CN115018171A - Combined operation method and system for new energy field station group and energy storage power station - Google Patents
Combined operation method and system for new energy field station group and energy storage power station Download PDFInfo
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Abstract
The embodiment of the application provides a joint operation method, a system, electronic equipment and a storage medium for a new energy field station group and an energy storage power station, wherein the method comprises the following steps: acquiring operation data of the new energy station group; calculating the power requirement of the new energy station group according to the operation data of the new energy station group; constructing a new energy station group cost model and an energy storage power station cost and income model; obtaining cost loss data according to a new energy station group cost model and power requirements; obtaining cost and income difference value data according to a cost and income model of the energy storage power station and power requirements; and carrying out optimization solution on the cost loss data and the cost and income difference value data to obtain an optimization result. By implementing the embodiment of the application, the new energy field station group and the energy storage power station can be operated more efficiently, and the economy and the stability of combined operation are improved.
Description
Technical Field
The application relates to the technical field of electric power operation, in particular to a method, a system, electronic equipment and a computer readable storage medium for joint operation of a new energy station group and an energy storage power station.
Background
The large-scale new energy grid-connected access easily causes the stability of the power system to be reduced, brings great challenges to the safe and stable operation of the power grid, and even directly influences the normal operation of the power grid. If an energy storage power station is to be constructed in a new energy station group in a matched manner, the economical efficiency must be considered.
However, the cost of the energy storage power station at the present stage is high, and most of the cooperative control methods for the new energy field station group and the energy storage power station are based on model control, the wind turbine generator set, the photovoltaic power generation unit and the energy storage unit in the combined system are respectively modeled, and output control is performed by using power prediction to meet power grid dispatching operation, but the accuracy of the power prediction can affect the actual output and also affect the overall optimized operation mode of the system, in addition, the construction operation of the energy storage power station with high energy storage cost at the present stage must consider the economy, and at present, the long-term operation reliability, the long-term economy, the stability and the like of the energy storage power station group and the energy storage power station under different working conditions in the combined operation are not optimistic.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, a system, an electronic device, and a computer-readable storage medium for joint operation of a new energy field group and an energy storage power station, which can more efficiently implement joint operation of the new energy field group and the energy storage power station, and improve economy and stability of the joint operation.
In a first aspect, an embodiment of the present application provides a method for jointly operating a new energy farm station group and an energy storage power station, where the method includes:
acquiring operation data of the new energy station group;
calculating the power requirement of the new energy station group according to the operation data of the new energy station group;
constructing a new energy station group cost model and an energy storage power station cost and income model;
obtaining cost loss data according to the new energy station group cost model and the power demand;
obtaining cost and income difference value data according to the energy storage power station cost and income model and the power demand;
and carrying out optimization solution on the cost loss data and the cost and income difference value data to obtain an optimization result.
In the implementation process, the cost loss data of the new energy field station group and the cost and income difference data of the energy storage power station are optimized and solved by constructing the new energy field station group cost model and the energy storage power station cost and income model, so that the new energy field station group and the energy storage power station can be operated more efficiently, and the economy and the stability of combined operation are improved.
Further, before the step of obtaining the operation data of the new energy yard group, the method further comprises:
and setting parameters of the new energy station group and parameters of the energy storage power station.
In the implementation process, the parameters of the new energy station group and the parameters of the energy storage power station are set, so that the accuracy of subsequent calculation can be improved.
Further, the step of calculating the power requirement of the new energy yard group according to the operation data of the new energy yard group comprises:
acquiring grid-connected point voltage data, grid-connected point frequency data and meteorological data in the operation data;
calculating voltage regulation requirements according to the voltage data of the grid-connected point;
calculating frequency modulation requirements according to the frequency data of the grid-connected point;
calculating the electricity abandoning quantity of electricity according to the meteorological data;
and obtaining the power demand according to the voltage regulation demand, the frequency modulation demand and the electricity abandoning capacity.
In the implementation process, the voltage regulation requirement, the frequency modulation requirement and the electricity abandoning quantity are calculated according to the voltage data of the grid-connected point, the frequency data of the grid-connected point and the meteorological data, and the operation conditions of the new energy station group can be truly and accurately reflected according to the voltage regulation requirement, the frequency modulation requirement and the electricity abandoning quantity.
Further, the step of performing optimization solution on the cost loss data and the cost and profit difference data to obtain an optimization result includes:
constructing an optimization model of the new energy field station group and the energy storage power station;
taking the cost loss data and the cost and profit difference data as sub-objective functions of the optimization model;
and carrying out optimization solution on the sub-objective functions to obtain an optimization result.
In the implementation process, the cost loss data and the cost and income difference value data are used as sub-objective functions of the optimization model, so that the optimization model can be optimized by taking the cost loss data and the cost and income difference value data as targets, and the optimization performance is improved.
Further, the step of performing optimization solution on the sub-objective functions to obtain an optimization result includes:
setting a constraint condition;
configuring decision parameters according to the operation data;
performing optimization iteration operation on the sub-objective functions according to an optimization algorithm, the constraint conditions and the decision parameters to obtain an iteration result;
and obtaining an optimization result according to the iteration result.
In the implementation process, the sub-objective function is optimized and iterated according to the optimization algorithm, the constraint conditions and the decision parameters, so that the sub-objective function can be optimized, the stability is higher, and the iteration result is more accurate.
Further, the obtaining of the optimization result according to the iteration result is further configured to:
judging whether the iteration result meets a preset threshold value or not;
and if so, taking the iteration result as an optimization result.
In the implementation process, whether the iteration result can be used as the optimization result is judged according to the preset threshold value, so that the accuracy and the practicability of the iteration result are ensured, and the iteration time is saved.
In a second aspect, an embodiment of the present application further provides a system for jointly operating a new energy source station group and an energy storage power station, where the system includes:
the acquisition module is used for acquiring the operation data of the new energy station group;
the calculation module is used for calculating the power requirement of the new energy station group according to the operation data of the new energy station group;
the building module is used for building a new energy field station group cost model and an energy storage power station cost and benefit model;
the data obtaining module is used for obtaining cost loss data according to the new energy station group cost model and the power demand; the energy storage power station cost and income model is also used for obtaining cost and income difference value data according to the energy storage power station cost and income model and the power demand;
and the optimization solving module is used for carrying out optimization solving on the cost loss data and the cost and income difference data to obtain an optimization result.
In the implementation process, the cost loss data of the new energy field station group and the cost and income difference data of the energy storage power station are optimized and solved by constructing the new energy field station group cost model and the energy storage power station cost and income model, so that the new energy field station group and the energy storage power station can be operated more efficiently, and the economy and the stability of combined operation are improved.
Further, the system further comprises a parameter setting module for:
and setting parameters of the new energy station group and parameters of the energy storage power station.
In the implementation process, the parameters of the new energy field station group and the parameters of the energy storage power station are set, so that the accuracy of subsequent calculation can be improved.
In a third aspect, an embodiment of the present application provides an electronic device, including: memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any of the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, cause the computer to perform the method according to any one of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product, which when run on a computer, causes the computer to perform the method according to any one of the first aspect.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure.
The present invention can be implemented in accordance with the content of the specification, and the following detailed description of the preferred embodiments of the present application is made with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a method for joint operation of a new energy farm and an energy storage power station according to an embodiment of the present disclosure;
fig. 2 is a schematic structural composition diagram of a combined operation system of a new energy station group and an energy storage power station according to an embodiment of the present application;
fig. 3 is a schematic structural component diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not construed as indicating or implying relative importance.
The following detailed description of embodiments of the present application will be described in conjunction with the accompanying drawings and examples. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.
Example one
Fig. 1 is a schematic flowchart of a method for jointly operating a new energy farm and an energy storage power station according to an embodiment of the present application, where as shown in fig. 1, the method includes:
s1, acquiring the operation data of the new energy station group;
s2, calculating the power requirement of the new energy station group according to the operation data of the new energy station group;
s3, constructing a new energy station group cost model and an energy storage power station cost and benefit model;
s4, obtaining cost loss data according to the cost model and the power demand of the new energy station group;
s5, obtaining cost and income difference data according to the cost and income model of the energy storage power station and the power demand;
and S6, carrying out optimization solution on the cost loss data and the cost and income difference data to obtain an optimization result.
In the implementation process, the cost loss data of the new energy field station group and the cost and income difference data of the energy storage power station are optimized and solved by constructing the new energy field station group cost model and the energy storage power station cost and income model, so that the new energy field station group and the energy storage power station can be operated more efficiently, and the economy and the stability of combined operation are improved.
The embodiment of the application aims at the problems of the prior art, and provides a method and a system for joint operation of a new energy field station group and an energy storage power station.
Further, before the step of obtaining the operation data of the new energy station group, the method further comprises:
and setting parameters of the new energy station group and parameters of the energy storage power station.
In the implementation process, the parameters of the new energy station group and the parameters of the energy storage power station are set, so that the accuracy of subsequent calculation can be improved.
The new energy field station group and the initial parameters of the energy storage power station are set through a parameter setting module, and the parameters at least comprise each new energy field station grid-connected node, installed capacity, on-line electricity price and planned output curve, the energy storage power station grid-connected node, rated power and capacity, the energy storage power station voltage regulation service price, frequency modulation service price, the price of electricity abandoning of energy storage purchase new energy field stations and the price of on-line electricity of the energy storage power station.
In S1, the operation data includes signals of the new energy station group, operation state information, grid-connected point voltage, frequency, wind speed and direction data, energy storage power station capacity, and the like.
Further, S2 includes:
acquiring grid-connected point voltage data, grid-connected point frequency data and meteorological data in the operation data;
calculating voltage regulation requirements according to the voltage data of the grid-connected point;
calculating frequency modulation requirements according to the frequency data of the grid-connected point;
calculating the electricity abandoning quantity of electricity according to the meteorological data;
and obtaining the power demand according to the voltage regulation demand, the frequency modulation demand and the electricity abandoning capacity.
In the implementation process, the voltage regulation requirement, the frequency modulation requirement and the electricity abandoning quantity are calculated according to the voltage data of the grid-connected point, the frequency data of the grid-connected point and the meteorological data, and the operation conditions of the new energy station group can be truly and accurately reflected according to the voltage regulation requirement, the frequency modulation requirement and the electricity abandoning quantity.
Calculating the voltage regulation requirement of each new energy station according to the monitored grid-connected point voltage of each new energy station of the new energy station group; calculating the frequency modulation requirement of each new energy source according to the monitored grid-connected point frequency of each new energy source station of the new energy source station group; and calculating theoretical output of each new energy station based on meteorological data such as wind speed, wind direction and the like of each new energy station, comparing the theoretical output with a planned output curve, and calculating the electric quantity of abandoned electricity.
In S3, a new energy station group cost model is constructed, and cost benefit configurations of a plurality of new energy stations are set.
In S4, according to the voltage regulation demand, the frequency modulation demand, and the electric power abandonment demand of the plurality of new energy stations, cost loss data of the plurality of new energy stations is set, including the voltage regulation cost, the frequency modulation cost, the punishment cost, and the electric power abandonment cost of the new energy stations, and the electric power abandonment benefit sold to the energy storage power station is deducted.
In S5, the energy storage power station cost benefit model comprises energy storage power station cost calculation and energy storage power station benefit calculation.
Energy storage power station cost calculation, purchase including energy storage power station and abandon electric cost, energy storage power station operation cost, wherein: the cost for purchasing electricity abandonment by the energy storage power station, namely the electricity abandonment income sold by the new energy station to the energy storage power station, is the product of the electricity abandonment price and the electricity abandonment quantity of the new energy station for purchasing the energy storage; the operation cost of the energy storage power station is the product of the discharge electric quantity of the energy storage power station and the normalized power cost of the energy storage power station.
And (3) calculating the profit of the energy storage power station, wherein the profit comprises the voltage regulation service profit of the energy storage power station, the frequency modulation service profit and the internet surfing profit of the energy storage power station, and the profit comprises the following steps: the energy storage power station voltage regulation service income, namely the voltage regulation cost of the new energy station, is the product of the voltage regulation service prices of the N new energy stations and the energy storage power station increase or decrease reactive power output; the energy storage power station frequency modulation service income, namely the frequency modulation cost of the new energy field station is the product of the frequency modulation service prices of the N new energy field stations and the modulation increase or modulation decrease active power output of the energy storage power station; the net surfing yield of the energy storage power station is the product of the net surfing electricity price of the energy storage power station and the net surfing electricity quantity of the energy storage power station.
Further, S6 includes:
constructing an optimization model of a new energy field station group and an energy storage power station;
taking the cost loss data and the cost and income difference value data as sub-objective functions of the optimization model;
and carrying out optimization solution on the sub-objective function to obtain an optimization result.
In the implementation process, the cost loss data and the cost and income difference value data are used as sub-objective functions of the optimization model, so that the optimization model can be optimized by taking the cost loss data and the cost and income difference value data as targets, and the optimization performance is improved.
Further, the step of performing optimization solution on the sub-objective function to obtain an optimization result includes:
setting constraint conditions;
configuring decision parameters according to the operation data;
performing optimized iterative operation on the sub-objective function according to the optimization algorithm, the constraint condition and the decision parameter to obtain an iterative result;
and obtaining an optimization result according to the iteration result.
In the implementation process, the sub-objective function is optimized and iterated according to the optimization algorithm, the constraint conditions and the decision parameters, so that the sub-objective function can be optimized, the stability is higher, and the iteration result is more accurate.
Further, obtaining the optimization result according to the iteration result is further used for:
judging whether the iteration result meets a preset threshold value or not;
and if so, taking the iteration result as an optimization result.
In the implementation process, whether the iteration result can be used as the optimization result is judged according to the preset threshold value, so that the accuracy and the practicability of the iteration result are ensured, and the iteration time is saved.
According to the embodiment of the application, an optimization model is established based on multifunctional application of voltage regulation, frequency modulation, reduction of electricity abandonment and the like which can be provided by the energy storage power station, electricity abandonment transaction and energy storage power station internet transaction of each new energy field station and the energy storage power station are considered, objective functions are established respectively from the angles of a plurality of new energy field stations and the energy storage power station, and benefit maximization of multiple subjects such as new energy field station groups and the energy storage power station is achieved.
Illustratively, if the new energy field station includes N new energy field stations, the cost loss data of the N new energy field stations is used as a sub-objective function, and the sub-objective function of each new energy field station covers several factors, such as a purchase voltage regulation service cost expenditure, a purchase frequency modulation service cost expenditure, a reduction punishment cost, and a power abandonment sale energy storage power station profit, of which the purchase voltage regulation and frequency modulation service cost minus the reduction punishment cost, and the power abandonment sale energy storage power station profit.
The energy storage power station cost and income difference value data can be used as an (N + 1) th sub-objective function, and the sub-objective functions of the energy storage power station cover the energy storage power station purchase electricity abandonment cost, the energy storage power station operation cost, the energy storage power station voltage regulation service income, the frequency modulation service income, the energy storage power station internet income and the like, and specifically comprise that the energy storage power station purchase electricity abandonment cost and the operation cost minus the energy storage power station voltage regulation, the frequency modulation service income and the internet income.
The constraint conditions comprise the upper and lower limits of the operating power of the energy storage power station, the state of charge constraint, the output constraint of each new energy field station, the reactive constraint of each power generation unit which is quitted gradually according to the fault level, the efficiency attenuation constraint in the service life of the energy storage power station, the voltage regulation service of the energy storage power station, the frequency modulation service, the consistency constraint of the purchase power abandonment and the charging and discharging states of the energy storage power station and other factors.
The optimization algorithm includes, but is not limited to, a multi-objective particle swarm algorithm, an artificial neural network optimization algorithm, and the like.
The optimization iterative operation can calculate data such as various adjustment requirements, energy storage charging and discharging power, cost of each new energy station, net income of the energy storage power station and the like at the next moment, and real-time iterative operation is carried out according to the change of the operation state and the environmental parameters of the power generation unit in operation.
After the iterative operation is optimized, whether the iterative result meets the iterative exit condition or not, namely a preset threshold value is judged, high-priority scheduling, preset shutdown time or manual operation termination control is used as an exit triggering condition, and the exit mode is in a descending order.
Illustratively, the exit evaluation criterion is that the voltage fluctuation of the grid-connected point does not exceed a voltage regulation threshold of a preset threshold, such as ± 10% of the voltage reference value, and the frequency fluctuation does not exceed a frequency modulation dead zone, such as ± 0.03 Hz.
After the optimization solution, the optimization result can be output, displayed and stored, various adjustment requirements, energy storage charging and discharging power and cost loss data of a plurality of new energy stations and cost and income difference data of the energy storage power station at each moment are output and displayed, and meanwhile, the data are stored in real time.
Example two
In order to implement the method corresponding to the above embodiment to achieve the corresponding functions and technical effects, the following provides a system for jointly operating a new energy farm group and an energy storage power station, as shown in fig. 2, the system includes:
the acquisition module 1 is used for acquiring the operation data of the new energy station group;
the calculation module 2 is used for calculating the power requirement of the new energy station group according to the operation data of the new energy station group;
the building module 3 is used for building a new energy field station group cost model and an energy storage power station cost and benefit model;
the data obtaining module 4 is used for obtaining cost loss data according to the new energy station group cost model and the power demand; the energy storage power station cost and income difference data are obtained according to the energy storage power station cost and income model and the power demand;
and the optimization solving module 5 is used for performing optimization solving on the cost loss data and the cost and income difference data to obtain an optimization result.
Further, the system further comprises a parameter setting module for:
and setting parameters of the new energy station group and parameters of the energy storage power station.
Further, the calculation module 2 is further configured to:
acquiring grid-connected point voltage data, grid-connected point frequency data and meteorological data in the operation data;
calculating voltage regulation requirements according to the voltage data of the grid-connected point;
calculating frequency modulation requirements according to the frequency data of the grid-connected point;
calculating the electricity abandoning quantity of electricity according to the meteorological data;
and obtaining the power demand according to the voltage regulation demand, the frequency modulation demand and the electricity abandoning quantity.
Further, the optimization solution module 5 is further configured to:
constructing an optimization model of a new energy field station group and an energy storage power station;
taking the cost loss data and the cost and income difference value data as sub-objective functions of the optimization model;
and carrying out optimization solution on the sub-objective function to obtain an optimization result.
Further, the optimization solution module 5 is further configured to:
setting constraint conditions;
configuring decision parameters according to the operation data;
performing optimized iterative operation on the sub-objective function according to the optimization algorithm, the constraint condition and the decision parameter to obtain an iterative result;
and obtaining an optimization result according to the iteration result.
Further, the optimization solution module 5 is further configured to:
judging whether the iteration result meets a preset threshold value or not;
and if so, taking the iteration result as an optimization result.
The combined operation system of the new energy station group and the energy storage power station can implement the method of the first embodiment. The alternatives in the first embodiment are also applicable to the present embodiment, and are not described in detail here.
The rest of the embodiments of the present application may refer to the contents of the first embodiment, and in this embodiment, details are not repeated.
EXAMPLE III
An embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the joint operation method of the new energy station group and the energy storage power station according to the first embodiment.
Alternatively, the electronic device may be a server.
Referring to fig. 3, fig. 3 is a schematic structural composition diagram of an electronic device according to an embodiment of the present disclosure. The electronic device may include a processor 31, a communication interface 32, a memory 33, and at least one communication bus 34. Wherein the communication bus 34 is used for realizing direct connection communication of these components. The communication interface 32 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. The processor 31 may be an integrated circuit chip having signal processing capabilities.
The Processor 31 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 31 may be any conventional processor or the like.
The Memory 33 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 33 has stored therein computer readable instructions which, when executed by the processor 31, enable the apparatus to perform the various steps involved in the method embodiment of fig. 1 described above.
Optionally, the electronic device may further include a memory controller, an input output unit. The memory 33, the memory controller, the processor 31, the peripheral interface, and the input/output unit are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components may be electrically connected to each other via one or more communication buses 34. The processor 31 is adapted to execute executable modules stored in the memory 33, such as software functional modules or computer programs comprised by the device.
The input and output unit is used for providing a task for a user to create and start an optional time period or preset execution time for the task creation so as to realize the interaction between the user and the server. The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 3 or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
In addition, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for jointly operating the new energy field group and the energy storage power station in the first embodiment is implemented.
Embodiments of the present application further provide a computer program product, which when running on a computer, causes the computer to execute the method described in the method embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A combined operation method of a new energy source station group and an energy storage power station is characterized by comprising the following steps:
acquiring operation data of the new energy station group;
calculating the power requirement of the new energy station group according to the operation data of the new energy station group;
constructing a new energy station group cost model and an energy storage power station cost and income model;
obtaining cost loss data according to the new energy station group cost model and the power demand;
obtaining cost and income difference value data according to the energy storage power station cost and income model and the power demand;
and carrying out optimization solution on the cost loss data and the cost and income difference value data to obtain an optimization result.
2. The method of claim 1, wherein prior to the step of obtaining operational data for the new group of energy sites, the method further comprises:
and setting parameters of the new energy station group and parameters of the energy storage power station.
3. The method of claim 1, wherein the step of calculating the power requirement of the new energy farm group based on the operational data of the new energy farm group comprises:
acquiring grid-connected point voltage data, grid-connected point frequency data and meteorological data in the operation data;
calculating voltage regulation requirements according to the voltage data of the grid-connected point;
calculating frequency modulation requirements according to the frequency data of the grid-connected point;
calculating electricity abandoning quantity according to the meteorological data;
and obtaining the power demand according to the voltage regulation demand, the frequency modulation demand and the electricity abandoning capacity.
4. The method of claim 2, wherein the step of performing the optimization solution on the cost loss data and the cost-to-profit difference data to obtain an optimized result comprises:
constructing an optimization model of the new energy field station group and the energy storage power station;
taking the cost loss data and the cost and profit difference data as sub-objective functions of the optimization model;
and carrying out optimization solution on the sub-objective functions to obtain an optimization result.
5. The method of claim 4, wherein the step of performing optimization solution on the sub-objective functions to obtain the optimized result comprises:
setting constraint conditions;
configuring decision parameters according to the operation data;
performing optimization iteration operation on the sub-objective functions according to an optimization algorithm, the constraint conditions and the decision parameters to obtain an iteration result;
and obtaining an optimization result according to the iteration result.
6. The system of claim 5, wherein the step of obtaining the optimized result according to the iteration result comprises:
judging whether the iteration result meets a preset threshold value or not;
and if so, taking the iteration result as an optimization result.
7. A combined operation system of a new energy yard group and an energy storage power station, the system comprising:
the acquisition module is used for acquiring the operation data of the new energy station group;
the calculation module is used for calculating the power requirement of the new energy station group according to the operation data of the new energy station group;
the building module is used for building a new energy field station group cost model and an energy storage power station cost and benefit model;
the data obtaining module is used for obtaining cost loss data according to the new energy station group cost model and the power demand; the energy storage power station cost and income model is also used for obtaining cost and income difference value data according to the energy storage power station cost and income model and the power demand;
and the optimization solving module is used for carrying out optimization solving on the cost loss data and the cost and income difference value data to obtain an optimization result.
8. The joint operating system of a new energy farm and an energy storage power plant of claim 7, characterized in that the system further comprises a parameter setting module for:
and setting parameters of the new energy station group and parameters of the energy storage power station.
9. An electronic device, comprising a memory for storing a computer program and a processor for executing the computer program to cause the electronic device to perform the method of joint operation of a new energy farm and an energy storage power plant according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when being executed by a processor, carries out the method of joint operation of a new energy farm group and an energy storage power plant according to any one of claims 1 to 6.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109492815A (en) * | 2018-11-15 | 2019-03-19 | 郑州大学 | Energy-accumulating power station addressing constant volume optimization method towards power grid under a kind of market mechanism |
| CN112821462A (en) * | 2021-01-05 | 2021-05-18 | 国网浙江省电力有限公司电力科学研究院 | Coordination optimization method for multiple adjustment resources of power grid |
| CN113067350A (en) * | 2021-03-31 | 2021-07-02 | 华中科技大学 | Economic analysis method and parameter optimization method based on combined frequency modulation |
| CN114256836A (en) * | 2021-12-13 | 2022-03-29 | 国网青海省电力公司清洁能源发展研究院 | Capacity optimization configuration method for new energy power station shared energy storage |
| CN114362139A (en) * | 2021-11-30 | 2022-04-15 | 国网甘肃省电力公司电力科学研究院 | Source-load-storage multilateral bargaining day-ahead response control method with new energy consumption as target |
| CN114498679A (en) * | 2022-02-08 | 2022-05-13 | 国网福建省电力有限公司经济技术研究院 | Power system energy storage demand quantification method and system considering frequency modulation rate and capacity |
-
2022
- 2022-06-15 CN CN202210682016.XA patent/CN115018171A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109492815A (en) * | 2018-11-15 | 2019-03-19 | 郑州大学 | Energy-accumulating power station addressing constant volume optimization method towards power grid under a kind of market mechanism |
| CN112821462A (en) * | 2021-01-05 | 2021-05-18 | 国网浙江省电力有限公司电力科学研究院 | Coordination optimization method for multiple adjustment resources of power grid |
| CN113067350A (en) * | 2021-03-31 | 2021-07-02 | 华中科技大学 | Economic analysis method and parameter optimization method based on combined frequency modulation |
| CN114362139A (en) * | 2021-11-30 | 2022-04-15 | 国网甘肃省电力公司电力科学研究院 | Source-load-storage multilateral bargaining day-ahead response control method with new energy consumption as target |
| CN114256836A (en) * | 2021-12-13 | 2022-03-29 | 国网青海省电力公司清洁能源发展研究院 | Capacity optimization configuration method for new energy power station shared energy storage |
| CN114498679A (en) * | 2022-02-08 | 2022-05-13 | 国网福建省电力有限公司经济技术研究院 | Power system energy storage demand quantification method and system considering frequency modulation rate and capacity |
Non-Patent Citations (1)
| Title |
|---|
| 孙财新: "储能联姻新能源三大问题：成本、能力和安全", 《能源》 * |
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