit does not need to be additionally arranged to participate in operation, and the generated energy in the period of the cascade hydropower station can meet the medium-long-term requirements of loads.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents and improvements made by those skilled in the art within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (3)

1. The clean energy delivery planning and operation optimization method considering the adaptability of the leading reservoir is characterized by comprising the following steps of: presetting a constraint hard index and an evaluation soft index, establishing an adaptive analysis index system, and presetting a power transmission scheme alternative library of a hydropower cluster to a load center;
the establishment of the adaptive analysis index system comprises the steps S1 to S7:
s1: dividing all indexes into constraint hard indexes and evaluation soft indexes according to preset index characteristics;
s2: forming an adaptive analysis index system by the soft indexes;
s3: forming an adaptive analysis index system;
s4: quantizing an index system;
s5: solving each index weight by using an entropy weight method, and reducing the dimension of an index system;
s6: constructing a weighted normalization matrix;
s7: determining the evaluation centralized membership degree;
the establishment of the power transmission scheme alternative library comprises steps S8 to S14:
s8, determining the horizontal year and the prospective year of the prospect of the future of the analysis and research target;
s9, analyzing and calculating the load demand and load characteristics of the load center;
s10, analyzing and calculating the installed capacity and the output characteristic of the hydropower cluster governed by the leading reservoir, and providing data support for the related calculation of the next partition balance power flow plan;
s11, obtaining a partition balance power flow plan through partition balance analysis;
s12, obtaining a hydropower cluster sending scheme and sending channel requirements according to the partition balance power flow plan;
s13: is the hydroelectricity cluster delivery scheme optimized after the faucet reservoir is commissioned? If yes, turning to the next step; if not, go to S27;
s14: according to the channel condition, the sending scheme is restrained and optimized;
s15: forming a power transmission scheme alternative library from the hydropower cluster to the load center;
according to the power transmission scheme alternative library, the power transmission scheme alternative library is evaluated in an entropy weight method evaluation mode to obtain a hydropower cluster power-off grid optimization scheme, and the method comprises the steps from S16 to S32;
s16: calculating the membership degree of each scheme corresponding to the evaluation set to which the scheme belongs, and determining alternative scheme ordering;
s17: selecting the current optimal hydropower cluster sending scheme;
s18: is the system safety and stability constraints satisfied? If yes, turning to the next step; if not, go to S21;
s19: can a critical failure be checked? If yes, turning to the next step; if not, go to S21;
s20: is the channel constraint satisfied? If not, turning to the next step; if yes, go to S26;
s21: deleting the current scheme from the alternative library;
s22: is there an alternative in the alternative library? If not, turning to the next step; if yes, go to S17;
s23: is the hydropower cluster development scale and commissioning timing optimizable? If yes, turning to the next step; if not, go to S25;
s24: optimizing the development scale and production time sequence of the hydropower cluster under the constraint boundary condition; turning to S11;
s25: optimizing partition balance and changing the power flow direction; turning to S12;
s26: forming a hydropower cluster sending scheme;
s27: acquiring the monthly power generation capacity of the hydropower cluster according to the output characteristics and the annual power generation capacity of the hydropower cluster after the faucet reservoir is put into operation;
s28: according to the intra-provincial load characteristics, the monthly power and electricity demand of the province is obtained;
s29: an annual electric power and quantity trading plan is formulated by taking the power generation capacity of the hydropower cluster, the intra-provincial electric power and quantity demand, the section conveying capacity and the channel conveying capacity as boundaries;
s30: arranging the operation mode of the thermal power generating unit;
s31: according to a trading plan, combining the water inflow condition and the tap reservoir adjusting capacity, arranging temporary trading and optimizing the operation mode of the hydropower cluster;
s32: and (6) ending.
2. The method of claim 1 for optimizing clean energy delivery planning and operation in consideration of faucet-reservoir adaptability, wherein: the S9 adopts a gray prediction method of a GM (1,1) model to judge the load demand and the load characteristics, and comprises the following three steps:
the method comprises the following steps: accumulating to generate a structure increasing number sequence, and listing a differential equation matrix;
the original data column and the data column formed by first-order accumulation are respectively shown as formulas (1) and (2);
x(0)={x(0)(1),x(0)(2),…,x(0)(k)},k=1,2,…,n(1)
x(1)={x(1)(1),x(1)(2),…,x(1)(k)},k=1,2,…,n(2)
in the formula (I), the compound is shown in the specification,
Figure FDA0003442750010000021
x(0)a column of raw data, i.e. historical data representing the load in the area under study; x is the number of(1)Data columns formed after the first-order accumulation are represented, namely accumulated data of loads in the research area;
the differential equation can thus be set forth as shown in equation (3):
Figure FDA0003442750010000022
in the formula, a and u are respectively development gray scale and endogenous gray scale parameters,
the differential equation (3) is rewritten into a matrix form as shown in equation (4):
Figure FDA0003442750010000031
step two: obtaining the time response function of the GM (1,1) model,
substituting the accumulated data values into a differential equation matrix to obtain the values of a and u, and substituting the values of a and u into the original differential equation to obtain an iterative equation shown in the formula (5):
Figure FDA0003442750010000032
step three: finally, a grey prediction model of the original sequence is obtained,
carrying out subtraction reduction on the formula (5) to obtain an original sequence X(0)According to the grey prediction model, different values are attached to k according to the number of the sequence numbers of the time to be predicted, the predicted value of the time point is calculated, and the prediction work is completed.
3. The method of claim 1 for optimizing clean energy delivery planning and operation in consideration of faucet-reservoir adaptability, wherein: and S11 and S12 obtain a partition balance power flow plan through partition balance analysis, and obtain a hydropower cluster delivery scheme and delivery channel requirements according to the power flow plan, so that an external delivery scheme library is formed preliminarily.
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CN108470249A (en) * 2018-03-16 2018-08-31 大连理工大学 A kind of Hydropower Stations short-term electricity generation dispatching method of coupling clustering and decision tree
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Publication number Priority date Publication date Assignee Title
CN108470249A (en) * 2018-03-16 2018-08-31 大连理工大学 A kind of Hydropower Stations short-term electricity generation dispatching method of coupling clustering and decision tree
CN109447370A (en) * 2018-11-13 2019-03-08 国电南瑞科技股份有限公司 Centralized safety analytical method, apparatus and system towards more power plant's multicomponent electricity Decentralized transactions

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