CN115173490A - Energy supply method and device for comprehensive energy station and electronic equipment - Google Patents
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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
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 leads to 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 the 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 generation station for power generation according to the power generation conversion degree and the power generation cost by adopting a particle swarm algorithm.
According to the energy supply method of the comprehensive energy station provided by the invention, 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 of 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 corresponding 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 a power generation conversion degree corresponding to the optimal beneficial degree value as the optimal power generation conversion degree;
in the above-mentioned formula (1),the rows of the input matrix representing the degree of conversion,a column of the conversion degree input matrix is represented,representing the degree of conversion in the input matrixGo to the firstThe power generation conversion degree of the column corresponds to a beneficial degree value,is shown asGo to the firstThe degree of power generation conversion of the column;the number of the types of the sources in the control area is shown,the number of the comprehensive energy stations in the control area is shown,denotes the firstGo to the firstAnd energy assignment corresponding to the power generation conversion degree of the row.
According to the energy supply method of the comprehensive energy station, 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 present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the integrated energy station energizing method 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 generation stations in the current control area are considered, and then at least one sub power generation 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 a particle swarm algorithm and continuous iterative optimization, an optimal sub-power station can be found for power generation so as to improve the energy supply efficiency of the comprehensive energy station.
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 provided by the present invention;
FIG. 2 is a schematic diagram illustrating comparison between the particle swarm optimization algorithm and the manual optimization method for energy conversion and utilization rates;
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 clearer, 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 large number of comprehensive energy stations exist in a city, each comprehensive energy station comprises multiple energy types, when power is supplied, an energy station worker selects one comprehensive energy station or multiple comprehensive energy stations to supply power according to experience or the current situation, and the energy types selected in each comprehensive energy station to realize power generation are also selected by technicians according to the current working situation 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 city a as an example, city a includes the integrated energy station 10, for example, and obtains the energy type of the sub-power generation station included in each integrated energy station.
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 comprehensive energy station to which the technician belongs as the conversion degree, theoretically, the conversion degree is different due to the influence of the environment where the energy station is located, and even if the same type of energy source is in different comprehensive energy stations, the corresponding conversion degree 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 station corresponding to the optimal power generation conversion degree as a target sub-power station for power generation.
For example, in this embodiment, a city is regarded as a particle swarm, and sub-power stations included in 10 integrated energy stations in the city are represented by the following matrix P.
The matrixEach 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 isThe conversion degree input matrix composed of the corresponding power generation conversion degrees of the five types of energy in the 10 comprehensive energy stations is as follows:
for example, the first column in the matrix sequentially shows 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 from top to bottom.
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;
in the above-mentioned formula (1),the rows of the input matrix representing the degree of conversion,a column of the conversion degree input matrix is represented,representing the degree of conversion in the input matrixGo to the firstThe beneficial degree value corresponding to the electricity generation conversion degree of the column,is shown asGo to the firstThe degree of power generation conversion of the train;the number of the types of the sources in the control area is shown,the number of the comprehensive energy stations in the control area is shown,is shown asGo to the firstEnergy valuation corresponding to the degree of conversion of power generation of a column, e.g. in the above-mentioned matrix P, at the point in the first row and first column due to the presence of energyAt that point in the second row and first column due to the absence of an energy source。
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 currently found by the whole particle population (i.e. the global optimal solution), i.e. the optimal point in the matrix P in this embodiment. After finding the optimal point, the particle updates its next step speed according to the following formula:
in the above formula (2), the first and second groups,indicating the location of the updated next time of day,andare fine particles inThe velocity vector and the position of the moment in time,is the position of the individual extrema of the particles,for the global optimal solution position of the particle,as a result of the inertial weight,andfor learning factors, in particular, in the present embodimentIs one by oneThe body learning factor is a function of the body learning factor,the larger the particle is, the higher the local searching capability of the particle is;is a social learning factor and is used as a social learning factor,the larger the particle size, the more likely it is to fall into local optima. In the verification of the present embodiment, it is shown that,andwhen the values are all 2, the updating of the speed of the particles can be rapidly realized.Andare random numbers greater than 0 and less than 1. The position change of the particles is formulated as。
In the embodiment, the power generation conversion degree and the power generation cost of the 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 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 energy conversion ratio of the particle swarm optimization algorithm is higher, the convergence rate is better, and the requirements of high efficiency and low consumption of the comprehensive energy station can be better met. 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 proper 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.
The second embodiment:
the present embodiment provides an energy supply device for an integrated energy station, as shown in fig. 4, the energy supply device of the present 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 station included in each comprehensive energy station in the 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 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.
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 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 position, or may be distributed on multiple 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, but 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 (10)
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;
and 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.
2. The method according to claim 1, wherein said determining at least one sub-power plant for generating power using a particle swarm algorithm based on the degree of conversion and cost of power generation 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;
and determining the sub-power station corresponding to the optimal power generation conversion degree as a target sub-power station for power generation.
3. The energy supply method for the integrated energy station according to claim 2, wherein 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 corresponding to the conversion degree input matrix comprises the steps of:
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;
in the above-mentioned formula (1),the rows of the input matrix representing the degree of conversion,a column of the conversion degree input matrix is represented,representing degree of conversion input matrixGo to the firstThe beneficial degree value corresponding to the electricity generation conversion degree of the column,is shown asGo to the firstThe degree of power generation conversion of the column;the number of the types of the sources in the control area is shown,the number of the comprehensive energy stations in the control area is shown,is shown asGo to the firstAnd energy assignment corresponding to the power generation conversion degree of the row.
4. The method of claim 2, wherein the trained particle swarm algorithm model has an inertial weight w =0.9 and an initial learning factor d =2.
5. The energy supply method for the comprehensive energy station according to claim 2, wherein the number of optimization iterations of the trained particle swarm algorithm model is 200 to 300.
6. The integrated energy station power method of claim 2, further comprising:
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.
7. The method of claim 6, further comprising:
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 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.
8. 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;
and the second processing module is used for determining at least one sub-power station to be used for power generation according to the power generation conversion degree and the power generation cost by adopting a particle swarm algorithm.
9. 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 of powering an integrated energy station according to any of claims 1 to 7.
10. 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 7.
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