CN117332963A - Dynamic optimization scheduling method and system for virtual power plant with collaborative source network and load storage - Google Patents
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Abstract
The invention discloses a source network load storage collaborative virtual power plant dynamic optimization scheduling method, a system, a device and a storage medium, which comprise a source load curve scene library, an input module, an optimization module and an output module, wherein the source load curve scene library is influenced by external factors; s1: providing input data attribute searching based on a source load curve scene library influenced by external factors, and meeting the basic data requirement of an optimization module on a source load curve; s2: the input module determines the corresponding scene and constraint condition of the calculation module; s3: the optimization module is responsible for optimizing the energy storage scheduling method; s4: the output module forms an energy storage scheduling scheme; according to the method, the typical daily source load curve under the influence of external factors such as investigation seasons, weather, working rules and the like is considered, the constraint of the energy storage charge and discharge rate, the charge and discharge power, the storage electric quantity and the constraint of the power grid capacity are considered, the maximum economic benefit of the virtual power plant is taken as a target, an optimal scheduling scheme of the energy storage equipment is provided, the aim of coordination and optimization of the source network load storage of the power system is achieved, and guidance is provided for the economic operation of the power grid.
Description
Technical Field
The invention relates to the technical field of power grid user load regulation and control, in particular to a method, a system, a device and a storage medium for dynamically optimizing and scheduling a virtual power plant with collaborative source network load storage.
Background
The virtual power plant is a virtualized power plant, and is a power coordination management system for realizing the aggregation and coordination collaborative optimization of distributed energy resources such as a distributed power supply, an energy storage system, a controllable load, a micro-grid, an electric automobile and the like through an advanced information communication technology and a software system, so that the virtual power plant is used as a special power plant to participate in the operation of a power market and a power grid. The cooperation of the source network and the load storage means that the power dynamic balance capacity of a power system is improved more economically, efficiently and safely through various interaction means by the four parts of a power supply, a power grid, a load and an energy storage, so that the utilization operation mode and the technology of energy resource maximization are realized.
According to the search discovery of the prior art document, china patent application No. 2023102687192 discloses a distributed source network charge storage scheduling optimization method based on a virtual power plant, and the method is characterized in that the power supply quantity of each energy storage power station at the centralized power supply moment is determined by analyzing the daily tendency power supply quantity of the energy storage power stations existing in an area, so that the power supply is carried out, and the reasonable and targeted distribution of the power supply resources of the energy storage power stations at the centralized power supply moment is realized. The patent considers the influence of the current atmospheric environment of the energy storage power station on the selection of the power supply, but does not consider the arrangement plan of the energy storage electric quantity, only depends on real-time data to make decisions, cannot realize the optimal scheduling scheme of the virtual power plant in the future, and lacks universality; meanwhile, when the energy storage electric quantity is insufficient, the source load coupling curve is not considered, and the influence of source load fluctuation change cannot be adapted.
At present, although a great deal of documents at home and abroad develop researches aiming at the problem of a virtual power plant scheduling method, the related source network charge storage coupling refinement degree is not high, the virtual power plant scheduling model cannot accurately predict the electricity utilization curve of energy storage after the energy storage participates in scheduling optimization, and the influence of external factors such as seasons, weather, working rules and the like on the virtual power plant scheduling method cannot be clarified. Therefore, it is necessary to build a virtual power plant optimization scheduling scene library, and to formulate an energy storage scheduling scheme by considering a source-load coupling curve under the influence of external factors, so that the comprehensive benefit of the virtual power plant is maximized on the basis of meeting the peak shaving requirement of the power grid.
Disclosure of Invention
The invention mainly solves the technical problems that a source network charge storage collaborative virtual power plant dynamic optimization scheduling method, a system, a device and a storage medium are provided, and the problem that in the prior art, the source network charge storage coupling refinement degree is not high, a virtual power plant scheduling model cannot accurately predict an electricity consumption curve after energy storage participates in scheduling optimization, and external factors such as seasons, weather, working rules and the like cannot be clearly influenced on virtual power plant scheduling is solved.
In order to solve the technical problems, the invention adopts a technical scheme that: the utility model provides a source network load storage collaborative virtual power plant dynamic optimization scheduling method, which is characterized in that: the method comprises the following steps:
step one, collecting power data of various power generation enterprises and power supply enterprises and meteorological data in a virtual power plant service range, and constructing a source load curve scene library based on the influence of external factors according to different scenes;
step two, retrieving data in a source load curve scene library based on the influence of external factors, and determining corresponding scenes and constraint conditions by combining the external influence factors, power load data and energy storage data;
optimizing an energy storage charge-discharge curve and a source charge-storage coupling curve according to the determined scene and constraint conditions and with the maximum economic benefit of the virtual power plant as a target;
and step four, outputting the optimized energy storage charge-discharge curve and the source charge storage coupling curve, and forming an energy storage scheduling scheme.
Further, in the first step, the method for constructing the source load curve scene library based on the influence of the external factors is as follows:
1) Forming a source load curve database by standardized processing according to the collected power data of various power generation enterprises and power supply enterprises by taking a day as a unit;
2) Forming a weather database by taking a day as a unit according to the collected weather data in the service range of the virtual power plant;
3) Forming a corresponding relation between a source load curve database and a meteorological database according to the date, and determining scene types by adopting a systematic clustering method;
4) And forming a typical daily source load curve which can be widely applied to each scene by adopting regression analysis, thereby forming a source load curve scene library based on the influence of external factors.
Further, the power data are power output curves and load curves of various power generation enterprises and power supply enterprises; the meteorological data are air temperature, sunlight, wind power, wind direction, rain and snow data in the service range of the virtual power plant.
In the second step, each external influence factor of the target day is determined according to weather prediction, power load data provided by a power generation enterprise and a power supply enterprise is provided by a virtual power plant, schedulable energy storage data is provided by the virtual power plant, and corresponding scene data and constraint data are formed by integrating the data.
Further, the power load data comprises power capacity, load data, power grid line capacity and transformer capacity; the energy storage data comprises an energy storage charge-discharge rate, charge-discharge power and storage electric quantity.
Further, in the third step, the energy storage scheduling method is optimized in the following manner:
1) The optimizing module calls the data of the input module, and the scene of the target day is defined by the external factors;
2) Combining the power capacity and load data with a typical daily source load curve to form a predicted source load curve, and superposing to form a source load coupling curve;
3) Forming constraint conditions based on the schedulable energy storage charge and discharge rate, charge and discharge power and storage electric quantity of the virtual power plant, the power grid line capacity and the transformer capacity in the service range of the virtual power plant and taking the energy storage technology feasibility and the power grid operation safety as the standard;
4) The income of energy storage operation scheduling is defined according to the price of energy storage charge and discharge participated in the electric market transaction, a mapping relation between input data and an energy storage charge and discharge power curve is formed, and an objective function with the maximum economic benefit of the virtual power plant as a target is established; and obtaining an energy storage charge-discharge curve and a source charge-storage coupling curve through optimization solution.
Further, in the fourth step, the energy storage charging and discharging curve can check the energy storage charging and discharging state at each moment to form an energy storage scheduling scheme; the source load storage coupling curve can check the running state of the power grid at each moment, and the dispatching effect of the virtual power plant is clear.
In order to solve the technical problems, the invention adopts a technical scheme that: the utility model provides a source net lotus stores up virtual power plant dynamic optimization scheduling system of cooperation which characterized in that: the system comprises a scene library, an input module, an optimization module and an output module which are sequentially connected, wherein:
the scene library: collecting power data of various power generation enterprises and power supply enterprises and meteorological data in a virtual power plant service range, and constructing a source load curve scene library based on the influence of external factors according to different scenes;
the input module: retrieving data in a source load curve scene library based on the influence of external factors, and determining corresponding scenes and constraint conditions by combining the external influence factors, power load data and energy storage data;
the optimizing module is used for: according to the determined scene and constraint conditions, optimizing an energy storage charge-discharge curve and a source charge-storage coupling curve by taking the maximum economic benefit of the virtual power plant as a target;
the output module: and outputting the optimized energy storage charge-discharge curve and the source charge storage coupling curve, and forming an energy storage scheduling scheme.
In order to solve the technical problems, the invention adopts a technical scheme that: the utility model provides a source net lotus stores up virtual power plant dynamic optimization scheduling device of cooperation which characterized in that includes:
a memory for storing a computer program;
and the processor is used for reading and executing the computer program stored in the memory, and when the computer program is executed, the processor executes a source network load storage collaborative virtual power plant dynamic optimization scheduling method.
In order to solve the technical problems, the invention adopts a technical scheme that: there is provided a computer-readable storage medium characterized in that: the computer readable storage medium stores instructions that, when executed on a computer, cause the computer to perform a source network load storage collaborative virtual power plant dynamic optimization scheduling method as described above.
The beneficial effects of the invention are as follows:
1. according to the invention, scene classification is carried out through external factor influence analysis, uncertainty and volatility of new energy power generation are constrained in various determined scenes, load characteristics are corresponding to external factors such as seasons, air temperatures, working days and the like, a source load curve scene library based on the influence of the external factors is formed, multi-factor multi-scene refined curve analysis is carried out in the aspect of defining the source load characteristics of a power grid, and typical reference data is provided for economic operation and flexible scheduling of the power grid.
2. According to the invention, the energy storage technology constraint and the power grid capacity constraint are synchronously considered, and the energy storage charge and discharge are used for optimizing and adjusting the source charge curve, wherein the source charge curve is provided by a power generation enterprise and a power supply enterprise, the time precision can reach 1-15 minutes, and the power precision is 0.001-1 kilowatt. The method can be used for properly providing a scheme of coordination optimization in combination with a scene, and the calculation accuracy meets the scheduling requirement of the virtual power plant.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments, as illustrated in the accompanying drawings.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only four of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow diagram of the method of the present application;
FIG. 2 is a block diagram of the system of the present application;
FIG. 3 is a block diagram of the apparatus of the present application;
fig. 4 is a block diagram of the structure of the storage medium of the present application.
Detailed Description
In order that the invention may be readily understood, a more particular description thereof will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. 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. The term "and/or" as used in this specification includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 shows a dynamic optimization scheduling method for a virtual power plant with collaborative source network and load storage, which is provided by the invention, and is characterized in that: the method comprises the following steps:
step S1, collecting power data of various power generation enterprises and power supply enterprises and meteorological data in a virtual power plant service range, and constructing a source load curve scene library based on the influence of external factors according to different scenes;
s2, retrieving data in a source load curve scene library based on the influence of external factors, and determining corresponding scenes and constraint conditions by combining the external influence factors, power load data and energy storage data;
step S3, optimizing an energy storage charge-discharge curve and a source charge-storage coupling curve according to the determined scene and constraint conditions and with the maximum economic benefit of the virtual power plant as a target;
and S4, outputting the optimized energy storage charge-discharge curve and the source charge-storage coupling curve, and forming an energy storage scheduling scheme.
In the step S1, the method for constructing the source load curve scene library based on the influence of the external factors is as follows:
1) Forming a source load curve database by standardized processing according to the collected power data of various power generation enterprises and power supply enterprises by taking a day as a unit;
2) Forming a weather database by taking a day as a unit according to the collected weather data in the service range of the virtual power plant;
3) Forming a corresponding relation between a source load curve database and a meteorological database according to the date, and determining scene types by adopting a systematic clustering method;
4) And forming a typical daily source load curve which can be widely applied to each scene by adopting regression analysis, thereby forming a source load curve scene library based on the influence of external factors.
The power data are power output curves and load curves of various power generation enterprises and power supply enterprises; the meteorological data are air temperature, sunlight, wind power, wind direction, rain and snow data in the service range of the virtual power plant.
The seasonal factors are marked as A and are classified into spring, summer, autumn and winter; the air temperature factor is marked as B, and the 5 ℃ is used as a first grade, and the extremely hot weather above 40 ℃ and the extremely cold weather below-20 ℃ are used as a first grade respectively; the sunshine factors are marked as C and are divided into sunny days, cloudy days and overcast and rainy days; the wind power factors are marked as D and are distinguished according to wind power levels; the wind direction factors are marked as E and are divided into northeast, southeast, southwest, northeast and northwest; the working day factor is F, which is divided into working day and rest day.
The source load curve is marked as G in the form of column vector 0 And P 0 ，G 0 Representing power daily power generation curve within service range of virtual power plant, P 0 Representing a daily load profile of the user within the service range of the virtual power plant. Wherein the power generation curve G 0 The load curve is determined by seasons, air temperatures, sun exposure, wind power and wind directions, whether working days are determined by the seasons, the air temperatures and working days, and the function relation between external factors and source load curves is set as follows:
G 0 ＝F G (A，B，C，D，E)＝[g 1 ，g 2 ，...，g n ] T (1)
P 0 ＝F P (A，B，F)＝[p 1 ，p 2 ，...，p n ] T (2)
n: recording the number of points of the daily curve; g i (i e {1,2,., n }): generating power of an ith recording point in the daily curve recording time; p is p i (i e {1,2,., n }): load power of the ith recording point in the day curve recording time.
In the step S2, various external influencing factors of the target are determined according to weather prediction, power load data provided by power generation enterprises and power supply enterprises, schedulable energy storage data provided by a virtual power plant, and corresponding scene data and constraint data are formed by integrating the above data.
The power load data comprise power capacity, load data, power grid line capacity and transformer capacity; the energy storage data comprises an energy storage charge-discharge rate, charge-discharge power and storage electric quantity.
In the step S3, the energy storage scheduling method is optimized in the following manner:
1) And the optimization module calls the data of the input module, and the scene to which the target day belongs is defined by external factors.
2) A predicted source load curve is formed by combining the power capacity and load data with a typical daily source load curve,
3) Forming constraint conditions based on the schedulable energy storage charge and discharge rate, charge and discharge power and storage electric quantity of the virtual power plant, the power grid line capacity and the transformer capacity in the service range of the virtual power plant and taking the energy storage technology feasibility and the power grid operation safety as the standard;
4) The income of energy storage operation scheduling is defined according to the price of energy storage charge and discharge participated in the electric market transaction, a mapping relation between input data and an energy storage charge and discharge power curve is formed, and an objective function with the maximum economic benefit of the virtual power plant as a target is established; and obtaining an energy storage charge-discharge curve and a source charge-storage coupling curve through optimization solution.
Wherein, the source-charge coupling curve S is formed by superposition 0 The following formula is shown:
s 0 ＝P 0 -G 0 ＝[s 1 ，s 2 ，...，s n ] T (3)
energy storage maximum charging power P based on virtual power plant schedulability im Maximum discharge power P om Maximum stored power W E Grid line capacity S within the service range of a virtual power plant L Transformer capacity S T The constraint conditions are formed by taking the feasibility of the energy storage technology and the operation safety of the power grid as the standard as follows:
0≤P i ≤P im (4)
0≤P o ≤P om (5)
P i : energy storage charging power; p (P) o : energy storage discharge power; η: energy storage cycle efficiency; d (D) E : energy storage depth of discharge; t: time intervals of adjacent recording points in the daily curve; t is t 1 : energy storage charging start time; t is t 2 : energy storageCharging end time; t is t 3 : the energy storage discharge start time; t is t 4 : the end time of energy storage discharge; s is S E : the energy storage charge-discharge curve is expressed in the form of column vector.
The benefits of participating in the power market trade are as follows:
R 1 ＝A·S E (9)
R 1 : the energy storage participates in the income of the electric power market trade; a: the real-time price fluctuation curve of the electric power market trade is expressed in a column vector form.
The objective function aiming at the maximum economic benefit of the virtual power plant is established as follows:
Y＝max(R 1 +R 2 +R 3 +R 4 ) (10)
R 2 : government subsidized revenues; r is R 3 : paid peak regulation and frequency modulation benefits; r is R 4 : share the energy storage lease revenue.
Obtaining an energy storage charging and discharging curve S through optimization solution E Coupling curve S with source charge storage GPE ：
S GPE ＝S 0 +S E (11)
In the step S4, the energy storage charging and discharging curve may check the energy storage charging and discharging state at each time to form an energy storage scheduling scheme; the source load storage coupling curve can check the running state of the power grid at each moment, and the dispatching effect of the virtual power plant is clear.
Referring to fig. 2, fig. 2 is a dynamic optimization scheduling system for a virtual power plant with collaborative source network and load storage, which is provided by the present application, and is characterized in that: the system comprises a scene library 21, an input module 22, an optimization module 23 and an output module 24 which are sequentially connected, wherein:
the scene library 21: collecting power data of various power generation enterprises and power supply enterprises and meteorological data in a virtual power plant service range, and constructing a source load curve scene library based on the influence of external factors according to different scenes;
the input module 22: retrieving data in a source load curve scene library based on the influence of external factors, and determining corresponding scenes and constraint conditions by combining the external influence factors, power load data and energy storage data;
the optimization module 23: according to the determined scene and constraint conditions, optimizing an energy storage charge-discharge curve and a source charge-storage coupling curve by taking the maximum economic benefit of the virtual power plant as a target;
the output module 24: and outputting the optimized energy storage charge-discharge curve and the source charge storage coupling curve, and forming an energy storage scheduling scheme.
Referring to fig. 3, fig. 3 is a dynamic optimization scheduling device 3 for a virtual power plant with collaborative source network load storage, provided in the present application, including:
a memory 31 for storing a computer program.
And a processor 32 for reading and executing the computer program stored in the memory, wherein the processor executes any one of the source network load storage collaborative virtual power plant dynamic optimization scheduling methods when the computer program is executed.
The processor 32 is configured to execute the program instructions stored in the memory 31, so as to implement the steps of any of the embodiments of the virtual power plant dynamic optimization scheduling method with source network load storage coordination. In a specific implementation scenario, a source network load storage coordinated virtual power plant dynamic optimization scheduling apparatus 3 may include, but is not limited to: the dynamic optimized dispatching device 3 of the virtual power plant with the cooperation of the source network and the load storage can also comprise mobile equipment such as a notebook computer, a tablet computer and the like, and is not limited herein.
Specifically, the processor 32 is configured to control itself and the memory 31 to implement the steps of any of the embodiments of the virtual power plant dynamic optimization scheduling method with source network load storage coordination described above. The processor 32 may also be referred to as a CPU (Central Processing Unit ). The processor 32 may be an integrated circuit chip having signal processing capabilities. The processor 32 may also be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific IntegratedCircuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. In addition, the processor 32 may be commonly implemented by an integrated circuit chip.
Referring to fig. 4, fig. 4 is a schematic diagram of a frame of an embodiment of a computer readable storage medium provided in the present application. The computer readable storage medium 4 stores program instructions 41 that can be executed by a processor, where the program instructions 41 are configured to implement the steps of any of the embodiments of the virtual power plant dynamic optimization scheduling method with source network load storage coordination.
In some embodiments, functions or modules included in an apparatus provided by the embodiments of the present disclosure may be used to perform a method described in the foregoing method embodiments, and specific implementations thereof may refer to descriptions of the foregoing method embodiments, which are not repeated herein for brevity.
The foregoing description of various embodiments is intended to highlight differences between the various embodiments, which may be the same or similar to each other and is not repeated herein for the sake of brevity.
In the several embodiments provided in the present application, it should be understood that the disclosed methods and apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical, or other forms.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all or part of the technical solution contributing to the prior art or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the present invention and the accompanying drawings, or direct or indirect application in other related technical fields, are included in the scope of the present invention.
The foregoing is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the present invention and the accompanying drawings, or direct or indirect application in other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A dynamic optimization scheduling method for a virtual power plant with collaborative source network and load storage is characterized by comprising the following steps of: the method comprises the following steps:
step one, collecting power data of various power generation enterprises and power supply enterprises and meteorological data in a virtual power plant service range, and constructing a source load curve scene library based on the influence of external factors according to different scenes;
step two, retrieving data in a source load curve scene library based on the influence of external factors, and determining corresponding scenes and constraint conditions by combining the external influence factors, power load data and energy storage data;
optimizing an energy storage charge-discharge curve and a source charge-storage coupling curve according to the determined scene and constraint conditions and with the maximum economic benefit of the virtual power plant as a target;
and step four, outputting the optimized energy storage charge-discharge curve and the source charge storage coupling curve, and forming an energy storage scheduling scheme.
2. The method for dynamically optimizing and scheduling the virtual power plant by the cooperation of source network and load storage according to claim 1 is characterized by comprising the following steps of: in the first step, the construction mode of the source load curve scene library based on the influence of external factors is as follows:
1) Forming a source load curve database by standardized processing according to the collected power data of various power generation enterprises and power supply enterprises by taking a day as a unit;
2) Forming a weather database by taking a day as a unit according to the collected weather data in the service range of the virtual power plant;
3) Forming a corresponding relation between a source load curve database and a meteorological database according to the date, and determining scene types by adopting a systematic clustering method;
4) And forming a typical daily source load curve which can be widely applied to each scene by adopting regression analysis, thereby forming a source load curve scene library based on the influence of external factors.
3. The method for dynamically optimizing and scheduling the virtual power plant by the cooperation of source network and load storage according to claim 2 is characterized in that: the power data are power output curves and load curves of various power generation enterprises and power supply enterprises; the meteorological data are air temperature, sunlight, wind power, wind direction, rain and snow data in the service range of the virtual power plant.
4. The method for dynamically optimizing and scheduling the virtual power plant by the cooperation of source network and load storage according to claim 1 is characterized by comprising the following steps of: in the second step, each external influence factor of the target day is determined according to weather prediction, power load data provided by a power generation enterprise and a power supply enterprise are provided by a virtual power plant, schedulable energy storage data are provided by the virtual power plant, and corresponding scene data and constraint data are formed by integrating the data.
5. The method for dynamically optimizing and scheduling the virtual power plant by the cooperation of source network and load storage according to claim 1 is characterized by comprising the following steps of: the power load data comprise power capacity, load data, power grid line capacity and transformer capacity; the energy storage data comprises an energy storage charge-discharge rate, charge-discharge power and storage electric quantity.
6. The method for dynamically optimizing and scheduling the virtual power plant by the cooperation of source network and load storage according to claim 1 is characterized by comprising the following steps of: in the third step, the energy storage scheduling method is optimized in the following manner:
1) The optimizing module calls the data of the input module, and the scene of the target day is defined by the external factors;
2) Combining the power capacity and load data with a typical daily source load curve to form a predicted source load curve, and superposing to form a source load coupling curve;
3) Forming constraint conditions based on the schedulable energy storage charge and discharge rate, charge and discharge power and storage electric quantity of the virtual power plant, the power grid line capacity and the transformer capacity in the service range of the virtual power plant and taking the energy storage technology feasibility and the power grid operation safety as the standard;
4) The income of energy storage operation scheduling is defined according to the price of energy storage charge and discharge participated in the electric market transaction, a mapping relation between input data and an energy storage charge and discharge power curve is formed, and an objective function with the maximum economic benefit of the virtual power plant as a target is established; and obtaining an energy storage charge-discharge curve and a source charge-storage coupling curve through optimization solution.
7. The method for dynamically optimizing and scheduling the virtual power plant by the cooperation of source network and load storage according to claim 1 is characterized by comprising the following steps of: in the fourth step, the energy storage charge-discharge state of each moment can be checked by the energy storage charge-discharge curve to form an energy storage scheduling scheme; the source load storage coupling curve can check the running state of the power grid at each moment, and the dispatching effect of the virtual power plant is clear.
8. A virtual power plant dynamic optimization scheduling system with collaborative source network and load storage is characterized in that: the system comprises a scene library, an input module, an optimization module and an output module which are sequentially connected, wherein:
the scene library: collecting power data of various power generation enterprises and power supply enterprises and meteorological data in a virtual power plant service range, and constructing a source load curve scene library based on the influence of external factors according to different scenes;
the input module: retrieving data in a source load curve scene library based on the influence of external factors, and determining corresponding scenes and constraint conditions by combining the external influence factors, power load data and energy storage data;
the optimizing module is used for: according to the determined scene and constraint conditions, optimizing an energy storage charge-discharge curve and a source charge-storage coupling curve by taking the maximum economic benefit of the virtual power plant as a target;
the output module: and outputting the optimized energy storage charge-discharge curve and the source charge storage coupling curve, and forming an energy storage scheduling scheme.
9. The utility model provides a source net lotus stores up virtual power plant dynamic optimization scheduling device in coordination which characterized in that includes:
a memory for storing a computer program;
a processor for reading and executing the computer program stored in the memory, which when executed performs a source network load store collaborative virtual power plant dynamic optimization scheduling method according to any one of claims 1-7.
10. A computer-readable storage medium, characterized by: the computer readable storage medium has instructions stored therein, which when executed on a computer, cause the computer to perform a source-network-load-storage collaborative virtual power plant dynamic optimization scheduling method as claimed in any one of claims 1-7.
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