CN113131528A - Method, device, equipment and storage medium for determining optimal capacity of wind fire bundling - Google Patents
Method, device, equipment and storage medium for determining optimal capacity of wind fire bundling Download PDFInfo
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- CN113131528A CN113131528A CN202110444535.8A CN202110444535A CN113131528A CN 113131528 A CN113131528 A CN 113131528A CN 202110444535 A CN202110444535 A CN 202110444535A CN 113131528 A CN113131528 A CN 113131528A
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- 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
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- 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
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- 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
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- 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]
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- 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
- H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel generators
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- 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
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- 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
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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- 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
- Y02E10/76—Power conversion electric or electronic aspects
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Abstract
The invention discloses a method, a device, equipment and a storage medium for determining the optimal capacity of wind-fire bundling, and relates to the technical field of power system planning. The method comprises the steps of obtaining a plurality of wind-fire capacity proportioning information according to the calculation boundary and the operation constraint of a regional power system; acquiring annual output time sequence data of the regional wind power plant according to the corresponding relation between the meteorological data and the wind speed output; according to the time sequence operation simulation model, acquiring annual time sequence operation data; performing temporary stability checking and adjustment on annual time sequence operation data to obtain annual operation data corresponding to wind-fire capacity ratio information; and screening the optimal annual operation data according to the annual operation data corresponding to all the wind-fire capacity matching information to obtain the optimal wind-fire capacity matching. The invention solves the problem that transient instability risks exist in a planning result because transient stability constraint of a wind fire bundling outgoing channel is difficult to take into account due to the existing wind fire bundling capacity ratio setting.
Description
Technical Field
The invention relates to the technical field of power system planning, in particular to a method, a device, equipment and a storage medium for determining the optimal capacity of wind fire bundling.
Background
In order to solve the problem of environmental pollution, a clean and sustainable electric energy pattern is constructed, and renewable energy represented by wind power is greatly supported in China. Offshore wind power has also begun to gain widespread attention as a large number of onshore wind farms are built and put into operation. Experience of onshore wind power delivery and consumption in the 'three-north' area shows that thermal power and wind power are bundled and then delivered, so that wind power fluctuation can be effectively stabilized, and the utilization rate of a delivery power transmission channel is improved. The construction of large-scale coal and electricity in coastal areas provides sufficient objective conditions for sending out offshore wind power by bundling wind and fire. In order to fully exert the advantages of the 'wind-fire bundling' sending mode, domestic scholars develop researches on wind-fire volume ratio.
The traditional wind-fire bundling capacity ratio setting method mainly calculates the utilization hours of wind power and thermal power by considering the operation cost of a thermal power generating unit, establishes an optimization model of wind-fire bundling capacity configuration, and provides a solution only from the economical point of view. However, for the 'wind-fire bundling' long-distance delivery channel, different wind-fire capacity ratios are directly related to the wind-fire output ratio, and the transmission capacity of the delivery channel is also limited by the potential transient stability constraint.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a storage medium for determining the optimal capacity of wind-fire bundling, which are used for solving the problem that the transient stability constraint of a wind-fire bundling outgoing channel is difficult to take into account due to the existing wind-fire bundling capacity ratio setting, so that the planning result has the risk of transient instability,
in order to achieve the above object, an embodiment of the present invention provides a method for determining an optimal capacity of wind fire bundling, including:
acquiring a plurality of wind-fire capacity proportioning information according to the calculation boundary and the operation constraint of the regional power system;
acquiring annual output time sequence data of the regional wind power plant according to the corresponding relation between the meteorological data and the wind speed output;
according to the time sequence operation simulation model, acquiring annual time sequence operation data; the time sequence operation simulation model aims at the lowest total operation cost of units of a thermal power plant and a wind power plant in an area and the highest clean energy consumption rate, and input data of the time sequence operation simulation model comprises wind-fire capacity ratio information and annual output time sequence data;
performing temporary stability checking and adjustment on the annual time sequence operation data to obtain annual operation data corresponding to the wind-fire capacity matching information;
and screening the optimal annual operation data according to the annual operation data corresponding to all the wind-fire capacity matching information to obtain the optimal wind-fire capacity matching.
Preferably, the performing transient stability checking and adjusting on the annual time sequence operation data to obtain annual operation data corresponding to the wind-fire capacity proportioning information includes:
performing transient stability checking on the annual time sequence operation data to obtain transient stability limited time;
adjusting the firepower power output proportion according to a preset proportion aiming at the transient stability limited moment to obtain updated time sequence operation data;
and taking the updated time sequence operation data as the input of the time sequence operation simulation model until the transient stability limited moment does not appear any more, and outputting the final wind-fire capacity proportioning information and the corresponding annual operation data.
Preferably, the computational boundaries include generator parameters, inter-regional tie-line transmission plans, annual load forecast data, and renewable energy output data; the operation constraints comprise power balance constraints, standby constraints, inter-subsystem transmission channel static stability limit constraints, generator climbing constraints and minimum start-stop time constraints.
Preferably, the time sequence operation simulation model adopts a time sequence decomposition and automatic rollback technology to realize the rapid solution of the model.
The embodiment of the invention also provides a device for determining the optimal capacity of wind fire bundling, which comprises:
the system comprises a proportioning information acquisition module, a calculation module and a control module, wherein the proportioning information acquisition module is used for acquiring a plurality of wind-fire capacity proportioning information according to the calculation boundary and the operation constraint of a regional power system;
the time sequence data acquisition module is used for acquiring annual output time sequence data of the regional wind power plant according to the corresponding relation between the meteorological data and the wind speed output;
the operation data acquisition module is used for operating the simulation model according to the time sequence to obtain annual time sequence operation data; the time sequence operation simulation model aims at the lowest total operation cost of units of a thermal power plant and a wind power plant in an area and the highest clean energy consumption rate, and input data of the time sequence operation simulation model comprises wind-fire capacity ratio information and annual output time sequence data;
the checking and adjusting module is used for carrying out temporary stability checking and adjusting on the annual time sequence operation data to obtain annual operation data corresponding to the wind-fire capacity matching information;
and the screening module is used for screening the optimal annual operation data according to the annual operation data corresponding to all the wind-fire capacity matching information to obtain the optimal wind-fire capacity matching.
Preferably, the check and adjustment module is specifically configured to:
performing transient stability checking on the annual time sequence operation data to obtain transient stability limited time;
adjusting the firepower power output proportion according to a preset proportion aiming at the transient stability limited moment to obtain updated time sequence operation data;
and taking the updated time sequence operation data as the input of the time sequence operation simulation model until the transient stability limited moment does not appear any more, and outputting the final wind-fire capacity proportioning information and the corresponding annual operation data.
Preferably, the computational boundaries include generator parameters, inter-regional tie-line transmission plans, annual load forecast data, and renewable energy output data; the operation constraints comprise power balance constraints, standby constraints, inter-subsystem transmission channel static stability limit constraints, generator climbing constraints and minimum start-stop time constraints.
Preferably, the time sequence operation simulation model adopts a time sequence decomposition and automatic rollback technology to realize the rapid solution of the model.
The embodiment of the invention also provides computer terminal equipment which comprises one or more processors and a memory. A memory coupled to the processor for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the wind fire bundling optimal capacity determination method according to any one of the embodiments.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for determining an optimal capacity for wind fire bundling according to any of the above embodiments is implemented.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method for determining the optimal capacity of wind fire bundling, which comprises the following steps: acquiring a plurality of wind-fire capacity proportioning information according to the calculation boundary and the operation constraint of the regional power system; acquiring annual output time sequence data of the regional wind power plant according to the corresponding relation between the meteorological data and the wind speed output; according to the time sequence operation simulation model, acquiring annual time sequence operation data; the time sequence operation simulation model aims at the lowest total operation cost of units of a thermal power plant and a wind power plant in an area and the highest clean energy consumption rate, and input data of the time sequence operation simulation model comprises wind-fire capacity ratio information and annual output time sequence data; performing temporary stability checking and adjustment on the annual time sequence operation data to obtain annual operation data corresponding to the wind-fire capacity matching information; and screening the optimal annual operation data according to the annual operation data corresponding to all the wind-fire capacity matching information to obtain the optimal wind-fire capacity matching. The method considers transient stability constraint and determines the optimal capacity of wind-fire bundling based on time sequence operation simulation. Transient stability limit of the power transmission channel is used for checking and adjusting transient stability of a time sequence operation result, and great influence on economy of the power transmission channel and the power transmission channel can be avoided on the premise of eliminating transient stability risk.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for determining an optimal capacity for wind fire bundling according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a wind fire bundling optimal capacity determination method according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of a simulation framework of a sequential operation simulation model according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of transient stability checking and adjusting in the method for determining the optimal capacity of wind fire bundling according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a situation of limited transient stability prediction for a day in a wind fire bundled power system according to an embodiment of the invention;
FIG. 6 is a schematic diagram of a wind curtailment situation with different configurations of offshore wind capacities according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a situation that transient stability of a channel is limited when offshore wind power with different capacities is configured according to an embodiment of the present invention;
FIG. 8 is a graph illustrating a change in economic indicators of a system configured with different capacity offshore wind power according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an optimal wind fire bundling capacity determining apparatus according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a computer terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for determining an optimal capacity of wind fire bundling according to an embodiment of the present invention. The method for determining the optimal capacity of wind fire bundling provided by the embodiment comprises the following steps:
step S110, acquiring a plurality of wind-fire capacity proportioning information according to the calculation boundary and the operation constraint of a regional power system;
step S120, acquiring annual output time sequence data of the regional wind power plant according to the corresponding relation between the meteorological data and the wind speed output;
step S130, acquiring annual time sequence operation data according to the time sequence operation simulation model; the method comprises the steps that a time sequence operation simulation model aims at the lowest total operation cost of a thermal power plant and a unit of a wind power plant in an area and the highest clean energy consumption rate, and input data of the time sequence operation simulation model comprise wind-fire capacity ratio information and annual output time sequence data;
step S140, carrying out temporary stability checking and adjustment on annual time sequence operation data to obtain annual operation data corresponding to wind-fire capacity proportioning information;
and S150, screening the optimal annual operation data according to the annual operation data corresponding to all the wind-fire capacity matching information to obtain the optimal wind-fire capacity matching.
In this embodiment of the present invention, step S140 performs transient stability checking and adjusting on the annual time series operation data to obtain annual operation data corresponding to the wind-fire capacity proportioning information, and includes: performing transient stability checking on annual time sequence operation data to obtain transient stability limited time; adjusting the firepower power output proportion according to a preset proportion aiming at the transient stability limited moment to obtain updated time sequence operation data; and (4) taking the updated time sequence operation data as the input of the time sequence operation simulation model until the transient stability limited moment does not appear any more, and outputting the final wind-fire capacity proportioning information and the corresponding annual operation data.
In the embodiment of the invention, the calculation boundary comprises generator parameters, an inter-area tie line transmission plan, annual load prediction data and renewable energy output data; the operation constraints comprise power balance constraints, standby constraints, inter-subsystem transmission channel static stability limit constraints, generator climbing constraints and minimum start-stop time constraints.
In the embodiment of the invention, the time sequence operation simulation model adopts the time sequence decomposition and automatic rollback technology to realize the rapid solution of the model.
Referring to fig. 2, fig. 2 is a schematic flow chart of a method for determining an optimal capacity of wind fire bundling according to another embodiment. In this embodiment, the data preparation method includes the following steps: (1) determining a calculation boundary and operation constraints, and setting n different wind-fire capacity proportioning levels. (2) And aiming at the wind power plant in the planning, generating annual output time sequence data of the wind power plant according to meteorological data and the corresponding relation between the wind speed and the output. (3) The method comprises the steps of establishing a small-scale time sequence operation simulation model by taking the lowest total operation cost of a unit and the highest consumption rate of clean energy as targets, inputting calculation data into the model, and achieving fast solving of the model by adopting a time sequence decomposition and automatic rollback technology to obtain annual time sequence operation data. (4) And (4) carrying out transient stability checking on the annual operation data obtained in the step (3) by using transient stability constraint of an outgoing channel obtained by transient simulation. And reducing the thermal power output in the period of possible transient stability risk until the transient stability limited risk is eliminated. The data obtained at this time is the final operation data under the wind-fire capacity ratio. (5) And (5) repeating the steps (2) to (4) under different wind-fire capacity ratios to obtain annual operation data of all the wind-fire capacity ratios. (6) And comparing the environmental protection, economic and safety indexes of operation under different wind-fire capacity ratios to finally obtain the optimal capacity ratio.
In the present embodiment, the time series operation simulation is a mixed integer linear programming model in the small scale. The objective function mainly includes: the system comprises startup and shutdown cost, power generation cost and wind and light abandoning punishment.
The channel transient stability limit used for checking the time sequence operation result is considered to be only related to thermal power output, and the channel transient stability limit obtaining method under any thermal power output comprises the following steps: a series of typical values of thermal power output at equal intervals from the minimum technical output to rated capacity are selected, and the wind power output is continuously improved under each thermal power output level until the transient stability limit of a power transmission channel is reached. Therefore, the transient stability limit of the power transmission channel under each typical thermal power output value can be obtained. And finally, solving the transient stability limit of the channel under any thermal power output through piecewise linear interpolation.
And alternately checking and adjusting the time sequence operation result until the checking result shows that the transient stability risk does not exist. (1) Checking for temporary stability: and inputting the operation result of the time sequence simulation, comparing the annual transient stability limit change curve with the annual actual transmission power of the channel, and screening out the time and limited power when transient stability is limited. The annual transient stability limit change curve is generated by combining an annual thermal power output time sequence curve with a thermal power output-transient stability limit change curve. (2) And (3) transient stability adjustment: and aiming at the transient stability limited moment screened out by transient stability checking, reducing all thermal power output according to the same proportion lambda to obtain an updated time sequence simulation result, and taking the result as the input of the next transient stability checking. The step length lambda which is adjusted down each time determines the precision of the checking result, the smaller the step length is, the more the checking result is fit with the transient stability limit, and the higher the utilization rate of the channel is. However, at the same time, the number of times adjustment is required is increased, which increases the time consumption.
Referring to fig. 3, fig. 3 is a schematic diagram of a simulation framework of a time sequence operation simulation model according to an embodiment of the present invention. In the present embodiment, the time series operation simulation model of the power system constructs a framework of the model using a simulation system. The simulation framework includes three phases: data preparation, running simulation and result output. In the data preparation stage, a calculation boundary and an operation constraint are determined, wherein the calculation boundary comprises generator parameters, an inter-area tie line transmission plan, annual load prediction data and renewable energy output data; the operation constraints comprise power balance constraints, standby constraints, inter-subsystem transmission channel static stability limit constraints, generator climbing constraints and minimum start-stop time constraints. And in the operation simulation stage, a small-level time sequence operation simulation model is established, and the model is solved by adopting a time sequence overlapping and automatic rollback technology. And the result output stage outputs the annual operation result of the system.
In one embodiment, The meteorological data is MERRA (The model Era retrospecific-analysis for Research and Applications) data, and The MERRA data is re-analyzed meteorological data made and distributed by a global modeling contracting office of The gordad space flight center.
Referring to fig. 4, fig. 4 is a schematic flow chart illustrating transient stability checking and adjusting in the method for determining the optimal capacity of wind fire bundling according to an embodiment of the present invention. In this embodiment, the transient stability checking and adjusting includes the following steps:
(1) and searching the transient stability limit of the power transmission channel under different wind-fire output ratios through transient simulation software, and performing piecewise linear interpolation to obtain a corresponding curve of thermal power output-channel transient stability limit.
Specifically, when the 4480MW thermal power full output is achieved, the transient stability limit of the channel is the lowest, which is 4900MW, only 420MW offshore wind power can be sent out at this time, and when the thermal power output is reduced to 57% of the maximum output, the transient stability limit of the power transmission channel can reach 7000MW, that is, the static stability limit of the power transmission channel. The transient stability limit under any other thermal power output is obtained by piecewise linear interpolation of typical values.
(2) Inputting the operation result of the time sequence simulation, obtaining a annual transient stability limit change curve by the annual thermal power output time sequence curve, then comparing the annual transient stability limit change curve with the annual actual transmission power of the channel, and screening the moment with transient stability limit.
(3) Aiming at the limited moment, reducing all thermal power output according to the same ratio lambda, and considering the self balance of a wind-fire bundling receiving end system due to the gap caused by reducing the thermal power output;
specifically, λ is set to 0.01, that is, only 1% of the thermal power output is adjusted down each time, so as to avoid the economic impact on the thermal power output caused by excessive adjustment down.
(4) Replacing the reduced thermal power output with the time sequence operation simulation data to obtain an updated time sequence simulation result; and acquiring a new round of annual transient stability limit curve of the outward delivery channel according to the thermal power output-transient stability limit corresponding curve.
(5) And (4) repeating the steps (2) to (4) by using the updated time sequence simulation result and the annual transient stability limit curve until the annual limited time number is 0 or the thermal power output is reduced to the minimum technical output.
Referring to fig. 5, fig. 5 is a schematic diagram illustrating a situation of transient stability limitation prediction of a wind fire bundled power system at a certain day according to an embodiment of the present invention. As shown in fig. 5(a), before transient stability adjustment, the sum of the wind-fire electric power outputs of 9:00-16:00 and 19:00-21:00, that is, the power to be sent through the power transmission channel exceeds the transient stability limit at this time, and the power transmission channel has a transient stability risk. After adjustment, as shown in fig. 5(b), the transient stability is not limited all the day, and the system security is ensured.
As shown in fig. 6, the wind curtailment situation is schematically shown when different offshore wind capacities are configured. It can be seen that the abandoned wind rate and the abandoned wind power are improved along with the increase of the capacity scale of the offshore wind, but the abandoned wind rate is less than 1% at all wind power capacity levels, and is still at a lower level, so that the environmental protection property meets the requirements.
As shown in fig. 7, a schematic diagram of a situation that the transient stability of the channel is limited when offshore wind power with different capacities is configured. It can be seen that, as the scale of the capacity of the accessed wind power increases, the number of the transient stability limited hours of the channel continuously increases, and the transient stability limited situation gradually increases. And the transient stability limited hours at all levels are reduced to 0 by the adjustment of the method provided by the invention, and the transient stability limited situation is improved.
As shown in fig. 8, a system economic index change curve chart when different capacities of offshore wind power are configured. It can be seen that the offshore wind power capacity of the access system is increased, the offshore wind power and the bundled thermal power "seize" the transmission capacity, and the annual utilization hours of the thermal power are reduced. On the other hand, the reduction of the thermal power output improves the transient stability limit of the power transmission channel, and the annual average utilization rate of the power transmission channel is improved. Therefore, by setting the expected thermal power economy and transmission channel economy indexes, the recommended capacity of accessing offshore wind can be obtained. If the number of the thermal power year average utilization hours is set to be not less than 4000 hours, the utilization rate of the power transmission channel is not less than 70%. The screening is carried out according to the two indexes, and the requirement is met only when the 4800MW capacity offshore wind power is accessed, so that the recommended accessed offshore wind power scale is 4800MW, and the wind-fire ratio is 1.07: 1.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an optimal wind fire bundling capacity determining device according to an embodiment of the present invention. The wind fire bundling optimum capacity determination device of the embodiment includes:
the ratio information obtaining module 210 is configured to obtain a plurality of wind-fire capacity ratio information according to a calculation boundary and an operation constraint of a regional power system;
the time sequence data acquisition module 220 is used for acquiring annual output time sequence data of the regional wind power plant according to the corresponding relation between the meteorological data and the wind speed output;
an operation data obtaining module 230, configured to obtain annual time series operation data according to the time series operation simulation model; the method comprises the steps that a time sequence operation simulation model aims at the lowest total operation cost of a thermal power plant and a unit of a wind power plant in an area and the highest clean energy consumption rate, and input data of the time sequence operation simulation model comprise wind-fire capacity ratio information and annual output time sequence data;
the checking and adjusting module 240 is used for performing transient stability checking and adjusting on the annual time sequence operation data to obtain annual operation data corresponding to the wind-fire capacity proportioning information;
and the screening module 250 is used for screening the optimal annual operation data according to the annual operation data corresponding to all the wind-fire capacity matching information to obtain the optimal wind-fire capacity matching.
In this embodiment of the present invention, the checking and adjusting module 240 is specifically configured to:
performing transient stability checking on annual time sequence operation data to obtain transient stability limited time;
adjusting the firepower power output proportion according to a preset proportion aiming at the transient stability limited moment to obtain updated time sequence operation data;
and (4) taking the updated time sequence operation data as the input of the time sequence operation simulation model until the transient stability limited moment does not appear any more, and outputting the final wind-fire capacity proportioning information and the corresponding annual operation data.
In the embodiment of the invention, the calculation boundary comprises generator parameters, an inter-area tie line transmission plan, annual load prediction data and renewable energy output data; the operation constraints comprise power balance constraints, standby constraints, inter-subsystem transmission channel static stability limit constraints, generator climbing constraints and minimum start-stop time constraints.
In the embodiment of the invention, the time sequence operation simulation model adopts the time sequence decomposition and automatic rollback technology to realize the rapid solution of the model.
The specific definition of the wind fire bundling optimum capacity determination device can be referred to the definition of the wind fire bundling optimum capacity method in the foregoing, and the detailed description is omitted here. The modules in the wind fire bundling optimal capacity determination device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Referring to fig. 10, fig. 10 is a schematic structural diagram of a computer terminal device according to an embodiment of the present invention. An embodiment of the present invention provides a computer terminal device, including one or more processors and a memory. The memory is coupled to the processor for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a wind fire bundling optimal capacity determination method as in any one of the embodiments described above.
The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the wind fire bundling optimal capacity determination method. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, for performing the wind fire bundling optimum capacity determination method described above, and achieving the technical effects consistent with the above-described method.
In another exemplary embodiment, there is also provided a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the wind fire bundling optimum capacity determination method in any one of the above embodiments. For example, the computer readable storage medium may be the above-described memory including program instructions executable by a processor of a computer terminal device to perform the above-described wind fire bundling optimum capacity determination method, and to achieve technical effects consistent with the above-described method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A wind-fire bundling optimal capacity determination method is characterized by comprising the following steps:
acquiring a plurality of wind-fire capacity proportioning information according to the calculation boundary and the operation constraint of the regional power system;
acquiring annual output time sequence data of the regional wind power plant according to the corresponding relation between the meteorological data and the wind speed output;
according to the time sequence operation simulation model, acquiring annual time sequence operation data; the time sequence operation simulation model aims at the lowest total operation cost of units of a thermal power plant and a wind power plant in an area and the highest clean energy consumption rate, and input data of the time sequence operation simulation model comprises wind-fire capacity ratio information and annual output time sequence data;
performing temporary stability checking and adjustment on the annual time sequence operation data to obtain annual operation data corresponding to the wind-fire capacity matching information;
and screening the optimal annual operation data according to the annual operation data corresponding to all the wind-fire capacity matching information to obtain the optimal wind-fire capacity matching.
2. The method for determining the optimal wind fire bundling capacity according to claim 1, wherein the performing transient stability checking and adjusting on the annual time series operation data to obtain annual operation data corresponding to the wind fire capacity proportioning information comprises:
performing transient stability checking on the annual time sequence operation data to obtain transient stability limited time;
adjusting the firepower power output proportion according to a preset proportion aiming at the transient stability limited moment to obtain updated time sequence operation data;
and taking the updated time sequence operation data as the input of the time sequence operation simulation model until the transient stability limited moment does not appear any more, and outputting the final wind-fire capacity proportioning information and the corresponding annual operation data.
3. The wind fire bundling optimal capacity determination method according to claim 1, wherein the calculation boundaries comprise generator parameters, inter-area link transmission plans, annual load prediction data and renewable energy output data; the operation constraints comprise power balance constraints, standby constraints, inter-subsystem transmission channel static stability limit constraints, generator climbing constraints and minimum start-stop time constraints.
4. The method for determining the optimal wind fire bundling capacity according to claim 1, wherein the time sequence operation simulation model adopts a time sequence decomposition and automatic rollback technology to realize the rapid solution of the model.
5. An apparatus for determining an optimal capacity for wind fire bundling, comprising:
the system comprises a proportioning information acquisition module, a calculation module and a control module, wherein the proportioning information acquisition module is used for acquiring a plurality of wind-fire capacity proportioning information according to the calculation boundary and the operation constraint of a regional power system;
the time sequence data acquisition module is used for acquiring annual output time sequence data of the regional wind power plant according to the corresponding relation between the meteorological data and the wind speed output;
the operation data acquisition module is used for operating the simulation model according to the time sequence to obtain annual time sequence operation data; the time sequence operation simulation model aims at the lowest total operation cost of units of a thermal power plant and a wind power plant in an area and the highest clean energy consumption rate, and input data of the time sequence operation simulation model comprises wind-fire capacity ratio information and annual output time sequence data;
the checking and adjusting module is used for carrying out temporary stability checking and adjusting on the annual time sequence operation data to obtain annual operation data corresponding to the wind-fire capacity matching information;
and the screening module is used for screening the optimal annual operation data according to the annual operation data corresponding to all the wind-fire capacity matching information to obtain the optimal wind-fire capacity matching.
6. The wind fire bundling optimal capacity determination apparatus according to claim 5, wherein the checking and adjusting module is specifically configured to:
performing transient stability checking on the annual time sequence operation data to obtain transient stability limited time;
adjusting the firepower power output proportion according to a preset proportion aiming at the transient stability limited moment to obtain updated time sequence operation data;
and taking the updated time sequence operation data as the input of the time sequence operation simulation model until the transient stability limited moment does not appear any more, and outputting the final wind-fire capacity proportioning information and the corresponding annual operation data.
7. The wind fire bundling optimal capacity determination apparatus according to claim 5, wherein the calculation boundaries comprise generator parameters, inter-area link transmission plans, annual load prediction data and renewable energy output data; the operation constraints comprise power balance constraints, standby constraints, inter-subsystem transmission channel static stability limit constraints, generator climbing constraints and minimum start-stop time constraints.
8. The wind fire bundling optimal capacity determination device according to claim 5, wherein the time sequence operation simulation model adopts time sequence decomposition and automatic rollback technology to realize fast solving of the model.
9. A computer terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the wind fire bundling optimal capacity determination method of any one of claims 1-4.
10. A computer-readable storage medium on which a computer program is stored, the computer program, when executed by a processor, implementing the wind fire bundling optimum capacity determination method according to any one of claims 1 to 4.
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