CN114825320A - Collaborative optimization method considering wind turbine-wind power plant double-layer system topology - Google Patents
Collaborative optimization method considering wind turbine-wind power plant double-layer system topology Download PDFInfo
- Publication number
- CN114825320A CN114825320A CN202111504033.6A CN202111504033A CN114825320A CN 114825320 A CN114825320 A CN 114825320A CN 202111504033 A CN202111504033 A CN 202111504033A CN 114825320 A CN114825320 A CN 114825320A
- Authority
- CN
- China
- Prior art keywords
- wind power
- power plant
- wind
- fan
- topology
- 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.)
- Pending
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a collaborative optimization method considering a wind turbine-wind power plant double-layer system topology, which comprises the following steps: acquiring the fan capacity, fan coordinates and fan output data of a wind power plant, the capacity, the coordinates and the output data of the wind power plant, transmission equipment information of a power system, a target area power transmission price and a power abandonment compensation price; generating a fan initial population through a fan topology optimization model in the wind power plant according to fan capacity, fan coordinates and fan output data to obtain optimal individuals in the current population; generating an initial wind power plant population through a wind power plant topology optimization model among wind power plants according to the coordinates and the optimal individuals of the wind power plants, calculating net income of each wind power plant, obtaining the optimal individuals in the offspring population of the wind power plants after crossing and variation, determining the optimal topology of the fan-wind power plant double-layer system, and performing collaborative optimization on the wind power plants and the fans according to the optimal topology of the fan-wind power plant double-layer system.
Description
Technical Field
The invention relates to the technical field of topology optimization of a wind power collection system, in particular to a collaborative optimization method considering the topology of a double-layer system of a fan-wind power plant.
Background
As the most active renewable clean energy power generation mode in the 21 st century, wind power generation makes great contribution to the construction of a low-carbon society and the promotion of new and old kinetic energy conversion and economic sustainable development. In recent years, wind power of all countries in the world is developed rapidly.
In recent years, the development of wind power has encountered the problems of wind abandon, network disconnection and the like caused by low utilization rate of wind resources, unbalance of wind power and other power resources and uncoordinated load sides of a power system. The fundamental method for solving the wind power consumption is to reasonably plan a wind power plant topological structure coupled with the wind power random characteristic, optimize the unit output of a wind power plant cluster by utilizing the smoothing effect of a large wind power plant, design an economic and reliable grid-connected conveying scheme and construct a collaborative optimization model considering the wind turbine-wind power plant double-layer system topology.
The large-scale wind power plant has a smoothing effect and random fluctuation characteristics, and has higher requirements on a wind turbine topological structure. In addition, under the era of 'flat price on-line', the large-scale reduction of power generation and transmission costs must be combined with the characteristics of a power grid, and the convergence system is designed and coordinately planned as a whole.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a collaborative optimization method considering the topology of a double-layer system of a wind turbine-wind power plant, which comprises the following steps:
acquiring wind power plant fan capacity, fan coordinates and fan output data, wind power plant capacity, wind power plant coordinates and wind power plant output data, power system transmission equipment information, a target area power transmission price and a power abandonment compensation price;
generating an initial fan population through a fan topology optimization model in the wind power plant according to fan capacity, fan coordinates and fan output data, calculating net earnings of all fans, generating a offspring population if the net earnings of the fans do not meet a termination criterion, crossing and mutating the offspring population, calculating the net earnings of all fans, and obtaining the optimal individual in the current population if the net earnings of the fans meet the termination criterion;
and thirdly, generating a wind power plant initial population through a wind power plant inter-wind power plant fan topology optimization model according to the wind power plant coordinates and the optimal individuals, calculating net income of each wind power plant, generating a wind power plant offspring population if the net income of the wind power plant does not meet a termination criterion, crossing and mutating the wind power plant offspring population, calculating net income of each wind power plant, obtaining the optimal individuals in the crossed and mutated wind power plant offspring population if the net income of the wind power plant meets the termination criterion, determining the optimal topology of the fan-wind power plant double-layer system, and performing collaborative optimization on the wind power plant and the fans according to the optimal topology of the fan-wind power plant double-layer system.
Furthermore, the fan-wind power plant double-layer system comprises a fan topology inside a wind power plant and a topology between wind power plants.
Further, the wind power plant internal fan topology optimization model is built based on wind power plant internal fan coordinates, capacity and output data; the topological optimization model among the wind power plants is obtained based on coordinates, capacity and output data of the wind power plants; the collaborative optimization model is obtained based on wind power plant internal fan information and wind power plant information.
Further, the wind turbine topology optimization model between the wind power plants is established based on a genetic algorithm and by combining wind turbine topology optimization results and wind power smoothing effects in the wind power plants.
A wind turbine-wind farm considered bilayer system comprising: wind power plant internal fan topology and wind power plant topology; the wind power plant topology comprises a fan and a transmission line, wherein the fan is connected with the fan or the wind power plant through the transmission line; the wind power plant topology comprises a wind power plant, a transmission line and a booster station, wherein the wind power plant is connected with a fan, the wind power plant and the booster station through the transmission line, and one or more computer programs are realized when being executed by one or more processors.
The invention has the beneficial effects that: 1. the method has the innovation points that a wind power smoothing effect is considered, a wind power plant internal fan topology optimization model is obtained through coordinates, capacity and output data of an internal fan of a wind power plant, and the wind power plant internal fan topology is optimized through a genetic algorithm.
2. On the basis, according to the optimal topology of the fans in the wind power plant, a wind power plant topology optimization model is established through wind power plant coordinates, and the optimal fan-wind power plant double-layer system topology is obtained.
Drawings
FIG. 1 is a flow chart of a collaborative optimization method considering a wind turbine-wind farm two-layer system topology;
fig. 2 is a schematic diagram of an offshore wind farm cluster access system according to the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a collaborative optimization method considering a wind turbine-wind farm double-layer system topology includes the following steps:
acquiring wind power plant fan capacity, fan coordinates and fan output data, wind power plant capacity, wind power plant coordinates and wind power plant output data, power system transmission equipment information, a target area power transmission price and a power abandonment compensation price;
generating an initial fan population through a fan topology optimization model in the wind power plant according to fan capacity, fan coordinates and fan output data, calculating net earnings of all fans, generating a offspring population if the net earnings of the fans do not meet a termination criterion, crossing and mutating the offspring population, calculating the net earnings of all fans, and obtaining the optimal individual in the current population if the net earnings of the fans meet the termination criterion;
and thirdly, generating a wind power plant initial population through a wind power plant inter-wind power plant fan topology optimization model according to the wind power plant coordinates and the optimal individuals, calculating net income of each wind power plant, generating a wind power plant offspring population if the net income of the wind power plant does not meet a termination criterion, crossing and mutating the wind power plant offspring population, calculating net income of each wind power plant, obtaining the optimal individuals in the crossed and mutated wind power plant offspring population if the net income of the wind power plant meets the termination criterion, determining the optimal topology of the fan-wind power plant double-layer system, and performing collaborative optimization on the wind power plant and the fans according to the optimal topology of the fan-wind power plant double-layer system.
Further, the fan-wind power plant double-layer system comprises a fan topology inside a wind power plant and a topology between wind power plants.
Further, the wind power plant internal fan topology optimization model is built based on wind power plant internal fan coordinates, capacity and output data; the topological optimization model among the wind power plants is obtained based on coordinates, capacity and output data of the wind power plants; the collaborative optimization model is obtained based on wind power plant internal fan information and wind power plant information.
Further, the wind turbine topology optimization model between the wind power plants is established based on a genetic algorithm and by combining wind turbine topology optimization results and wind power smoothing effects in the wind power plants.
A wind turbine-wind farm considered bilayer system comprising: wind power plant internal fan topology and wind power plant topology; the wind power plant topology comprises a fan and a transmission line, wherein the fan is connected with the fan or the wind power plant through the transmission line; the wind power plant topology comprises a wind power plant, a transmission line and a booster station, wherein the wind power plant is connected with a fan, the wind power plant and the booster station through the transmission line, and one or more computer programs are realized when being executed by one or more processors.
Specifically, the collaborative optimization model considering the wind turbine-wind power plant double-layer system topology comprises the following steps:
s1, acquiring the capacity, coordinates and output data of a fan in the wind power plant, the capacity, coordinates and output data of the wind power plant, transmission equipment information of a power system, a power transmission price of a target area and a power abandonment compensation price;
s2, inputting the capacity, coordinates and output data of the fans in the wind power plant into a constructed collaborative optimization model, solving the model by using a genetic algorithm, firstly generating an initial population, calculating the net income of each fan, if the net income does not meet a termination criterion, generating a filial population, then calculating the net income of each fan through crossing and variation, and if the net income meets the termination criterion, obtaining the optimal individual in the current population;
s3, inputting coordinates of the wind power plant into the established collaborative optimization model, generating an initial population according to the optimal individual obtained by wind power plant internal fan topology optimization, calculating net income of each wind power plant, generating a sub-generation population if the termination criterion is not met, then calculating net income of each wind power plant through crossing and variation, obtaining the optimal individual in the current population if the termination criterion is met, and determining the optimal topology of the fan-wind power plant double-layer system.
For a sending-out line of a wind power plant or a wind power plant cluster, if the sending-out line is configured according to installed capacity, the power transmission line is often lightly loaded, and the asset utilization rate is low; if the capacity of the outgoing line is too low, a large amount of abandoned wind is caused. Therefore, an economic comprehensive optimization model needs to be established, and the construction capacity and the abandoned wind loss of the power transmission line are coordinated. The comprehensive benefits of the power transmission line are divided into cost and income which are given by net present value
In the formula：B total Annual revenue; c L Annual cost; r is interest rate; and N is the operation life.
The electric energy sent out by the wind power plant is a revenue source of the line, and because the wind abandon caused by the transmission capacity limitation, namely the electric quantity is not sent out, the electric energy also needs to be used as a fine to be added into the line. Neglecting the price difference of the electric energy sent by the wind power plant in different time periods and the annual income B of the wind power plant total Income from wind and electricity B TE Electricity abandonment compensation cost C cur Determining, wherein B TE Mainly determined by annual energy production of wind power station, C cur Annual wind power curtailment mainly caused by transmission capacity limitation
C cur ＝[(1+r) N -1]/[r(1+r) N ]p b E cur
B TE ＝[(1+r) N -1]/[r(1+r) N ]p o E
In the formula: b TE The wind power electric quantity is gained; c cur The cost is compensated for electricity abandonment; p is a radical of o 、p b Is a price parameter; e cur The annual electric quantity of the wind power plant is abandoned; and E is the annual energy production of the wind power plant.
And (4) according to the annual continuous output curve representing the wind power generation output characteristic, calculating the annual abandoned power quantity and the annual generated power quantity of the wind power plant. Annual continuous output curve of a wind farm as a function P of the cumulative duration t dur (t) of (d). Outlet line active power limit of wind farm is P LL Therefore, the theoretical output of the wind power plant is greater than P LL The portion that exceeds the line power constraint will be discarded. T is LL The output of the wind power plant is not less than P LL Cumulative duration of year. The area of a region enclosed by the annual continuous output curve and the coordinate axes is the theoretical annual electricity generation quantity E of the wind power plant 0 Annual energy loss for shadow part area E cur And the blank area annual energy production E.
According to the definition, the annual power loss E of the wind power plant cur The annual energy production amount E can be calculated by the following formula
In the formula, P dur,max The annual maximum output of the wind power plant. According to the relationship among annual electricity generation quantity, annual electricity generation quantity and annual electricity abandonment quantity of the wind power plant, the equivalent of the above formula is as follows:
the constraint conditions of the topological optimization of the current collection system mainly comprise submarine cable transmission capacity constraint, submarine cable carrying capacity constraint, submarine cable fan number bearing constraint and submarine cable cross evasion constraint.
When the submarine cable is selected, the capacity of all fans connected with the submarine cable is ensured not to exceed the limited capacity of the submarine cable, and the expression mode is as follows:
wherein S ij Indicating the capacity, n, of a fan j connected to a sea cable i i Indicating the number of fans connected to the sea cable i, S imax The maximum transmission capacity of the sea cable i is indicated and N indicates the number of sea cables.
The current-carrying capacity constraint of the submarine cable comprises single-fan submarine cable current-carrying capacity constraint and collection submarine cable current-carrying capacity constraint, and the expression is as follows:
in which I ij Indicating the current-carrying capacity, P, of a cable i connected to a fan j wj Indicating the rated output, U, of fan j ij Indicating the nominal voltage of the sea cable ij,
representing the power factor, I, of fan j i Represents that n is connected to i Current carrying capacity, U, of cable i of desk fan i Indicating the rated voltage of the umbilical i.
Because the current-carrying capacity of the submarine cable is limited, the number n of the fans which can be collected by each submarine cable i Limited, its expression is as follows:
wherein I max i denotes the maximum ampacity of the collector cable i,
representing the power factor, P, of the umbilical i w The average rated output of the wind turbine connected to the collection sea cable i is shown.
The current collection submarine cables of offshore wind power are usually installed in a blowing and burying mode, and the current collection submarine cables are not allowed to intersect with each other in consideration of actual engineering requirements. The input information of the model is the position coordinates of the fan, so that whether the submarine cables are crossed or not is judged by using the coordinate information of the nodes, and the crossed constraint of the submarine cables is
Wherein P is 1 P 2 And Q 1 Q 2 Two line segments that cannot be intersected are represented,
and represents the cross product and dot product calculations.
The grid-connected operation of the offshore wind farm group needs to be collected to the offshore booster station and then conveyed, so that the capacity of the offshore booster station is not less than the capacity of each collection cable, and the expression is as follows:
wherein S i Representing cable capacity, N i Indicating the number of cables collected to the booster station, S asc Representing the capacity of the offshore booster station.
In the invention, the model is optimized based on genetic algorithm according to the optimization model.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A collaborative optimization method considering wind turbine-wind power plant double-layer system topology is characterized by comprising the following steps:
acquiring wind power plant fan capacity, fan coordinates and fan output data, wind power plant capacity, wind power plant coordinates and wind power plant output data, information such as the model and the sectional area of power system transmission equipment, a target area power transmission price and a power curtailment compensation price;
generating a fan initial population through a fan topology optimization model in the wind power plant according to fan capacity, fan coordinates and fan output data, calculating net profits of all fans, generating a sub population if the net profits of the fans do not meet a termination criterion, crossing and varying the sub population, calculating the net profits of all fans, and obtaining the optimal individual in the current population if the net profits of the fans meet the termination criterion;
and thirdly, generating an initial wind power plant population through a wind power plant topology optimization model among the wind power plants according to the coordinates and the optimal individuals of the wind power plants, calculating net income of each wind power plant, generating a wind power plant offspring population if the net income of the wind power plants does not meet a termination criterion, crossing and mutating the wind power plant offspring population, calculating the net income of each wind power plant, obtaining the optimal individuals in the crossed and mutated wind power plant offspring population if the net income of the wind power plants meets the termination criterion, determining the optimal topology of the wind power plant-wind power plant double-layer system, and performing collaborative optimization on the wind power plant and the wind turbine according to the optimal topology of the wind power plant-wind power plant double-layer system.
2. The collaborative optimization method considering wind turbine-wind farm double-layer system topology according to claim 1, wherein the wind turbine-wind farm double-layer system includes wind farm internal wind turbine topology and wind farm inter-topology.
3. The collaborative optimization method considering wind turbine-wind farm double-layer system topology according to claim 2, characterized in that the wind farm internal wind turbine topology optimization model is built based on wind farm internal wind turbine coordinates, capacity and output data; the topological optimization model among the wind power plants is obtained based on coordinates, capacity and output data of the wind power plants; the collaborative optimization model is obtained based on wind power plant internal fan information and wind power plant information.
4. The cooperative optimization method considering wind turbine-wind farm double-layer system topology according to claim 1, wherein the wind turbine topology optimization model between wind farms is established based on a genetic algorithm by combining a wind turbine topology optimization result and a wind power smoothing effect in the wind farm.
5. The wind turbine-wind farm double-layer system of the collaborative optimization method considering the wind turbine-wind farm double-layer system topology according to any one of claims 1 to 4, characterized by comprising: wind power plant internal fan topology and wind power plant topology; the wind power plant internal fan topology comprises a fan and a transmission line, wherein the fan is connected with the fan or the wind power plant through the transmission line; the topology among the wind power plants comprises a wind power plant, a transmission line and a booster station, wherein the wind power plant is connected with a fan, the wind power plant and the booster station through the transmission line, and one or more computer programs are realized when being executed by one or more processors.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111504033.6A CN114825320A (en) | 2021-12-06 | 2021-12-06 | Collaborative optimization method considering wind turbine-wind power plant double-layer system topology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111504033.6A CN114825320A (en) | 2021-12-06 | 2021-12-06 | Collaborative optimization method considering wind turbine-wind power plant double-layer system topology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114825320A true CN114825320A (en) | 2022-07-29 |
Family
ID=82525480
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111504033.6A Pending CN114825320A (en) | 2021-12-06 | 2021-12-06 | Collaborative optimization method considering wind turbine-wind power plant double-layer system topology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114825320A (en) |
-
2021
- 2021-12-06 CN CN202111504033.6A patent/CN114825320A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wu et al. | Optimization of the wind turbine layout and transmission system planning for a large-scale offshore windfarm by AI technology | |
| CN109598377B (en) | AC/DC hybrid power distribution network robust planning method based on fault constraint | |
| CN108599235B (en) | Constant volume method for distributed photovoltaic network access | |
| CN112398678B (en) | Operation mode topological structure feature extraction method suitable for multi-source heterogeneous power grid | |
| Goswami et al. | Development of a grid connected solar-wind hybrid system with reduction in levelized tariff for a remote island in India | |
| Chen et al. | Integrated planning of distribution systems with distributed generation and demand side response | |
| CN114362143A (en) | Topological optimization solving algorithm for current collecting system of offshore wind farm | |
| CN112836957B (en) | Regional comprehensive energy system planning method considering power supply reliability | |
| CN114118787A (en) | Dispatching optimization method for urban distributed source network load storage based on LSTM algorithm | |
| CN114491924A (en) | Offshore booster station site selection method considering fan-wind power plant collaborative optimization | |
| CN117239792A (en) | Regional distributed energy storage, site selection and volume determination method and device | |
| CN117239754A (en) | Carbon emission considered wind-light-fire multi-power coordination operation method and system | |
| CN105207255B (en) | A kind of power system peak regulation computational methods suitable for wind power output | |
| CN114825320A (en) | Collaborative optimization method considering wind turbine-wind power plant double-layer system topology | |
| CN110867902A (en) | Power generation prediction-based micro-grid distributed power supply de-centering optimized operation method | |
| CN116306181A (en) | Low-carbon economic source-load storage active power distribution network coordination planning method | |
| CN112039076B (en) | Power distribution network load flow dynamic equivalence method and system integrating distributed power sources and loads | |
| CN113612191A (en) | Method and device for rapidly recovering power supply of power distribution network | |
| CN115411719A (en) | Distributed power supply planning method based on source load uncertainty and voltage stability | |
| Du et al. | A bi-level multi-objective planning method for microgrid with offshore wind power | |
| CN112865101A (en) | Linear transaction method considering uncertainty of output of renewable energy | |
| Jiang et al. | Expansion planning method of offshore multiplatform power system with wind power considering cable size selection | |
| Li et al. | Optimization of the offshore wind power grid-connected structure based on an improved genetic algorithm | |
| Chan-Mook et al. | Optimal wind-thermal coordination scheduling considering reserve requirement | |
| Prakash et al. | Optimal DG Planning Incorporating Energy Management for an Economical and Resilient Smart Distribution System |
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 |