CN116131340A - Method, device, equipment and storage medium for matching power station with load area - Google Patents
Method, device, equipment and storage medium for matching power station with load area Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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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
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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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
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a method, a device, equipment and a storage medium for matching a power station with a load area. The method comprises the following steps: acquiring weather information corresponding to a distributed power station; inputting the meteorological information into a power generation amount prediction model to perform power generation amount prediction, and obtaining predicted power generation amount data at the current moment based on the output of the power generation amount prediction model; determining a load grade corresponding to a load area at the current moment, and determining load quantity data at the current moment based on the load grade; according to the predicted generating capacity data at the current moment, the load capacity data at the current moment and a preset gap threshold value, a load area matched with the distributed power station at the current moment is determined, the load area and the distributed power station can be reasonably matched, and therefore safety and stability of a power distribution system are improved.
Description
Technical Field
The present invention relates to the field of electric energy planning technologies, and in particular, to a method, an apparatus, a device, and a storage medium for matching a power station with a load area.
Background
With the wide popularization of green low-carbon production life modes, domestic wind power generation, photovoltaic power generation and other distributed new energy power generation forms are rapidly developed. And the grid connection of large-scale renewable energy power generation has the characteristics of fluctuation and randomness, which leads to the reduction of the electric energy quality of a power grid end and brings great influence to the safe and stable operation of a power system.
In the traditional scheme, the actual power supply quantity required when the load dynamically changes is not considered, the load area and the distributed power stations are directly subjected to fixed combination planning, so that the load area is unreasonably matched with the distributed power stations, the area self-digestion capacity of the distributed power stations is not improved, and the safety and stability of a power distribution system are further reduced.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for matching a power station with a load area, so as to reasonably match the load area with a distributed power station, and further improve the safety and stability of a power distribution system.
According to an aspect of the invention, a method of matching a power plant to a load area is provided. The method comprises the following steps:
acquiring weather information corresponding to a distributed power station;
Inputting the meteorological information into a power generation amount prediction model to perform power generation amount prediction, and obtaining predicted power generation amount data at the current moment based on the output of the power generation amount prediction model;
determining a load grade corresponding to a load area at the current moment, and determining load quantity data at the current moment based on the load grade;
and determining a load area matched with the distributed power station at the current moment according to the predicted power generation amount data at the current moment, the load amount data at the current moment and a preset gap threshold.
According to another aspect of the invention, a device for matching a power plant to a load area is provided. The device comprises:
the weather information acquisition module is used for acquiring weather information corresponding to the distributed power station;
the generating capacity data prediction module is used for inputting the meteorological information into a generating capacity prediction model to predict generating capacity, and obtaining predicted generating capacity data at the current moment based on the output of the generating capacity prediction model;
the load data determining module is used for determining a load grade corresponding to a load area at the current moment and determining load data at the current moment based on the load grade;
And the power station and load area matching module is used for determining a load area matched with the distributed power station at the current moment according to the predicted power generation amount data at the current moment, the load amount data at the current moment and a preset gap threshold value.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of matching a power plant to a load area according to any one of the embodiments of the present invention.
According to another aspect of the invention there is provided a computer readable storage medium storing computer instructions for causing a processor to perform a method of matching a power plant to a load area according to any of the embodiments of the invention.
According to the technical scheme, the meteorological information corresponding to the distributed power station is obtained. The meteorological information is input into a power generation amount prediction model to perform power generation amount prediction, and based on the output of the power generation amount prediction model, predicted power generation amount data at the current moment can be automatically and accurately obtained. And determining a load grade corresponding to the load area at the current moment, and determining load quantity data at the current moment based on the load grade. According to the predicted generating capacity data at the current moment, the load capacity data at the current moment and a preset gap threshold value, a load area matched with the distributed power station at the current moment is determined, so that the problem that the load area is unreasonable to be matched with the distributed power station is solved, the self-absorption capacity of the area of the distributed power station is fully improved, and the safety and stability of a power distribution system are further improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for matching a power plant to a load area according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a method for matching a power plant to a load area according to a second embodiment of the present invention;
fig. 3 is a schematic structural view of an apparatus for matching a power station with a load area according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a method for matching a power plant to a load area according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for matching a power station with a load area, where the method may be performed by a device for matching a power station with a load area, where the device for matching a power station with a load area may be implemented in hardware and/or software, and the device for matching a power station with a load area may be configured in an electronic device. As shown in fig. 1, the method includes:
s101, acquiring meteorological information corresponding to the distributed power station.
The distributed power station can be a distributed clean energy power station such as wind power generation, photovoltaic power generation, hydroelectric power generation and the like. The stability of the power generation amount data of the distributed power generation station is poor, and the power generation amount data of the distributed power generation station is associated with weather information corresponding to the distributed power generation station. The weather information may include ambient temperature, ambient humidity, dew point temperature, wind speed, wind direction, air pressure, total solar radiation, rainfall, ground temperature (including ground surface temperature, shallow ground temperature and deep ground temperature), soil humidity, and soil water potential, etc.
Specifically, weather information corresponding to all distributed power stations in multiple areas can be acquired based on weather automatic acquisition equipment and/or manual acquisition modes.
S102, inputting the meteorological information into a power generation amount prediction model to perform power generation amount prediction, and obtaining predicted power generation amount data at the current moment based on the output of the power generation amount prediction model.
The power generation amount prediction model is used for predicting power generation amount data of the distributed power station at the current moment according to weather information of the current moment. The generating capacity prediction model is obtained by training historical meteorological data, generating power corresponding to the historical meteorological data and generating capacity data in advance. The predicted power generation amount data may refer to power generation data corresponding to the distributed power generation station predicted and determined based on the power generation amount prediction model.
Specifically, weather information corresponding to the distributed power station is input into a power generation amount prediction model to perform power generation amount prediction, and predicted power generation amount data at the current moment is obtained based on the output of the power generation amount prediction model.
Illustratively, the training process of the power generation amount prediction model includes: acquiring historical meteorological data corresponding to a distributed power station and generating power corresponding to the historical meteorological data; determining a forgetting door activation value at the current moment based on a forgetting door activation function, weather data at the current moment, generated energy power corresponding to the weather data at the current moment, historical weather data at the last moment, historical generated energy power corresponding to the historical weather data at the last moment and model weight; determining the state information of the current moment based on the forgetting door activation value of the current moment, the state information of the last moment, the input door activation value of the current moment and the initialization state information of the current moment; determining model loss corresponding to a power generation amount prediction model based on the state information of the current moment, the output door activation value of the current moment and the power generation amount data corresponding to the current moment, and adjusting the model weight based on the model loss; and under the condition that the preset ending condition is met, taking the adjusted generated energy prediction model as a generated energy prediction model after training.
Illustratively, historical meteorological data of each distributed power station region and power generation corresponding to the historical meteorological data in each distributed power station are obtained by using intelligent measuring equipment. After the historical meteorological data is acquired, the acquired historical meteorological data is required to be subjected to data processing, and due to the fact that data are abnormal due to the influences of electromagnetic interference, data transmission limitation and the like of data acquisition and storage equipment, the abnormal data are deleted firstly, then the normal data on two sides of the abnormal data are subjected to mean value supplementation, and the abnormal data and the missing data are subjected to mean value supplementation by using a median theorem, so that the data cleaning processing is completed. The forgetting door activation value at the current moment can be calculated and determined by utilizing the forgetting door activation function, the weather data at the current moment, the generated energy power corresponding to the weather data at the current moment, the historical weather data at the last moment, the historical generated energy power corresponding to the historical weather data at the last moment, the model weight and other data. And calculating the state information of the current moment according to the forgetting door activation value of the current moment, the state information of the last moment, the input door activation value of the current moment and the initialization state information of the current moment. And comparing the obtained state information at the current moment, the output door activation value at the current moment and the generated energy data corresponding to the current moment, determining the model loss corresponding to the generated energy prediction model, adjusting the model weight according to the model loss, and taking the generated energy prediction model obtained by adjusting the model parameters as a trained generated energy prediction model under the condition that model training meets the preset ending condition.
Illustratively, the determining the forgetting door activation value at the current time includes:
integrating the meteorological data corresponding to the current moment and the generated energy power corresponding to the meteorological data at the current moment into a current moment data matrix, and integrating the historical generated energy power corresponding to the historical meteorological data at the last moment and the historical generated energy power corresponding to the historical meteorological data at the last moment into a last moment data matrix; and calculating the forgetting gate activation value at the current moment based on the forgetting gate activation function, the current moment data matrix, the model weight corresponding to the current moment data matrix, the last moment data matrix and the weight corresponding to the last moment data matrix.
Specifically, the meteorological data corresponding to the current moment and the generated energy power corresponding to the meteorological data of the current moment are integrated, so that a current moment data matrix can be obtained. And integrating the historical meteorological data corresponding to the previous moment with the historical generated energy power corresponding to the historical meteorological data of the previous moment to obtain a data matrix of the previous moment. According to the forgetting gate activation function, the current time data matrix, the model weight corresponding to the current time data matrix, the last time data matrix and the model weight corresponding to the last time data matrix, the forgetting gate activation value at the current time can be calculated. The forgetting gate activation function can be any one of a sigmoid function and a tanh function.
Illustratively, determining the forgetting gate activation value at the current time may be accomplished by:
f t ＝σ(W xf x t +W hf h t-1 +b f )
wherein x is t For the current moment data matrix, W xf The model weight corresponding to the current moment data matrix is h t-1 For the data matrix at the previous time, W hf B, corresponding to the model weight of the data matrix at the previous moment f Biasing the forgetful gate.
Note that the forgetting gate functions to control discarding redundant information in the state information at the previous time. The forgetting gate can read the network output value at the last moment and the network input value at the current moment, and the activation function is used for controlling the activation value of the forgetting gate to output a value in a range of 0-1, wherein the degree of the state information reserved to the current state at the previous moment is represented by the fact that the degree of the reserved state information is lower when the state information is closer to 0, and the degree of the reserved state information is higher when the state information is closer to 1.
The determining the state information of the current moment is implemented by the following method, which is exemplified by the embodiment, based on the forgetting door activation value of the current moment, the state information of the last moment, the input door activation value of the current moment and the initialization state information of the current moment:
wherein c t F is state information at the current time t C, forgetting the door activation value at the current moment t-1 I is the state information of the last moment t The gate activation value is entered for the current time,
the initialization state information is the current time.
It should be noted that, in the embodiment of the present invention, the input gate is activated at the current timeValue i t And initialization state information at the current time
Is calculated in a similar way to the forgetting door activation value f at the current moment t The gate activation value i is input at the current moment t Initialization state information of activation function and current moment +.>
The activation function of (a) can be selected from sigmoid function or tanh function.
Illustratively, the model loss may be determined by:
h t ＝o t tanh(c t )
wherein o is t Outputting a gate activation value, c, for the current time t H is state information at the current moment t And the generated energy data corresponding to the current moment. It should be noted that the output gate activation value o at the current time t Is determined by the method and the forgetting door activation value f t The determination of (2) is the same and will not be described in detail herein.
It should be noted that, the power generation amount prediction model can save or forget information in the training process, so that it can save data information contained in long-sequence data. The historical meteorological data can be used for obtaining the predicted power generation data of the predicted distributed power supply under the corresponding weather conditions.
S103, determining a load grade corresponding to the load area at the current moment, and determining load quantity data at the current moment based on the load grade.
Wherein the load levels may be divided into a first level and a second level. The first level may include, among other things, transferable loads, interruptible loads, electric vehicle loads, and energy storage device loads. The second level may refer to a generally conventional load.
Further, the transferable load may be a load with energy time shifting characteristics, a part of resident load and industrial load have electricity standby time controllability, electricity utilization time transfer can be achieved within a certain time, such as irrigation electricity utilization in resident electricity utilization, planned production in industrial production and the like, planning and arrangement can be conducted according to time and electricity price, and electricity utilization time shifting can be achieved within a certain period of time. Interruptible loads may refer to loads that may be subjected to an interrupt power supply, such as temperature controlled loads, where, although such loads may interrupt the power supply, powering them down may affect the comfort of the associated user, should reduce the amount of load based on such compliance with user comfort. The electric vehicle load can be a load with bidirectional interaction characteristics of energy interaction between a power grid and an electric vehicle, the electric vehicle can be used as a load and a power supply, and the electric vehicle has unique random charge and discharge characteristics, and is different from the traditional load transfer, but the electric vehicle belongs to one load which can be transferred. The energy storage device can be an important implementation mode of distributed power supply energy storage and peak clipping and valley filling, and the main means of the energy storage device include pumped storage, electric conversion, storage batteries and the like. The energy storage device can store redundant electric energy when the distributed power supply output is larger than the user power consumption, and discharge when the distributed power supply output is smaller than the user power consumption, so that the user power consumption is ensured. The general traditional load mainly comprises most resident loads and most industrial electric loads, and mainly comprises daily life electricity consumption of residents, such as daily loads of residents like electric lamps, refrigerators, televisions, air conditioners and the like, office electricity consumption and industrial production electricity consumption and the like, and the partial loads relate to the guarantee of daily life and industrial normal production of the residents.
Specifically, in order to effectively control the terminal load in the area, the regulation and control optimization of the regional power distribution network is improved, and the terminal electricity load in the area is classified. And according to the load grade corresponding to the load area, adopting a corresponding mode to determine the load quantity data at the current moment.
Illustratively, the determining load amount data at the current time based on the load level includes: under the condition that the load level is the first level, carrying out load quantity acquisition on the load area through an intelligent measuring device to obtain load quantity data at the current moment; and under the condition that the load level is the second level, inputting the load quantity prediction data at the previous moment into a load quantity prediction model to perform load quantity prediction, and obtaining the load quantity prediction data at the current moment based on the output of the load quantity prediction model.
Specifically, under the condition that the load level of the load area is determined to be the first level, the load amount data at the current moment is obtained by manually measuring or automatically collecting the load amount of the load area through an intelligent measuring device. And under the condition that the load level of the load area is determined to be the second level, inputting the load quantity prediction data at the previous moment into a load quantity prediction model to perform load quantity prediction, and obtaining the load quantity prediction data at the current moment according to the output of the load quantity prediction model. It should be noted that, the training process of the load quantity prediction model is similar to the training process of the power generation quantity prediction model, and will not be described here again.
It should be noted that, data transmission between the intelligent measurement devices in different areas gathers data to the cloud platform through the intelligent gateway. The intelligent gateway used in the invention supports various network modes, such as GPRS, 4G, 5G, beidou, 1.8G power line wireless private network, optical fiber private network and the like, and can also support various communication protocols, such as DL/T645, DL/T698, TCP, UDP and the like.
S104, determining a load area matched with the distributed power station at the current moment according to the predicted power generation amount data at the current moment, the load amount data at the current moment and a preset gap threshold.
The preset gap threshold may refer to a gap range that needs to be satisfied when the distributed power station is matched with the load area.
Specifically, for each distributed power station, calculating a difference value between predicted power generation amount data of the distributed power station at the current moment and load amount data corresponding to each load area, and determining that the load areas are matched with the distributed power station under the condition that the difference value is smaller than a preset difference threshold value, wherein the difference value is only used for determining all load areas matched with the distributed power station at the current moment.
According to the technical scheme, the meteorological information corresponding to the distributed power station is obtained. The meteorological information is input into a power generation amount prediction model to perform power generation amount prediction, and based on the output of the power generation amount prediction model, predicted power generation amount data at the current moment can be automatically and accurately obtained. And determining a load grade corresponding to the load area at the current moment, and determining load quantity data at the current moment based on the load grade. According to the predicted generating capacity data at the current moment, the load capacity data at the current moment and a preset gap threshold value, a load area matched with the distributed power station at the current moment is determined, so that the problem that the load area is unreasonable to be matched with the distributed power station is solved, the self-absorption capacity of the area of the distributed power station is fully improved, and the safety and stability of a power distribution system are further improved.
Example two
Fig. 2 is a flowchart of a method for matching a power plant with a load area according to a second embodiment of the present invention, where how to determine an optimal matching combination is further defined based on the above embodiments. As shown in fig. 2, the method includes:
s201, acquiring meteorological information corresponding to the distributed power station.
S202, inputting the meteorological information into a power generation amount prediction model to perform power generation amount prediction, and obtaining predicted power generation amount data at the current moment based on the output of the power generation amount prediction model.
S203, determining a load level corresponding to the load area at the current moment, and determining load quantity data at the current moment based on the load level.
S204, determining a load area matched with the distributed power station at the current moment according to the predicted power generation amount data at the current moment, the load amount data at the current moment and a preset gap threshold.
S205, according to the determined distributed power stations with the matched load areas, the dynamic combination of the load areas and the power stations is established.
Specifically, according to predicted power generation data obtained by prediction and load data corresponding to each load area, the invention dynamically combines a plurality of distributed power stations with the load areas according to supply-demand relations, and realizes load power supply of the distributed power stations to the load areas.
The invention dynamically combines the distributed power station and the load area, wherein if a distributed power supply is started to supply power to the load power supply area, the parameter is set to be 1, and if the distributed power supply is disconnected, the parameter is set to be 0, so that a numerical matrix of the distributed power station and the load area is generated. The obtained numerical matrix of the power supply switch dynamically changes along with the time sequence of the power supply data and the load data, so that a conditional distributed power station consumption dynamic planning scheme set is generated, and the power supply planning scheme is dynamically adjusted.
S206, calculating the minimum planning cost aiming at each load area and power station combination, and determining the load area and power station combination corresponding to the minimum planning cost as the best matching combination based on the construction cost of the distributed power station, the annual maintenance cost of the distributed power station and the annual electric quantity acquisition cost of the load area and power station combination.
The construction cost of the distributed power station may refer to the construction cost of the distributed power station, the annual maintenance cost of the distributed power station may refer to the annual maintenance cost of the distributed power station, and the annual electricity acquisition cost of the load area and the power station combination may refer to the cost of the annual electricity acquisition cost of the load area and the power station combination from the power grid. It should be noted that when the annual electricity acquisition cost of the load area and the power plant combination is a positive number, the cost of the load area and the power plant combination for acquiring electricity from the power grid is indicated. When the annual electric quantity acquisition cost of the load area and the power station combination is negative, the load area and the power station combination are indicated to transfer the rights of the own electric quantity to the power grid, or supply power to the power grid.
In particular, because there may be multiple matrices of power switch values for each instant of time that meet the power distribution requirements. The invention further provides an optimized distributed power supply planning optimization scheme based on minimizing the total cost of the power distribution network. And calculating the minimum planning cost aiming at each load area and power station combination, and determining the load area and power station combination corresponding to the minimum planning cost as the best matching combination based on the construction cost of the distributed power station and the annual maintenance cost of the distributed power station and the annual electric quantity acquisition cost of the load area and power station combination.
Illustratively, the minimum planning cost is calculated based on the construction cost of the distributed power station, the annual maintenance cost of the distributed power station and the annual electricity acquisition cost of the combination of the load area and the power station, and the minimum planning cost is realized by the following steps:
wherein mu i For the weight coefficient of the distributed power station i,
for the construction costs of the distributed power station i +.>
Maintenance costs for annual operation of distributed power station i, < >>
Annual electric quantity acquisition cost, lambda, for load area and power station combination i And the cost coefficient is the electricity purchasing cost coefficient.
According to the technical scheme provided by the embodiment of the invention, the dynamic planning scheme of the distributed power supply mode changing along with time is realized by combining the dynamic characteristics of the data sequence changing along with time based on the predicted power generation data and the load data, and the optimal planning scheme is obtained based on the realization cost minimization. According to the planning scheme, based on historical meteorological data, optimal planning can be performed by selecting the historical meteorological data of different areas, the regional self-digestion capacity of the distributed power station is fully improved, and then the safety and stability of a power distribution system are improved.
Example III
Fig. 3 is a schematic structural diagram of a device for matching a power station with a load area according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: the system comprises a meteorological information acquisition module 301, a power generation amount data prediction module 302, a load amount data determination module 303 and a power generation station and load area matching module 304. Wherein, the liquid crystal display device comprises a liquid crystal display device,
the weather information acquisition module 301 is configured to acquire weather information corresponding to the distributed power station;
the generating capacity data prediction module 302 is configured to input the meteorological information into a generating capacity prediction model to perform generating capacity prediction, and obtain predicted generating capacity data at the current moment based on the output of the generating capacity prediction model;
the load data determining module 303 is configured to determine a load level corresponding to a load area at a current moment, and determine load data at the current moment based on the load level;
and the power station and load area matching module 304 is configured to determine a load area matched with the distributed power station at the current moment according to the predicted power generation amount data at the current moment, the load amount data at the current moment and a preset gap threshold.
According to the technical scheme, the meteorological information corresponding to the distributed power station is obtained. The meteorological information is input into a power generation amount prediction model to perform power generation amount prediction, and based on the output of the power generation amount prediction model, predicted power generation amount data at the current moment can be automatically and accurately obtained. And determining a load grade corresponding to the load area at the current moment, and determining load quantity data at the current moment based on the load grade. According to the predicted generating capacity data at the current moment, the load capacity data at the current moment and a preset gap threshold value, a load area matched with the distributed power station at the current moment is determined, so that the problem that the load area is unreasonable to be matched with the distributed power station is solved, the self-absorption capacity of the area of the distributed power station is fully improved, and the safety and stability of a power distribution system are further improved.
On the basis of the above embodiments, the device comprises a power generation amount prediction model training module.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the generating capacity prediction model training module can comprise a forgetting door activation value determining unit, a power generation standby data obtaining unit, a state information determining unit, a model loss determining unit and a generating capacity prediction model determining unit. Wherein, the liquid crystal display device comprises a liquid crystal display device,
the power generation standby data acquisition module is used for acquiring historical meteorological data corresponding to the distributed power station and power generation corresponding to the historical meteorological data;
the forgetting door activation value determining unit is used for determining a forgetting door activation value at the current moment based on a forgetting door activation function, weather data at the current moment, generated energy power corresponding to the weather data at the current moment, historical weather data at the last moment, historical generated energy power corresponding to the historical weather data at the last moment and model weight;
the state information determining unit is used for determining the state information of the current moment based on the forgetting door activation value of the current moment, the state information of the last moment, the input door activation value of the current moment and the initialization state information of the current moment;
the model loss determining unit is used for determining model loss corresponding to a power generation amount prediction model based on the state information of the current moment, the output door activation value of the current moment and the power generation amount data corresponding to the current moment, and adjusting the model weight based on the model loss;
And the generating capacity prediction model determining unit is used for taking the adjusted generating capacity prediction model as a trained generating capacity prediction model under the condition that the preset ending condition is met.
On the basis of the above embodiments, the forgetting gate activation value determining unit is specifically configured to: integrating the meteorological data corresponding to the current moment and the generated energy power corresponding to the meteorological data at the current moment into a current moment data matrix, and integrating the historical generated energy power corresponding to the historical meteorological data at the last moment and the historical generated energy power corresponding to the historical meteorological data at the last moment into a last moment data matrix;
and calculating the forgetting gate activation value at the current moment based on the forgetting gate activation function, the current moment data matrix, the model weight corresponding to the current moment data matrix, the last moment data matrix and the weight corresponding to the last moment data matrix.
On the basis of the above embodiments, the workflow of the state information determining unit is specifically implemented by the following manner:
wherein c t F is state information at the current time t C, forgetting the door activation value at the current moment t-1 I is the state information of the last moment t The gate activation value is entered for the current time, 
Is temporary state information at the current time.
On the basis of the above embodiments, the load data determining module may be specifically configured to:
under the condition that the load level is the first level, carrying out load quantity acquisition on the load area through an intelligent measuring device to obtain load quantity data at the current moment;
and under the condition that the load level is the second level, inputting the load quantity prediction data at the previous moment into a load quantity prediction model to perform load quantity prediction, and obtaining the load quantity prediction data at the current moment based on the output of the load quantity prediction model.
On the basis of the above embodiments, the device further includes: the best combination determination module. Wherein, the liquid crystal display device comprises a liquid crystal display device,
the optimal combination determining module is used for establishing a combination of the load area and the power station according to the determined distributed power station with the matched load area;
and calculating the minimum planning cost aiming at each load area and power station combination, and determining the load area and power station combination corresponding to the minimum planning cost as the optimal combination based on the construction cost of the distributed power station, the annual maintenance cost of the distributed power station and the annual electric quantity acquisition cost of the load area and power station combination.
On the basis of the above embodiments, the best combination determining module may be implemented as follows:
wherein mu i For the weight coefficient of the distributed power station i,
for the construction costs of the distributed power station i +.>
Maintenance costs for annual operation of distributed power station i, < >>
Annual electric quantity acquisition cost, lambda, for load area and power station combination i And the cost coefficient is the electricity purchasing cost coefficient.
The device for matching the power station with the load area provided by the embodiment of the invention can execute the method for matching the power station with the load area provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, e.g. the method power plant is matched to the load area.
In some embodiments, the method power plant and load zone matching may be implemented as a computer program tangibly embodied on a computer readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method described above for power plant matching to load areas may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method power plant to load region matching by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of matching a power plant to a load area, comprising:
acquiring weather information corresponding to a distributed power station;
inputting the meteorological information into a power generation amount prediction model to perform power generation amount prediction, and obtaining predicted power generation amount data at the current moment based on the output of the power generation amount prediction model;
determining a load grade corresponding to a load area at the current moment, and determining load quantity data at the current moment based on the load grade;
And determining a load area matched with the distributed power station at the current moment according to the predicted power generation amount data at the current moment, the load amount data at the current moment and a preset gap threshold.
2. The method of claim 1, wherein the training process of the power generation capacity prediction model comprises:
acquiring historical meteorological data corresponding to a distributed power station and generating power corresponding to the historical meteorological data;
determining a forgetting door activation value at the current moment based on a forgetting door activation function, weather data at the current moment, generated energy power corresponding to the weather data at the current moment, historical weather data at the last moment, historical generated energy power corresponding to the historical weather data at the last moment and model weight;
determining the state information of the current moment based on the forgetting door activation value of the current moment, the state information of the last moment, the input door activation value of the current moment and the initialization state information of the current moment;
determining model loss corresponding to a power generation amount prediction model based on the state information of the current moment, the output door activation value of the current moment and the power generation amount data corresponding to the current moment, and adjusting the model weight based on the model loss;
And under the condition that the preset ending condition is met, taking the generated energy prediction model obtained by adjusting the model parameters as a generated energy prediction model after training.
3. The method of claim 2, wherein determining the forget gate activation value for the current time comprises:
integrating the meteorological data corresponding to the current moment and the generated energy power corresponding to the meteorological data at the current moment into a current moment data matrix, and integrating the historical generated energy power corresponding to the historical meteorological data at the last moment and the historical generated energy power corresponding to the historical meteorological data at the last moment into a last moment data matrix;
and calculating the forgetting gate activation value at the current moment based on the forgetting gate activation function, the current moment data matrix, the model weight corresponding to the current moment data matrix, the last moment data matrix and the model weight corresponding to the last moment data matrix.
4. The method according to claim 1, wherein the determining the state information at the current time based on the forget gate activation value at the current time, the state information at the last time, the input gate activation value at the current time, and the initialization state information at the current time is implemented by:
Wherein c t F is state information at the current time t C, forgetting the door activation value at the current moment t-1 I is the state information of the last moment t The gate activation value is entered for the current time,
the initialization state information is the current time.
5. The method of claim 1, wherein the determining load amount data for the current time based on the load class comprises:
under the condition that the load level is the first level, carrying out load quantity acquisition on the load area through an intelligent measuring device to obtain load quantity data at the current moment;
and under the condition that the load level is the second level, inputting the load quantity prediction data at the previous moment into a load quantity prediction model to perform load quantity prediction, and obtaining the load quantity prediction data at the current moment based on the output of the load quantity prediction model.
6. The method of claim 1, further comprising, after said determining a distributed power plant matching the load zone:
according to the determined distributed power stations with the matched load areas, establishing dynamic combination of the load areas and the power stations;
and calculating the minimum planning cost aiming at each load area and power station combination, and determining the load area and power station combination corresponding to the minimum planning cost as the best matching combination based on the construction cost of the distributed power station and the annual maintenance cost of the distributed power station and the annual electric quantity acquisition cost of the load area and power station combination.
7. The method of claim 5, wherein the calculating the minimum planning cost based on the construction cost of the distributed power plant, the annual maintenance cost of the distributed power plant, and the annual power harvesting cost of the load area and power plant combination is accomplished by:
wherein mu i For the weight coefficient of the distributed power station i,
for the construction costs of the distributed power station i +.>
Maintenance costs for annual operation of distributed power station i, < >>
Annual electric quantity acquisition cost, lambda, for load area and power station combination i And the cost coefficient is the electricity purchasing cost coefficient.
8. A device for matching a power plant to a load area, comprising:
the weather information acquisition module is used for acquiring weather information corresponding to the distributed power station;
the generating capacity data prediction module is used for inputting the meteorological information into a generating capacity prediction model to predict generating capacity, and obtaining predicted generating capacity data at the current moment based on the output of the generating capacity prediction model;
the load data determining module is used for determining a load grade corresponding to a load area at the current moment and determining load data at the current moment based on the load grade;
and the power station and load area matching module is used for determining a load area matched with the distributed power station at the current moment according to the predicted power generation amount data at the current moment, the load amount data at the current moment and a preset gap threshold value.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of matching a power plant to a load area of any one of claims 1-4.
10. A computer-readable storage medium, characterized in that it stores computer instructions for causing a processor to execute the method of matching a power plant with a load area according to any one of claims 1-4.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116706905A (en) * | 2023-08-08 | 2023-09-05 | 国网浙江省电力有限公司宁波供电公司 | Multi-energy prediction and scheduling method, equipment and storage medium based on power system |
| CN116780660A (en) * | 2023-08-22 | 2023-09-19 | 国网浙江宁波市鄞州区供电有限公司 | Layered cooperative control method and system for distributed photovoltaic |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116706905A (en) * | 2023-08-08 | 2023-09-05 | 国网浙江省电力有限公司宁波供电公司 | Multi-energy prediction and scheduling method, equipment and storage medium based on power system |
| CN116706905B (en) * | 2023-08-08 | 2023-11-03 | 国网浙江省电力有限公司宁波供电公司 | Multi-energy prediction and scheduling method, equipment and storage medium based on power system |
| CN116780660A (en) * | 2023-08-22 | 2023-09-19 | 国网浙江宁波市鄞州区供电有限公司 | Layered cooperative control method and system for distributed photovoltaic |
| CN116780660B (en) * | 2023-08-22 | 2024-03-12 | 国网浙江宁波市鄞州区供电有限公司 | Layered cooperative control method and system for distributed photovoltaic |
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