CN114297249B - New energy project optimal selection ordering method considering power grid demand degree - Google Patents
New energy project optimal selection ordering method considering power grid demand degree Download PDFInfo
- Publication number
- CN114297249B CN114297249B CN202111669119.4A CN202111669119A CN114297249B CN 114297249 B CN114297249 B CN 114297249B CN 202111669119 A CN202111669119 A CN 202111669119A CN 114297249 B CN114297249 B CN 114297249B
- Authority
- CN
- China
- Prior art keywords
- project
- wind power
- photovoltaic
- mode
- power
- 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
Links
Images
Classifications
-
- 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
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a method for optimizing and sequencing new energy projects. And collecting the existing and built new energy installation project conditions of each partition, acquiring the grid structure, load data, power distribution and power output conditions of various types of power in the planning year, and providing a basic database for load flow calculation. And determining the regional bearing capacity under the conditions of regional wind power and photovoltaic 'N-1' and in a normal mode sequentially through load flow calculation and 'N-1' check, and providing a criterion for determining the demand of a new energy project on a power grid. The method has the advantages that the condition of accessing the new energy project into the power grid and the resource quality are combined, the influence of the demand degree of the power grid on the new energy project is innovatively considered, the cooperativity of new energy project development and power grid construction is improved, and more scientific and reliable guidance is provided for reasonable and ordered development of new energy under the 'double-carbon' background.
Description
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a new energy project optimal sorting method considering power grid demand.
Background
By researching the influence analysis of the new energy project on the power grid demand degree, the power grid planning construction can be further coordinated to be adaptive to the new energy development. The demand degree of a power grid is considered, the new energy projects are preferably sequenced, scientific reference can be provided for further coordinating the development scale and speed of the new energy, and the method has important significance for constructing the development of a novel power system taking the new energy as a main body.
At present, the development of new energy of a regional power grid mainly focuses on a bearing capacity measuring and calculating method, and two types of models are mainly adopted: one is based on different cooperation strategies of new energy and energy storage or other peak regulation power supplies, and the consumption capacity of the new energy is evaluated according to the determined power supply structure and the peak regulation capacity; the other type is developed from the viewpoint of considering the peak shaving capacity of the power grid. And analyzing the electric power and electric quantity balance condition of the whole power grid to determine the new energy consumption capacity of the power grid. At present, a method for multi-factor comprehensive optimal sequencing of new energy projects considering the demand degree of a power grid is lacked.
In view of this, a new energy project optimal sorting method considering the power grid demand degree is researched, multiple factors are integrated to conduct optimal sorting on new energy projects, and the method has important practical value for solving the practical difficulties faced by planning designers and project decision makers.
Disclosure of Invention
Aiming at the technical problems, the invention provides a new energy project optimal selection ordering method considering the power grid demand degree.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a new energy project optimal sorting method considering power grid demand degree comprises the following steps:
step S100: acquiring power grid data information, counting and planning new energy project distribution information, partitioning and sequencing project resources according to the power grid data information and the new energy project distribution information to obtain a partition project sequencing table;
step S200: collecting the existing and new energy installation project conditions of each partition, acquiring a grid structure, load data, various power supply distributions and power supply output conditions of a planned year, and obtaining a basic database in a small mode and a large noon mode;
step S300: carrying out load flow calculation by using a basic database in a small-scale mode, adjusting the scale of wind power installation in a region, obtaining the bearing capacity of regional wind power meeting the requirement of N-1 by using the load flow calculation and the N-1 calculation, adjusting the scale of the wind power installation in the region, obtaining the bearing capacity of regional wind power meeting normal delivery by using the load flow calculation, and judging the power grid demand degree of the wind power projects in the partition project sequencing table by combining the bearing capacity of regional wind power under the N-1 condition and the bearing capacity of regional wind power meeting the normal delivery;
step S400: on the basis that each partition obtained in the step S300 meets the internal wind power bearing capacity under the condition of ' N-1 ', performing load flow calculation by using a basic database under a Feng ' S noon mode, adjusting the scale of the photovoltaic installation in the region, obtaining the regional photovoltaic bearing capacity under the condition of meeting ' N-1 ' by using the load flow calculation and the ' N-1 ' calculation, adjusting the scale of the photovoltaic installation in the region, obtaining the regional photovoltaic bearing capacity meeting normal delivery by using the load flow calculation, and performing power grid demand judgment on the photovoltaic projects in the partition project sequencing table by combining the regional photovoltaic bearing capacity under the condition of ' N-1 ' and the regional wind power bearing capacity meeting normal delivery;
step S500: and sequencing the new energy projects by using a comprehensive optimization sequencing method by combining the partition project sequencing table, the power grid demand of the wind power project and the power grid demand of the photovoltaic project to obtain a new energy project optimization sequencing table.
Preferably, step S100 includes:
step S110: acquiring power grid data information and counting new energy project information, wherein the new energy project information comprises installed capacity of each project, geographical position of each project, resource information of each project and access power grid distance of each project;
step S120: obtaining the goodness and the badness of each project resource according to each project resource information, partitioning the new energy project resources according to the power grid data information and the geographic position of each project, and obtaining a new energy project condition table under each partition according to the installed capacity of each project and the goodness and the badness of each project resource;
step S130: and sequencing the new energy projects in the subareas according to the access distance of each project to the power grid to obtain a subarea project sequencing list.
Preferably, step S200 includes:
step S210: collecting project information of existing and new energy installation machines in each partition, wherein the project information comprises the sum of the existing and new energy installation machines in the partition and the sum of the existing and new energy installation machines in the partition;
step S220: acquiring a grid structure, load data, various power distributions and power output information of a planned year, acquiring load data in a small-abundance mode and a large-noon mode according to the grid structure and the load data of the planned year, acquiring the sum of the output of various power supplies except new energy in the small-abundance mode and the sum of the output of various power supplies except new energy in the large-noon mode according to the power distributions and the power output information, and acquiring a wind power total output value in the small-abundance mode, a wind power total output value in the large-noon mode and a photovoltaic total output value in the large-noon mode according to the power output information; obtaining a wind power output coefficient of the Feng mode, a wind power output coefficient of the Feng mode and a photovoltaic output coefficient of the Feng mode according to the wind power total output value of the Feng mode, the photovoltaic total output value of the Feng mode, the sum of the existing partitioned wind power installations and the sum of the existing partitioned photovoltaic installations;
step S230: constructing a basic database under the Feng Xiao mode and the Feng Mian mode according to a grid structure of a planned year, load data under the Feng Xiao mode and the Feng Mian mode, distribution of various power supplies, sum of output of various power supplies except new energy in the Feng Xiao mode, sum of output of various power supplies except new energy in the Feng Mian mode, existing subareas, sum of output of built-in wind power installations, subarea existing and built-in photovoltaic installation sum, feng Mian mode wind power output coefficient, feng Xiao mode wind power output coefficient and Feng Mian mode photovoltaic output coefficient.
Preferably, step S220 specifically includes:
wherein, C fx0 For wind power total output value in small mode, C fw0 For wind power total output value in Feng Wu mode, C gw0 For the Toyomi mode photovoltaic total output value, F 0 For the division of the existing and under construction wind installation assemblies, G 0 For the sum of the existing photovoltaic installation machines in different regions and the photovoltaic installation machines under construction, lambda x is a wind power output coefficient in a small mode, lambda w is a wind power output coefficient in a mid-day mode, and kw is a photovoltaic output coefficient in a mid-day mode.
Preferably, step S300 includes:
step S310: performing load flow calculation according to the basic database in the small and large mode to obtain a basic load flow calculation result in the small and large mode;
step S320: based on a basic load flow calculation result in a small-size mode, sequentially increasing the wind power installed scale in the region according to the item sequence in the partition item sequence table, and obtaining the wind power bearing capacity of the region under the condition of meeting the requirement of N-1 by utilizing load flow calculation and N-1 calculation;
step S330: based on a basic load flow calculation result in a small-size mode, sequentially increasing the installed scale of wind power in the region according to the item sequence in the partition item sequence table, and obtaining the wind power bearing capacity of the region under the condition of meeting the requirement of normal delivery by utilizing load flow calculation;
step S340: and judging the power grid demand degree of the wind power projects in the partition project sequencing list according to the regional wind power bearing capacity under the condition of meeting the 'N-1' requirement and the regional wind power bearing capacity under the condition of meeting the normal sending-out requirement.
Preferably, step S320 is specifically:
F'＝F 0 +F 0 '+F 1 +F 2 …F k1
Z x ＝F 0 ·λ x +F 0 '·λ x +∑F i ·λ x +P x -D x (i∈[1,k 1 ]),
k 1 ∈[1,M]
wherein F ' is regional wind power bearing capacity under the condition of meeting the requirement of ' N-1 ', and F 0 For partitioning of the sum of existing wind installations, F 0 ' wind installation sum under construction for sub-district, F i Planning the installed capacity of the ith wind power project in a subarea, F k1 Plan kth for intra-partition 1 Installed capacity, λ, of individual wind power projects x The wind power output coefficient under the rich and small mode, px is the sum of the output of various power supplies except new energy under the rich and small mode, D x Load data in a small-scale mode, M is the total number of wind power plant items, Z x Surplus of regional electric power in a small and large mode;
step S330 specifically includes:
F”＝F 0 +F 0 '+F 1 +…F k2
Z x ＝F 0 ·λ x +F 0 '·λ x +∑F i *λ x +P x -D x (i∈[1,k 2 ]),
k 2 ∈[k 1 ,M]
wherein F' is the regional wind power bearing capacity under the condition of meeting normal delivery, and F k2 Plan kth for intra-partition 2 Installed capacity of each wind power project.
The step S340 includes:
when the wind power project i belongs to [1, k ] 1 ]Judging that the demand degree of the wind power project on the power grid is A type;
when the wind power project i belongs to (k) 1 ,k 2 ]Judging that the demand degree of the wind power project on the power grid is B type;
when the wind power project i belongs to (k) 2 ,M]And judging that the demand degree of the wind power project on the power grid is type C.
Preferably, step S400 includes:
step S410: carrying out load flow calculation according to a basic database in the Feng noon mode and combining wind power projects with the power grid demand degree of A type to obtain a basic load flow calculation result in the Feng noon mode;
step S420: based on a basic load flow calculation result in a Feng' ang mode, sequentially increasing the photovoltaic installed scale in the region according to item sequencing in a partition item sequencing table, and obtaining the photovoltaic bearing capacity of the region under the condition of meeting the requirement of N-1 by utilizing load flow calculation and N-1 calculation;
step S430: based on a basic load flow calculation result in a Feng' ang mode, sequentially increasing the photovoltaic installed scale in the region according to the item sequence in the partition item sequence table, and obtaining the photovoltaic bearing capacity of the region under the condition of meeting the normal sending condition by utilizing load flow calculation;
step S440: and judging the power grid demand degree of the photovoltaic projects in the partition project sequencing list according to the regional photovoltaic bearing capacity under the condition of meeting the 'N-1' requirement and the regional photovoltaic bearing capacity under the condition of meeting the normal sending-out requirement.
Preferably, step S420 is specifically:
G'＝G 0 +G 0 '+G 1 +…G b1
Z w ＝G 0 ·k w +G 0 '·k w +∑G j ·k w +F'·λ w +P w -D w j∈[1,b 1 ]
b 1 ∈[1,N]
wherein G ' is the regional photovoltaic bearing capacity under the condition of satisfying ' N-1 ', G 0 Is a sub-area of the existing photovoltaic installation sum, G' 0 For zoning under construction photovoltaic installation assembly, G j Planning installed capacity, G, of jth photovoltaic project in a partition b1 Planning within a partition 1 Installed capacity, λ, of individual photovoltaic projects w Feng Wu modeLower wind power output coefficient, k w Is the photovoltaic output coefficient, P, in the Feng noon mode w The sum of the output of various power supplies except new energy in the Feng' ang mode, D w Load data in the mode of Toyobo noon, N is the total number of wind power plant items, Z w Surplus of regional power in the Feng-noon mode;
the step S430 specifically includes:
G”＝G 0 +G 0 '+G 1 +…G b2
Z w ＝G 0 ·k w +G 0 '·k w +∑G j ·k w +F'·λw+P x -D x j∈[1,b 2 ]
b 2 ∈[b 1 ,N]
wherein G' satisfies the regional photovoltaic bearing capacity under normal delivery, G b2 Planning within a partition 2 Installed capacity of individual photovoltaic projects.
Step S440 includes:
when the photovoltaic item j belongs to [1, b ] 1 ]Judging the demand degree of the photovoltaic project on the power grid to be type A;
when the photovoltaic item j belongs to (b) 1 ,b 2 ]Judging the demand degree of the photovoltaic project on the power grid to be type B;
when the photovoltaic item j belongs to the element (b) 2 ,N]And judging that the demand degree of the photovoltaic project on the power grid is type C.
Preferably, step S500 includes:
step S510: calculating the optimal value of each project of the new energy by combining the installed capacity, the access power grid distance and the resource goodness and badness of the new energy project in the partition project ranking table, the power grid demand of the wind power project and the power grid demand of the photovoltaic project;
step S520: and sequencing the new energy projects according to the optimized values of the projects of the new energy.
Preferably, step S510 specifically includes:
Y i ＝αF i +βL fi +δR fi +ηQ fi
Y j ＝αG j +βL gj +δR gj +ηQ gj
wherein, Y i Planning the preferred value of the ith wind power project for the subarea, F i Planning the installed capacity, L, of the ith wind power project in the subarea fi Planning the distance, R, of the ith wind power project to be connected into the power grid in the subarea fi Planning the quality degree, Q, of the ith wind power project resource in the subarea fi Planning the power grid demand degree of the ith wind power project for the subarea; y is j Planning preference value, G, of jth photovoltaic project in subarea j Planning installed capacity, L, of jth photovoltaic project for a zone gj Planning the distance, R, of the j-th photovoltaic project to be connected into the power grid in the subarea gj Planning the quality degree, Q, of the j-th photovoltaic project resource in the subarea gi And planning the power grid demand degree of the jth photovoltaic project in the subarea.
The method for optimizing and sequencing the new energy projects comprises the steps of firstly counting and planning the partition distribution condition of the new energy projects, obtaining a partition project sequencing table by combining with power grid data information, and providing a basis for optimizing and sequencing the new energy projects. And collecting the existing and built new energy installation project conditions of each partition, acquiring the grid structure, load data, power distribution and power output conditions of various types of power in the planning year, and providing a basic database for load flow calculation. And determining the regional bearing capacity under the conditions of regional wind power and photovoltaic 'N-1' and in a normal mode sequentially through load flow calculation and 'N-1' check, and providing a criterion for determining the demand of a new energy project on a power grid. The influence of the demand degree of the power grid on the new energy project is innovatively considered by combining the wind area project sequencing table, the cooperativity of the new energy project development and the power grid construction is improved, and more scientific and reliable guidance is provided for the reasonable and ordered development of the new energy under the 'double-carbon' background.
Drawings
Fig. 1 is a flowchart of a new energy project optimization ranking method considering a grid demand degree according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention is further described in detail below with reference to the accompanying drawings.
In one embodiment, as shown in fig. 1, a new energy project optimization ranking method considering grid demand degree includes the following steps:
step S100: and acquiring power grid data information, counting and planning new energy project distribution information, partitioning and sequencing project resources according to the power grid data information and the new energy project distribution information, and acquiring a partition project sequencing table.
Specifically, the power grid data information refers to the distribution condition of a 500kV transformer substation and the open-loop condition of power grid operation.
In one embodiment, step S100 includes:
step S110: acquiring power grid data information and counting new energy project information, wherein the new energy project information comprises installed capacity of each project, geographical position of each project, resource information of each project and access distance of each project to a power grid;
step S120: obtaining the goodness and the badness of each project resource according to each project resource information, partitioning the new energy project resources according to the power grid data information and the geographic position of each project, and obtaining a new energy project condition table under each partition according to the installed capacity of each project and the goodness and the badness of each project resource;
step S130: and sequencing the new energy projects in the subareas according to the access distance of each project to the power grid to obtain a subarea project sequencing list.
Specifically, the resource information of each project is used to obtain the goodness and badness of the resource, for example, the resource information of the wind power refers to indexes such as wind speed and wind resource density. The photovoltaic resource information refers to a series of conditions such as a total solar radiation value and the like, and the quality of the resource can be obtained according to the resource information. The photoelectric items and the partitioned photovoltaics need to be sorted respectively, and the partition item sorting table obtained in step S130 specifically includes:
partition 1 wind power project sequencing table
Zone 1 photovoltaic project sequencing table
| Photovoltaic project numbering | Photovoltaic project capacity | Distance to grid | Degree of superiority and inferiority of resource |
| 1 | G 1 | L g1 | R g1 |
| 2 | G 2 | L g2 | R g2 |
| … | … | … | … |
| j | G j | L gj | R gj |
| … | … | … | … |
| N | G N | L gN | R gN |
Wherein L is f1 ＜L f2 ＜...L fi ＜...L fM
L g1 ＜L g2 ＜...L gj ＜...L gN
Wherein, F 1 、F 2 …F i …F M Planning the installed capacity, L, of the ith wind power project in the subarea f1 、L f2 …L fi …L fM Planning the distance, R, of the ith wind power project to be connected into the power grid in the subarea f1 、R f2 …R fi …R fM And planning the quality degree of the ith wind power project resource in the partition.
Wherein, G 1 、G 2 …G j …G N Planning installed capacity, L, of jth photovoltaic project for a zone g1 、L g2 …L gj …L gN Planning the distance, R, of the j-th photovoltaic project to be connected into the power grid in the subarea g1 、R g2 …R gj …R gN And planning the resource quality degree of the jth photovoltaic project in the subarea.
Step S200: collecting the existing and new energy installation project conditions of each partition, acquiring the grid structure, load data, various power supply distributions and power supply output conditions of the planned year, and obtaining a basic database in the small and large modes and the mid-day mode.
Specifically, the small-size mode refers to a specific mode (generally 2 to 00 in the morning in 3 to 5 months. The Feng noon mode refers to a specific mode (generally, 12-00 noon in 3-5 months) in the midstream of the water-laden period, and in the mode, the wind power output is slightly smaller than that in the Feng noon mode, the photovoltaic output is larger, the area delivery is larger, and the mode can be used for determining the maximum bearing capacity of the photovoltaic installation.
Acquiring a grid structure (including substation parameters and line parameters), load data (of each substation), power distribution conditions (which substation each type of substation is connected to) and power output conditions of a planned year, establishing a basic power flow calculation database in a Fengmu mode and a Fengmen mode, and performing power flow calculation and 'N-1' checking by using special software such as BPA (Business Process analysis).
In one embodiment, step S200 includes:
step S210: collecting project information of the existing and new energy installation projects of each subarea, wherein the project information comprises the sum of the existing and new energy installation projects of each subarea and the sum of the existing and new energy installation projects of each subarea;
step S220: acquiring a grid structure, load data, various power distributions and power output information of a planned year, obtaining load data in a small-size mode and a large-size mode according to the grid structure and the load data of the planned year, obtaining the sum of the power outputs of various types of power supplies except new energy in the small-size mode and the sum of the power outputs of various types of power supplies except the new energy in the large-size mode according to the power distributions and the power output information, and obtaining a wind power total output value in the small-size mode, a wind power total output value in the large-size mode and a photovoltaic total output value in the large-size mode according to the power output information; obtaining a wind power output coefficient of the large and small mode, a wind power output coefficient of the large and small mode and a photovoltaic output coefficient of the large and small mode according to the wind power total output value of the large and small mode, the photovoltaic total output value of the large and small mode, the sum of the partition existing wind power installations and the sum of the partition existing photovoltaic installations;
step S230: constructing a basic database under the Feng Xiao mode and the Feng Wu mode according to a grid structure of a planned year, load data under the Feng Xiao mode and the Feng Wu mode, distribution of various power supplies, total sum of output of various power supplies except new energy in the Feng Xiao mode, total sum of output of various power supplies except new energy in the Feng Wu mode, current sum of partition and wind power installation under construction, total sum of partition and photovoltaic installation under construction, feng Wu mode wind power output coefficient, feng Xiao mode wind power output coefficient and Feng Wu mode photovoltaic output coefficient.
Further, the output coefficient of the photovoltaic small-size mode is 0.
In one embodiment, step S220 specifically includes:
wherein, C fx0 For wind power total output value in small mode, C fw0 For wind power generation of the Feng-noon mode, C gw0 For the Toyomi mode photovoltaic total output value, F 0 Installation of wind sums for zoning existing and under construction, G 0 For the sum of the existing photovoltaic installation machines in different regions and the photovoltaic installation machines under construction, lambda x is a wind power output coefficient in a small mode, lambda w is a wind power output coefficient in a mid-day mode, and kw is a photovoltaic output coefficient in a mid-day mode.
Step S300: carrying out load flow calculation by using a basic database in a small-scale mode, adjusting the scale of wind power installation in the region, obtaining the bearing capacity of regional wind power meeting the requirement of N-1 by using the load flow calculation and the calculation of N-1, adjusting the scale of the wind power installation in the region, obtaining the bearing capacity of regional wind power meeting normal delivery by using the load flow calculation, and judging the power grid demand degree of the wind power projects in the partition project sequencing table by combining the bearing capacity of regional wind power under the condition of N-1 and the bearing capacity of regional wind power meeting normal delivery.
In one embodiment, step S300 includes:
step S310: performing load flow calculation according to the basic database in the small and large mode to obtain a basic load flow calculation result in the small and large mode;
step S320: based on a basic load flow calculation result in a small-size mode, sequentially increasing the wind power installed scale in the region according to the item sequence in the partition item sequence table, and obtaining the wind power bearing capacity of the region under the condition of meeting the requirement of N-1 by utilizing load flow calculation and N-1 calculation;
step S330: based on a basic load flow calculation result in a small-size mode, sequentially increasing the scale of the regional internal wind power installations according to the item sequence in the partition item sequence table, and obtaining regional wind power bearing capacity under the condition of meeting normal delivery by utilizing load flow calculation;
step S340: and judging the power grid demand degree of the wind power projects in the partition project sequencing list according to the regional wind power bearing capacity under the condition of meeting the 'N-1' requirement and the regional wind power bearing capacity under the condition of meeting the normal sending-out requirement.
Specifically, sequentially increasing the wind power installed scale means that under the condition of the original installed scale, the capacity F1 of one wind power plant is increased, whether the N-1 check is met or not is calculated, if yes, the capacity F2 of the wind power plant is increased, and the calculation is performed again until the limit value is calculated.
In one embodiment, step S320 specifically includes:
F'＝F 0 +F 0 '+F 1 +F 2 …F k1
Z x ＝F 0 ·λ x +F 0 '·λ x +∑F i ·λ x +P x -D x (i∈[1,k 1 ]),
k 1 ∈[1,M]
wherein F ' is regional wind power bearing capacity meeting the condition of ' N-1 ', and F 0 For partitioning of the sum of existing wind installations, F 0 ' for a partition building wind installation aggregate, F i Planning the installed capacity of the ith wind power project in a subarea, F k1 Plan kth for intra-partition 1 Installed capacity, λ, of individual wind power projects x The wind power output coefficient under the rich and small mode, px is the sum of the output of various power supplies except new energy under the rich and small mode, D x Load data in a small-scale mode, M is the total number of wind power plant items, Z x The surplus of regional power in a small and large mode is achieved.
Specifically, the surplus Z of regional power meets the requirement of load flow calculation N-1 check, namely after any line or transformer N-1 is disconnected, a main transformer and line power meet the requirements of overload capacity of the main transformer and the limit transmission capacity of the line, and at the moment, a wind power project i belongs to [1, k ] 1 ]The method can meet the delivery requirement of the wind power project without adding any equipment on the power grid side, and can judge that the demand of the wind power on the power grid is type A.
Step S330 specifically includes:
F”＝F 0 +F 0 '+F 1 +…F k2
Z x ＝F 0 ·λ x +F 0 '·λ x +∑F i ·λ x +P x -D x (i∈[1,k 2 ]),
k 2 ∈[k 1 ,M]
wherein F' is the regional wind power bearing capacity under the condition of meeting normal delivery, and F k2 Plan kth for intra-partition 2 Installed capacity of each wind power project.
The requirement of normal sending is met, namely, the main transformer and the line power meet the requirement of main transformer capacity and line limit transmission capacity in a normal mode. However, after the line or the main transformer N-1 is disconnected, the main transformer and the line power do not meet the requirements of the overload capacity of the main transformer and the limit transmission capacity of the line.
At the moment, the wind power project i belongs to (k) 1 ,k 2 ]The safety and stability requirements of the wind power plant access system can be met only by configuring a stability control generator tripping device on the side of the power grid, and the requirement degree of the project on the power grid can be judged to be B type.
Wind power project i e (k) 2 ,M]The requirement of the power grid for further reinforcing the network can meet the requirement of a wind power plant access system, and the requirement of the project on the power grid can be judged to be type C. The obtained partition wind power project sequencing table is as follows:
step S400: on the basis that each partition obtained in the step S300 meets the internal wind power bearing capacity under the condition of 'N-1', performing load flow calculation by using a basic database under the Feng noon mode, adjusting the scale of the photovoltaic installation in the region, obtaining the regional photovoltaic bearing capacity under the condition of meeting 'N-1' by using the load flow calculation and the 'N-1' calculation, adjusting the scale of the photovoltaic installation in the region, obtaining the regional photovoltaic bearing capacity meeting normal delivery by using the load flow calculation, and performing power grid demand degree judgment on the photovoltaic projects in the partition project ranking table by combining the regional photovoltaic bearing capacity under the condition of 'N-1' and the regional wind power bearing capacity meeting normal delivery.
Specifically, when calculating the basic trend of the Toyoho noon, the existing wind power F0 in the region and the installed scale of all the A-type wind power projects calculated in the previous step need to be considered.
In one embodiment, step S400 includes:
step S410: carrying out load flow calculation according to a basic database in the Feng noon mode and in combination with a wind power project with the power grid demand degree of A type to obtain a basic load flow calculation result in the Feng noon mode;
step S420: based on a basic load flow calculation result in a Feng' ang mode, sequentially increasing the photovoltaic installed scale in the region according to item sequencing in a partition item sequencing table, and obtaining the photovoltaic bearing capacity of the region under the condition of meeting the requirement of N-1 by utilizing load flow calculation and N-1 calculation;
step S430: based on a basic load flow calculation result in a Feng-noon mode, sequentially increasing the photovoltaic installed scale in the region according to the item sequence in the partition item sequence table, and obtaining the photovoltaic bearing capacity of the region under the condition of meeting the requirement of normal delivery by utilizing load flow calculation;
step S440: and judging the power grid demand degree of the photovoltaic projects in the partition project sequencing list according to the regional photovoltaic bearing capacity under the condition of meeting the 'N-1' requirement and the regional photovoltaic bearing capacity under the condition of meeting the normal sending-out requirement.
In one embodiment, step S420 specifically includes:
G'＝G 0 +G 0 '+G 1 +…G b1
Z w ＝G 0 ·k w +G 0 '·k w +∑G j ·k w +F'·λ w +P w -D w j∈[1,b 1 ]
b 1 ∈[1,N]
wherein G ' is the regional photovoltaic bearing capacity under the condition of satisfying ' N-1 ', G 0 Is the total sum of the partition existing photovoltaic installation machines, G' 0 Building photovoltaic installation assembly for zoning, G j Planning installed capacity, G, of jth photovoltaic project in a partition b1 Planning the b-th in the subarea 1 Installed capacity, λ, of individual photovoltaic projects w Is the wind power output coefficient k in the Feng noon mode w Is the photovoltaic output coefficient in the Feng noon mode, P w The sum of the output of various power supplies except new energy in the Feng' ang mode, D w Load data in the mode of Toyobo noon, N is the total number of wind power plant items, Z w The surplus of regional electric power in the mode of abundance at noon.
And the surplus Z of the regional power meets the requirement of load flow calculation N-1 check, namely after any line or transformer N-1 is disconnected, the main transformer and the line power meet the requirements of overload capacity of the main transformer and the limit transmission capacity of the line. When the photovoltaic term j = [1, b ] 1 ]The requirement for sending out the photovoltaic project can be met without adding any equipment on the power grid side, and the requirement of the photovoltaic project on the power grid can be judged to be type A.
The step S430 specifically includes:
G”＝G 0 +G 0 '+G 1 +…G b2
Z w ＝G 0 ·k w +G 0 '·k w +∑G j ·k w +F'·λw+P x -D x j∈[1,b 2 ]
b 2 ∈[b 1 ,N]
wherein G' is the area photovoltaic bearing capacity under the condition of normal delivery, G b2 Planning within a partition 2 Installed capacity of individual photovoltaic projects.
The surplus of regional electric power meets the requirement of normal sending, namely, under a normal mode, the main transformer and the line power meet the requirements of main transformer capacity and line limit transmission capacity. However, after the line or the main transformer N-1 is disconnected, the main transformer and the line power do not meet the requirements of overload capacity of the main transformer and the limit transmission capacity of the line.
At the moment, the photovoltaic item j belongs to (b) 1 ,b 2 ]And the safety and stability requirements of the photovoltaic field access system can be met only by configuring a stability control generator tripping device on the side of the power grid, and the requirement degree of the project on the power grid can be judged to be B type.
Photovoltaic item j e (b) 2 ,N]The requirement of a power grid for further reinforcing the network can meet the requirement of a photovoltaic power station access system, and the requirement of the project on the power grid can be judged to be type C. The obtained partitioned photovoltaic project sequencing table is as follows:
step S500: and sequencing the new energy projects by using a comprehensive optimization sequencing method by combining the partition project sequencing table, the power grid demand of the wind power project and the power grid demand of the photovoltaic project to obtain a new energy project optimization sequencing table.
In one embodiment, step S500 includes:
step S510: and calculating the optimal value of each project of the new energy by combining the installed capacity, the access power grid distance and the resource goodness and badness of the new energy project, the power grid demand of the wind power project and the power grid demand of the photovoltaic project in the partition project ranking table.
In one embodiment, step S510 specifically includes:
Y i ＝αF i +βL fi +δR fi +ηQ fi
Y j ＝αG j +βL gj +δR gj +ηQ gj
wherein, Y i Planning the preferred value of the ith wind power project for the subarea, F i Planning the installed capacity, L, of the ith wind power project for the subarea fi Planning the distance of the ith wind power project to be connected into the power grid in the subarea, R fi Planning the quality degree, Q, of the ith wind power project resource in the subarea fi Planning the power grid demand degree of the ith wind power project for the subarea; y is j Planning preference value, G, of jth photovoltaic project in subarea j Planning installed capacity, L, of jth photovoltaic project for a zone gj Planning the distance, R, of the j-th photovoltaic project to be connected into the power grid in the subarea gj Planning the quality degree, Q, of the j-th photovoltaic project resource in the subarea gi And planning the power grid demand degree of the jth photovoltaic project in the subarea.
Step S520: and sequencing the new energy projects according to the optimized values of the projects of the new energy.
Specifically, the smaller the preferred value Y is, the better the item development degree is, specifically:
| photovoltaic project number | Project capacity | Access line length | Degree of superiority and inferiority of resource | Electric network demand degree | Preferred value |
| 1 | G 1 | L f1 | R g1 | A | Y 1 |
| … | … | … | … | A | … |
| b1 | G b1 | L k1 | Rg k1 | A | Y b1 |
| b1+1 | G b1+1 | L k1+1 | Rg k1+1 | B | Y b1+1 |
| … | … | … | … | B | … |
| b2 | G b2 | L fi | Rg fi | B | Y b2 |
| b2+1 | G b2+1 | L fi | Rg fi | C | Y b2+1 |
| … | … | … | … | C | … |
| N | G N | L fm | Rg fm | C | Y N |
The invention discloses a new energy project optimization sequencing method considering power grid requirements. And collecting the existing and built new energy installation project conditions of each partition, acquiring the grid structure, load data, power distribution and power output conditions of the planning year, and providing a basic database for load flow calculation. Through load flow calculation and N-1 check, the regional bearing capacity under the conditions of regional wind power and photovoltaic N-1 and in a normal mode is determined in sequence, and a criterion is provided for determining the demand of a new energy project on a power grid. The method has the advantages that the condition of accessing the new energy project into the power grid and the resource quality are combined, the influence of the demand degree of the power grid on the new energy project is innovatively considered, the cooperativity of new energy project development and power grid construction is improved, and more scientific and reliable guidance is provided for reasonable and ordered development of new energy under the 'double-carbon' background.
The method for optimally ordering the new energy project considering the power grid requirement provided by the invention is described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the core concepts of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (10)
1. A new energy project optimal sorting method considering power grid demand degree is characterized by comprising the following steps:
step S100: acquiring power grid data information, counting and planning new energy project distribution information, partitioning and sequencing project resources according to the power grid data information and the new energy project distribution information to obtain a partition project sequencing table;
step S200: collecting the existing and new energy installation project conditions of each partition, acquiring a grid structure, load data, various power supply distributions and power supply output conditions of a planned year, and obtaining a basic database in a small mode and a large noon mode;
step S300: carrying out load flow calculation by using the basic database in the small-scale mode, adjusting the scale of wind power installation in the region, obtaining the bearing capacity of regional wind power meeting the requirement of N-1 by using the load flow calculation and the calculation of N-1, adjusting the scale of the wind power installation in the region, obtaining the bearing capacity of regional wind power meeting the requirement of normal delivery by using the load flow calculation, and carrying out power grid demand degree judgment on the wind power projects in the partition project sequencing table by combining the bearing capacity of regional wind power under the condition of N-1 and the bearing capacity of regional wind power meeting the requirement of normal delivery;
step S400: on the basis that each partition obtained in the step S300 meets the internal wind power bearing capacity under the condition of N-1, performing load flow calculation by using the basic database under the Feng noon mode, adjusting the scale of the photovoltaic installation in the region, obtaining the regional photovoltaic bearing capacity under the condition of meeting N-1 by using the load flow calculation and the N-1 calculation, adjusting the scale of the photovoltaic installation in the region, obtaining the regional photovoltaic bearing capacity meeting normal delivery by using the load flow calculation, and determining the power grid demand degree of the photovoltaic projects in the partition project ranking table by combining the regional photovoltaic bearing capacity under the condition of N-1 and the regional wind power bearing capacity meeting normal delivery;
step S500: and sequencing the new energy projects by using a comprehensive optimal sequencing method by combining the partition project sequencing list, the power grid demand degree of the wind power project and the power grid demand degree of the photovoltaic project to obtain a new energy project optimal sequencing list.
2. The method according to claim 1, wherein step S100 comprises:
step S110: acquiring power grid data information and counting new energy project information, wherein the new energy project information comprises installed capacity of each project, geographical position of each project, resource information of each project and access distance of each project to a power grid;
step S120: obtaining the goodness and the badness of each project resource according to the information of each project resource, partitioning the new energy project resource according to the power grid data information and the geographical position of each project, and obtaining a new energy project condition table under each partition according to the installed capacity of each project and the goodness and the badness of each project resource;
step S130: and sequencing the new energy projects in the subareas according to the distance of each project accessed to the power grid to obtain a subarea project sequencing list.
3. The method according to claim 2, wherein step S200 comprises:
step S210: collecting project information of existing and new energy installation machines in each partition, wherein the project information comprises the sum of the existing and new energy installation machines in the partition and the sum of the existing and new energy installation machines in the partition;
step S220: acquiring a grid structure, load data, various power distributions and power output information of a planned year, obtaining load data in a small-to-large and high-noon mode according to the grid structure and the load data of the planned year, obtaining the sum of the power outputs of various power supplies except new energy in the small-to-large mode and the sum of the power outputs of various power supplies except the new energy in the high-noon mode according to the power distributions and the power output information, and obtaining a wind power total output value in the small-to-large mode, a wind power total output value in the high-noon mode and a photovoltaic total output value in the high-noon mode according to the power output information; obtaining a wind power output coefficient of the large and small mode, a wind power output coefficient of the large and small mode and a photovoltaic output coefficient of the large and small mode according to the total wind power output value of the large and small mode, the total photovoltaic output value of the large and small mode, the sum of the partitioned existing wind power installations and the sum of the partitioned existing photovoltaic installations;
step S230: and constructing a basic database under the Feng Xiao mode and the Feng Wu mode according to the grid structure of the planned year, the load data under the Feng Xiao and Feng Wu modes, the distribution of various power supplies, the sum of the power supplies of the Feng Xiao modes except new energy, the sum of the power supplies of the Feng Wu modes except new energy, the sum of the partition existing and under-construction wind power installations, the sum of the partition existing and under-construction photovoltaic installations, the Feng Wu mode wind power output coefficient, the Feng Xiao mode wind power output coefficient and the Feng Wu mode photovoltaic output coefficient.
4. The method according to claim 3, wherein step S220 is specifically:
wherein, C fx0 For wind power total output value in small mode, C fw0 For wind power generation of the Feng-noon mode, C gw0 For the Toyomi mode photovoltaic total output value, F 0 For partitioning of the sum of existing wind installations, G 0 The method is characterized in that lambda x is the wind power output coefficient of the small mode, lambda w is the wind power output coefficient of the mid-day mode, and kw is the photovoltaic output coefficient of the mid-day mode.
5. The method according to claim 4, wherein step S300 comprises:
step S310: performing load flow calculation according to the basic database in the small and large mode to obtain a basic load flow calculation result in the small and large mode;
step S320: based on the basic load flow calculation result in the small-size mode, sequentially increasing the installed scale of the wind power in the region according to the item sequence in the partition item sequence table by using load flow calculation and N-1 calculation to obtain the wind power bearing capacity of the region under the condition of meeting the N-1;
step S330: based on the basic load flow calculation result in the small-size mode, sequentially increasing the scale of the regional internal wind power installations according to the item sequence in the partition item sequence table, and obtaining regional wind power bearing capacity under the condition of meeting the normal delivery condition by utilizing load flow calculation;
step S340: and judging the power grid demand degree of the wind power projects in the partition project sequencing list according to the regional wind power bearing capacity under the condition of meeting the 'N-1' and the regional wind power bearing capacity under the condition of meeting the normal sending.
6. The method according to claim 5, wherein step S320 is specifically:
F'＝F 0 +F 0 '+F 1 +F 2 …F k1
Z x ＝F 0 ·λ x +F 0 '·λ x +∑F i ·λ x +P x -D x (i∈[1,k 1 ]),
k 1 ∈[1,M]
wherein F ' is regional wind power bearing capacity under the condition of meeting the requirement of ' N-1 ', and F 0 For zoning the existing wind installations, F 0 ' for a partition building wind installation aggregate, F i Planning the installed capacity of the ith wind power project in a subarea, F k1 Plan kth for intra-partition 1 Installed capacity, λ, of individual wind power projects x The wind power output coefficient under the rich and small mode, px is the sum of the output of various power supplies except new energy under the rich and small mode, D x Load data in a small-scale mode, M is the total number of wind power plant items, Z x Surplus of regional electric power in a small and large mode;
step S330 specifically includes:
F”＝F 0 +F 0 '+F 1 +…F k2
Z x ＝F 0 ·λ x +F 0 '·λ x +∑F i *λ x +P x -D x (i∈[1,k 2 ]),
k 2 ∈[k 1 ,M]
wherein F' is the regional wind power bearing capacity under the condition of meeting normal delivery, and F k2 Plan kth for intra-partition 2 Installed capacity of each wind power project.
The step S340 includes:
when the wind power project i belongs to [1, k ] 1 ]Judging that the demand degree of the wind power project on the power grid is A type;
when the wind power project i belongs to (k) 1 ,k 2 ]Judging that the demand degree of the wind power project on the power grid is B type;
when the wind power project i belongs to (k) 2 ,M]And judging that the demand degree of the wind power project on the power grid is type C.
7. The method of claim 6, wherein step S400 comprises:
step S410: carrying out load flow calculation according to the basic database in the Feng noon mode and in combination with the wind power project with the power grid demand degree of A type to obtain a basic load flow calculation result in the Feng noon mode;
step S420: based on the basic load flow calculation result in the Feng noon mode, sequentially increasing the photovoltaic installed scale in the region according to the item sequence in the partition item sequence table by utilizing load flow calculation and N-1 calculation to obtain the photovoltaic bearing capacity of the region under the condition of meeting the condition of N-1;
step S430: based on the basic load flow calculation result in the Feng' ang mode, sequentially increasing the photovoltaic installed scale in the region according to the item sequence in the partition item sequence table, and obtaining the photovoltaic bearing capacity of the region under the condition of meeting the requirement of normal delivery by utilizing load flow calculation;
step S440: and judging the power grid demand degree of the photovoltaic projects in the partition project sequencing list according to the regional photovoltaic bearing capacity under the condition of meeting the 'N-1' and the regional photovoltaic bearing capacity under the condition of meeting the normal sending.
8. The method according to claim 7, wherein step S420 specifically comprises:
G'＝G 0 +G 0 '+G 1 +…G b1
Z w ＝G 0 ·k w +G 0 '·k w +∑G j ·k w +F'·λ w +P w -D w j∈[1,b 1 ]
b 1 ∈[1,N]
wherein G ' is the regional photovoltaic bearing capacity under the condition of satisfying ' N-1 ', G 0 Is a sub-area of the existing photovoltaic installation sum, G' 0 For zoning under construction photovoltaic installation assembly, G j Planning installed capacity, G, of the jth photovoltaic project in a partition b1 Planning the b-th in the subarea 1 Installed capacity, λ, of individual photovoltaic projects w Is the wind power output coefficient k in the Feng noon mode w Is the photovoltaic output coefficient, P, in the Feng noon mode w The sum of the output of various power supplies except new energy in the Feng' ang mode, D w Load data in the mode of Toyobo noon, N is the total number of wind power plant items, Z w Surplus of regional electric power in the Feng Wu mode;
step S430 specifically includes:
G”＝G 0 +G 0 '+G 1 +…G b2
Z w ＝G 0 ·k w +G 0 '·k w +∑G j ·k w +F'·λw+P x -D x j∈[1,b 2 ]
b 2 ∈[b 1 ,N]
wherein G' satisfies the regional photovoltaic bearing capacity under normal delivery, G b2 Planning within a partition 2 Installed capacity of individual photovoltaic projects.
Step S440 includes:
when the photovoltaic item j belongs to [1, b ] 1 ]Judging the demand degree of the photovoltaic project on the power grid to be type A;
when the photovoltaic item j belongs to (b) 1 ,b 2 ]Judging that the demand degree of the photovoltaic project on the power grid is B type;
when the photovoltaic item j belongs to (b) 2 ,N]And judging that the demand degree of the photovoltaic project on the power grid is C type.
9. The method of claim 8, wherein step S500 comprises:
step S510: calculating the optimal value of each project of the new energy by combining the installed capacity, the access power grid distance and the resource goodness of the new energy project in the partition project ranking table, the power grid demand of the wind power project and the power grid demand of the photovoltaic project;
step S520: and sequencing the new energy projects according to the optimized values of the projects of the new energy.
10. The method according to claim 9, wherein step S510 specifically includes:
Y i ＝αF i +βL fi +δR fi +ηQ fi
Y j ＝αG j +βL gj +δR gj +ηQ gj
wherein Y is i Planning the preferred value of the ith wind power project for the subarea, F i Planning the installed capacity, L, of the ith wind power project for the subarea fi Planning the distance of the ith wind power project to be connected into the power grid in the subarea, R fi Planning the quality degree, Q, of the ith wind power project resource in the subarea fi Planning the power grid demand degree of the ith wind power project for the subareas; y is j Planning preference value, G, of jth photovoltaic project in subarea j Planning installed capacity, L, of jth photovoltaic project for a zone gj Planning the distance, R, of the j-th photovoltaic project to be connected into the power grid in the subarea gj Planning the quality degree, Q, of the j-th photovoltaic project resource in the subarea gi And planning the power grid demand degree of the jth photovoltaic project in the subarea.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111669119.4A CN114297249B (en) | 2021-12-31 | 2021-12-31 | New energy project optimal selection ordering method considering power grid demand degree |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111669119.4A CN114297249B (en) | 2021-12-31 | 2021-12-31 | New energy project optimal selection ordering method considering power grid demand degree |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114297249A CN114297249A (en) | 2022-04-08 |
| CN114297249B true CN114297249B (en) | 2023-01-20 |
Family
ID=80974396
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111669119.4A Active CN114297249B (en) | 2021-12-31 | 2021-12-31 | New energy project optimal selection ordering method considering power grid demand degree |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114297249B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115619102B (en) * | 2022-11-14 | 2023-04-07 | 中国能源建设集团湖南省电力设计院有限公司 | Electrical calculation checking method based on new energy power abandonment rate |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577901A (en) * | 2013-11-22 | 2014-02-12 | 国家电网公司 | Method of intertidal zone wind power for accessing power grid |
| CN106874630A (en) * | 2017-03-28 | 2017-06-20 | 上海理工大学 | Based on the regional power grid new energy development potential evaluation method that electricity is dissolved |
-
2021
- 2021-12-31 CN CN202111669119.4A patent/CN114297249B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577901A (en) * | 2013-11-22 | 2014-02-12 | 国家电网公司 | Method of intertidal zone wind power for accessing power grid |
| CN106874630A (en) * | 2017-03-28 | 2017-06-20 | 上海理工大学 | Based on the regional power grid new energy development potential evaluation method that electricity is dissolved |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114297249A (en) | 2022-04-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ismail et al. | A comprehensive review on optimal location and sizing of reactive power compensation using hybrid-based approaches for power loss reduction, voltage stability improvement, voltage profile enhancement and loadability enhancement | |
| CN112467722B (en) | Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station | |
| CN111030188A (en) | Hierarchical control strategy containing distributed and energy storage | |
| CN102074954A (en) | Comprehensive energy-conservation evaluation and decision-making method for urban and rural distribution network | |
| CN103729685A (en) | Method for photovoltaic power station group region power prediction on basis of improved RBF neural network | |
| Yan et al. | Many-objective charging optimization for electric vehicles considering demand response and multi-uncertainties based on Markov chain and information gap decision theory | |
| CN110535129A (en) | A kind of distribution network planning method under energy internet | |
| CN110034561B (en) | Wind power plant energy storage capacity optimization method based on cloud energy storage lease service | |
| CN114003860B (en) | Method for measuring and calculating new energy bearing capacity of regional power grid considering interval power exchange | |
| CN112001598A (en) | Energy storage configuration evaluation and operation optimization method for different users based on energy storage type selection | |
| CN106529737A (en) | Planning and distribution method for peak load regulation power source on supply side of power distribution network | |
| CN114638502A (en) | Power distribution network line reinforcement strategy considering flexibility of demand side resources | |
| CN114297249B (en) | New energy project optimal selection ordering method considering power grid demand degree | |
| Manas et al. | Optimal distributed generator sizing and placement by analytical method and fuzzy expert system: a case study in Tezpur University, India | |
| CN113222241B (en) | Taxi quick-charging station planning method considering charging service guide and customer requirements | |
| CN113570282B (en) | Capacity configuration and cost allocation method for multi-main-body micro-grid group combined energy storage system | |
| CN112270072B (en) | Method for participating in power spot market by self-provided enterprises | |
| CN111612271B (en) | Clean energy delivery planning and operation optimization method considering clean energy utilization rate | |
| CN113315173B (en) | Distribution network planning method, equipment, system and storage medium based on big data analysis and supply and demand double-side collaborative optimization | |
| Ren et al. | Decision-making approach in charging mode for electric vehicle based on cumulative prospect theory | |
| Kaneko et al. | Proposal and evaluation of determination method for multiobjective output dispatch of frequency control generators to compensate for renewable energy system fluctuation in multiarea system operation | |
| CN118134289B (en) | Incremental power distribution network planning system based on big data analysis | |
| CN111612267B (en) | Hydropower cluster delivery net rack optimization method considering distant view horizontal year | |
| CN111612266B (en) | Hydropower cluster delivery net rack optimization method considering clean energy utilization rate | |
| CN111612272B (en) | Hydropower cluster delivery net rack optimization method considering leading reservoir |
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 |