CN111667119B - Hydropower station MILP model optimal representative head selection method and system - Google Patents

Hydropower station MILP model optimal representative head selection method and system Download PDF

Info

Publication number
CN111667119B
CN111667119B CN202010523887.8A CN202010523887A CN111667119B CN 111667119 B CN111667119 B CN 111667119B CN 202010523887 A CN202010523887 A CN 202010523887A CN 111667119 B CN111667119 B CN 111667119B
Authority
CN
China
Prior art keywords
head
representative
flow
day
water
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010523887.8A
Other languages
Chinese (zh)
Other versions
CN111667119A (en
Inventor
王亮
苗树敏
滕予非
魏巍
陈刚
王永灿
丁理杰
杜成锐
张弛
王金龙
王莉丽
过夏明
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Electric Power Research Institute of State Grid Sichuan Electric Power Co Ltd
Original Assignee
Electric Power Research Institute of State Grid Sichuan Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Electric Power Research Institute of State Grid Sichuan Electric Power Co Ltd filed Critical Electric Power Research Institute of State Grid Sichuan Electric Power Co Ltd
Priority to CN202010523887.8A priority Critical patent/CN111667119B/en
Publication of CN111667119A publication Critical patent/CN111667119A/en
Application granted granted Critical
Publication of CN111667119B publication Critical patent/CN111667119B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a method and a system for selecting an optimal representative head of an MILP model of a hydropower station, wherein the method for selecting the optimal representative head comprises the following steps: establishing a representative water head hydropower station MILP model; the representative head hydropower station MILP model comprises an objective function and operation simulation constraints; establishing a characteristic matrix M selected by an optimal representative head according to historical operating data of the hydropower station and a representative head hydropower station MILP model; and obtaining the optimal representative waterhead of the dispatching day x according to the predicted average warehousing flow, the predicted average output and the characteristic matrix M of the dispatching day x. The invention aims to provide a hydropower station MILP model optimal representative head selection method and system, which can realize hydropower fine scheduling by mining historical data rules and combining the average warehousing flow and average output prediction results of hydropower stations, ensure water quantity matching between the upstream and downstream hydropower stations in the step level in actual hydropower scheduling and improve the utilization efficiency of watershed water resources.

Description

Hydropower station MILP model optimal representative head selection method and system
Technical Field
The invention relates to the technical field of hydropower dispatching operation, in particular to a method and a system for selecting an optimal representative head of an MILP model of a hydropower station.
Background
The conventional solving methods for the hydropower scheduling problem comprise linear programming, nonlinear programming, mixed integer linear programming, dynamic programming, heuristic modern intelligent algorithm and the like, wherein the mixed integer linear programming method is one of important methods for solving the hydropower scheduling problem in power grid scheduling. Through the high-speed development of hydropower for many years, the Sichuan hydropower has been greatly developed, and by the end of 2019, the installed capacity of the whole-social caliber hydropower of the Sichuan power grid is 7840.3 ten thousand kilowatts, the percentage of the installed capacity in the power supply structure exceeds 79%, and the Sichuan power grid is the first provincial power grid in China. The high-density and ultra-large-scale hydropower station in operation in the Sichuan power grid enables the situation that a plurality of same channels are connected into a plurality of drainage basin power stations and a plurality of delivery channels are connected into the same drainage basin to appear in the power grid structure, the hydraulic-electric coupling relation is very complex, and the characteristics of large scale, high dimension and nonlinearity of hydropower dispatching per se bring great challenges to the Sichuan power grid hydropower dispatching.
Therefore, when the mixed integer linear programming is applied to a sichuan power grid with large-scale hydropower, a Mixed Integer Linear Programming (MILP) scheduling model considering head influence cannot be established for each hydropower station, otherwise, a situation that the variable is too much to solve occurs. The current common processing mode in the actual scheduling process is to take hydropower stations with the adjustment performance of daily adjustment or below as fixed water heads to participate in scheduling, and design water heads are usually adopted. However, in the operation process of the hydropower station, the water head is influenced by the upstream dam front water level, the downstream tail water level and the like, the correlation between the hydropower station power generation and the ex-warehouse flow is difficult to accurately describe only by adopting the designed water head, and the simulation precision is poor. Especially restricted by the management level, partial power station still has design data disappearance, imperfect scheduling problem, has further increaseed the deviation between traditional fixed flood peak simulation and actual operation operating mode, easily causes the water volume of upper and lower trip to mismatch, increases and abandons water, the reservoir risk of drawing empty.
Disclosure of Invention
The invention aims to provide a hydropower station MILP model optimal representative head selection method and system, which can realize hydropower fine scheduling by mining historical data rules and combining the average warehousing flow and average output prediction results of hydropower stations, ensure water quantity matching between the upstream and downstream hydropower stations in the step level in actual hydropower scheduling and improve the utilization efficiency of watershed water resources.
The invention is realized by the following technical scheme:
a hydropower station MILP model optimal representative head selection method comprises the following steps:
s1: establishing a representative water head hydropower station MILP model; the representative head hydropower station MILP model comprises an objective function and an operation simulation constraint;
s2: establishing a characteristic matrix M selected by an optimal representative head according to historical operating data of the hydropower station and the representative head hydropower station MILP model;
s3: average flow rate of entering warehouse according to prediction of scheduling day x
Figure BDA0002533118140000028
Predicted average output
Figure BDA0002533118140000029
And the characteristic matrix M acquires the optimal representative water head of the dispatching day x.
Further, the S1 includes the following sub-steps:
s11: acquiring historical operating data of the hydropower station;
s12: fitting the objective function and the operational simulation constraints according to the historical operational data; the operation simulation constraints comprise output constraints, water balance constraints, ex-warehouse flow balance constraints, power station output characteristic constraints, ex-warehouse flow constraints, power generation flow constraints and warehouse capacity constraints.
Further, the objective function is:
Figure BDA0002533118140000021
wherein:
Figure BDA0002533118140000022
historical outlet flow of the power station in a time period t; qtSimulating the ex-warehouse flow for the power station at the time t;
Figure BDA0002533118140000023
actual water discharge of the power station in a period t; stSimulating water abandoning flow for the power station at the time t; t is the total number of periods counted.
Further, the power plant output characteristic constraint is obtained by:
Pt＝1000*A*qt*H；
wherein A is the comprehensive output coefficient of the power station; h represents a water head of the hydropower station; q. q.stAnd (4) simulating the power generation flow of the power station in the time period t.
Further, the S2 includes the following sub-steps:
s21: at the minimum head H of the hydroelectric power stationminAnd maximum head HmaxConstructing a water head interval for a boundary, and dispersing the water head interval into n representative water heads by taking delta as an interval in the water head interval; wherein Hmin＝H1＜H2＜…＜Hn＝Hmax；
S22: acquiring the time-interval ex-warehouse flow, the water discharge and the output data of the hydropower station on the ith day in the historical operation data, respectively substituting the time-interval ex-warehouse flow, the water discharge, the output data and the n representative water heads on the ith day into the MILP model of the hydropower station, and acquiring n flow deviation data F corresponding to the representative water heads on the ith dayi 1，Fi 2，…，Fi n(ii) a Wherein the representative head at which the flow deviation data is minimized on the ith day is an optimal representative head
Figure BDA0002533118140000025
Wherein i is 1,2,3 … m;
s23: acquiring the average warehousing flow of the n representative water heads in the ith day
Figure BDA0002533118140000026
And average output
Figure BDA0002533118140000027
And the average warehousing flow rate of the ith day
Figure BDA0002533118140000031
Said average output
Figure BDA0002533118140000032
And the optimal representative head
Figure BDA0002533118140000033
Representative head characteristic vector composing day i
Figure BDA0002533118140000034
S24: establishing an optimal representative head selected feature matrix M according to the representative head feature vector:
Figure BDA0002533118140000035
wherein the content of the first and second substances,
Figure BDA0002533118140000036
represents the average warehousing traffic on day m;
Figure BDA0002533118140000037
represents the mean output on day m;
Figure BDA0002533118140000038
representing the optimal representative head on day m.
In a hydroelectric power station, the operating head of the hydroelectric power station is generally closely related to the average flow rate entering the reservoir and the daily average output, in other words, the average flow rate entering the reservoir and the daily average output reflect the quality of the head to some extent. In the scheme, based on the incidence relation among the three parts, by acquiring historical average warehousing flow and daily average output data, rules among the average warehousing flow, the daily average output and the optimal representative head are deeply mined, and a corresponding characteristic matrix M is established.
Further, the S3 includes the following sub-steps:
s31: acquiring the predicted average warehousing flow of the scheduling day x according to the hydrologic forecast result and the electric quantity transaction result before the day
Figure BDA0002533118140000039
And predicting the average output
Figure BDA00025331181400000310
S32: according to the predicted average warehousing flow
Figure BDA00025331181400000311
And said predicted average output
Figure BDA00025331181400000312
Calculating Euclidean distances between the Euclidean distances and the daily average flow and the average output under the condition that the characteristic matrix M has historically occurred one by one:
Figure BDA00025331181400000313
s32: select the minimum Euclidean distance min [ omega ]1,ω2,...,ωmCalculating the optimal representative head of the adjustment day x by using the representative head corresponding to the adjustment day x as a model
Figure BDA00025331181400000314
S33: repeating the steps S22-S23, and adjusting the average warehousing flow of the day x
Figure BDA00025331181400000315
Mean output force
Figure BDA00025331181400000316
And an optimal representative head
Figure BDA0002533118140000041
Updating the feature matrix M.
The characteristic matrix M is composed of representative water head characteristic vectors of a plurality of days, and the representative water head characteristic vectors of any day comprise average warehousing flow, average output and optimal representative water head; when the optimal representative head of a certain day needs to be obtained, the average warehousing flow and the predicted average output of the day are compared with the daily average warehousing flow and the daily predicted average output in the characteristic matrix M for similarity, the daily average warehousing flow and the daily predicted average output with the closest similarity are selected, the optimal representative head corresponding to the day is obtained, and the optimal representative head of the day is placed into the representative head hydropower station MILP model for inspection.
A hydropower station MILP model optimal representative head selection system comprises a modeling module, a construction module and an acquisition module;
the modeling module is used for establishing a representative head hydropower station MILP model; the representative head hydropower station MILP model comprises an objective function and an operation simulation constraint;
the construction module is used for establishing an optimal representative head selected characteristic matrix M according to historical operating data of the hydropower station and the representative head hydropower station MILP model;
the acquisition module is used for predicting average warehousing flow according to the scheduling day x
Figure BDA0002533118140000046
Predicted average output
Figure BDA0002533118140000047
And the characteristic matrix M acquires the optimal representative water head of the dispatching day x.
Further, the modeling module includes the following processes:
acquiring historical operating data of the hydropower station;
fitting the objective function and the operational simulation constraints according to the historical operational data; the operation simulation constraints comprise output constraints, water balance constraints, ex-warehouse flow balance constraints, power station output characteristic constraints, ex-warehouse flow constraints, power generation flow constraints and warehouse capacity constraints.
Further, the construction module includes the following processes:
at the minimum head H of the hydroelectric power stationminAnd maximum head HmaxConstructing a water head interval for a boundary, and dispersing the water head interval into n representative water heads by taking delta as an interval in the water head interval; wherein Hmin＝H1＜H2＜…＜Hn＝Hmax；
Acquiring the time-interval ex-warehouse flow, the water discharge and the output data of the hydropower station on the ith day in the historical operation data, respectively substituting the time-interval ex-warehouse flow, the water discharge, the output data and the n representative water heads on the ith day into the MILP model of the hydropower station, and acquiring n flow deviation data F corresponding to the representative water heads on the ith dayi 1，Fi 2，…，Fi n(ii) a Wherein, make a firstThe representative water head with the minimum flow deviation data in the i day is the optimal representative water head
Figure BDA0002533118140000043
Wherein i is 1,2,3 … m;
acquiring the average warehousing flow of the n representative water heads in the ith day
Figure BDA0002533118140000044
And average output
Figure BDA0002533118140000045
And the average warehousing flow rate of the ith day
Figure BDA0002533118140000051
Said average output
Figure BDA0002533118140000052
And the optimal representative head
Figure BDA0002533118140000053
Representative head characteristic vector composing day i
Figure BDA0002533118140000054
Establishing an optimal representative head selected feature matrix M according to the representative head feature vector:
Figure BDA0002533118140000055
wherein the content of the first and second substances,
Figure BDA0002533118140000056
represents the average warehousing traffic on day m;
Figure BDA0002533118140000057
represents the mean output on day m;
Figure BDA0002533118140000058
representing the optimal representative head on day m.
Further, the acquiring module comprises the following processing procedures:
obtaining the predicted average warehousing flow of the scheduling day x
Figure BDA0002533118140000059
And predicting the average output
Figure BDA00025331181400000510
According to the predicted average warehousing flow
Figure BDA00025331181400000511
And said predicted average output
Figure BDA00025331181400000512
Calculating Euclidean distances between the Euclidean distances and the daily average flow and the average output under the condition that the characteristic matrix M has historically occurred one by one:
Figure BDA00025331181400000513
s32: select the minimum Euclidean distance min [ omega ]1,ω2,...,ωmCalculating the optimal representative head of the adjustment day x by using the representative head corresponding to the adjustment day x as a model
Figure BDA00025331181400000514
Average warehousing traffic of scheduling day x
Figure BDA00025331181400000515
Mean output force
Figure BDA00025331181400000516
And an optimal representative head
Figure BDA00025331181400000517
Updating the feature matrix M.
In the actual scheduling process, a common processing mode is to use hydropower stations with the adjustment performance of daily adjustment and below as fixed water heads to participate in scheduling, and design water heads are usually adopted. However, the water head is influenced by the upstream dam front water level, the downstream tail water level and the like in the operation process of the hydropower station, and the correlation between the power generation and the delivery flow of the hydropower station is difficult to accurately describe only by adopting the designed water head, and the simulation precision is relatively poor.
Based on the method, compared with a mode of adopting a fixed design head, the method/system effectively utilizes the incidence relation between daily average warehousing flow and daily average output in the operation process of the hydropower station and the operation head of the hydropower station, obtains the optimal representative head corresponding to the given predicted average warehousing flow and the average output through historical operation data simulation and similarity search, and can accurately reflect the conversion relation between water and electricity of the hydropower station through the screened optimal representative head, thereby solving the problem that the water quantity of the hydropower station on the upstream and the downstream of the cascade is not matched due to the adoption of the MILP scheduling model of the fixed design head.
Meanwhile, the historical data of the hydropower station can reflect the 'water-electricity' conversion relation of the hydropower station, the forecast information of the hydropower station can reflect the overall operation state of the hydropower station in the future, and the method/the system organically combines the historical data and the forecast information of the hydropower station, so that the flow process of the calculated flow of the hydropower station in the power dispatching process of the power grid is closer to the actual flow process of the hydropower station in the delivery process of the power grid, the matching precision of the upstream and downstream water quantities of the gradient hydropower station in the power dispatching plan made by the power grid is improved, and the optimal utilization of water resources is realized.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the general flow of the method for selecting the optimal representative head of the MILP model of the hydropower station by utilizing historical operation data of the hydropower station is provided, and the problem that the water quantity of hydropower stations upstream and downstream of the cascade is not matched due to the fixed design head MILP hydropower scheduling model is solved;
(2) the method can be closer to the actual delivery flow process of the hydropower station, is favorable for improving the upstream and downstream water quantity matching precision of the gradient hydropower station in the power grid planning and dispatching plan, and realizes the optimal utilization of water resources.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a schematic block diagram of the computational process of the present invention;
FIG. 2 is a schematic diagram of a 2-month simulation scheduling ex-warehouse traffic deviation in an embodiment of the present invention;
FIG. 3 is a schematic diagram of the warehouse-out flow process from 2 months to 1 day time by time in the embodiment of the present invention;
FIG. 4 is a schematic diagram of the warehouse-out flow process from 2 months to 10 days every moment in the embodiment of the present invention;
FIG. 5 is a schematic diagram of the warehouse-out flow process of 2 months and 20 days every moment in the embodiment of the invention;
FIG. 6 is a schematic diagram of a flow deviation of a 6-month simulation dispatch warehouse-out in an embodiment of the present invention;
FIG. 7 is a schematic diagram of the warehouse-out flow process from 6 months to 1 day time by time in the embodiment of the present invention;
FIG. 8 is a schematic diagram of the warehouse-out flow process from 6 months to 10 days time by time in the embodiment of the present invention;
fig. 9 is a schematic diagram of the warehouse-out flow process from 6 months to 20 days every moment in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
A hydropower station MILP model optimal representative head selection method comprises the following steps:
s1: establishing a representative water head hydropower station MILP model; the representative head hydropower station MILP model comprises an objective function and operation simulation constraints;
s2: establishing a characteristic matrix M selected by an optimal representative head according to historical operating data of the hydropower station and a representative head hydropower station MILP model;
s3: average flow rate of entering warehouse according to prediction of scheduling day x
Figure BDA0002533118140000071
Predicted average output
Figure BDA0002533118140000072
And acquiring the optimal representative water head of the adjustment day x by the characteristic matrix M.
Wherein, S1 includes the following substeps:
s11: acquiring historical operating data of a hydropower station; the historical operation data comprises the ex-warehouse flow, the water discharge, the output condition, the in-warehouse flow, the storage capacity and the like of the hydropower station.
S12: fitting a target function and operation simulation constraints according to historical operation data; the operation simulation constraints comprise output constraints, water balance constraints, ex-warehouse flow balance constraints, power station output characteristic constraints, ex-warehouse flow constraints, power generation flow constraints and warehouse capacity constraints.
In this particular embodiment, the objective function representing the head hydro-power plant MILP model is:
Figure BDA0002533118140000073
wherein the content of the first and second substances,
Figure BDA0002533118140000074
historical outlet flow of the power station in a time period t; qtSimulating the ex-warehouse flow for the power station at the time t;
Figure BDA0002533118140000075
actual water discharge of the power station in a period t; stSimulating water abandoning flow for the power station at the time t; t is the total number of periods counted.
The output constraint is as follows:
Pt＝Pt real
wherein, PtSimulating output for the hydropower station in the time period t; pt realHistorical output of the hydropower station is obtained in a time period t;
the water balance constraint is as follows:
Figure BDA0002533118140000078
wherein, VtSimulating the terminal storage capacity for the power station at the time t;
Figure BDA0002533118140000079
the actual warehousing flow of the power station in the time period t is obtained; Δ t is the scheduling period step;
the balance constraint of the ex-warehouse flow is as follows:
Qt＝qt+St
wherein q istSimulating the power generation flow of the power station in a time period t;
the power station output characteristic constraints are as follows:
Pt＝1000*A*qt*H；
wherein A is the comprehensive output coefficient of the power station; h represents a water head of the hydropower station; q. q.stSimulating the power generation flow of the power station in a time period t;
the outbound flow constraint is:
Figure BDA0002533118140000081
wherein the content of the first and second substances,
Figure BDA0002533118140000082
respectively the minimum and maximum ex-warehouse flow of the power station in the time period t;
the generated current constraint is as follows:
Figure BDA0002533118140000083
wherein the content of the first and second substances,
Figure BDA0002533118140000084
limiting the maximum generating flow of the time-interval power station;
the library capacity constraint is:
Vt min≤Vt≤Vt max
wherein: vt min、Vt maxRespectively the minimum and maximum allowed storage capacity of the power station during the period t.
Further, S2 includes the following sub-steps:
s21: at minimum head H of hydroelectric power stationminAnd maximum head HmaxConstructing a water head interval for the boundary, and dispersing the water head interval into n representative water heads by taking delta as an interval in the water head interval; wherein Hmin＝H1＜H2＜…＜Hn＝Hmax；
S22: acquiring the time-interval ex-warehouse flow, the water discharge and the output data of the hydropower station on the ith day in the historical operation data, respectively substituting the time-interval ex-warehouse flow, the water discharge, the output data and the n representative water heads on the ith day into the MILP model of the hydropower station, and obtaining n flow deviation data F corresponding to the representative water heads on the ith dayi 1，Fi 2，…，Fi n(ii) a Wherein, the representative water head with the minimum flow deviation data in the ith day is the optimal representative water head
Figure BDA0002533118140000088
Wherein i is 1,2,3 … m;
s23: acquiring average warehousing flow of n representative water heads in the ith day
Figure BDA0002533118140000089
And average output
Figure BDA00025331181400000810
And average warehousing flow rate of the ith day
Figure BDA00025331181400000811
Mean output force
Figure BDA00025331181400000812
And an optimal representative head
Figure BDA00025331181400000813
Representative head characteristic vector composing day i
Figure BDA00025331181400000814
S24: establishing an optimal representative head selected feature matrix M according to the representative head feature vector:
Figure BDA0002533118140000091
wherein the content of the first and second substances,
Figure BDA0002533118140000092
represents the average warehousing traffic on day m;
Figure BDA0002533118140000093
represents the mean output on day m;
Figure BDA0002533118140000094
representing the optimal representative head on day m.
Further, S3 includes the following sub-steps:
s31: obtaining the predicted average warehousing flow of the scheduling day x
Figure BDA0002533118140000095
And predicting the average output
Figure BDA0002533118140000096
S32: average flow to warehouse based on prediction
Figure BDA0002533118140000097
And predicting the average output
Figure BDA0002533118140000098
Calculating Euclidean distances between the daily average flow and the average output under the condition that the historical occurrence in the feature matrix M one by one:
Figure BDA0002533118140000099
s32: select the minimum Euclidean distance min [ omega ]1,ω2,...,ωmThe representative head corresponding to the water level is used as the representative head of the model calculation adjustment day x
Figure BDA00025331181400000910
S33: repeating the steps S22-S23, and adjusting the average warehousing flow of the day x
Figure BDA00025331181400000911
Mean output force
Figure BDA00025331181400000912
And an optimal representative head
Figure BDA00025331181400000913
And updating the feature matrix M.
A hydropower station MILP model optimal representative head selection system comprises a modeling module, a construction module and an acquisition module;
the modeling module is used for establishing a representative head hydropower station MILP model; the representative head hydropower station MILP model comprises an objective function and operation simulation constraints;
the construction module is used for establishing an optimal representative head selected characteristic matrix M according to historical operating data of the hydropower station and a representative head hydropower station MILP model;
an obtaining module for predicting average warehousing flow according to the scheduling day x
Figure BDA00025331181400000914
Predicted average output
Figure BDA00025331181400000915
And acquiring the optimal representative water head of the adjustment day x by the characteristic matrix M.
Wherein, the modeling module comprises the following processing procedures:
acquiring historical operating data of a hydropower station;
fitting a target function and operation simulation constraints according to historical operation data; the operation simulation constraints comprise output constraints, water balance constraints, ex-warehouse flow balance constraints, power station output characteristic constraints, ex-warehouse flow constraints, power generation flow constraints and warehouse capacity constraints.
In this particular embodiment, the objective function representing the head hydro-power plant MILP model is:
Figure BDA0002533118140000101
wherein the content of the first and second substances,
Figure BDA0002533118140000102
historical outlet flow of the power station in a time period t; qtSimulating the ex-warehouse flow for the power station at the time t;
Figure BDA0002533118140000103
actual water discharge of the power station in a period t; stSimulating water abandoning flow for the power station at the time t; t is the total number of periods counted.
The output constraint is as follows:
Pt＝Pt real
wherein, PtSimulating output for the hydropower station in the time period t; pt realHistorical output of the hydropower station is obtained in a time period t;
the water balance constraint is as follows:
Figure BDA0002533118140000106
wherein, VtIs electricitySimulating the end storage capacity in a time period t;
Figure BDA0002533118140000107
the actual warehousing flow of the power station in the time period t is obtained; Δ t is the scheduling period step;
the balance constraint of the ex-warehouse flow is as follows:
Qt＝qt+St
wherein q istSimulating the power generation flow of the power station in a time period t;
the power station output characteristic constraints are as follows:
Pt＝1000*A*qt*H；
wherein A is the comprehensive output coefficient of the power station; h represents a water head of the hydropower station; q. q.stSimulating the power generation flow of the power station in a time period t;
the outbound flow constraint is:
Figure BDA0002533118140000108
wherein the content of the first and second substances,
Figure BDA0002533118140000109
respectively the minimum and maximum ex-warehouse flow of the power station in the time period t;
the generated current constraint is as follows:
Figure BDA0002533118140000111
wherein the content of the first and second substances,
Figure BDA0002533118140000112
limiting the maximum generating flow of the time-interval power station;
the library capacity constraint is:
Vt min≤Vt≤Vt max
wherein: vt min、Vt maxRespectively minimum and maximum allowed storage capacity of the power station during the period t
Further, the construction module includes the following processes:
at minimum head H of hydroelectric power stationminAnd maximum head HmaxConstructing a water head interval for the boundary, and dispersing the water head interval into n representative water heads by taking delta as an interval in the water head interval; wherein Hmin＝H1＜H2＜…＜Hn＝Hmax；
Acquiring the time-interval ex-warehouse flow, the water discharge and the output data of the hydropower station on the ith day in the historical operation data, respectively substituting the time-interval ex-warehouse flow, the water discharge, the output data and the n representative water heads on the ith day into the MILP model of the hydropower station, and obtaining n flow deviation data F corresponding to the representative water heads on the ith dayi 1，Fi 2，…，Fi n(ii) a Wherein, the representative water head with the minimum flow deviation data in the ith day is the optimal representative water head
Figure BDA0002533118140000116
Wherein i is 1,2,3 … m;
acquiring average warehousing flow of n representative water heads in the ith day
Figure BDA0002533118140000117
And average output
Figure BDA0002533118140000118
And average warehousing flow rate of the ith day
Figure BDA0002533118140000119
Mean output force
Figure BDA00025331181400001110
And an optimal representative head
Figure BDA00025331181400001111
Representative head characteristic vector composing day i
Figure BDA00025331181400001112
Establishing an optimal representative head selected feature matrix M according to the representative head feature vector:
Figure BDA00025331181400001113
wherein the content of the first and second substances,
Figure BDA00025331181400001114
represents the average warehousing traffic on day m;
Figure BDA00025331181400001115
represents the mean output on day m;
Figure BDA00025331181400001116
representing the optimal representative head on day m.
Further, the acquisition module comprises the following processing procedures:
obtaining the predicted average warehousing flow of the scheduling day x
Figure BDA00025331181400001117
And predicting the average output
Figure BDA00025331181400001118
Average flow to warehouse based on prediction
Figure BDA0002533118140000121
And predicting the average output
Figure BDA0002533118140000122
Calculating Euclidean distances between the daily average flow and the average output under the condition that the historical occurrence in the feature matrix M one by one:
Figure BDA0002533118140000123
s32: choose minimumIs the Euclidean distance min [ omega ]1,ω2,...,ωmCalculating the optimal representative head of the adjustment day x by using the representative head corresponding to the model
Figure BDA0002533118140000124
Average warehousing traffic of scheduling day x
Figure BDA0002533118140000125
Mean output force
Figure BDA0002533118140000126
And an optimal representative head
Figure BDA0002533118140000127
And updating the feature matrix M.
The present solution is illustrated below by means of specific examples:
the method is characterized in that a hydropower station is adjusted at a certain day in the Sichuan power grid as a research object, basic parameters of the hydropower station are shown in table 1, data of the hydropower station 30 days before scheduling is used as a representative water head selection characteristic matrix, 1 hour is used as a scheduling period step length, 2018 actual operation data is used as a basis, and simulation scheduling is carried out respectively on the representative basis of a dry period of 2 months and a flood period of 6 months so as to verify the effectiveness of the method.
Figure BDA0002533118140000128
TABLE 1
Building an MILP model in MATLAB, calling a Cplex software package to perform daily simulation scheduling on the operating data of the power station calendar from 1 month and 1 day in 2018 to 1 month and 31 in 2018 respectively to form an initial characteristic matrix 1; the simulation schedule is developed for 2 months and 1 day to 2 months and 28 days.
Calculating and analyzing:
the designed water head in month 2, the daily actual flow calculated based on the representative water head predicted by the method, the calculated flow deviation of the model and the daily minimum flow deviation of the MILP model are shown in figure 2, and the typical daily actual delivery flow, the simulated delivery flow of the designed water head and the simulated delivery flow of the optimized representative water head are shown in figures 3-5.
As shown in fig. 2, when the representative water head predicted by the method is used for scheduling the hydropower station, the calculated deviation of the flow leaving the reservoir in the 2 months of the dry season is basically consistent with the minimum flow deviation fitted by the MILP in the current day. As shown in the typical moment-by-moment ex-warehouse flow process shown in fig. 3-5, the representative water head optimized by the method can be adopted to better fit the actual ex-warehouse flow process in the dry season.
And after the day-by-day simulation scheduling is carried out on the operation data of the 31-month calendar from 1/2018 to 5/2018, the original characteristic matrix is expanded, and the simulation scheduling from 1/6/30 is carried out. The design water head of 6 months, the daily actual flow calculated based on the representative water head predicted by the method, the model calculation flow deviation and the daily minimum flow deviation of the MILP model are shown in fig. 6, and the typical daily actual ex-warehouse flow, the design water head simulated ex-warehouse flow and the process of optimizing the representative water head simulated ex-warehouse flow are shown in fig. 7-9.
As shown in fig. 6, when the representative water head of the method is used for flood season hydropower station scheduling, the deviation from the actual flow process is mostly smaller than the simulation deviation of the designed water head. As shown in the typical daily moment-by-moment ex-warehouse flow process in the flood season shown in fig. 7-9, the representative water head optimized by the method can be well fitted with the actual ex-warehouse.
In a word, the hydropower station MILP model optimal representative head selection method and system based on the operation data provides a general flow for performing the hydropower station MILP model optimal representative head selection method by using historical operation data of the hydropower station, and solves the problem that the water quantity of hydropower stations upstream and downstream of the steps is not matched due to the fixed design head MILP hydropower scheduling model adopted at present.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (9)

1. A hydropower station MILP model optimal representative head selection method is characterized by comprising the following steps:
s1: establishing a representative water head hydropower station MILP model; the representative head hydropower station MILP model comprises an objective function and an operation simulation constraint;
the objective function is:
Figure FDA0003514737590000011
wherein:
Figure FDA0003514737590000012
historical outlet flow of the power station in a time period t; qtSimulating the ex-warehouse flow for the power station at the time t;
Figure FDA0003514737590000013
actual water discharge of the power station in a period t; stSimulating water abandoning flow for the power station at the time t; t is the total number of the calculated time periods;
s2: establishing a characteristic matrix M selected by an optimal representative head according to historical operating data of the hydropower station and the representative head hydropower station MILP model;
s3: average flow rate of entering warehouse according to prediction of scheduling day x
Figure FDA0003514737590000014
Predicted average output
Figure FDA0003514737590000015
And the characteristic matrix M acquires the optimal representative water head of the dispatching day x.
2. The method for selecting the optimal representative head of the MILP model of the hydropower station according to claim 1, wherein the S1 comprises the following sub-steps:
s11: acquiring historical operating data of the hydropower station;
s12: fitting the objective function and the operational simulation constraints according to the historical operational data; the operation simulation constraints comprise output constraints, water balance constraints, ex-warehouse flow balance constraints, power station output characteristic constraints, ex-warehouse flow constraints, power generation flow constraints and warehouse capacity constraints.
3. The method of claim 2, wherein the plant output characteristic constraints are obtained by:
Pt＝1000*A*qt*H；
wherein A is the comprehensive output coefficient of the power station; h represents a water head of the hydropower station; q. q.stAnd (4) simulating the power generation flow of the power station in the time period t.
4. The method for selecting the optimal representative head of the MILP model of the hydropower station according to claim 3, wherein the S2 comprises the following sub-steps:
s21: at the minimum head H of the hydroelectric power stationminAnd maximum head HmaxConstructing a water head interval for a boundary, and dispersing the water head interval into n representative water heads by taking delta as an interval in the water head interval; wherein Hmin＝H1＜H2＜…＜Hn＝Hmax；
S22: acquiring the time-interval ex-warehouse flow, the water discharge and the output data of the hydropower station on the ith day in the historical operation data, respectively substituting the time-interval ex-warehouse flow, the water discharge, the output data and the n representative water heads on the ith day into the MILP model of the hydropower station, and acquiring n flow deviation data F corresponding to the representative water heads on the ith dayi 1,Fi 2,…,Fi n(ii) a Wherein the representative head at which the flow deviation data is minimized on the ith day is an optimal representative head
Figure FDA0003514737590000021
Wherein i is 1,2,3 … m;
s23: acquiring the average warehousing flow of the n representative water heads in the ith day
Figure FDA0003514737590000022
And average output
Figure FDA0003514737590000023
And the average warehousing flow rate of the ith day
Figure FDA0003514737590000024
Said average output
Figure FDA0003514737590000025
And the optimal representative head
Figure FDA0003514737590000026
Representative head characteristic vector composing day i
Figure FDA0003514737590000027
S24: establishing an optimal representative head selected feature matrix M according to the representative head feature vector:
Figure FDA0003514737590000028
wherein the content of the first and second substances,
Figure FDA0003514737590000029
represents the average warehousing traffic on day m;
Figure FDA00035147375900000210
represents the mean output on day m;
Figure FDA00035147375900000211
representing the optimal representative head on day m.
5. The method for selecting the optimal representative head of the MILP model of the hydropower station according to claim 4, wherein the S3 comprises the following sub-steps:
s31: obtaining the predicted average warehousing flow of the scheduling day x
Figure FDA00035147375900000212
And predicting the average output
Figure FDA00035147375900000213
S32: according to the predicted average warehousing flow
Figure FDA00035147375900000214
And said predicted average output
Figure FDA00035147375900000215
Calculating Euclidean distances between the Euclidean distances and the daily average flow and the average output under the condition that the characteristic matrix M has historically occurred one by one:
Figure FDA00035147375900000216
s32: select the minimum Euclidean distance min [ omega ]1,ω2,...,ωmCalculating the optimal representative head of the adjustment day x by using the representative head corresponding to the adjustment day x as a model
Figure FDA00035147375900000217
S33: repeating the steps S22-S23, and adjusting the average warehousing flow of the day x
Figure FDA00035147375900000218
Mean output force
Figure FDA00035147375900000219
And an optimal representative head
Figure FDA0003514737590000031
Updating the feature matrix M.
6. A hydropower station MILP model optimal representative head selection system is characterized by comprising a modeling module, a construction module and an acquisition module;
the modeling module is used for establishing a representative head hydropower station MILP model; the representative head hydropower station MILP model comprises an objective function and an operation simulation constraint;
the objective function is:
Figure FDA0003514737590000032
wherein:
Figure FDA0003514737590000033
historical outlet flow of the power station in a time period t; qtSimulating the ex-warehouse flow for the power station at the time t;
Figure FDA0003514737590000034
actual water discharge of the power station in a period t; stSimulating water abandoning flow for the power station at the time t; t is the total number of the calculated time periods;
the construction module is used for establishing an optimal representative head selected characteristic matrix M according to historical operating data of the hydropower station and the representative head hydropower station MILP model;
the acquisition module is used for predicting average warehousing flow according to the scheduling day x
Figure FDA0003514737590000035
Predicted average output
Figure FDA0003514737590000036
And the characteristic matrix M acquires the optimal representative water head of the dispatching day x.
7. The hydropower station MILP model optimal representative head selection system according to claim 6, wherein the modeling module comprises the following processing procedures:
acquiring historical operating data of the hydropower station;
fitting the objective function and the operational simulation constraints according to the historical operational data; the operation simulation constraints comprise output constraints, water balance constraints, ex-warehouse flow balance constraints, power station output characteristic constraints, ex-warehouse flow constraints, power generation flow constraints and warehouse capacity constraints.
8. The hydropower station MILP model optimal representative head selection system of claim 7, wherein the construction module comprises the following processes:
at the minimum head H of the hydroelectric power stationminAnd maximum head HmaxConstructing a water head interval for a boundary, and dispersing the water head interval into n representative water heads by taking delta as an interval in the water head interval; wherein Hmin＝H1＜H2＜…＜Hn＝Hmax；
Acquiring the time-interval warehouse-out flow, the water discharge and the output data of the hydropower station on the ith day in the historical operation data, respectively substituting the time-interval warehouse-out flow, the water discharge, the output data and the n representative water heads on the ith day into the MILP model of the hydropower station, and acquiring n flow deviation data corresponding to the representative water heads on the ith day
Figure FDA0003514737590000041
Wherein the representative head at which the flow deviation data is minimized on the ith day is an optimal representative head
Figure FDA0003514737590000042
Wherein i is 1,2,3 … m;
acquiring the average warehousing flow of the n representative water heads in the ith day
Figure FDA0003514737590000043
And average output
Figure FDA0003514737590000044
And the average warehousing flow rate of the ith day
Figure FDA0003514737590000045
Said average output
Figure FDA0003514737590000046
And the optimal representative head
Figure FDA0003514737590000047
Representative head characteristic vector composing day i
Figure FDA0003514737590000048
Establishing an optimal representative head selected feature matrix M according to the representative head feature vector:
Figure FDA0003514737590000049
wherein the content of the first and second substances,
Figure FDA00035147375900000410
represents the average warehousing traffic on day m;
Figure FDA00035147375900000411
represents the mean output on day m;
Figure FDA00035147375900000412
representing the optimal representative head on day m.
9. The hydropower station MILP model optimal representative head selection system of claim 8, wherein the obtaining module comprises the following processing procedures:
obtaining the predicted average warehousing flow of the scheduling day x
Figure FDA00035147375900000413
And predicting the average output
Figure FDA00035147375900000414
According to the predicted average warehousing flow
Figure FDA00035147375900000415
And said predicted average output
Figure FDA00035147375900000416
Calculating Euclidean distances between the Euclidean distances and the daily average flow and the average output under the condition that the characteristic matrix M has historically occurred one by one:
Figure FDA00035147375900000417
s32: select the minimum Euclidean distance min [ omega ]1,ω2,...,ωmCalculating the optimal representative head of the adjustment day x by using the representative head corresponding to the adjustment day x as a model
Figure FDA00035147375900000418
Average warehousing traffic of scheduling day x
Figure FDA00035147375900000419
Mean output force
Figure FDA00035147375900000420
And an optimal representative head
Figure FDA00035147375900000421
Updating the feature matrix M.
CN202010523887.8A 2020-06-10 2020-06-10 Hydropower station MILP model optimal representative head selection method and system Active CN111667119B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010523887.8A CN111667119B (en) 2020-06-10 2020-06-10 Hydropower station MILP model optimal representative head selection method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010523887.8A CN111667119B (en) 2020-06-10 2020-06-10 Hydropower station MILP model optimal representative head selection method and system

Publications (2)

Publication Number Publication Date
CN111667119A CN111667119A (en) 2020-09-15
CN111667119B true CN111667119B (en) 2022-04-01

Family

ID=72386554

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010523887.8A Active CN111667119B (en) 2020-06-10 2020-06-10 Hydropower station MILP model optimal representative head selection method and system

Country Status (1)

Country Link
CN (1) CN111667119B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113361818B (en) * 2021-07-07 2023-09-22 西安热工研究院有限公司 Medium-and-long-term power generation optimization method and system for hydroelectric generating set
CN115858955B (en) * 2023-02-21 2023-05-05 南京图德科技有限公司 Hydropower station warehouse-in flow and generating capacity time sequence analysis method, device and storage medium

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107844864A (en) * 2017-11-17 2018-03-27 大连理工大学 A kind of mixed-integer nonlinear programming model of Solving Hydropower Unit Commitment Problem

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9811061B1 (en) * 2001-05-18 2017-11-07 The Energy Authority, Inc. Method for management and optimization of hydropower generation and consumption

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107844864A (en) * 2017-11-17 2018-03-27 大连理工大学 A kind of mixed-integer nonlinear programming model of Solving Hydropower Unit Commitment Problem

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Hydro Unit Commitment With a Head-Sensitive Reservoir and Multiple Vibration Zones Using MILP;Chuntian Cheng 等;《IEEE TRANSACTIONS ON POWER SYSTEMS》;20161130;第31卷(第6期);第4842-4852页 *
求解水电站日负荷优化分配的混合整数非线性规划模型;申建建 等;《电力系统自动化》;20181010;第42卷(第19期);第34-40页 *
考虑机组组合的梯级水电站短期调峰MILP模型;苏承国 等;《电网技术》;20180630;第42卷(第6期);第1883-1891页 *

Also Published As

Publication number Publication date
CN111667119A (en) 2020-09-15

Similar Documents

Publication Publication Date Title
CN107240932B (en) Photovoltaic power station capacity optimization method in water-light complementary system
Jacobs et al. SOCRATES: A system for scheduling hydroelectric generation under uncertainty
Yuan et al. Stochastic optimization model for the short-term joint operation of photovoltaic power and hydropower plants based on chance-constrained programming
CN101705671A (en) Yellow River upstream cascade hydroelectric station operation design and optimized dispatching method as well as equipment
Cheng et al. Stochastic short-term scheduling of a wind-solar-hydro complementary system considering both the day-ahead market bidding and bilateral contracts decomposition
CN112016747B (en) Optimization method suitable for source-load-storage flexible resource overall planning and operation
Zhao et al. Flexibility evaluation of wind-PV-hydro multi-energy complementary base considering the compensation ability of cascade hydropower stations
CN111667119B (en) Hydropower station MILP model optimal representative head selection method and system
CN108574303A (en) A kind of multiple-energy-source coordination optimization dispatching method considering peak-frequency regulation demand
CN107679679A (en) Cogeneration machine unit scheduling operation method
CN115117940A (en) Wind power, photovoltaic and load uncertainty considered wind-light-water-fire storage system low-carbon scheduling model modeling method
CN109978331B (en) Method for decomposing daily electric quantity in high-proportion water-electricity spot market
Yu et al. Optimal scheduling strategy of cascade hydropower plants under the joint market of day-ahead energy and frequency regulation
CN113363976B (en) Scene graph-based wind-solar-water complementary power generation system medium-term optimization scheduling method
Li et al. Research on short-term joint optimization scheduling strategy for hydro-wind-solar hybrid systems considering uncertainty in renewable energy generation
CN113872253A (en) Pumped storage power station and new energy combined power generation optimal scheduling method and device
CN112598175A (en) Watershed type virtual power plant scheduling method
Hjelmeland et al. Combined SDDP and simulator model for hydropower scheduling with sales of capacity
CN104951650A (en) Method for evaluating outer power transmission trading capacity of power exchange point of large-scale wind power grid
CN112750039B (en) Cross-region transaction spot market clearing model coupled with complex hydraulic connection
CN112994087B (en) Multi-source power system medium-term optimization scheduling method based on conditional risk constraint
Jing et al. Benefit compensation of hydropower-wind-photovoltaic complementary operation in the large clean energy base
CN111709605A (en) Reservoir power station peak regulation capacity evaluation method based on multiple counterregulation effects
CN116562572B (en) Annual planned electric quantity curve decomposition method for cascade hydropower station group
CN113887154B (en) Assessment method for medium-long term power generation capacity of cascade hydropower station group

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant