CN107506873B - Water-wind photoelectric station group collaborative optimization scheduling method and system based on adjustment performance - Google Patents

Water-wind photoelectric station group collaborative optimization scheduling method and system based on adjustment performance Download PDF

Info

Publication number
CN107506873B
CN107506873B CN201710861481.9A CN201710861481A CN107506873B CN 107506873 B CN107506873 B CN 107506873B CN 201710861481 A CN201710861481 A CN 201710861481A CN 107506873 B CN107506873 B CN 107506873B
Authority
CN
China
Prior art keywords
hydropower
station
stabilizing
local
hydropower station
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
CN201710861481.9A
Other languages
Chinese (zh)
Other versions
CN107506873A (en
Inventor
王现勋
梅亚东
王浩
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.)
Wuhan University WHU
Original Assignee
Wuhan University WHU
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 Wuhan University WHU filed Critical Wuhan University WHU
Priority to CN201710861481.9A priority Critical patent/CN107506873B/en
Publication of CN107506873A publication Critical patent/CN107506873A/en
Application granted granted Critical
Publication of CN107506873B publication Critical patent/CN107506873B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063Operations research, analysis or management
    • G06Q10/0631Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Health & Medical Sciences (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Public Health (AREA)
  • Primary Health Care (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides a cooperative combination partitioning method and system of a hydroelectric power station group based on regulation performance. The invention carries out the echelon combination division of the cooperative operation of the hydropower station group, the wind power group and the photoelectric group by automatically refining the regulating performance of the hydropower station and the space distance of the power station group, thereby achieving the purpose of cooperative optimization, improving the fineness of the cooperative operation optimization of various power supplies, being beneficial to the scheduling optimization of a complex power system containing various power supplies, having important significance for improving the development and utilization of clean energy and having important popularization and use values.

Description

Water-wind photoelectric station group collaborative optimization scheduling method and system based on adjustment performance
Technical Field
The invention belongs to the technical field of hydropower, wind power and photoelectric cooperative operation analysis, and particularly relates to a cooperative optimization scheduling method and system for a hydropower station group based on regulation performance.
Background
The research on the cooperative operation of the hydropower station group, the wind power station group and the photoelectric group is mainly used for scheduling and optimizing a complex power system containing multiple types of power supplies, wind power and photoelectricity are adjusted through hydropower compensation to reduce the amount of abandoned wind and abandoned light, the resource utilization rate is improved, and technical support is provided for the development and utilization of clean energy. The effect of improving the cooperative operation among various power supply groups is beneficial to excavating the cooperative optimization potential of the power system, and the dispatching technical level of the power system is favorably improved. At present, the scheduling optimization technology of complex power systems at home and abroad is limited to research on cooperative operation among different power supplies, namely, a power station is selected from a certain type of power supplies to be used as compensation cooperation for researching the power supplies on behalf. Generally, a power source of a certain type in an electric power system is a power station group consisting of a plurality of power stations, such as a hydropower group including a plurality of hydropower stations, a wind power group including a plurality of wind power plants, and a photovoltaic group including a plurality of photovoltaic stations. The research scale is not refined into the power supply in the prior art in the field, the difference of the adjusting characteristics among different power stations in a hydroelectric group is not considered, and the optimization effect of the cooperative operation of various power supplies is reduced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a water-wind photovoltaic power station group collaborative optimization scheduling method and system based on adjustment performance, which are used for carrying out optimization scheduling aiming at adjustment characteristic differences among different hydropower stations, excavating compensation adjustment capability of the water-wind photovoltaic power station group, improving collaborative operation effects among various power supply groups and better providing technical support for clean energy development and utilization.
In order to solve the technical problems, the invention adopts the following technical scheme:
a water-wind photovoltaic station group collaborative optimization scheduling method based on adjustment performance comprises the following steps:
step 1, calculating a reservoir capacity coefficient of the hydropower stations, setting the number of the hydropower stations in the hydropower group to be N, wherein N is more than or equal to 3, sequencing the hydropower stations from upstream to downstream in sequence, recording the serial number of the hydropower stations as a step serial number N, and recording the coordinate of the hydropower stations as a step serial number N, wherein N is 1,2
Figure GDA0002679370700000011
The storage capacity coefficient of the nth hydropower station is recorded as betan
Step 2, the hydropower cascade segmentation is divided into a segment A, wherein A is more than or equal to 1, the segmentation is carried out according to the following mode,
firstly, extracting a global stabilizing hydropower station, and marking the maximum value of the storage capacity coefficient obtained in the step 1 as betamaxThe largest value of the storage capacity coefficient is recorded as beta2nd(ii) a Reservoir capacity coefficient beta for each hydropower station obtained in step 1nWill betanThe hydropower station with the size of more than or equal to lambda is called a global stabilizing hydropower station, and the step serial number of the a-th global stabilizing hydropower station is marked as gloaA, a is the total number of the global stabilizing hydropower stations, λ is a stabilizing function discrimination coefficient, and 0<λ≤βmax(ii) a The non-global stabilizing hydropower station is called a local stabilizing hydropower station, and the cascade number of the b-th local stabilizing hydropower station is lcobB is the total number of the local stabilizing hydropower stations;
if λ>β2ndIf A is 1, all the hydropower stations are regarded as a step section; if λ ≦ β2ndThen A is>1, the 1 st hydropower station to the glo according to the cascade sequence number of the hydropower stations1Dividing each hydropower station into 1 st step and dividing the glo1+1 hydropower station to glo2Individual hydroelectric plants are divided into 2 nd step, …, the gloa-1+1 hydropower station to gloaThe individual hydroelectric plants are divided into the a-th step, …, and glo is dividedA-1+1 to Nth hydropower station is designated as gloAA step section; obtaining A steps, wherein each step comprises a global stabilizing hydropower station;
step 3, initially establishing a local stabilizing subgroup and a global stabilizing subgroup, wherein the implementation mode is as follows,
each local stabilizing hydropower station obtained in the step 2 is independently constructed into local stabilizing subgroups, and B local stabilizing subgroups can be obtained; building all the hydropower stations in each step section of the A step sections obtained in the step 2 into a global stabilizing subgroup to obtain A global stabilizing subgroups;
step 4, dividing a local stabilizing subgroup of the hydropower group, the wind power group and the photoelectric group which run cooperatively, wherein the local stabilizing subgroup is further divided according to the initial building result of the local stabilizing subgroup in the step 3 and the distance between the hydropower station and the wind power plant or the photoelectric station;
step 5, dividing the global stabilizing subgroups of the hydropower group, the wind power group and the photoelectric group which run in a coordinated mode, wherein the step 4 comprises the step of correspondingly adding B local stabilizing subgroups to the global stabilizing subgroups to which the hydropower stations belong by taking the hydropower stations in the groups as identification marks to obtain updated A global stabilizing subgroups; and B local stabilizing subgroups obtained in the step 4 and A global stabilizing subgroups obtained in the step 5 are the optimal scheduling results of the cooperative operation echelon of the hydroelectric and wind power station group based on the hydroelectric regulation performance.
In step 1, the reservoir capacity coefficient of the hydropower station is calculated in such a manner that,
Figure GDA0002679370700000021
wherein
Figure GDA0002679370700000022
For the regulated storage capacity of the nth hydroelectric station, and
Figure GDA0002679370700000023
Figure GDA0002679370700000024
the normal water storage level of the nth hydropower station corresponds to the storage capacity,
Figure GDA0002679370700000025
corresponding to the dead water level of the nth hydropower station, WnThe mean runoff over years for the nth hydroelectric power station,
Figure GDA0002679370700000026
wherein
Figure GDA0002679370700000027
Is the average runoff over years for the nth hydroelectric station,
Figure GDA0002679370700000028
is the average annual second of many years.
In step 4, the local flattening sub-group division is performed as follows,
step 4.1, setting that the wind power group comprises M wind power plants, and recording the coordinates of the wind power plants as
Figure GDA0002679370700000029
M is the serial number of the wind power plant, and M is 1,2, … and M; for the mth wind farm, the distance to the mth local stabilizing hydropower station is calculated and recorded as dism_b
Figure GDA0002679370700000031
Will dism_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure GDA0002679370700000032
Then the mth wind farm is added to the mth wind farm obtained in step 3
Figure GDA0002679370700000033
A local suppressor subgroup;
step 4.2, the photoelectric group is set to contain P photoelectric stations, and the coordinates of the photoelectric stations are recorded as
Figure GDA0002679370700000034
P is the number of the optical power station, and P is 1,2, … and P; for the p-th photovoltaic power station, the distance to the b-th locally-stabilized hydropower station is calculated and is recorded as disp_b
Figure GDA0002679370700000035
Will disp_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure GDA0002679370700000036
Then the p-th photovoltaic station is added to the p-th photovoltaic station
Figure GDA0002679370700000037
A subset of local plateaus.
The invention provides a water-wind photoelectric station group collaborative optimization scheduling system based on regulation performance, which comprises the following units: the hydropower station storage capacity calculation method comprises a first unit, wherein the first unit is used for calculating a hydropower station storage capacity coefficient, the number of hydropower stations in a hydropower group is N, N is more than or equal to 3, the hydropower stations are sequentially sequenced from upstream to downstream, the sequence number of the hydropower stations is recorded as a step sequence number N, N is 1,2
Figure GDA0002679370700000038
The storage capacity coefficient of the nth hydropower station is recorded as betan
The second unit is used for the subsection of the hydroelectric cascade and is divided into A sections, A is more than or equal to 1, the subsection is carried out according to the following mode,
firstly, extracting a global stabilizing hydropower station, and marking the maximum value of the storage capacity coefficient obtained by a first unit as betamaxThe largest value of the storage capacity coefficient is recorded as beta2nd(ii) a The reservoir capacity coefficient beta of each hydropower station obtained for the first unitnWill betanThe hydropower station with the size of more than or equal to lambda is called a global stabilizing hydropower station, and the step serial number of the a-th global stabilizing hydropower station is marked as gloaA, a is the total number of globally regulated hydropower stations,λ is the discrimination coefficient of the smoothing function, 0<λ≤βmax(ii) a The non-global stabilizing hydropower station is called a local stabilizing hydropower station, and the cascade number of the b-th local stabilizing hydropower station is lcobB is the total number of the local stabilizing hydropower stations;
if λ>β2ndIf A is 1, all the hydropower stations are regarded as a step section; if λ ≦ β2ndThen A is>1, the 1 st hydropower station to the glo according to the cascade sequence number of the hydropower stations1Dividing each hydropower station into 1 st step and dividing the glo1+1 hydropower station to glo2Individual hydroelectric plants are divided into 2 nd step, …, the gloa-1+1 hydropower station to gloaThe individual hydroelectric plants are divided into the a-th step, …, and glo is dividedA-1+1 to Nth hydropower station is designated as gloAA step section; obtaining A steps, wherein each step comprises a global stabilizing hydropower station;
a third unit for initially building a local and global stationarity subgroup, realized as follows,
each local stabilizing hydropower station obtained by the second unit is independently constructed into local stabilizing subgroups, and B local stabilizing subgroups can be obtained; building all hydropower stations in each step section of the A step sections obtained by the second unit into a global stabilizing subgroup to obtain A global stabilizing subgroups;
the fourth unit is used for dividing the local stabilizing subgroups of the hydropower station, the wind power station and the photoelectric group which run cooperatively, and further dividing the local stabilizing subgroups according to the initial building result of the local stabilizing subgroups of the third unit and the distance between the hydropower station and the wind power station or the photoelectric station;
a fifth unit, configured to divide a global stabilizing subgroup in which the hydropower station group, the wind power group, and the photovoltaic group operate cooperatively, where the fifth unit includes adding the B local stabilizing subgroups obtained in step 4 to the global stabilizing subgroup to which the hydropower station belongs, using the hydropower stations in the group as identification tags, and obtaining updated a global stabilizing subgroups; the B local stabilizing subgroups obtained by the fourth unit and the A global stabilizing subgroups obtained by the fifth unit are optimal scheduling results of the cooperative operation echelon of the hydroelectric and wind power station group based on hydroelectric regulation performance.
In the first unit, moreover, the storage capacity coefficient of the hydropower station is calculated in such a manner that,
Figure GDA0002679370700000041
wherein
Figure GDA0002679370700000042
For the regulated storage capacity of the nth hydroelectric station, and
Figure GDA0002679370700000043
Figure GDA0002679370700000044
the normal water storage level of the nth hydropower station corresponds to the storage capacity,
Figure GDA0002679370700000045
corresponding to the dead water level of the nth hydropower station, WnThe mean runoff over years for the nth hydroelectric power station,
Figure GDA0002679370700000046
wherein
Figure GDA0002679370700000047
Is the average runoff over years for the nth hydroelectric station,
Figure GDA0002679370700000048
is the average annual second of many years.
In the fourth cell, the local flattening sub-group division is performed as follows,
let the wind power group contain M wind power plants, and the coordinates of the wind power plants are recorded as
Figure GDA0002679370700000049
M is the serial number of the wind power plant, and M is 1,2, … and M; for the mth wind farm, the distance to the mth local stabilizing hydropower station is calculated and recorded as dism_b
Figure GDA00026793707000000410
Will dism_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure GDA00026793707000000411
Then the mth wind farm is added to the third unit to get the th
Figure GDA00026793707000000412
A local suppressor subgroup;
let the photovoltaic group contain P photovoltaic stations, and the coordinates of the photovoltaic stations are recorded as
Figure GDA00026793707000000413
P is the number of the optical power station, and P is 1,2, … and P; for the p-th photovoltaic power station, the distance to the b-th locally-stabilized hydropower station is calculated and is recorded as disp_b
Figure GDA00026793707000000414
Will disp_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure GDA00026793707000000415
Then the p-th photovoltaic station is added to the p-th photovoltaic station
Figure GDA00026793707000000416
A subset of local plateaus.
The cooperative optimization scheduling scheme of the hydropower station group based on the regulation performance provided by the invention divides the hydropower group and the cooperative operation combination of the wind power group and the photoelectric group by automatically analyzing the regulation performance of hydropower and the spatial distribution characteristics of the power station group, and provides a new division method, the result is simple and clear, and the implementation is simple and easy. In the dispatching optimization application of a complex power system containing multiple types of power supplies, the regulating performance of hydropower and the space distance between power station groups can be automatically judged by taking the hydropower regulating reservoir capacity, the runoff and the spatial distribution coordinates of the power station groups as input, the cooperative operation echelon combination is divided, and the cooperative operation optimization scales of the multiple power supplies are further refined from the power supplies to the power supplies. Compared with the prior art, the cooperative operation optimization scheduling based on the adjusting performance of water and electricity and the space distance of a power station is provided for the first time, the refinement degree of the cooperative operation optimization of multiple power sources is improved, the method is an important innovation in the technical field, the scheduling optimization of a complex power system containing multiple power sources is facilitated, the method has an important significance for improving the development and utilization of clean energy, has an important popularization and use value, and has a great market application value.
Drawings
Fig. 1 is a schematic diagram showing the result of dividing the hydroelectric group into stages according to the embodiment of the present invention.
Fig. 2 is a schematic diagram of a scheduling result of the cooperative optimization of the water-wind photovoltaic station group according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be described below with reference to the embodiments of the present invention and the accompanying drawings.
The invention provides a water-wind photovoltaic station group collaborative optimization scheduling method based on adjustment performance, which comprises the following steps: step 1: calculating the reservoir capacity coefficient of the hydropower stations, and setting the number of the hydropower stations in the hydropower group as N, wherein N is more than or equal to 3;
further, the calculation of the hydropower station capacity coefficient in the step 1 is carried out according to the following mode:
in a hydropower station group consisting of two or more hydropower stations in hydraulic connection upstream and downstream, the hydropower stations are called cascade hydropower stations. A certain hydropower group is provided with N (N is more than or equal to 3) cascade hydropower stations, the hydropower stations are sequentially sequenced from upstream to downstream, the sequence number of the hydropower stations is recorded as a cascade sequence number N, N is 1,2, and N, and the coordinate of the hydropower station is recorded as a cascade sequence number N
Figure GDA0002679370700000051
hy is the identity of the water and electricity. The storage capacity coefficient of the nth hydropower station is recorded as betan
Figure GDA0002679370700000052
Wherein
Figure GDA0002679370700000053
For the regulated storage capacity of the nth hydroelectric station, and
Figure GDA0002679370700000054
Figure GDA0002679370700000055
the normal water storage level of the nth hydropower station corresponds to the storage capacity,
Figure GDA0002679370700000056
corresponding to the dead water level of the nth hydropower station, WnThe mean runoff over years for the nth hydroelectric power station,
Figure GDA0002679370700000057
wherein
Figure GDA0002679370700000058
Is the average runoff over years for the nth hydroelectric station,
Figure GDA0002679370700000059
is the average annual second number of a plurality of years,
Figure GDA00026793707000000510
referring to fig. 1, a hydroelectric group contains 5 cascade hydroelectric stations, and the coordinates of the 1 st hydroelectric station are recorded
Figure GDA00026793707000000511
Coordinates of the 2 nd hydropower station are recorded as
Figure GDA00026793707000000512
Coordinates of the 3 rd hydroelectric station are recorded
Figure GDA00026793707000000513
Coordinates of the 4 th hydropower station are recorded as
Figure GDA00026793707000000514
Coordinates of the 5 th hydropower station are recorded as
Figure GDA00026793707000000515
Step 2: the hydropower cascade is segmented into A segments, wherein A is more than or equal to 1;
further, the step 2 of segmenting the hydroelectric step is carried out according to the following modes:
the invention divides the whole cascade into a plurality of sections according to the global stabilizing hydropower station, firstly extracts the global stabilizing hydropower station: marking the maximum value of the storage capacity coefficient obtained in the step 1 as betamaxThe largest value of the storage capacity coefficient is recorded as beta2nd(ii) a Reservoir capacity coefficient beta for each hydropower station obtained in step 1nWill betanThe hydropower station with the size of more than or equal to lambda is called a global stabilizing hydropower station, and the step serial number of the a-th global stabilizing hydropower station is marked as gloaA, a is the total number of the global stabilizing hydropower stations, λ is a stabilizing function discrimination coefficient, and 0<λ≤βmax(ii) a For ease of distinction, the non-global stabilizing hydropower stations are referred to as local stabilizing hydropower stations, and the b-th local stabilizing hydropower station has a step number of lcobB is the total number of the local stabilizing hydropower stations.
If λ>β2ndIf A is 1, the segmentation is not needed in the case, all the hydropower stations are regarded as a cascade stage, and the hydropower cascade stage segmentation is not needed;
if λ ≦ β2ndThen A is>1, performing hydropower cascade segmentation, namely, segmenting the 1 st hydropower station to the glo according to the cascade sequence number of the hydropower station1Dividing each hydropower station into 1 st step and dividing the glo1+1 hydropower station to glo2Individual hydroelectric plants are divided into 2 nd step, …, the gloa-1+1 hydropower station to gloaThe individual hydroelectric plants are divided into the a-th step, …, and glo is dividedA-1+1 to Nth hydropower station is designated as gloAA step section.
A number of step levels can be obtained in the step, and each step level comprises a global stabilizing hydropower station. During specific implementation, a person skilled in the art can preset the value of the lambda according to the requirement of the power system on the hydropower station group to bear the global stabilizing function, and the larger the requirement is, the smaller the lambda value is. Global settling is the peak clipping valley filling in the field, and local settling is the output fluctuation settling in the field.
Referring to fig. 1, in the embodiment, the dividing result of the hydroelectric group step is a to 2, and two steps are obtained.
And step 3: initially establishing a local peaceful subgroup and a global peaceful subgroup;
further, step 3, the initial building of the local and global stationary subgroups is performed as follows:
each local stabilizing hydropower station obtained in the step 2 is independently constructed into local stabilizing subgroups, and then B local stabilizing subgroups can be obtained; and (3) building all the hydropower stations in the A step sections obtained in the step (2) into a global stabilizing subgroup, so as to obtain the A global stabilizing subgroups.
And 4, step 4: dividing a local stabilizing subgroup of the hydropower group, the wind power group and the photoelectric group which operate cooperatively; and (3) further dividing the local stabilizing subgroups according to the initial building result of the local stabilizing subgroups and the distance between the hydropower station and the wind power station or the photoelectric station, wherein the number of the wind power stations in the wind power group is set to be M, M is larger than or equal to 3, the number of the photoelectric fields in the photoelectric group is set to be P, and P is larger than or equal to 3.
Further, in the step 4, the local stabilizing subgroup division of the cooperative operation of the hydroelectric group, the wind power group and the photovoltaic group is performed as follows:
step 4.1, the wind power group comprises M wind power plants, and the coordinates of the wind power plants are recorded as
Figure GDA0002679370700000061
M is the serial number of the wind power plant, M is 1,2, …, and M, w are the identifications of the wind power. For the mth wind farm, the distance between the mth wind farm and the mth local stabilizing hydropower station is calculated and is recorded as dism_b
Figure GDA0002679370700000062
Will dism_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure GDA0002679370700000063
Then the mth wind farm is added to the mth wind farm obtained in step 3
Figure GDA0002679370700000064
A subset of local plateaus.
Step 4.2, the photoelectric group comprises P photoelectric stations, and the coordinates of the photoelectric stations are recorded as
Figure GDA0002679370700000071
P is the serial number of the photovoltaic station, P is 1,2, …, and P, ph are photoelectric identifiers. For the p-th photovoltaic power station, the distance between the p-th photovoltaic power station and the b-th local stabilizing hydropower station is calculated and is recorded as disp_b
Figure GDA0002679370700000072
Will disp_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure GDA0002679370700000073
Then the p-th photovoltaic station is added to the p-th photovoltaic station
Figure GDA0002679370700000074
A subset of local plateaus. So far, step 4 may obtain updated B local settlement subgroups, each local settlement subgroup having one local settlement hydropower station.
And 5: global stabilizing subgroup for dividing hydropower group and cooperatively operating wind power group and photoelectricity group
Further, in step 5, the global stabilizing subgroup division of the cooperative operation of the hydroelectric group, the wind power group and the photovoltaic group is performed as follows:
and (4) correspondingly adding the B local stabilizing subgroups obtained in the step (4) to the global stabilizing subgroup to which the hydropower station belongs by taking the hydropower stations in the group as identification marks. Thus, step 5 may obtain updated a global inhibitor subgroups. And the B local stabilizing subgroups in the step 4 and the A global stabilizing subgroups in the step 5 are the optimal scheduling results of the cooperative operation echelon of the hydroelectric and wind power station group based on the hydroelectric regulation performance.
The step obtains the optimal scheduling result of the cooperative operation echelon of the water-wind photovoltaic station group shown in the figure 2. Referring to fig. 2, in the embodiment, the dividing result of the hydropower group ladder section is a-2 and B-3, and three parts are obtainedA first partial suppressor subgroup comprising hydroelectric power stations
Figure GDA0002679370700000075
Wind power station
Figure GDA0002679370700000076
And a photovoltaic station
Figure GDA0002679370700000077
The first local subgroup of suppressors is added to the initial first global subgroup of suppressors
Figure GDA0002679370700000078
The first global suppressor subgroup obtained is a hydropower station
Figure GDA0002679370700000079
Wind power station
Figure GDA00026793707000000710
And a photovoltaic station
Figure GDA00026793707000000711
The second partial suppressor subgroup obtained in the same way comprises the hydropower station
Figure GDA00026793707000000712
Wind power station
Figure GDA00026793707000000713
And a photovoltaic station
Figure GDA00026793707000000714
Third local suppressor subgroup hydroelectric station
Figure GDA00026793707000000715
Wind power station
Figure GDA00026793707000000716
And a photovoltaic station
Figure GDA00026793707000000717
All add an initial second global suppressor subgroup
Figure GDA00026793707000000718
The second global suppressor subgroup obtained was a hydropower station
Figure GDA00026793707000000719
Figure GDA00026793707000000720
Wind power station
Figure GDA00026793707000000721
And a photovoltaic station
Figure GDA00026793707000000722
Fig. 2 shows that the technical scheme provided by the invention is simple, clear and feasible, and the validity of the technical scheme provided by the invention is verified.
In specific implementation, the method provided by the invention can realize automatic operation flow based on software technology, and can also realize a corresponding system in a modularized mode. The embodiment of the invention provides a water-wind photoelectric station group collaborative optimization scheduling system based on regulation performance, which comprises the following units:
the hydropower station storage capacity calculation method comprises a first unit, wherein the first unit is used for calculating a hydropower station storage capacity coefficient, the number of hydropower stations in a hydropower group is N, N is more than or equal to 3, the hydropower stations are sequentially sequenced from upstream to downstream, the sequence number of the hydropower stations is recorded as a step sequence number N, N is 1,2
Figure GDA0002679370700000081
The storage capacity coefficient of the nth hydropower station is recorded as betan
The second unit is used for the subsection of the hydroelectric cascade and is divided into A sections, A is more than or equal to 1, the subsection is carried out according to the following mode,
firstly, extracting a global stabilizing hydropower station, and marking the maximum value of the storage capacity coefficient obtained by a first unit as betamaxThe largest value of the storage capacity coefficient is recorded as beta2nd(ii) a For each of the hydroelectric power stations obtained by the first unitCoefficient of storage capacity betanWill betanThe hydropower station with the size of more than or equal to lambda is called a global stabilizing hydropower station, and the step serial number of the a-th global stabilizing hydropower station is marked as gloaA, a is the total number of the global stabilizing hydropower stations, λ is a stabilizing function discrimination coefficient, and 0<λ≤βmax(ii) a The non-global stabilizing hydropower station is called a local stabilizing hydropower station, and the cascade number of the b-th local stabilizing hydropower station is lcobB is the total number of the local stabilizing hydropower stations;
if λ>β2ndIf A is 1, all the hydropower stations are regarded as a step section; if λ ≦ β2ndThen A is>1, the 1 st hydropower station to the glo according to the cascade sequence number of the hydropower stations1Dividing each hydropower station into 1 st step and dividing the glo1+1 hydropower station to glo2Individual hydroelectric plants are divided into 2 nd step, …, the gloa-1+1 hydropower station to gloaThe individual hydroelectric plants are divided into the a-th step, …, and glo is dividedA-1+1 to Nth hydropower station is designated as gloAA step section; obtaining A steps, wherein each step comprises a global stabilizing hydropower station;
a third unit for initially building a local and global stationarity subgroup, realized as follows,
each local stabilizing hydropower station obtained by the second unit is independently constructed into local stabilizing subgroups, and B local stabilizing subgroups can be obtained; building all hydropower stations in each step section of the A step sections obtained by the second unit into a global stabilizing subgroup to obtain A global stabilizing subgroups;
the fourth unit is used for dividing the local stabilizing subgroups of the hydropower station, the wind power station and the photoelectric group which run cooperatively, and further dividing the local stabilizing subgroups according to the initial building result of the local stabilizing subgroups of the third unit and the distance between the hydropower station and the wind power station or the photoelectric station;
a fifth unit, configured to divide a global stabilizing subgroup in which the hydropower station group, the wind power group, and the photovoltaic group operate cooperatively, where the fifth unit includes adding the B local stabilizing subgroups obtained in step 4 to the global stabilizing subgroup to which the hydropower station belongs, using the hydropower stations in the group as identification tags, and obtaining updated a global stabilizing subgroups; the B local stabilizing subgroups obtained by the fourth unit and the A global stabilizing subgroups obtained by the fifth unit are optimal scheduling results of the cooperative operation echelon of the hydroelectric and wind power station group based on hydroelectric regulation performance.
The specific implementation of each module can refer to the corresponding step, and the detailed description of the invention is omitted.
According to the embodiment results, the technical scheme provided by the invention provides the optimal scheduling result of the water-wind photovoltaic station group cooperative operation echelon, and the effectiveness of the invention is illustrated. Therefore, the method can automatically and effectively analyze the regulation performance of hydropower and the space distribution characteristics of the power station group, carry out optimized scheduling based on the regulation performance and the space distribution characteristics, refine the optimized scale of the cooperative operation of various power supplies from the power supplies to the power supplies, and provide decision support for the optimized scheduling of the power system containing various power supplies.
The invention is mainly applied to the optimal scheduling of the cooperative operation echelon of the water-wind-power photoelectric station group, and in the optimal scheduling application of an electric power system containing various types of power supplies, the regulation performance of water and electricity and the space distance between the power station groups can be automatically judged by taking the water and electricity regulation reservoir capacity, the runoff and the space distribution coordinates of the power station groups as input, the cooperative operation echelon combination is divided, and the cooperative operation optimization scale of various power supplies is further refined from the power supplies to the inside of the power supplies. Compared with the prior art, the invention is innovative in that the cooperative operation optimization scheduling based on the regulation performance of hydropower and the space distance of the power station is firstly provided. In view of this, the invention and the prior art are simultaneously applied to the optimized scheduling of the cooperative operation of the water-wind photovoltaic station group, so as to verify the rationality of the technical scheme of the invention.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but other embodiments derived from the technical solutions of the present invention by those skilled in the art are also within the scope of the present invention.

Claims (4)

1. A water-wind photovoltaic station group collaborative optimization scheduling method based on regulation performance is characterized in that in the optimized scheduling application of an electric power system containing multiple types of power supplies, the following procedures are operated in a software mode:
step 1, calculating a reservoir capacity coefficient of the hydropower stations, setting the number of the hydropower stations in the hydropower group to be N, wherein N is more than or equal to 3, sequencing the hydropower stations from upstream to downstream in sequence, recording the serial number of the hydropower stations as a step serial number N, and recording the coordinate of the hydropower stations as a step serial number N, wherein N is 1,2
Figure FDA0002679370690000011
The storage capacity coefficient of the nth hydropower station is recorded as betan
Step 2, the hydropower cascade segmentation is divided into a segment A, wherein A is more than or equal to 1, the segmentation is carried out according to the following mode,
firstly, extracting a global stabilizing hydropower station, and marking the maximum value of the storage capacity coefficient obtained in the step 1 as betamaxThe largest value of the storage capacity coefficient is recorded as beta2nd(ii) a Reservoir capacity coefficient beta for each hydropower station obtained in step 1nWill betanThe hydropower station with the size of more than or equal to lambda is called a global stabilizing hydropower station, and the step serial number of the a-th global stabilizing hydropower station is marked as gloaA, a is the total number of the global stabilizing hydropower stations, λ is a stabilizing function discrimination coefficient, and 0<λ≤βmax(ii) a The non-global stabilizing hydropower station is called a local stabilizing hydropower station, and the cascade number of the b-th local stabilizing hydropower station is lcobB is the total number of the local stabilizing hydropower stations;
if λ>β2ndIf A is 1, all the hydropower stations are regarded as a step section; if λ ≦ β2ndThen A is>1, the 1 st hydropower station to the glo according to the cascade sequence number of the hydropower stations1Dividing each hydropower station into 1 st step and dividing the glo1+1 hydropower station to glo2Individual hydroelectric plants are divided into 2 nd step, …, the gloa-1+1 hydropower station to gloaThe individual hydroelectric plants are divided into the a-th step, …, and glo is dividedA-1+1 to Nth hydropower station is designated as gloAA step section; obtaining A steps, wherein each step comprises a global stabilizing hydropower station;
step 3, initially establishing a local stabilizing subgroup and a global stabilizing subgroup, wherein the implementation mode is as follows,
each local stabilizing hydropower station obtained in the step 2 is independently constructed into local stabilizing subgroups, and B local stabilizing subgroups can be obtained; building all the hydropower stations in each step section of the A step sections obtained in the step 2 into a global stabilizing subgroup to obtain A global stabilizing subgroups;
step 4, dividing a local stabilizing subgroup of the hydropower group, the wind power group and the photoelectric group which run cooperatively, wherein the local stabilizing subgroup is further divided according to the initial building result of the local stabilizing subgroup in the step 3 and the distance between the hydropower station and the wind power plant or the photoelectric station;
in step 4, the local flattening sub-group division is performed as follows,
step 4.1, setting that the wind power group comprises M wind power plants, and recording the coordinates of the wind power plants as
Figure FDA0002679370690000012
M is the serial number of the wind power plant, and M is 1,2, … and M; for the mth wind farm, the distance to the mth local stabilizing hydropower station is calculated and recorded as dism_b
Figure FDA0002679370690000013
Will dism_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure FDA0002679370690000014
Then the mth wind farm is added to the mth wind farm obtained in step 3
Figure FDA0002679370690000015
A local suppressor subgroup;
step 4.2, the photoelectric group is set to contain P photoelectric stations, and the coordinates of the photoelectric stations are recorded as
Figure FDA0002679370690000021
P is the number of the optical power station, and P is 1,2, … and P; for the p-th photovoltaic station, the b-th local stabilizing hydropower station is calculatedDistance between stations, denoted disp_b
Figure FDA0002679370690000022
Will disp_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure FDA0002679370690000023
Then the p-th photovoltaic station is added to the p-th photovoltaic station
Figure FDA0002679370690000024
A local suppressor subgroup;
step 5, dividing the global stabilizing subgroups of the hydropower group, the wind power group and the photoelectric group which run in a coordinated mode, wherein the step 4 comprises the step of correspondingly adding B local stabilizing subgroups to the global stabilizing subgroups to which the hydropower stations belong by taking the hydropower stations in the groups as identification marks to obtain updated A global stabilizing subgroups; and B local stabilizing subgroups obtained in the step 4 and A global stabilizing subgroups obtained in the step 5 are water-wind photovoltaic station group cooperative operation echelon optimization scheduling results based on hydropower regulation performance, and multiple power supply cooperative operation optimization scales are further refined from power supplies to the interior of the power supplies.
2. The performance-adjusting-based water-wind photovoltaic station group collaborative optimization scheduling method according to claim 1, characterized in that: in the step 1, the calculation mode of the capacity coefficient of the hydropower station is,
Figure FDA0002679370690000025
wherein
Figure FDA0002679370690000026
For the regulated storage capacity of the nth hydroelectric station, and
Figure FDA0002679370690000027
Figure FDA0002679370690000028
the normal water storage level of the nth hydropower station corresponds to the storage capacity,
Figure FDA0002679370690000029
corresponding to the dead water level of the nth hydropower station, WnThe mean runoff over years for the nth hydroelectric power station,
Figure FDA00026793706900000210
wherein
Figure FDA00026793706900000211
Is the average runoff over years for the nth hydroelectric station,
Figure FDA00026793706900000212
is the average annual second of many years.
3. A coordinated optimization scheduling system of a water-wind photovoltaic station group based on regulation performance is characterized by being used for optimization scheduling application of an electric power system containing multiple types of power supplies and comprising the following units:
the hydropower station storage capacity calculation method comprises a first unit, wherein the first unit is used for calculating a hydropower station storage capacity coefficient, the number of hydropower stations in a hydropower group is N, N is more than or equal to 3, the hydropower stations are sequentially sequenced from upstream to downstream, the sequence number of the hydropower stations is recorded as a step sequence number N, N is 1,2
Figure FDA00026793706900000213
The storage capacity coefficient of the nth hydropower station is recorded as betan
The second unit is used for the subsection of the hydroelectric cascade and is divided into A sections, A is more than or equal to 1, the subsection is carried out according to the following mode,
firstly, extracting a global stabilizing hydropower station, and marking the maximum value of the storage capacity coefficient obtained by a first unit as betamaxThe largest value of the storage capacity coefficient is recorded as beta2nd(ii) a The reservoir capacity coefficient beta of each hydropower station obtained for the first unitnWill betanThe hydropower station with the size of more than or equal to lambda is called a global stabilizing hydropower station, and the step sequence number of the a-th global stabilizing hydropower station is markedIs gloaA, a is the total number of the global stabilizing hydropower stations, λ is a stabilizing function discrimination coefficient, and 0<λ≤βmax(ii) a The non-global stabilizing hydropower station is called a local stabilizing hydropower station, and the cascade number of the b-th local stabilizing hydropower station is lcobB is the total number of the local stabilizing hydropower stations;
if λ>β2ndIf A is 1, all the hydropower stations are regarded as a step section; if λ ≦ β2ndThen A is>1, the 1 st hydropower station to the glo according to the cascade sequence number of the hydropower stations1Dividing each hydropower station into 1 st step and dividing the glo1+1 hydropower station to glo2Individual hydroelectric plants are divided into 2 nd step, …, the gloa-1+1 hydropower station to gloaThe individual hydroelectric plants are divided into the a-th step, …, and glo is dividedA-1+1 to Nth hydropower station is designated as gloAA step section; obtaining A steps, wherein each step comprises a global stabilizing hydropower station;
a third unit for initially building a local and global stationarity subgroup, realized as follows,
each local stabilizing hydropower station obtained by the second unit is independently constructed into local stabilizing subgroups, and B local stabilizing subgroups can be obtained; building all hydropower stations in each step section of the A step sections obtained by the second unit into a global stabilizing subgroup to obtain A global stabilizing subgroups;
the fourth unit is used for dividing the local stabilizing subgroups of the hydropower station, the wind power station and the photoelectric group which run cooperatively, and further dividing the local stabilizing subgroups according to the initial building result of the local stabilizing subgroups of the third unit and the distance between the hydropower station and the wind power station or the photoelectric station;
in the fourth cell, the local flattening sub-group division is performed as follows,
let the wind power group contain M wind power plants, and the coordinates of the wind power plants are recorded as
Figure FDA0002679370690000031
M is the serial number of the wind power plant, and M is 1,2, … and M; for the mth wind farm, calculate anddistance between the b-th locally stabilised hydroelectric station, denoted dism_b
Figure FDA0002679370690000032
Will dism_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure FDA0002679370690000033
Then the mth wind farm is added to the third unit to get the th
Figure FDA0002679370690000034
A local suppressor subgroup;
let the photovoltaic group contain P photovoltaic stations, and the coordinates of the photovoltaic stations are recorded as
Figure FDA0002679370690000035
P is the number of the optical power station, and P is 1,2, … and P; for the p-th photovoltaic power station, the distance to the b-th locally-stabilized hydropower station is calculated and is recorded as disp_b
Figure FDA0002679370690000036
Will disp_bThe sequence number of the local stabilizing hydropower station step corresponding to the minimum value is marked as
Figure FDA0002679370690000037
Then the p-th photovoltaic station is added to the p-th photovoltaic station
Figure FDA0002679370690000038
A local suppressor subgroup;
a fifth unit, configured to divide a global stabilizing subgroup in which the hydropower station group, the wind power group, and the photovoltaic group operate cooperatively, where the fifth unit includes adding the B local stabilizing subgroups obtained in step 4 to the global stabilizing subgroup to which the hydropower station belongs, using the hydropower stations in the group as identification tags, and obtaining updated a global stabilizing subgroups; b local stabilizing subgroups obtained by the fourth unit and A global stabilizing subgroups obtained by the fifth unit are water-wind photoelectric station group cooperative operation echelon optimization scheduling results based on hydropower regulation performance, and multiple power supply cooperative operation optimization scales are further refined from power supplies to power supplies.
4. The performance-adjusting-based water-wind photovoltaic station group collaborative optimization scheduling system according to claim 3, wherein: in the first unit, the reservoir capacity coefficient of the hydropower station is calculated in such a manner that,
Figure FDA0002679370690000039
wherein
Figure FDA00026793706900000310
For the regulated storage capacity of the nth hydroelectric station, and
Figure FDA00026793706900000311
Figure FDA00026793706900000312
the normal water storage level of the nth hydropower station corresponds to the storage capacity,
Figure FDA00026793706900000313
corresponding to the dead water level of the nth hydropower station, WnThe mean runoff over years for the nth hydroelectric power station,
Figure FDA00026793706900000314
wherein
Figure FDA00026793706900000315
Is the average runoff over years for the nth hydroelectric station,
Figure FDA0002679370690000041
is the average annual second of many years.
CN201710861481.9A 2017-09-21 2017-09-21 Water-wind photoelectric station group collaborative optimization scheduling method and system based on adjustment performance Active CN107506873B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710861481.9A CN107506873B (en) 2017-09-21 2017-09-21 Water-wind photoelectric station group collaborative optimization scheduling method and system based on adjustment performance

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710861481.9A CN107506873B (en) 2017-09-21 2017-09-21 Water-wind photoelectric station group collaborative optimization scheduling method and system based on adjustment performance

Publications (2)

Publication Number Publication Date
CN107506873A CN107506873A (en) 2017-12-22
CN107506873B true CN107506873B (en) 2020-10-30

Family

ID=60697749

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710861481.9A Active CN107506873B (en) 2017-09-21 2017-09-21 Water-wind photoelectric station group collaborative optimization scheduling method and system based on adjustment performance

Country Status (1)

Country Link
CN (1) CN107506873B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109472715B (en) * 2018-11-08 2020-12-29 湖南万盟环境科技有限公司 Digital industrial energy management and control service system
CN109886473B (en) * 2019-01-24 2020-05-05 河海大学 Watershed wind-solar water system multi-objective optimization scheduling method considering downstream ecology
CN117543721B (en) * 2024-01-05 2024-03-15 河海大学 Optimized scheduling method, device, equipment and medium for cascade water wind-solar complementary system

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3892433A (en) * 1973-09-21 1975-07-01 Martin Marietta Corp Direct solar hydro-electric integrated system and concentrating heliostat for same
CN102184472A (en) * 2011-05-03 2011-09-14 西安交通大学 Wind, water and fire united dispatching method based on power grid dispatching side demand
CN104158217A (en) * 2014-08-22 2014-11-19 东北电力大学 Output power fluctuation characteristic description method for clustered wind-solar combined power generation system
CN106786802A (en) * 2017-02-15 2017-05-31 中国能源建设集团江苏省电力设计院有限公司 A kind of photovoltaic plant based on CCHP is exerted oneself quick regulator control system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9300141B2 (en) * 2010-11-18 2016-03-29 John J. Marhoefer Virtual power plant system and method incorporating renewal energy, storage and scalable value-based optimization

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3892433A (en) * 1973-09-21 1975-07-01 Martin Marietta Corp Direct solar hydro-electric integrated system and concentrating heliostat for same
CN102184472A (en) * 2011-05-03 2011-09-14 西安交通大学 Wind, water and fire united dispatching method based on power grid dispatching side demand
CN104158217A (en) * 2014-08-22 2014-11-19 东北电力大学 Output power fluctuation characteristic description method for clustered wind-solar combined power generation system
CN106786802A (en) * 2017-02-15 2017-05-31 中国能源建设集团江苏省电力设计院有限公司 A kind of photovoltaic plant based on CCHP is exerted oneself quick regulator control system

Also Published As

Publication number Publication date
CN107506873A (en) 2017-12-22

Similar Documents

Publication Publication Date Title
Miranda et al. Technical-economic potential of PV systems on Brazilian rooftops
CN107506873B (en) Water-wind photoelectric station group collaborative optimization scheduling method and system based on adjustment performance
CN113435923B (en) Power consumption prediction method and device and electronic equipment
CN109740808B (en) Wind-solar-water complementary power generation plan calculation method and system
CN113128113B (en) Lean information building load prediction method based on deep learning and transfer learning
CN107480825B (en) Photovoltaic power station optimization planning method considering capacity credibility
Mukhoty et al. Sequence to sequence deep learning models for solar irradiation forecasting
CN104638672A (en) Determining method of photovoltaic transmission power limit considering variable correlation
CN109978277B (en) Regional internet load prediction method and device in photovoltaic power generation
Pfluger et al. Impact of renewable energies on conventional power generation technologies and infrastructures from a long-term least-cost perspective
CN113052389A (en) Distributed photovoltaic power station ultra-short-term power prediction method and system based on multiple tasks
CN103500997B (en) Electric power system dispatching method based on hybrid multi-objective lambda iteration method and Newton method
CN113872253A (en) Pumped storage power station and new energy combined power generation optimal scheduling method and device
Li et al. Research on short-term joint optimization scheduling strategy for hydro-wind-solar hybrid systems considering uncertainty in renewable energy generation
CN103366225A (en) Wind power prediction error identification method
CN116780643B (en) Confidence output calculation method and system for new energy participation in electric power balance
CN105512763A (en) Method and system for predicting photovoltaic power station middle-short term power generation
CN109193772B (en) Energy storage optimal configuration system and method based on wind-solar micro-grid
CN109409604B (en) Cold load prediction method based on genetic algorithm-support vector machine
CN110247399A (en) A kind of power distribution network photovoltaic maximum consumption method and system based on Monte Carlo simulation
CN112448411A (en) Method for planning gathering station site selection and delivery capacity of multi-wind power plant access system
CN114330923A (en) Photovoltaic power generation power prediction method based on public meteorological data
CN104331748A (en) Method for forecasting continuous power curve of wind power plant group in planning target year
CN110717694B (en) Energy storage configuration random decision method and device based on new energy consumption expected value
CN109149644B (en) Light-storage integrated online strategy matching and collaborative optimization method based on big data analysis

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