WO2021253291A1 - 风电场布局的优化方法、优化系统及计算机可读存储介质 - Google Patents

风电场布局的优化方法、优化系统及计算机可读存储介质 Download PDF

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WO2021253291A1
WO2021253291A1 PCT/CN2020/096605 CN2020096605W WO2021253291A1 WO 2021253291 A1 WO2021253291 A1 WO 2021253291A1 CN 2020096605 W CN2020096605 W CN 2020096605W WO 2021253291 A1 WO2021253291 A1 WO 2021253291A1
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cable
wind
wind farm
objective function
position coordinates
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PCT/CN2020/096605
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English (en)
French (fr)
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侯鹏
朱江生
金荣森
陈乐�
孟晓刚
缪骏
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上海电气风电集团股份有限公司
上海电气风电集团欧洲科创中心有限公司
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Priority to PCT/CN2020/096605 priority Critical patent/WO2021253291A1/zh
Priority to EP20941242.8A priority patent/EP4170850A4/en
Publication of WO2021253291A1 publication Critical patent/WO2021253291A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • This application relates to the technical field of wind farm planning, and in particular to an optimization method, an optimization system and a computer-readable storage medium for the layout of a wind farm.
  • Wind is one of the energy sources without pollution, and it is inexhaustible and inexhaustible. For coastal islands, grassland scenic areas, mountainous areas and plateaus that lack water, fuel and inconvenient transportation, it is very suitable and promising to use wind power according to local conditions.
  • Wind power refers to the conversion of wind energy into electrical energy.
  • the use of wind power generation is very environmentally friendly and has a huge amount of wind energy, so it is increasingly being valued by countries all over the world.
  • the cable layout of the wind farm and the location of the booster station have a great influence on the cost of the wind farm. How to optimize the cable layout and the location of the booster station has become an important task for the optimization of the layout of the wind farm.
  • This application provides an improved optimization method, optimization system, and computer-readable storage medium for the layout of a wind farm.
  • the wind farm includes a plurality of nodes, and the plurality of nodes includes a booster station and a plurality of wind turbines.
  • the optimization method includes: obtaining all The position coordinates of the wind turbines of the multiple wind turbines; and according to the position coordinates of the wind turbines, the meta-heuristic algorithm is used to solve the optimization problem with the objective of minimizing the objective function of the wind farm cable cost to obtain the position coordinates of the booster station
  • an optimization system for the layout of a wind farm includes one or more processors for implementing the above optimization method.
  • a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, the foregoing optimization method is implemented.
  • the optimization method of some embodiments of the application considers the influence of the power flow of the wind farm to consider the influence of the power loss of the cable on the cost of the wind farm cable. Simultaneous optimization of cables for all paths can greatly improve the cost optimization effect, and can significantly improve the economic performance of the electrical design of the overall wind farm.
  • Fig. 1 shows a flowchart of an embodiment of a method for optimizing the layout of a wind farm according to the present application.
  • Fig. 2 shows a sub-flow chart of the steps of using the meta-heuristic algorithm in the optimization method shown in Fig. 1 to solve the optimization problem with the objective of minimizing the objective function of the wind farm cable cost.
  • Fig. 3 shows a sub-flow chart of the steps of determining a feasible solution of the objective function shown in Fig. 2.
  • Fig. 4 shows a sub-flow chart of the steps of performing power flow calculation shown in Fig. 2.
  • Figure 5 shows a schematic diagram of the location of the wind turbines of a wind farm.
  • FIG. 6 is a schematic diagram of a wind farm layout obtained after optimizing the layout of the wind farm in FIG. 5 according to an embodiment of the application.
  • Fig. 7 shows a block diagram of an embodiment of a system for optimizing the layout of a wind farm according to the present application.
  • the related technology cable power loss cost model only involves the average annual utilization hours of the wind farm and the rated power of the wind turbine.
  • the cable power loss cost model ignores the influence of the voltage drop along the cable of the wind farm and the reactive power; on the other hand, the cable power loss cost model assumes that all wind turbines output energy under the rated power state, but the wind turbines in the field Most of the time, it is in a non-rated working state, and due to the wake effect between the wind turbines, the output power of different wind turbines in the same time field is not the same. Therefore, the calculation process and results are unreasonable, so there is a big gap between the optimal results generated by the cable power loss cost model and the actual optimal results.
  • the embodiment of the present application provides a method for optimizing the layout of a wind farm.
  • the optimization method can be used for the layout optimization of an offshore wind farm, and can also be used for the layout optimization of an onshore wind farm.
  • the wind turbines collect wind energy and convert the wind energy into electrical energy.
  • a booster station is built.
  • the booster station is electrically connected to multiple wind turbines, and the electricity generated by multiple wind turbines is aggregated and boosted for long distance Transmission and transmission to the substation connected to the booster station.
  • offshore wind farms have built sea booster stations, which aggregate and boost the electric energy of wind turbines and send them to onshore substations.
  • the wind farm includes multiple nodes, including booster stations and multiple wind turbines.
  • the optimization method includes: obtaining the wind turbine position coordinates of multiple wind turbines; and using the meta-heuristic algorithm to solve the optimization problem with the objective of minimizing the objective function of the wind farm cable cost according to the wind turbine position coordinates, and obtain the booster station.
  • the connection relationship represents whether two or two of the multiple nodes are connected.
  • the objective function includes the power loss of the cable determined according to the power flow of the wind farm.
  • the optimization method of the embodiment of the present application considers the influence of the power flow of the wind farm, thereby considering the voltage drop along the cable and the influence of the reactive power.
  • the meta-heuristic algorithm is used to increase the voltage
  • the site selection of the station, the connection relationship of the nodes and the cables of each path are optimized simultaneously.
  • the factors considered in the optimization process are more in line with the actual working state of the wind turbine, and the factors considered are more comprehensive, so that the optimization process and results are more reasonable and can be closer.
  • the optimization results of the actual optimal results can improve the cost optimization effect to a greater extent, and can significantly improve the economic performance of the electrical design of the overall wind farm.
  • Fig. 1 shows a flowchart of an embodiment of a method 100 for optimizing the layout of a wind farm.
  • the wind farm includes multiple nodes, including booster stations and multiple wind turbines.
  • the optimization method 100 includes steps 101 and 102.
  • step 101 the position coordinates of the wind turbines of multiple wind turbines are acquired.
  • the position coordinates of the fan may include the coordinates of the fan in a Cartesian coordinate system.
  • the position coordinates of the wind turbine may include the latitude and longitude coordinates of the wind turbine.
  • the position coordinates of the fan may include the two-dimensional coordinates of the fan. From west to east is the positive direction of the x-axis of the two-dimensional coordinate system, from south to north is the positive direction of the y-axis of the two-dimensional coordinate system, and the two-dimensional coordinates of the wind turbine are the coordinates in the two-dimensional coordinate system.
  • the latitude and longitude coordinates or two-dimensional coordinates of the wind turbine may be obtained and converted into coordinates in a Cartesian coordinate system.
  • the position coordinates of the wind turbine may be read from a document (such as a Text document, an Excel document) in which the position coordinates of the wind turbine are recorded.
  • a document such as a Text document, an Excel document
  • the position coordinates of the wind turbine input by the user may be received.
  • step 102 according to the position coordinates of the wind turbine, the meta-heuristic algorithm is used to solve the optimization problem with the objective of minimizing the objective function of the wind farm cable cost, and the optimized solution of the position coordinates of the booster station and the connection of multiple nodes are obtained.
  • the connection relationship represents whether two or two of the multiple nodes are connected.
  • the objective function includes the power loss of the cable determined according to the power flow of the wind farm.
  • MetaHeuristic Algorigthm is an improvement of heuristic algorithm, which is the product of the combination of random algorithm and local search algorithm.
  • Meta-heuristic algorithms can include tabu search algorithm, simulated annealing algorithm, genetic algorithm, ant colony optimization algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, artificial neural network algorithm, etc.
  • the meta-heuristic algorithm can be used to iteratively search to find the global optimal solution or the approximate optimal solution of the optimization problem.
  • the "optimized solution” can be a global optimal solution or an approximate optimal solution.
  • the wind farm cable cost includes cable laying cost, cable material cost, and cable power loss cost.
  • the cost of cable laying is mainly the cost of laying and buried cables, which is mainly related to the length of the cable. The longer the cable length, the higher the cable laying cost.
  • the cable material cost can be the price of the purchased cable, which is related to the length and cross-sectional area of the cable. When the cross-sectional area is constant, the longer the cable length, the higher the cable material cost. When the length is constant, the larger the cross-sectional area of the cable, the higher the cable material cost.
  • the cable material cost can be equal to the product of the cable material cost per unit length and the cable length.
  • the cable power loss cost is the power loss cost of the cable between the wind turbines during the life of the wind farm, which is related to the length of the cable and the power carried by the cable.
  • the power carried by the cable is constant, the longer the cable length, the higher the power loss cost of the cable; when the length of the cable is constant, the greater the power carried by the cable, the higher the power loss cost of the cable.
  • the greater the power carried by the cable the larger the cross-sectional area of the cable needs to be.
  • the investment cost of the wind farm cable including the cable laying cost and the cable material cost can account for 10% of the total investment cost.
  • the cost of cable power loss also occupies a larger portion of the wind farm's life cycle. Therefore, in the design stage, it is especially important to consider the cost of cable power loss to optimize the cost of wind farm cables.
  • the position coordinates of the booster station may include the position coordinates of the booster station in a Cartesian coordinate system.
  • the position coordinates of the booster station may include the latitude and longitude coordinates of the booster station. In other embodiments, the position coordinates of the booster station may include the two-dimensional coordinates of the booster station. In some embodiments, the latitude and longitude coordinates or two-dimensional coordinates of the booster station can be obtained and converted to coordinates in a Cartesian coordinate system.
  • connection relationship represents whether multiple fans are connected and between the fans and the booster station.
  • “1" may be used to indicate connection
  • “0” may be used to indicate no connection.
  • “0” may be used to indicate connection
  • "1" may be used to indicate no connection.
  • the connection relationship of multiple nodes reflects the cable connection structure. The position of the wind turbine is known. After the position coordinates of the booster station and the connection relationship between multiple nodes are obtained, the length of each cable connecting the two nodes can be determined. The connection relationship is different, the cable path is different, the cable length may be different, and the number of fans carried by each cable may be different.
  • connection relationship affects the cost of the wind farm cable, so optimizing the connection relationship can improve the economic performance of the wind farm.
  • the cable information may include at least one of a cross-sectional area, a resistance value per unit length, and a current-carrying capacity. In some embodiments, the cable information may also include the price per unit length. In some embodiments, the cable information may include the cable type, and the cross-sectional area of different cables corresponds to different cable types. Choose the right cable and keep the cost as low as possible on the basis of meeting the current carrying capacity.
  • the location of the booster station, the connection relationship and the cable of each path all have an impact on the cost of the wind farm cable, and the location and connection relationship of the booster station affect each other, and affect the cable of each path, so the location of the booster station
  • the coordinates, the connection relationship of multiple nodes and the cables of each path are optimized at the same time, which can be comprehensively optimized to make the cost as small as possible.
  • the objective function includes the power loss of the cable, considering the influence of the power loss of the cable on the cost of the wind farm cable.
  • the power loss of the cable can be determined according to the power flow of the wind farm, and the non-convexity of the power flow of the wind farm is considered, so as to consider the influence of the cable power loss of the wind farm on the cost of the wind farm cable.
  • the optimization method considers the influence of the power flow of the wind farm, the influence of the voltage drop along the cable and the reactive power, and the influence of the power loss of the cable on the cost of the wind farm cable.
  • the meta-heuristic algorithm is used to select the location and node of the booster station. The connection relationship and the cables of each path are optimized at the same time.
  • the factors considered in the optimization process are more in line with the actual working state of the fan, and the factors considered are more comprehensive, so that the optimization process and results are more reasonable, and the optimization closer to the actual optimal results can be obtained.
  • the cost optimization effect can be improved to a greater extent, and the economic performance of the electrical design of the overall wind farm can be significantly improved.
  • Step 102 includes steps 201-207.
  • step 102 includes performing an optimization iteration step until the iteration end condition is satisfied.
  • the optimization iteration steps include steps 201-206.
  • a feasible solution of the objective function is determined.
  • the feasible solution includes the first dimensional information that characterizes the position coordinates of the booster station, the second dimensional information that characterizes the connection relationship of multiple nodes, and the path of each path that characterizes the corresponding connection relationship.
  • the third dimension of cable information is determined.
  • determining the feasible solution of the objective function includes initializing the feasible solution.
  • feasible solutions are initialized first, and feasible solutions are randomly generated in the solution space.
  • the meta-heuristic algorithm includes a particle swarm algorithm, by which the particle swarm algorithm is used to solve the optimization problem to obtain the optimized solution of the position coordinates of the booster station, the optimized solution of the connection relationship of multiple nodes, and Optimized solution for cable information.
  • a particle is a feasible solution
  • the initial feasible solution includes initializing the position and velocity of the particle, that is, the position and velocity of the particle randomly generated in the D-dimensional search space.
  • the D-dimensional search space is a three-dimensional search space.
  • the parameters of the meta-heuristic algorithm are initialized before the feasible solution is initialized.
  • the parameters can include inertia weight w, learning factors C1, C2, and random probability values.
  • determining the feasible solution of the objective function includes updating the feasible solution. After one iteration is completed, the feasible solution is updated and the next iteration is performed. In some embodiments, updating the feasible solution includes updating the position and velocity of the particles.
  • multiple nodes are used as vertices, and the booster station is used as the initial vertices, and a random tree is established to determine the second dimension information and obtain the connection relationship of the multiple nodes.
  • the electric energy of the wind turbine is concentrated to the booster station, so the booster station is used as the initial vertex, and the other vertices of the random tree are the wind turbines.
  • Each branch of the random tree is a path connecting two nodes, so the random tree is generated to obtain the connection relationship of the nodes.
  • the length of the branches of the random tree can be determined, thereby determining the length of the cable connecting the nodes.
  • the length of the branches of the random tree can be determined according to the first dimension information (the position coordinates of the booster station) and the coordinates of the wind turbine, that is, the coordinates of the initial vertex and the coordinates of other vertices.
  • building the random tree includes: using a meta-heuristic algorithm to select vertices from multiple wind turbines until multiple wind turbines are selected. After the booster station is selected as the initial vertex, the meta-heuristic algorithm is used to randomly select other vertices to formulate branches, thereby generating a random tree. In some embodiments, a particle swarm algorithm may be used to select vertices from multiple wind turbines.
  • the wind turbines and booster stations of the wind farm can be numbered, and the following sets and matrices can be defined:
  • Set I includes the vertices that have been connected in the random tree
  • Set II includes the vertices that have not been connected in the random tree
  • Set II I Including the length of each branch (that is, each path) in Set I;
  • Adjacency matrix includes the distance between each pair of adjacent vertices.
  • a random tree layout is randomly made, and sets I, II, and IV are empty, and all vertices are stored in set II.
  • the vertex is transferred from set II to set I.
  • a new vertex (fan) in set II is randomly selected through meta-heuristic algorithm, transferred from set II to set I, and the branch is made.
  • the newly formulated branch length (cable length) is added to set III, and the number of fans assumed by the corresponding branch in set IV is increased by one. Select vertices in this way until all vertices are selected, set II is empty, and terminate the process.
  • the meta-heuristic algorithm is used to select vertices from multiple wind turbines until multiple wind turbines are selected, and the booster station is used as the initial vertex to establish a random tree, and obtain the number of wind turbines carried by each path and each path The length of the cable.
  • the adjacency matrix can be determined according to the first dimension information (the position coordinates of the booster station) and the coordinates of the wind turbine.
  • the step 201 of determining a feasible solution of the objective function includes steps 301-303.
  • step 301 the number of fans carried by each path is determined according to the second dimension information. After the connection relationship is determined, the number of wind turbines carried by each path can be obtained.
  • the minimum cross-sectional area of the cable of each path is determined according to the number of fans.
  • the minimum cross-sectional area of the cable for each path is the minimum cross-sectional area that meets the current carrying capacity. According to the number of fans carried by each path, the current carrying capacity of each path can be determined, so that the minimum cross-sectional area can be determined.
  • the third-dimensional information is determined according to the minimum cross-sectional area, and the cable cross-sectional area of each path corresponding to the third-dimensional information is not less than the minimum cross-sectional area.
  • the cross-sectional area of the cable corresponding to the third-dimensional information of the feasible solution is not less than the minimum cross-sectional area.
  • the cable cross-sectional area corresponding to the third-dimensional information of the feasible solution is greater than the minimum cross-sectional area. So as to ensure that the selected cable can meet the requirements of current carrying capacity.
  • the third dimension information may include the cable type. In other embodiments, the third-dimensional information may include the cable cross-sectional area. A collection of various cable information can be obtained, and the cable of each path can be selected from the collection to ensure that the cross-sectional area of the cable of each path is not less than the minimum cross-sectional area of the cable of the path.
  • the cable information may include cable type and/or cable cross-sectional area. In some embodiments, the cable information may also include the resistance per unit length, the current carrying capacity, and the price per unit length of the cables corresponding to different cable cross-sectional areas.
  • the number of fans carried by each branch is stored in set IV, so that the minimum cable cross-sectional area of each branch can be determined.
  • the selection range of the third-dimensional information only includes cable information that is not less than the minimum cable cross-sectional area.
  • step 202 the power flow calculation is performed according to the position coordinates of the wind turbine, the first dimension information, the second dimension information, and the third dimension information to obtain the power flow of the wind farm.
  • the power flow calculation is performed based on the particles determined this time to obtain the power flow of the wind farm.
  • the particles include first-dimensional information, second-dimensional information, and third-dimensional information.
  • the third-dimensional information may include the resistance value, capacitance value, and inductance value per unit length of the cable, which is used for power flow calculation.
  • the position coordinates of the wind turbine, the first dimension information, the second dimension information, and the third dimension information may be input into the cost calculation model including the objective function of the wind farm cable cost, and the cost calculation model calculates the power flow.
  • the step 202 of calculating the power flow includes steps 401 and 402.
  • step 401 the topology of multiple nodes is determined according to the position coordinates of the wind turbine, the first dimension information, and the second dimension information.
  • the topology includes the position of nodes, the relative positions and connections between nodes.
  • the topology includes the position of the fan, the position of the booster station, the relative position between the fans and the fan and the booster station, the connection relationship between the fans and the fan and the booster station.
  • a coordinate matrix is determined, and the coordinate matrix includes wind turbine position coordinates and first-dimensional information (booster station position coordinates). According to the coordinate matrix, determine the relative position matrix between nodes.
  • a topology of multiple nodes is generated according to the relative position matrix and the second dimension information (connection relationship).
  • the topology reflects the layout of the wind farm. According to the relative position relationship between nodes, the length of each path can be determined.
  • a power flow calculation is performed according to the topology and third-dimensional information to obtain the power flow of the wind farm.
  • the third dimension information may include the resistance value, capacitance value, and inductance value per unit length of the cable.
  • the position of the fan According to the position of the fan, the position of the booster station, the relative position between the fans and the fan and the booster station, the connection relationship between the fans and the fan and the booster station, and the resistance value and capacitance value of the unit length of the cable And the inductance value, calculate the power flow of the wind farm, the voltage of the node and the current on each path can be obtained. In this way, accurate wind farm power flow calculations can be carried out.
  • the optimization method 100 includes: obtaining various wind condition information of the wind farm.
  • wind resource data may be collected.
  • Wind rose diagrams can be drawn using wind resource data.
  • the wind resource data can be divided into multiple groups according to the wind direction, for example, 36 groups, each with 10°.
  • the wind distribution of each group is represented by the Weibull distribution, and the Weibull distribution of wind speed is obtained.
  • wind rose diagrams can be drawn.
  • the wind rose diagram reflects the distribution of wind direction and wind speed in a certain period of time in the wind farm.
  • the wind condition information may include the wind direction, the wind speed range and the probability within the corresponding wind speed range in the wind rose chart.
  • the output power of each fan under multiple wind conditions is determined according to the position coordinates of the fan and wind condition information. In some embodiments, the output power is determined by the wake model according to the position coordinates of the wind turbine and the wind condition information. Through the wake model, determine the output power of each wind turbine under various wind conditions. Under different wind conditions, consider the impact of wake effects on the output power of the wind turbines, and calculate the power generation of each wind turbine under different wind conditions.
  • power flow calculations are performed based on the output power of multiple wind turbines under various wind conditions to obtain the power flow of the wind farm under various wind conditions.
  • the power flow of the wind farm under each wind condition is calculated separately.
  • the power flow calculation is performed according to the output power of multiple wind turbines under the corresponding wind condition, and the power flow of the wind farm under the corresponding wind condition is obtained. Due to the wake effect between wind turbines, the output power of different wind turbines in a wind farm at the same time is not the same.
  • the wake model is used to reasonably estimate the output power of the wind turbine under different wind conditions, taking into account the distribution characteristics of wind resources of the wind farm, and considering the wake effect, so that the power flow of the wind farm can be calculated more accurately, which is more in line with the actual situation of the wind farm. Get a more optimized layout of wind farms. Through accurate power flow calculations, more specific technical restrictions can be imposed on the wind farm. For example, the voltage drop along the line can be specified to be no more than 5%, the voltage variation range of each path can be specified within a specified range, and the voltage phase angle change can be specified Within the specified phase angle range.
  • multiple wind conditions are calculated The tide of the wind farm under.
  • the power flow of the wind farm under the corresponding wind condition is calculated according to the output power of multiple wind turbines, the topology of multiple nodes and the third-dimensional information of feasible solutions. In this way, the power flow of the wind farm is accurately calculated.
  • step 203 the power loss of the cable is determined according to the power flow of the wind farm.
  • the Newton-Raphson method can be used to calculate the power flow and calculate the power loss of the cable.
  • the cable power loss of the wind farm under various wind conditions is determined according to the power flow of the wind farm under various wind conditions. In each wind condition, determine the cable power loss of the wind farm under the corresponding wind condition according to the power flow of the wind farm under the corresponding wind condition.
  • step 204 the value of the objective function is determined according to the power loss of the cable.
  • the value of the objective function is obtained, and the value of the objective function is the cable cost of the wind farm, so that the value of the wind farm cable cost including the power loss cost of the cable is obtained.
  • the objective function may be referred to as the fitness function, and the value of the objective function is referred to as the fitness value.
  • the value of the objective function is determined according to the power loss of the cable under various wind conditions.
  • the objective function includes the sum of cable power loss under various wind conditions
  • the cost of the wind farm includes the sum of cable power loss costs under various wind conditions. Substituting the cable power loss under various wind conditions into the objective function, the value of the wind farm cable cost including the annual power loss cost of the wind farm is obtained.
  • the optimization iteration step includes: determining the cable dielectric loss according to the third-dimensional information and the power flow of the wind farm; and determining the value of the objective function according to the cable dielectric loss.
  • the value of the objective function includes the cost of cable power loss and the cost of cable dielectric loss.
  • the third-dimensional information includes the resistance value of the cable of each path, and the power flow of the wind farm includes the current flowing on the cable of each path and/or the voltage at both ends of the cable.
  • the cable dielectric loss can be calculated based on the resistance value and current (or voltage).
  • Wind farm cable cost includes cable dielectric loss cost.
  • the wind farm cable cost is calculated, and the value of wind farm cable cost including cable power loss cost and cable dielectric loss cost can be obtained, which can be more comprehensive Considering the loss cost of the wind farm, the optimization is more in line with the actual situation and more perfect.
  • the wind farm includes a substation for receiving electrical energy output by the booster station.
  • the substation can further transmit electrical energy to the main grid.
  • the substation can be connected to multiple booster stations and receive electric energy from multiple booster stations.
  • the booster station is a sea booster station, and the substation is an onshore substation.
  • the optimization method includes: obtaining the position coordinates of the substation.
  • the position coordinates of the substation can include coordinates in a Cartesian coordinate system, or latitude and longitude coordinates, or two-dimensional coordinates.
  • the location of the substation is determined. In other embodiments, the location of the substation can be solved by a meta-heuristic algorithm.
  • the objective function includes the cost of the transmission cable from the booster station to the substation.
  • the cost of the transmission cable is related to the cable length and the cable cross-sectional area between the booster station and the substation.
  • the location of the booster station affects the cable length, and the cable cross-sectional area is related to the power transmitted between the booster station and the substation.
  • the transmission cable cost includes the circuit power loss cost, and in some embodiments, it may also include the cable dielectric loss.
  • the optimization iteration steps include: determining the cost of the transmission cable according to the location coordinates of the substation and the first dimension information; and determining the value of the objective function according to the cost of the transmission cable.
  • the objective function further includes the cost of the transmission cable.
  • the length of the transmission cable can be determined according to the position coordinates of the substation and the position coordinates of the booster station, and then the cost of the transmission cable can be determined.
  • the cost of the transmission cable is determined based on the location coordinates of the substation, the first dimension information, and the power flow of the wind farm.
  • the electric energy that flows into the substation can be determined according to the power flow of the wind farm, the cross-sectional area of the transmission cable can be determined, and then the cost of the transmission cable can be determined.
  • the value of the objective function includes the cost of the transmission cable, so that the cost of the wind farm cable can be considered more comprehensively, so that the optimization is more in line with the actual situation and more complete.
  • a wind farm includes a power collection system and a power transmission system.
  • the power collection system includes multiple wind turbines and cables connecting the wind turbines and booster stations.
  • the power transmission system includes booster stations, substations, and cables connecting booster stations and substations. In this embodiment, both the cable cost of the power collection system and the cable cost of the power transmission system are considered, and the cable cost of the wind farm is fully considered in the optimization process.
  • the value of the objective function can be calculated by the cost calculation model of the objective function described above.
  • the minimum value of the objective function is found as the optimal value of the objective function.
  • the current objective function value (fit value) is compared with the individual optimal value. If it is better than the individual optimal value, the position of the individual optimal value is the current particle position, that is, the current The feasible solution is the solution corresponding to the individual optimal value; further compare the value of the current objective function with the optimal value of the entire group. If the value of the current objective function is better than the optimal value of the entire group, then the position corresponding to the optimal value of the entire group is set The position of the current particle, that is, the current feasible solution is the solution corresponding to the optimal value of the entire population.
  • the minimum value of the objective function is sought as the optimal value of the objective function, so that each iteration seeks the minimum value of the wind farm cable cost. Iterate in this way to find the individual with the least economic cost.
  • step 206 it is determined whether the iteration end condition is satisfied. Satisfying the iteration end condition may include reaching the maximum number of iterations and/or reaching the iteration time threshold. If the conditions for the end of the iteration are not met, the iteration continues.
  • the first dimension information of the feasible solution corresponding to the optimal value of the current objective function is used as the optimized solution of the position coordinates of the booster station, and the second dimension information is used as the optimal solution of the multiple nodes.
  • the optimized solution of the connection relationship uses the third-dimensional information as the optimized solution of the cable information.
  • the optimal solution of the current objective function is found, and the corresponding feasible solution is used as the optimization result.
  • the cable information includes the cable type.
  • the cable information includes the cross-sectional area of the cable, etc., and the corresponding cable type can be selected according to the cable information.
  • FIG. 5 shows a schematic diagram of the location of the wind turbines of a wind farm.
  • FIG. 6 is a schematic diagram of a wind farm layout obtained after optimizing the layout of the wind farm in FIG. 5 according to an embodiment of the application. From Figure 5, we can see the location of each wind turbine in the wind farm and the average annual equivalent full load hours. The fans are numbered. S1 in Fig. 6 is the position of the booster station. From Fig. 4, it can be seen that the position of the optimized booster station, the connection relationship between the fans and the fan and the booster station, and the cross-sectional area of the cable. The method of the embodiment of the present application considers the influence of the power flow of the wind farm on the power loss cost of the cable, and the layout optimization is more reasonable.
  • FIG. 7 shows a block diagram of an embodiment of a system 700 for optimizing the layout of a wind farm.
  • the optimization system 700 includes one or more processors 701 for implementing the optimization method 100.
  • the optimization system 700 may include a computer-readable storage medium 704, which may store a program that can be called by the processor 701, and may include a non-volatile storage medium.
  • the optimization system 700 may include a memory 703 and an interface 702. In some embodiments, the optimization system 700 may also include other hardware according to actual applications.
  • the computer-readable storage medium 704 of the embodiment of the present application has a program stored thereon, and when the program is executed by the processor 701, the optimization method 100 is implemented.
  • This application can take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program codes.
  • Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology.
  • the information can be computer-readable instructions, data structures, program modules, or other data.
  • Examples of computer-readable storage media include, but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only Memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage , Magnetic cassette tape, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by computing devices.
  • PRAM phase change memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • RAM random access memory
  • ROM read-only Memory
  • EEPROM electrically erasable programmable read-only memory
  • flash memory or other memory technology
  • CD-ROM compact disc
  • DVD digital versatile disc
  • Magnetic cassette tape magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by computing devices.

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Abstract

一种风电场布局的优化方法、优化系统及计算机可读存储介质。风电场包括多个节点，多个节点包括升压站和多台风机。优化方法包括：获取多台风机的风机位置坐标（101）；及根据所述风机位置坐标，利用元启发式算法对以风电场电缆成本的目标函数最小化为目标的优化问题进行求解，得到升压站的位置坐标的优化解、多个节点的连接关系的优化解，及连接多个节点的多条路径的电缆信息的优化解（102）。其中，连接关系表征多个节点中两两节点是否连接。目标函数包括根据风电场的潮流确定的电缆功率损耗。

Description

风电场布局的优化方法、优化系统及计算机可读存储介质 技术领域
本申请涉及风电场规划技术领域，尤其涉及一种风电场布局的优化方法、优化系统及计算机可读存储介质。
背景技术
风是没有公害的能源之一，而且取之不尽，用之不竭。对于缺水、缺燃料和交通不便的沿海岛屿、草原牧区、山区和高原地带，因地制宜地利用风力发电，非常适合，大有可为。风力发电是指把风能转为电能。利用风力发电非常环保，且风能蕴量巨大，因此日益受到世界各国的重视。风电场的电缆的布局和升压站的选址对风电场的成本有很大的影响，如何优化电缆布局和升压站选址成为风电场布局优化的一项重要任务。
发明内容
本申请提供一种改进的风电场布局的优化方法、优化系统及计算机可读存储介质。
根据本申请实施例的一个方面，提供一种风电场布局的优化方法，所述风电场包括多个节点，所述多个节点包括升压站和多台风机，所述优化方法包括：获取所述多台风机的风机位置坐标；及根据所述风机位置坐标，利用元启发式算法对以风电场电缆成本的目标函数最小化为目标的优化问题进行求解，得到所述升压站的位置坐标的优化解、所述多个节点的连接关系的优化解，及连接所述多个节点的多条路径的电缆信息的优化解； 其中，所述连接关系表征所述多个节点中两两节点是否连接，所述目标函数包括根据所述风电场的潮流确定的电缆功率损耗。
根据本申请实施例的另一个方面，提供一种风电场布局的优化系统，优化系统包括一个或多个处理器，用于实现上述优化方法。
根据本申请实施例的另一个方面，提供一种计算机可读存储介质，其上存储有程序，该程序被处理器执行时，实现上述优化方法。
本申请一些实施例的优化方法考虑风电场的潮流的影响，以考虑电缆功率损耗对风电场电缆成本的影响，且利用元启发式算法，对升压站的选址、节点的连接关系和每条路径的电缆进行同时优化，可以更大程度地提升对成本的优化效果，可明显提升整体风电场的电气设计的经济性能。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1所示为本申请风电场布局的优化方法的一个实施例的流程图。
图2所示为图1所示的优化方法的利用元启发式算法对以风电场电缆成本的目标函数最小化为目标的优化问题进行求解的步骤的子流程图。
图3所示为图2所示的确定目标函数的可行解的步骤的子流程图。
图4所示为图2所示的进行潮流计算的步骤的子流程图。
图5所示为一个风电场的风机位置示意图。
图6所示为本申请实施例方法对图5的风电场进行优化布局后获得的风电场布局的示意图。
图7所示为本申请风电场布局的优化系统的一个实施例的模块框图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本申请相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本申请的一些方面相一致的装置和方法的例子。
在本申请使用的术语是仅仅出于描述特定实施例的目的，而非旨在限制本申请。在本申请和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”可以指单数形式，也可包括多数形式，除非上下文清楚地表示其他含义。还应当理解，本文中使用的术语“和/或”是指并包含一个或多个相关联的列出项目的任何或所有可能组合。除非另行指出，“连接”或者“相连”等类似的词语并非限定于物理的或者机械的连接，而且可以包括电性的连接，不管是直接的还是间接的。
相关技术的电缆功率损耗成本模型只涉及风电场的年平均利用小时数与风机额定功率。一方面，该电缆功率损耗成本模型忽略了风电场的电缆沿线电压降幅以及无功功率的影响；另一方面，该电缆功率损耗成本模型假设所有风机在额定功率状态下输出能量，然而场内风机大部分时间处 于非额定工作状态，并且由于风机间的尾流效应，同一时间场内不同风机输出的功率也并不相同。因此，计算过程及结果并不合理，所以根据该电缆功率损耗成本模型生成的优化结果与实际最优结果存在较大差距。
本申请实施例提供风电场布局的优化方法，优化方法可以用于海上风电场的布局优化，也可以用于陆上风电场的布局优化。风电场分布有多台风机，风机采集风能，将风能转换为电能。对于长距离传输电能的风电场，尤其是远海风电场，为了减少电缆传输损耗，建设升压站，升压站与多台风机电连接，将多台风机发出的电能汇总升压后，进行长距离传输，输送给与升压站连接的变电站。例如，海上风电场建设有海上升压站，将风机的电能汇总升压后输送到陆上变电站。
风电场包括多个节点，多个节点包括升压站和多台风机。优化方法包括：获取多台风机的风机位置坐标；及根据所述风机位置坐标，利用元启发式算法对以风电场电缆成本的目标函数最小化为目标的优化问题进行求解，得到升压站的位置坐标的优化解、多个节点的连接关系的优化解，及连接多个节点的多条路径的电缆信息的优化解。其中，连接关系表征多个节点中两两节点是否连接。目标函数包括根据风电场的潮流确定的电缆功率损耗。
本申请实施例的优化方法考虑风电场的潮流的影响，从而考虑电缆沿线电压降幅以及无功功率的影响，如此考虑电缆功率损耗对风电场电缆成本的影响，利用元启发式算法，对升压站的选址、节点的连接关系和每条路径的电缆进行同时优化，优化过程考虑的因素更符合风机实际工作状态，考虑的因素更全面充分，从而优化过程和结果更加合理，可以得到更接近实际最优结果的优化结果，可以更大程度地提升对成本的优化效果，可明显提升整体风电场的电气设计的经济性能。
下面结合附图，对本申请的风电场布局的优化方法、优化系统及计算机可读存储介质进行详细说明。在不冲突的情况下，下述的实施例及实 施方式中的特征可以相互组合。
图1所示为风电场布局的优化方法100的一个实施例的流程图。风电场包括多个节点，多个节点包括升压站和多台风机。优化方法100包括步骤101和102。
在步骤101中，获取多台风机的风机位置坐标。
在一些实施例中，风机位置坐标可以包括在笛卡尔坐标系中的风机的坐标。在一些实施例中，风机位置坐标可以包括风机的经纬度坐标。在另一些实施例中，风机位置坐标可以包括风机的二维坐标。自西向东为二维坐标系的x轴的正方向，自南向北为二维坐标系的y轴的正方向，风机的二维坐标为在二维坐标系中的坐标。在一些实施例中，可以获取风机的经纬度坐标或二维坐标，转换为笛卡尔坐标系中的坐标。
在一些实施例中，可以从记录有风机位置坐标的文档(例如Text文档、Excel文档)中读取风机位置坐标。在另一些实施例中，可以接收用户输入的风机位置坐标。
在步骤102中，根据风机位置坐标，利用元启发式算法对以风电场电缆成本的目标函数最小化为目标的优化问题进行求解，得到升压站的位置坐标的优化解、多个节点的连接关系的优化解，及连接多个节点的多条路径的电缆信息的优化解。其中，连接关系表征多个节点中两两节点是否连接。目标函数包括根据风电场的潮流确定的电缆功率损耗。
元启发式算法(MetaHeuristic Algorigthm)是启发式算法的改进，是随机算法与局部搜索算法相结合的产物。元启发式算法可以包括禁忌搜索算法、模拟退火算法、遗传算法、蚁群优化算法、粒子群优化算法、人工鱼群算法、人工蜂群算法、人工神经网络算法等。可以利用元启发式算法，迭代寻优，找到优化问题的全局最优解或者近似最优解。“优化解”可以为全局最优解或近似最优解。
优化问题为风电场电缆成本的目标函数最小化。在一些实施例中，风电场电缆成本包括电缆铺设成本、电缆材料成本和电缆功率损耗成本。电缆铺设成本主要为铺设填埋电缆的成本，主要与电缆的长度相关。电缆的长度越长，电缆铺设成本越高。电缆材料成本可以为购买到的电缆的价格，与电缆的长度和截面积相关。截面积一定时，电缆的长度越长，电缆材料成本越高。长度一定时，电缆的截面积越大，电缆材料成本越高。电缆材料成本可以等于单位长度的电缆材料成本与电缆长度的乘积。电缆功率损耗成本为风电场的寿命期间风机间电缆的功率损耗成本，与电缆的长度和电缆承载的功率相关。电缆承载的功率一定时，电缆长度越长，电缆功率损耗成本越高；电缆的长度一定时，电缆承载的功率越大，电缆功率损耗成本越高。电缆承载的功率越大，电缆的截面积需越大。例如，在海上风电场中，包括电缆铺设成本和电缆材料成本的风电场电缆的投资成本可占总投资成本的10％。除了投资成本外，电缆功率损耗成本在风电场全寿命周期内同样占据较大一部分比重。因此在设计阶段，考虑电缆功率损耗成本，以对风电场电缆成本进行优化也尤其重要。
基于风机位置已知，寻找升压站的位置坐标的优化解、多个节点的连接关系的优化解，及连接多个节点的多条路径的电缆信息的优化解，以使风电场电缆成本尽可能小，实现对风电场的布局的优化。升压站的位置不同，风机到升压站的电缆长度不同，从而影响风电场电缆成本，因此寻找升压站的位置坐标的优化解可以有利于优化风电场的成本。升压站的位置坐标可以包括在笛卡尔坐标系中的升压站的位置坐标。在一些实施例中，升压站的位置坐标可以包括升压站的经纬度坐标。在另一些实施例中，升压站的位置坐标可以包括升压站的二维坐标。在一些实施例中，可以得到升压站的经纬度坐标或二维坐标，转换为笛卡尔坐标系中的坐标。
连接关系表征多台风机之间及风机与升压站之间是否连接。在一些实施例中，可以用“1”表示连接，“0”表示不连接。在另一些实施例中， 可以用“0”表示连接，“1”表示不连接。多个节点的连接关系体现电缆连接结构。风机的位置已知，升压站的位置坐标和多个节点的连接关系得到后，每条连接两节点路径的电缆的长度可以确定。连接关系不同，电缆路径不同，电缆长度可能不同，且每条电缆承载的风机数可能不同。电缆连接的风机不同，电缆承载的风机数不同时，电缆两端的电压降幅和流过的电流可能不同，电缆承载的功率可能不同。因此连接关系影响风电场电缆成本，所以对连接关系进行优化可以提升风电场的经济性能。
在一些实施例中，电缆信息可以包括截面积、单位长度的电阻值和载流量中的至少一个。在一些实施例中，电缆信息还可以包括单位长度价格。在一些实施例中，电缆信息可以包括电缆类型，不同的电缆的截面积对应不同的电缆类型。选择合适的电缆，在满足载流量的基础上使成本尽可能低。升压站的位置、连接关系和每条路径的电缆对风电场电缆成本均有影响，而且升压站的位置和连接关系互相影响，且影响每条路径的电缆，因此对升压站的位置坐标、多个节点的连接关系和每条路径的电缆同时进行优化，可综合优化使成本尽可能小。
目标函数包括电缆功率损耗，考虑电缆功率损耗对风电场电缆成本的影响。电缆功率损耗可以根据风电场的潮流确定，考虑风电场的潮流非凸性，从而考虑风电场的电缆功率损耗对风电场电缆成本的影响。优化方法考虑风电场的潮流的影响，考虑电缆沿线电压降幅以及无功功率的影响，如此考虑电缆功率损耗对风电场电缆成本的影响，利用元启发式算法，对升压站的选址、节点的连接关系和每条路径的电缆进行同时优化，优化过程考虑的因素更符合风机实际工作状态，考虑的因素更全面充分，从而优化过程和结果更加合理，可以得到更接近实际最优结果的优化结果，可以更大程度地提升对成本的优化效果，可明显提升整体风电场的电气设计的经济性能。
图2所示为利用元启发式算法对以风电场电缆成本的目标函数最小 化为目标的优化问题进行求解的步骤102的子流程图。步骤102包括步骤201-207。在一些实施例中，步骤102包括执行优化迭代步骤，直至满足迭代结束条件。优化迭代步骤包括步骤201-206。
在步骤201中，确定目标函数的可行解，可行解包括表征升压站的位置坐标的第一维度信息、表征多个节点的连接关系的第二维度信息和表征对应连接关系的每条路径的电缆信息的第三维度信息。
在一些实施例中，确定目标函数的可行解包括初始化可行解。在第一次迭代时，先初始化可行解，在求解空间内随机生成可行解。在一些实施例中，元启发式算法包括粒子群算法，利用所述粒子群算法，对优化问题进行求解，得到升压站的位置坐标的优化解、多个节点的连接关系的优化解，及电缆信息的优化解。一个粒子为一个可行解，初始化可行解包括初始化粒子的位置与速度，即在D维搜索空间中随机产生粒子的位置和速度。在本实施例中，D维搜索空间为三维搜索空间。
在一些实施例中，在初始化可行解之前，初始化元启发式算法的参数。初始化粒子群算法的参数，参数可包括惯性权重w、学习因子C1、C2和随机概率值。
在一些实施例中，确定目标函数的可行解包括更新可行解。在一次迭代完成后，更新可行解，进行下一次迭代。在一些实施例中，更新可行解包括更新粒子的位置和速度。
在一些实施例中，以多个节点为顶点，其中升压站作为初始顶点，建立随机树，以确定第二维度信息，得到多个节点的连接关系。风机的电能汇聚至升压站，因此以升压站作为初始顶点，随机树的其他顶点为风机。随机树的每个分支为连接两个节点的路径，因此生成随机树即获得节点的连接关系。在一些实施例中，可以确定随机树的分支的长度，从而确定连接节点的电缆的长度。可以根据第一维度信息(升压站的位置坐标)和风 机的坐标，即初始顶点的坐标和其他顶点的坐标，确定随机树的分支的长度。
在一些实施例中，建立随机树包括：利用元启发式算法，从多台风机中选择顶点，直至多台风机均被选择。升压站作为初始顶点被选择后，利用元启发式算法随机选择其他顶点，以制定分支，从而生成随机树。在一些实施例中，可以利用粒子群算法从多台风机中选择顶点。
在一些实施例中，可以对风电场的风机和升压站进行编号，并定义如下集合和矩阵：
集合Ⅰ：包括已经在随机树中连接的顶点；
集合Ⅱ：包括尚未在随机树中连接的顶点；
集合ⅡⅠ：包括集合Ⅰ中每个分支(即每条路径)的长度；
集合IV：包括随机树中每个分支承载的风机数；
邻接矩阵：包括每对相邻的顶点之间的距离。
初始时，随机制定随机树布局，并且集合Ⅰ、ⅡⅠ和IV为空，所有顶点都存储在集合II中。随机树生成过程中，从给定的顶点(升压站)开始，将该顶点从集合Ⅱ转移到集合Ⅰ中。然后通过元启发式算法随机选择集合Ⅱ中的一个新顶点(风机)，从集合Ⅱ转移到集合Ⅰ中，并制定分支。同时，根据邻接矩阵中的相应信息，将新制定的分支的长度(电缆长度)添加到集合Ⅲ，并将集合IV中的对应分支承担的风机数加1。如此选择顶点直到所有的顶点都被选择，集合Ⅱ为空，终止该过程。如此，利用元启发式算法，从多台风机中选择顶点，直至多台风机均被选择，且以升压站作为初始顶点，建立随机树，并且得到每条路径承载的风机数和每条路径的电缆长度。邻接矩阵可以根据第一维度信息(升压站的位置坐标)和风机的坐标确定。
在一些实施例中，如图3所示，确定目标函数的可行解的步骤201 包括步骤301-303。在步骤301中，根据第二维度信息，确定每条路径承载的风机数。连接关系确定后，可以得到每条路径承载的风机数量。
在步骤302中，根据风机数，确定每条路径的电缆的最小截面积。每条路径的电缆的最小截面积为最小满足载流量的截面积。根据每条路径承载的风机数，可以确定每条路径的载流量，从而可以确定最小截面积。
在步骤303中，根据最小截面积，确定第三维度信息，第三维度信息对应的每条路径的电缆截面积不小于最小截面积。可行解的第三维度信息对应的电缆截面积不小于最小截面积。在一些实施例中，可行解的第三维度信息对应的电缆截面积大于最小截面积。从而保证选择的电缆可以满足载流量的要求。
在一些实施例中，第三维度信息可以包括电缆类型。在另一些实施例中，第三维度信息可以包括电缆截面积。可以获取多种电缆信息的集合，可以从集合中选择每条路径的电缆，以保证每条路径的电缆的截面积不小于该条路径的电缆的最小截面积。在一些实施例中，电缆信息可以包括电缆类型和/或电缆截面积。在一些实施例中，电缆信息还可以包括对应不同电缆截面积的电缆的单位长度电阻、载流量和单位长度价格。
在一些实施例中，通过上文所述的方法，每个分支承载的风机数存储在集合Ⅳ中，如此可以确定每个分支的最小电缆截面积。第三维度信息的选择范围仅包含不小于最小电缆截面积对应的电缆信息。
回到图2，在步骤202中，根据风机位置坐标、第一维度信息、第二维度信息和第三维度信息，进行潮流计算，获得风电场的潮流。
在一些实施例中，根据本次确定的粒子，进行潮流计算，获得风电场的潮流。粒子包括第一维度信息、第二维度信息和第三维度信息。根据风机位置坐标、升压站的位置坐标、节点连接关系和电缆信息，进行潮流计算。第三维度信息可以包括电缆的单位长度的电阻值、电容值和电感值， 用于潮流计算。在一些实施例中，可以将风机位置坐标、第一维度信息、第二维度信息和第三维度信息输入包括风电场电缆成本的目标函数的成本计算模型中，成本计算模型计算潮流。
在一些实施例中，如图4所示，计算潮流的步骤202包括步骤401和402。在步骤401中，根据风机位置坐标、第一维度信息和第二维度信息，确定多个节点的拓扑。拓扑包括节点的位置、节点之间的相对位置和连接关系。拓扑包括风机位置、升压站位置、风机之间及风机与升压站之间的相对位置、风机之间及风机与升压站之间的连接关系。在一些实施例中，确定坐标矩阵，坐标矩阵包括风机位置坐标和第一维度信息(升压站位置坐标)。根据坐标矩阵，确定节点之间的相对位置矩阵。以升压站为初始顶点，根据相对位置矩阵和第二维度信息(连接关系)，生成多个节点的拓扑。拓扑体现风电场的布局。根据节点之间的相对位置关系，可以确定每条路径的长度。
在步骤402中，根据拓扑和第三维度信息，进行潮流计算，获得风电场的潮流。第三维度信息可以包括电缆的单位长度的电阻值、电容值和电感值。
根据风机位置、升压站位置、风机之间及风机与升压站之间的相对位置、风机之间及风机与升压站之间的连接关系，以及电缆的单位长度的电阻值、电容值和电感值，计算风电场的潮流，可以获得节点的电压和每条路径上的电流。如此可以进行精确的风电场的潮流计算。
在一些实施例中，优化方法100包括：获取风电场的多种风况信息。在一些实施例中，在风电场建设的规划阶段，可以收集风资源数据。利用风资源数据可以绘制风玫瑰图。在一些实施例中，可以根据风向将风资源数据划分为多组，例如36组，则每组10°。每组的风分布通过威布尔分布表示，得到风速威布尔分布。根据不同的风向和相应的风速威布尔分布，可以绘制风玫瑰图。风玫瑰图体现风电场某一时间段内的风向和风速的分 布情况。风况信息可以包括风玫瑰图中的风向、风速范围和相应风速范围内的概率。
在一些实施例中，根据风机位置坐标和风况信息，确定每台风机在多种风况下的输出功率。在一些实施例中，根据风机位置坐标和风况信息，通过尾流模型确定输出功率。通过尾流模型，确定多种风况下每台风机的输出功率。在不同的风况下，考虑尾流效应对风机输出功率的影响，计算每台风机在不同风况下的发电量。
在一些实施例中，根据多台风机在多种风况下的输出功率进行潮流计算，获得多种风况下的风电场的潮流。分别计算获得每种风况下的风电场的潮流。在每一风况下，根据对应风况下的多台风机的输出功率进行潮流计算，得到对应风况下的风电场的潮流。由于风机间的尾流效应，同一时间风电场内的不同风机输出的功率并不相同。通过尾流模型合理估算不同风况下的风机的输出功率，考虑了风电场的风资源分布特性，考虑了尾流效应，如此更加精确地计算风电场的潮流，更符合风电场实际情况，可以得到更优化的风电场布局。通过精确的潮流计算，可以对风电场做更加具体的技术限制，例如可以规定沿线压降不高于5％，可以规定每条路径的电压变动范围在规定的范围内，可以规定电压相角变化在规定的相角范围内。
在一些实施例中，根据风电场的多台风机在多种风况下的输出功率、多个节点的拓扑和可行解的第三维度信息(每条路径的电缆信息)，计算多种风况下的风电场的潮流。在每一风况下，根据多台风机的输出功率、多个节点的拓扑和可行解的第三维度信息，计算对应风况下的风电场的潮流。如此对风电场的潮流进行精确计算。
回到图2，在步骤203中，根据风电场的潮流，确定电缆功率损耗。
在一些实施例中，可以通过牛顿—拉夫逊法(Newton-Raphson)进 行潮流计算，并计算电缆功率损耗。
在一些实施例中，根据多种风况下的风电场的潮流，确定多种风况下的风电场的电缆功率损耗。在每种风况下，根据对应的风况的风电场的潮流，确定对应风况下的风电场的电缆功率损耗。
在步骤204中，根据电缆功率损耗，确定目标函数的值。
将确定的电缆功率损耗代入目标函数中，得到目标函数的值，目标函数的值为风电场的电缆成本，如此得到包含电缆功率损耗成本的风电场电缆成本的值。在一些实施例中，粒子群算法中，目标函数可称作适应度函数，目标函数的值称作适应值。
在一些实施例中，根据多种风况下的电缆功率损耗，确定目标函数的值。在一些实施例中，目标函数包括多种风况下的电缆功率损耗的总和，风电场成本包括多种风况下的电缆功率损耗成本的总和。将多种风况下的电缆功率损耗代入目标函数中，得到包括风电场全年的功率损耗成本的风电场电缆成本的值。
在一些实施例中，优化迭代步骤包括：根据第三维度信息和风电场的潮流，确定电缆介质损耗；根据电缆介质损耗，确定目标函数的值。目标函数的值包括电缆功率损耗成本和电缆介质损耗成本。第三维度信息包括每条路径的电缆的电阻值，风电场的潮流包括每条路径的电缆上流过的电流和/或电缆两端的电压。可以根据电阻值和电流(或电压)计算得到电缆介质损耗。风电场电缆成本包括电缆介质损耗成本，根据电缆功率损耗成本和电缆介质损耗，计算风电场电缆成本，得到包含电缆功率损耗成本和电缆介质损耗成本的风电场电缆成本的值，从而可以更全面地考虑风电场的损耗成本，使得优化更符合实际情况，更完善。
在一些实施例中，风电场包括用于接收升压站输出的电能的变电站。变电站可以进一步将电能传输至主电网。变电站可以与多个升压站连接， 接收多个升压站的电能。在一些实施例中，升压站为海上升压站，变电站为陆上变电站。优化方法包括：获取变电站的位置坐标。变电站的位置坐标可以包括在笛卡尔坐标系中的坐标，或经纬度坐标，或二维坐标。在一些实施例中，变电站的位置确定。在另一些实施例中，变电站的位置可以通过元启发式算法求解。
目标函数包括升压站至变电站的输电电缆成本。输电电缆成本与升压站和变电站之间的电缆长度和电缆截面积有关，升压站的位置影响电缆长度，电缆截面积与升压站和变电站之间传输的电能有关。输电电缆成本包括电路功率损耗成本，在一些实施例中，还可以包括电缆介质损耗。
优化迭代步骤包括：根据变电站的位置坐标和第一维度信息，确定输电电缆成本；及根据输电电缆成本，确定目标函数的值。目标函数进一步包括输电电缆成本。根据变电站的位置坐标和升压站的位置坐标可以确定输电电缆的长度，进而可以确定输电电缆的成本。在一些实施例中，根据变电站的位置坐标、第一维度信息和风电场的潮流，确定输电电缆成本。可以根据风电场的潮流确定汇入变电站的电能，可以确定输电电缆的截面积，进而确定输电电缆的成本。
目标函数的值包括输电电缆成本，从而可以更全面地考虑风电场电缆成本，使得优化更符合实际情况，更完善。风电场包括集电系统和输电系统，集电系统包括多台风机和连接风机和升压站的电缆，输电系统包括升压站、变电站和连接升压站和变电站的电缆。在本实施例中，既考虑集电系统的电缆成本，也考虑输电系统的电缆成本，在优化过程中对风电场的电缆成本进行充分考虑。可以通过上文所述的目标函数的成本计算模型计算目标函数的值。
继续参考图2，在步骤205中，寻找目标函数的最小值作为目标函数的最优值。在一些实施例中，在粒子群算法中，比较当前目标函数的值(适应值)与个体最优值，如果优于个体最优值，则个体最优值的位置就 是当前粒子位置，即将当前可行解作为个体最优值对应的解；进一步比较当前目标函数的值与群体全体最优值，如果当前目标函数的值优于群体全体最优值，则设置群体全体最优值对应的位置就是当前粒子的位置，即当前可行解作为群体全体最优值对应的解。每次迭代中，寻找目标函数的最小值作为目标函数的最优值，如此每次迭代寻找风电场电缆成本的最小值。如此更新迭代，寻找经济成本最小的个体。
在步骤206中，判断是否满足迭代结束条件。满足迭代结束条件可以包括达到最大迭代次数和/或达到迭代时间阈值。未满足迭代结束条件时，继续迭代。
在步骤207中，在满足迭代结束条件时，将当前目标函数的最优值对应的可行解的第一维度信息作为升压站的位置坐标的优化解，将第二维度信息作为多个节点的连接关系的优化解，将第三维度信息作为电缆信息的优化解。迭代结束时，找到当前的目标函数的最优解，其对应的可行解作为优化结果。如此得到风电场布局的优化结果，确定升压站的位置、风机的连接关系、风机和升压站的连接关系，和每条路径的电缆信息。在一些实施例中，电缆信息包括电缆类型。在另一些实施例中，电缆信息包括电缆的截面积等，可以根据电缆信息，选择相应的电缆类型。
图5所示为一个风电场的风机位置示意图。图6所示为本申请实施例方法对图5的风电场进行优化布局后获得的风电场布局的示意图。从图5可以看出风电场的每台风机的位置和年平均等效满负荷小时数。对风机进行了编号。图6中S1为升压站的位置，从图4中可以看出优化后的升压站的位置、风机之间及风机和升压站之间的连接关系，以及电缆的截面积。本申请实施例方法考虑风电场的潮流对电缆功率损耗成本的影响，布局优化更合理。
图7所示为风电场布局的优化系统700的一个实施例的模块框图。优化系统700包括一个或多个处理器701，用于实现优化方法100。在一些 实施例中，优化系统700可以包括计算机可读存储介质704，计算机可读存储介质可以存储有可被处理器701调用的程序，可以包括非易失性存储介质。在一些实施例中，优化系统700可以包括内存703和接口702。在一些实施例中，优化系统700还可以根据实际应用包括其他硬件。
本申请实施例的计算机可读存储介质704，其上存储有程序，该程序被处理器701执行时，实现优化方法100。
本申请可采用在一个或多个其中包含有程序代码的存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。计算机可读存储介质包括永久性和非永久性、可移动和非可移动媒体，可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机可读存储介质的例子包括但不限于：相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。
需要说明的是，在本文中，诸如“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上对本申请实施例所提供的方法和装置进行了详细介绍，本文中应用了具体个例对本申请的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的一般技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。
本专利文件披露的内容包含受版权保护的材料。该版权为版权所有人所有。版权所有人不反对任何人复制专利与商标局的官方记录和档案中所存在的该专利文件或者该专利披露。

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  1. 一种风电场布局的优化方法，所述风电场包括多个节点，所述多个节点包括升压站和多台风机，其特征在于，所述优化方法包括：
    获取所述多台风机的风机位置坐标；及
    根据所述风机位置坐标，利用元启发式算法对以风电场电缆成本的目标函数最小化为目标的优化问题进行求解，得到所述升压站的位置坐标的优化解、所述多个节点的连接关系的优化解，及连接所述多个节点的多条路径的电缆信息的优化解；其中，所述连接关系表征所述多个节点中两两节点是否连接，所述目标函数包括根据所述风电场的潮流确定的电缆功率损耗。
  2. 根据权利要求1所述的优化方法，其特征在于，所述利用元启发式算法对以风电场电缆成本的目标函数最小化为目标的优化问题进行求解，包括：执行优化迭代步骤，直至满足迭代结束条件，所述优化迭代步骤包括：
    确定所述目标函数的可行解，所述可行解包括表征所述升压站的位置坐标的第一维度信息、表征所述多个节点的连接关系的第二维度信息和表征对应所述连接关系的每条路径的电缆信息的第三维度信息；
    根据所述风机位置坐标、所述第一维度信息、所述第二维度信息和所述第三维度信息，进行潮流计算，获得所述风电场的潮流；
    根据所述风电场的潮流，确定所述电缆功率损耗；
    根据所述电缆功率损耗，确定所述目标函数的值；
    寻找所述目标函数的最小值作为所述目标函数的最优值；
    在满足所述迭代结束条件时，将当前所述目标函数的最优值对应的所述可行解的所述第一维度信息作为所述升压站的位置坐标的优化解，将所述第二维度信息作为所述多个节点的连接关系的优化解，将所述第三维度信息作为所述电缆信息的优化解。
  3. 根据权利要求2所述的优化方法，其特征在于，所述根据所述风机位置坐标、所述第一维度信息、所述第二维度信息和所述第三维度信息，进行潮流计算，获得所述风电场的潮流，包括：
    根据所述风机位置坐标、所述第一维度信息和所述第二维度信息，确定所述多个节点的拓扑，所述拓扑包括节点的位置、节点之间的相对位置和连接关系；
    根据所述拓扑和所述第三维度信息，进行潮流计算，获得所述风电场的潮流。
  4. 根据权利要求2所述的优化方法，其特征在于，所述优化方法包括：获取所述风电场的多种风况信息；
    所述优化迭代步骤包括：
    根据所述风机位置坐标和所述风况信息，确定每台所述风机在多种风况下的输出功率；
    根据所述多台风机在所述多种风况下的所述输出功率进行潮流计算，获得所述多种风况下的所述风电场的潮流；
    根据所述多种风况下的所述风电场的潮流，确定所述多种风况下的所述电缆功率损耗；及
    根据所述多种风况下的所述电缆功率损耗，确定所述目标函数的值。
  5. 根据权利要求4所述的优化方法，其特征在于，所述根据所述风机位置坐标和所述风况信息，确定每台所述风机在多种风况下的输出功率，包括：
    根据所述风机位置坐标和所述风况信息，通过尾流模型确定所述输出功率。
  6. 根据权利要求2所述的优化方法，其特征在于，所述确定所述目标函数的可行解，包括：
    根据所述第二维度信息，确定每条所述路径承载的风机数；
    根据所述风机数，确定每条所述路径的电缆的最小截面积；及
    根据所述最小截面积，确定所述第三维度信息，所述第三维度信息对应的每条所述路径的电缆截面积不小于所述最小截面积。
  7. 根据权利要求6所述的优化方法，其特征在于，所述确定所述目标函数的可行解，包括：
    以所述多个节点为顶点，其中所述升压站作为初始顶点，建立随机树，以确定所述第二维度信息。
  8. 根据权利要求7所述的优化方法，其特征在于，所述建立随机树，包括：
    利用元启发式算法，从所述多台风机中选择顶点，直至所述多台风机均被选择。
  9. 根据权利要求2所述的优化方法，其特征在于，所述优化迭代步骤包括：
    根据所述第三维度信息和所述风电场的潮流，确定所述电缆介质损耗；
    根据所述电缆介质损耗，确定所述目标函数的值。
  10. 根据权利要求2所述的优化方法，其特征在于，所述风电场包括用于接收所述升压站输出的电能的变电站，所述目标函数包括所述升压站至所述变电站的输电电缆成本；所述优化方法包括：获取所述变电站的位置坐标；
    所述优化迭代步骤包括：
    根据所述变电站的位置坐标和所述第一维度信息，确定所述输电电缆成本；及
    根据所述输电电缆成本，确定所述目标函数的值。
  11. 根据权利要求1-10中任一项所述的优化方法，其特征在于，所述元启发式算法包括粒子群算法；所述利用元启发式算法对以风电场电缆成本的目标函数最小化为目标的优化问题进行求解，包括：
    利用所述粒子群算法，对所述优化问题进行求解，得到所述升压站的位置坐标的优化解、所述多个节点的连接关系的优化解，及所述电缆信息 的优化解。
  12. 一种风电场布局的优化系统，其特征在于，包括一个或多个处理器，用于实现如权利要求1-11中任一项所述的优化方法。
  13. 一种计算机可读存储介质，其特征在于，其上存储有程序，该程序被处理器执行时，实现如权利要求1-11中任一项所述的优化方法。
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