CN115173490B - Energy supply method and device for comprehensive energy station and electronic equipment - Google Patents

Energy supply method and device for comprehensive energy station and electronic equipment Download PDF

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CN115173490B
CN115173490B CN202211076095.6A CN202211076095A CN115173490B CN 115173490 B CN115173490 B CN 115173490B CN 202211076095 A CN202211076095 A CN 202211076095A CN 115173490 B CN115173490 B CN 115173490B
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power generation
energy
station
degree
conversion degree
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CN115173490A (en
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俎云霄
周杰
齐国红
陈文斌
吕新
马洪亮
李景云
黄超
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Xinjiang Tianfu Energy Co ltd
Beijing University of Posts and Telecommunications
Shihezi University
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Xinjiang Tianfu Energy Co ltd
Beijing University of Posts and Telecommunications
Shihezi University
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • 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 relates to the technical field of energy utilization, in particular to an energy supply method and device for a comprehensive energy station and electronic equipment. The energy supply method of the comprehensive energy station comprises the following steps: acquiring the type of the power generation energy of the sub-power generation station included by each comprehensive energy station in the control area; acquiring the power generation conversion degree and the power generation cost corresponding to each power generation energy type during power generation; and determining at least one sub-power station for power generation by adopting a particle swarm algorithm according to the power generation conversion degree and the power generation cost. According to the invention, through continuous iterative optimization by adopting a particle swarm algorithm, an optimal sub-power station can be found for power generation, so that the energy supply efficiency of the comprehensive energy station is improved.

Description

Energy supply method and device for comprehensive energy station and electronic equipment
Technical Field
The invention relates to the technical field of energy utilization, in particular to an energy supply method and device for a comprehensive energy station and electronic equipment.
Background
In the 21 st century, the countries emphasize green color and sustainable development, and require energy enterprises to change to clean green sustainable direction. The regional difference causes different energy distribution, and a single type of energy is not enough to support production and life. In order to coordinate the balance relationship between energy utilization and environmental protection, energy collaborative planning is taken as an idea, energy types such as water, electricity, steam and heat are integrated, and an energy system is constructed to realize complementary characteristics and synergistic effects of different energy forms. The comprehensive energy system has the characteristics of high available energy density, high load utilization hours, increased proportion of renewable energy sources, diversified energy production and utilization forms and the like, and is an effective implementation way for promoting large-scale local consumption of the renewable energy sources, improving the comprehensive utilization efficiency of the energy sources and realizing the aims of energy conservation and emission reduction.
In the prior art, the single scheduling mode is the main reason of low energy utilization efficiency. The comprehensive energy station combines various energy sources (gas, water, steam, heat energy and light energy) with various energy supply and energy saving devices (a generator set, a waste heat boiler, a refrigerating unit, a solar panel and the like), and centrally manages, regulates, controls and conveys production and energy sources through the Internet, so that an independent energy island is created, and the resource integration and comprehensive utilization of regional energy sources are realized. Generally, a city comprises a plurality of comprehensive energy source stations, when a city needs to supply power, workers usually select a mode of parallel power generation by a plurality of energy sources or a mode of alternate power generation by a plurality of energy sources according to experience to supply power, and the power generation and supply mode causes the energy efficiency of the comprehensive energy source stations in the city to be lower.
Disclosure of Invention
The invention provides an energy supply method and device for a comprehensive energy station and electronic equipment, which are used for solving the technical problem that in the prior art, workers select a power generation mode according to experience so that the energy efficiency of the comprehensive energy station is low.
In one aspect, the invention provides an energy supply method for an integrated energy station, which comprises the following steps:
acquiring the type of the power generation energy of the sub-power generation station included by each comprehensive energy station in the control area;
acquiring the power generation conversion degree and the power generation cost corresponding to each power generation energy type during power generation;
and determining at least one sub-power station for power generation by adopting a particle swarm algorithm according to the power generation conversion degree and the power generation cost.
According to the energy supply method of the comprehensive energy station, the step of determining at least one sub-power station for power generation by adopting a particle swarm algorithm according to the power generation conversion degree and the power generation cost comprises the following steps:
respectively determining a conversion degree input matrix and a cost input matrix according to the power generation conversion degree and the power generation cost of each sub-power station;
inputting the conversion degree input matrix and the cost input matrix into a pre-trained particle swarm algorithm model to obtain at least one corresponding optimal power generation conversion degree in the conversion degree input matrix;
and determining the sub-power station corresponding to the optimal power generation conversion degree as a target sub-power station for power generation.
According to the energy supply method for the comprehensive energy station provided by the invention, the step of inputting the conversion degree input matrix and the cost input matrix into a pre-trained particle swarm algorithm model to obtain at least one optimal power generation conversion degree in the conversion degree input matrix comprises the following steps:
determining a beneficial degree value of each sub-power station by adopting the following formula (1) according to a conversion degree input matrix and a cost input matrix by a pre-trained particle swarm algorithm model, determining at least one optimal beneficial degree value through multiple iterative optimization, and determining the power generation conversion degree corresponding to the optimal beneficial degree value as the optimal power generation conversion degree;
Figure 284302DEST_PATH_IMAGE001
in the above-mentioned formula (1),
Figure 629833DEST_PATH_IMAGE002
the rows of the input matrix representing the degree of conversion,
Figure 186716DEST_PATH_IMAGE003
a column of the conversion degree input matrix is represented,
Figure 719328DEST_PATH_IMAGE004
representing the degree of conversion in the input matrix
Figure 760972DEST_PATH_IMAGE005
Go to the first
Figure 633113DEST_PATH_IMAGE003
The beneficial degree value corresponding to the electricity generation conversion degree of the column,
Figure 485531DEST_PATH_IMAGE006
is shown as
Figure 708702DEST_PATH_IMAGE005
Go to the first
Figure 789922DEST_PATH_IMAGE007
The degree of power generation conversion of the train;
Figure 516569DEST_PATH_IMAGE008
the number of the types of the sources in the control area is shown,
Figure 539889DEST_PATH_IMAGE009
the number of the comprehensive energy stations in the control area is shown,
Figure 984777DEST_PATH_IMAGE010
denotes the first
Figure 633801DEST_PATH_IMAGE011
Go to the first
Figure 746114DEST_PATH_IMAGE012
And energy assignment corresponding to the power generation conversion degree of the row.
According to the energy supply method for the comprehensive energy station, provided by the invention, the inertia weight w of the trained particle swarm algorithm model is =0.9, and the initial learning factor d =2.
According to the comprehensive energy station energy supply method provided by the invention, the trained particle swarm algorithm model has the optimization iteration times of 200-300.
According to the energy supply method of the comprehensive energy station provided by the invention, the method further comprises the following steps:
the method comprises the steps that the power generation conversion degree and the power generation cost of sub-power stations included in each comprehensive energy station in a plurality of areas are obtained in advance to form a plurality of groups of training data;
and training the initial particle swarm algorithm model by adopting the multiple groups of training data to obtain the trained particle swarm algorithm model.
According to the energy supply method of the comprehensive energy station provided by the invention, the method further comprises the following steps:
the method comprises the steps that the power generation conversion degree and the power generation cost of sub-power generation stations included in each comprehensive energy station in at least one region are obtained in advance to form at least one group of verification data;
and verifying the trained particle swarm algorithm model by adopting the verification data so as to determine whether the trained particle swarm algorithm model meets the preset requirement.
In another aspect, the present invention further provides an energy supply device for an integrated energy station, including:
the acquisition module is used for acquiring the type of the power generation energy of the sub-power generation station included by each comprehensive energy station in the control area;
the first processing module is used for acquiring the power generation conversion degree and the power generation cost corresponding to each power generation energy type during power generation;
and the second processing module is used for determining at least one sub-power station for power generation according to the power generation conversion degree and the power generation cost by adopting a particle swarm algorithm.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the energy supply method of the comprehensive energy station.
The invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of powering an integrated energy station as described in any of the above.
According to the energy supply method of the comprehensive energy station, the power generation conversion degree and the power generation cost of all sub power stations in the current control area are considered, and then at least one sub power station is determined to be used for power generation according to the power generation conversion degree and the power generation cost by adopting a particle swarm algorithm. By adopting the particle swarm optimization, the optimal sub-power station can be found for power generation through continuous iterative optimization, so that the energy supply efficiency of the comprehensive energy station is improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method for powering an integrated energy plant according to the present invention;
FIG. 2 is a schematic diagram illustrating comparison between energy conversion and utilization rates of a particle swarm optimization algorithm and a manual optimization method provided by the invention;
FIG. 3 is a schematic diagram of comparing the cost consumption of the particle swarm optimization algorithm and the manual optimization method provided by the present invention;
FIG. 4 is a schematic structural diagram of an energy supply device of the integrated energy station provided by the invention;
fig. 5 is a schematic physical structure diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, a lot of comprehensive energy stations exist in a city, each comprehensive energy station comprises a plurality of energy types, when power is supplied, energy station workers select one comprehensive energy station or a plurality of comprehensive energy stations to realize power supply according to experience or current conditions, wherein the energy types are selected for realizing power generation in each comprehensive energy station, and technicians select the energy types according to the current working conditions and experience, so that the functional efficiency of the whole functional system of the city is low, namely, the energy conversion rate is low. The invention considers the energy supply system of the whole city as a whole, uses the sub-power stations in each comprehensive energy station as a particle, uses the power generation conversion degree and the power generation cost of each sub-energy station during power generation as the limiting conditions, and finds the optimal sub-power station or sub-power stations for power generation through multiple iterations of the particle swarm algorithm so as to improve the energy conversion efficiency of the energy supply system of the whole city.
The technical solution of the present invention is further explained below with reference to fig. 1 to 5.
The first embodiment is as follows:
the embodiment provides an energy supply method of an integrated energy station, as shown in fig. 1, the energy supply method comprises the following steps:
step 101: and acquiring the type of the generating energy of the sub-power station included by each comprehensive energy station in the control area.
The existing energy types for power generation generally comprise five types of wind energy, water energy, fire energy, natural gas and photovoltaic, and a general comprehensive energy station has no more than three types of energy at the same time. Taking a city a as an example, for example, the energy sources of the sub-power plants included in the city a include the integrated energy station 10, the energy type of the sub-power plant included in each integrated energy station is obtained.
Step 102: and acquiring the power generation conversion degree and the power generation cost corresponding to each power generation energy type during power generation. In this embodiment, the technician obtains the power generation conversion rate of each energy source in the integrated energy station to which the technician belongs as the conversion degree, and theoretically, the conversion degree is different due to the influence of the environment in which the energy station is located, and even if the same type of energy source is in different integrated energy stations, the corresponding conversion degrees may be different.
Step 103: and determining at least one sub-power station for power generation by adopting a particle swarm algorithm according to the power generation conversion degree and the power generation cost. In the embodiment, the conversion degree and the power generation cost of each sub-energy station during power generation are used as limiting conditions, and through multiple iterations of a particle swarm algorithm, the optimal sub-power station or sub-power stations are found for power generation, so that the energy conversion efficiency of the energy supply system of the whole city is improved.
Specifically, in step 103, the embodiment first determines a conversion degree input matrix and a cost input matrix according to the power generation conversion degree and the power generation cost of each sub-power station; inputting the conversion degree input matrix and the cost input matrix into a pre-trained particle swarm algorithm model to obtain at least one corresponding optimal power generation conversion degree in the conversion degree input matrix; and finally, determining the sub-power stations corresponding to the optimal power generation conversion degree as target sub-power stations for power generation.
For example, in this embodiment, a city is regarded as a particle swarm, and sub-power plants included in 10 integrated energy stations in the city are represented by the following matrix P.
Figure 674756DEST_PATH_IMAGE013
The matrix
Figure 606940DEST_PATH_IMAGE014
Each column represents an integrated energy station, a 1 in each column represents that the location has energy, and a 0 represents that the location has no energy. And each column respectively represents wind energy, water energy, fire energy, natural gas and photovoltaic from top to bottom.
In this embodiment, the cost input matrix composed of the power generation costs of the five energy sources of wind energy, water energy, fire energy, natural gas and photovoltaic is
Figure 29962DEST_PATH_IMAGE015
The conversion degree input matrix composed of the power generation conversion degrees of the five types of energy in the 10 comprehensive energy stations is as follows:
Figure 262360DEST_PATH_IMAGE016
for example, the first column in the matrix sequentially represents that the power generation conversion degree of wind energy in the first comprehensive energy source station is 0.718, the power generation conversion degree of water energy in the first comprehensive energy source station is 0.683, the power generation conversion degree of fire energy in the first comprehensive energy source station is 0.957, the power generation conversion degree of natural gas in the first comprehensive energy source station is 0.823, and the power generation conversion degree of photovoltaic in the first comprehensive energy source station is 0.768.
In this embodiment, the cost input matrix and the conversion degree input matrix are input into a pre-trained particle swarm algorithm model, and the iteration number and the constraint condition of the particle swarm algorithm model are set at the same time, for example, in this embodiment, the number of particle swarm individuals is set to 20, the inertia weight w =0.9 of the trained particle swarm algorithm model is set, and the initial learning factor d =2; the optimization iteration times of the trained particle swarm algorithm model are set to be 200, and the number of optimal points (namely the optimal power generation conversion degree) needing to be found is set to be 1. Meanwhile, the position and the speed of the particle swarm algorithm model are initialized in a chaotic manner. And then, iteration is started until the optimal 1 power generation conversion degree is found or the iteration number reaches the set value, so that the sub-power generation station corresponding to the determined optimal power generation conversion degree meets the requirements of the comprehensive energy station on high efficiency and low consumption. According to the embodiment, two or more optimal sub-power generation stations can be found according to needs to generate power at the same time, the multi-energy complementary comprehensive energy station formed by energy supply optimization through a particle swarm algorithm is adopted, and the energy utilization rate and the economic benefit are improved to some extent.
In the embodiment, the pre-trained particle swarm algorithm model determines the beneficial degree value V of each sub-power station by adopting the following formula (1) according to the conversion degree input matrix and the cost input matrix, determines at least one optimal beneficial degree value V through multiple iterative optimization, and determines the power generation conversion degree corresponding to the optimal beneficial degree value V as the optimal power generation conversion degree;
Figure 361903DEST_PATH_IMAGE017
in the above-mentioned formula (1),
Figure 781383DEST_PATH_IMAGE018
the rows of the input matrix representing the degree of conversion,
Figure 506631DEST_PATH_IMAGE019
a column of the conversion degree input matrix is represented,
Figure 124695DEST_PATH_IMAGE020
representing the degree of conversion in the input matrix
Figure 863980DEST_PATH_IMAGE021
Go to the first
Figure 770757DEST_PATH_IMAGE022
The power generation conversion degree of the column corresponds to a beneficial degree value,
Figure 535581DEST_PATH_IMAGE023
denotes the first
Figure 273730DEST_PATH_IMAGE024
Go to the first
Figure 590442DEST_PATH_IMAGE003
The degree of power generation conversion of the train;
Figure 109148DEST_PATH_IMAGE008
the number of the types of the sources in the control area is shown,
Figure 379461DEST_PATH_IMAGE025
the number of the comprehensive energy stations in the control area is shown,
Figure 175379DEST_PATH_IMAGE026
is shown as
Figure 725309DEST_PATH_IMAGE027
Go to the first
Figure 731311DEST_PATH_IMAGE022
Energy assignment for the degree of conversion of power generation to a column, e.g., in the above-described matrix P, at a point in the first row and first column due to the presence of energy
Figure 962572DEST_PATH_IMAGE028
At that point in the second row and first column due to the absence of an energy source
Figure 488363DEST_PATH_IMAGE029
。
In the particle swarm optimization iterative process, the position, the speed, the learning factor and the inertia weight of the particles are updated in each iterative process. In this embodiment, the benefit degree value corresponding to each power generation conversion factor is obtained in each iteration process.
In each optimization process, the particles track two optimal points, one is the optimal point (i.e. individual extreme value) found by the particles themselves, namely the degree of conversion of power generation in the matrix P of the embodiment; the other is the optimal point (i.e. the global optimal solution) currently found by the whole population of particles, i.e. the optimal point in the matrix P in this embodiment. After finding the optimum point, the particle updates its next speed according to the following formula:
Figure 412456DEST_PATH_IMAGE030
in the above formula (2), the first and second groups,
Figure 905755DEST_PATH_IMAGE031
indicating the location of the updated next time of day,
Figure 940707DEST_PATH_IMAGE032
and
Figure 553960DEST_PATH_IMAGE033
are fine particles in
Figure 648955DEST_PATH_IMAGE034
The velocity vector and the position of the moment in time,
Figure 895128DEST_PATH_IMAGE035
is the position of the individual extrema of the particles,
Figure 468192DEST_PATH_IMAGE036
for the global optimal solution position of the particle,
Figure 702995DEST_PATH_IMAGE037
in order to be the inertial weight,
Figure 968892DEST_PATH_IMAGE038
and
Figure 171203DEST_PATH_IMAGE039
for learning factors, in particular, in the present embodiment
Figure 813537DEST_PATH_IMAGE040
The factors are learned for the individual in question,
Figure 944259DEST_PATH_IMAGE038
the larger the particle size is, the stronger the local searching capability of the particle is;
Figure 381056DEST_PATH_IMAGE041
is a social learning factor and is used as a social learning factor,
Figure 805085DEST_PATH_IMAGE042
the larger the particle size, the more likely it is to fall into local optima. In the embodiment, it is verified that,
Figure 719951DEST_PATH_IMAGE043
and
Figure 194926DEST_PATH_IMAGE044
when the values are all 2, the updating of the speed of the particles can be rapidly realized.
Figure 68204DEST_PATH_IMAGE045
And
Figure 979528DEST_PATH_IMAGE046
are random numbers greater than 0 and less than 1. The position change of the particles is formulated as
Figure 698085DEST_PATH_IMAGE047
。
In the embodiment, the power generation conversion degree and the power generation cost of the sub-power stations included in each comprehensive energy station in a plurality of areas are obtained in advance to form a plurality of groups of training data; and training the initial particle swarm algorithm model by adopting a plurality of groups of training data to obtain the trained particle swarm algorithm model.
In order to verify the accuracy of the trained particle swarm algorithm model, the embodiment also obtains the power generation conversion degree and the power generation cost of the sub-power generation station included in each comprehensive energy station in at least one region in advance to form at least one group of verification data; and verifying the trained particle swarm algorithm model by adopting verification data to determine whether the trained particle swarm algorithm model meets the preset requirements, and if not, continuing to train the particle swarm algorithm model until the requirements are met.
In this embodiment, relevant data of a function scheme in the city a for a period of time is collected, and a method of manually setting an energy supply manner is compared with a method of determining an energy supply manner by using the particle swarm optimization of this embodiment, as shown in fig. 2, it can be seen that a suitable energy supply scheme can be obtained in a continuous iteration process by using the particle swarm optimization and the manual setting optimization. But the particle swarm optimization algorithm has higher energy conversion ratio and better convergence rate, and can better meet the requirements of high efficiency and low consumption of the comprehensive energy station. The multi-energy complementary comprehensive energy station formed by optimizing energy supply through the particle swarm algorithm improves the energy utilization rate and the economic benefit.
As shown in fig. 3, it can be seen that the particle swarm optimization algorithm and the manual setting optimization method can both obtain a suitable energy supply scheme in a continuous iteration process. But the energy conversion consumption cost of the particle swarm optimization algorithm is lower, the convergence rate is better, and the requirements of high efficiency and low consumption of a comprehensive energy station can be better met.
Example two:
the embodiment provides an energy supply device for an integrated energy station, as shown in fig. 4, the energy supply device of the embodiment includes: the device comprises an acquisition module 201, a first processing module 202 and a second processing module 203.
The obtaining module 201 is configured to obtain a power generation energy type of a sub-power plant included in each comprehensive energy station in the management and control area; the first processing module 202 is configured to obtain a power generation conversion degree and a power generation cost corresponding to each power generation energy type during power generation; the second processing module 203 is used for determining at least one sub-power station for power generation according to the power generation conversion degree and the power generation cost by adopting a particle swarm algorithm.
In this embodiment, the implementation method of the functions of the modules is the same as that in the first embodiment, and is not described here again.
Example three:
fig. 5 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 5: a processor (processor) 301, a communication Interface (communication Interface) 302, a memory (memory) 303 and a communication bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the communication bus 304. The processor 301 may call logic instructions in the memory 303 to perform the method for powering an integrated energy station provided in the first embodiment, the method comprising: acquiring the power generation energy type of sub-power stations included in each comprehensive energy station in a control area; acquiring the power generation conversion degree and the power generation cost corresponding to each power generation energy type during power generation; and determining at least one sub-power station for power generation by adopting a particle swarm algorithm according to the power generation conversion degree and the power generation cost.
In addition, the logic instructions in the memory 303 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a method for providing energy for an integrated energy plant provided by the above methods, the method comprising: acquiring the type of the power generation energy of the sub-power generation station included by each comprehensive energy station in the control area; acquiring the power generation conversion degree and the power generation cost corresponding to each power generation energy type during power generation; and determining at least one sub-power station for power generation by adopting a particle swarm algorithm according to the power generation conversion degree and the power generation cost.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. An energy supply method for an integrated energy station, comprising:
acquiring the type of the power generation energy of the sub-power generation station included by each comprehensive energy station in the control area;
acquiring the power generation conversion degree and the power generation cost corresponding to each power generation energy type during power generation;
determining at least one sub-power generation station for power generation according to the power generation conversion degree and the power generation cost by adopting a particle swarm algorithm;
wherein the determining at least one sub-power generation station for power generation according to the power generation conversion degree and the power generation cost by adopting a particle swarm algorithm comprises:
respectively determining a conversion degree input matrix and a cost input matrix according to the power generation conversion degree and the power generation cost of each sub-power station;
inputting the conversion degree input matrix and the cost input matrix into a pre-trained particle swarm algorithm model to obtain at least one corresponding optimal power generation conversion degree in the conversion degree input matrix;
determining the sub-power station corresponding to the optimal power generation conversion degree as a target sub-power station for power generation;
wherein, the inputting the conversion degree input matrix and the cost input matrix into a pre-trained particle swarm algorithm model to obtain at least one corresponding optimal power generation conversion degree in the conversion degree input matrix comprises:
determining a beneficial degree value of each sub-power station by adopting the following formula (1) according to a conversion degree input matrix and a cost input matrix by a pre-trained particle swarm algorithm model, determining at least one optimal beneficial degree value through multiple iterative optimization, and determining a power generation conversion degree corresponding to the optimal beneficial degree value as the optimal power generation conversion degree;
Figure 372864DEST_PATH_IMAGE001
in the above-mentioned formula (1),
Figure 937969DEST_PATH_IMAGE002
the rows of the input matrix representing the degree of conversion,
Figure 557169DEST_PATH_IMAGE003
a column representing the conversion degree input matrix,
Figure 886519DEST_PATH_IMAGE004
representing degree of conversion input matrix
Figure 391144DEST_PATH_IMAGE005
Go to the first
Figure 122340DEST_PATH_IMAGE006
The power generation conversion degree of the column corresponds to a beneficial degree value,
Figure 663174DEST_PATH_IMAGE007
is shown as
Figure 948662DEST_PATH_IMAGE008
Go to the first
Figure 216832DEST_PATH_IMAGE003
The degree of power generation conversion of the column;
Figure 271376DEST_PATH_IMAGE009
the number of the types of the sources in the control area is shown,
Figure 481646DEST_PATH_IMAGE010
the number of the comprehensive energy stations in the control area is shown,
Figure 988851DEST_PATH_IMAGE011
denotes the first
Figure 60712DEST_PATH_IMAGE012
Go to the first
Figure 235341DEST_PATH_IMAGE013
The power generation conversion degree of the column corresponds to the energy assignment,
Figure DEST_PATH_IMAGE014
denotes the first
Figure 383557DEST_PATH_IMAGE015
The power generation cost of the corresponding energy.
2. The method of claim 1, wherein the trained particle swarm algorithm model has an inertial weight w =0.9 and an initial learning factor d =2.
3. The energy supply method for the comprehensive energy station according to claim 1, wherein the number of optimization iterations of the trained particle swarm algorithm model is 200 to 300.
4. The integrated energy station power method of claim 1, further comprising:
the method comprises the steps that the power generation conversion degree and the power generation cost of sub-power generation stations included in each comprehensive energy station in a plurality of areas are obtained in advance to form a plurality of groups of training data;
and training an initial particle swarm algorithm model by adopting the multiple groups of training data to obtain the trained particle swarm algorithm model.
5. The method of claim 4, further comprising:
the method comprises the steps that the power generation conversion degree and the power generation cost of sub-power generation stations included in each comprehensive energy station in at least one region are obtained in advance to form at least one group of verification data;
and verifying the trained particle swarm algorithm model by adopting the verification data so as to determine whether the trained particle swarm algorithm model meets the preset requirement.
6. An energy supply device of an integrated energy station, comprising:
the acquisition module is used for acquiring the type of the power generation energy of the sub-power generation station included by each comprehensive energy station in the control area;
the first processing module is used for acquiring the power generation conversion degree and the power generation cost corresponding to each power generation energy type during power generation;
the second processing module is used for determining at least one sub-power station for power generation according to the power generation conversion degree and the power generation cost by adopting a particle swarm algorithm;
wherein the determining at least one sub-power generation station for power generation according to the power generation conversion degree and the power generation cost by adopting a particle swarm algorithm comprises:
respectively determining a conversion degree input matrix and a cost input matrix according to the power generation conversion degree and the power generation cost of each sub-power station;
inputting the conversion degree input matrix and the cost input matrix into a pre-trained particle swarm algorithm model to obtain at least one corresponding optimal power generation conversion degree in the conversion degree input matrix;
determining the sub-power station corresponding to the optimal power generation conversion degree as a target sub-power station for power generation;
wherein, the inputting the conversion degree input matrix and the cost input matrix into a pre-trained particle swarm algorithm model to obtain at least one corresponding optimal power generation conversion degree in the conversion degree input matrix comprises:
determining a beneficial degree value of each sub-power station by adopting the following formula (1) according to a conversion degree input matrix and a cost input matrix by a pre-trained particle swarm algorithm model, determining at least one optimal beneficial degree value through multiple iterative optimization, and determining a power generation conversion degree corresponding to the optimal beneficial degree value as the optimal power generation conversion degree;
Figure 643637DEST_PATH_IMAGE001
in the above-mentioned formula (1),
Figure 253610DEST_PATH_IMAGE002
the rows of the input matrix representing the degree of conversion,
Figure 532013DEST_PATH_IMAGE003
a column representing the conversion degree input matrix,
Figure 569239DEST_PATH_IMAGE004
representing degree of conversion input matrix
Figure 316616DEST_PATH_IMAGE005
Go to the first
Figure 730279DEST_PATH_IMAGE006
The beneficial degree value corresponding to the electricity generation conversion degree of the column,
Figure 364654DEST_PATH_IMAGE007
is shown as
Figure 838361DEST_PATH_IMAGE008
Go to the first
Figure 807454DEST_PATH_IMAGE003
The degree of power generation conversion of the column;
Figure 759229DEST_PATH_IMAGE009
the number of the types of the sources in the control area is shown,
Figure 12225DEST_PATH_IMAGE010
the number of the comprehensive energy stations in the control area is shown,
Figure 391254DEST_PATH_IMAGE011
denotes the first
Figure 847643DEST_PATH_IMAGE012
Go to the first
Figure 603109DEST_PATH_IMAGE013
The power generation conversion degree of the column corresponds to the energy assignment,
Figure 212076DEST_PATH_IMAGE014
is shown as
Figure DEST_PATH_IMAGE016
The power generation cost of the corresponding energy.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method for powering an integrated energy station according to any of the claims 1 to 5.
8. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when being executed by a processor, implements the integrated energy station energizing method according to any one of claims 1 to 5.
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