CN117559426A - Multi-type power supply and new energy integrated control method based on self-organizing map network - Google Patents
Multi-type power supply and new energy integrated control method based on self-organizing map network Download PDFInfo
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- CN117559426A CN117559426A CN202311543168.2A CN202311543168A CN117559426A CN 117559426 A CN117559426 A CN 117559426A CN 202311543168 A CN202311543168 A CN 202311543168A CN 117559426 A CN117559426 A CN 117559426A
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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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- 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
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy 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
- H02J3/38—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
- 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
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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
-
- 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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Abstract
The invention provides a self-organizing map network-based multi-type power supply and new energy integrated control method, which comprises the following steps: collecting real-time data from new energy plants, including capacity, consumption, cost and environmental impact; determining a network structure; the weight value of the winning node and the neighbor nodes thereof is adjusted; dynamically adjusting the capacity and scheduling of each power supply according to the output of the self-organizing map network; evaluating stability and efficiency of the configuration scheme; monitoring the running state of the whole power supply system in real time; and adjusting the control strategy according to the real-time data and the prediction model. The method solves the problem that the energy storage is not included in the electric energy scheduling in the prior art, so that the electric energy scheduling is not accurate enough.
Description
Technical Field
The invention relates to a multi-type power supply and new energy integrated control method based on a self-organizing map network, and belongs to the technical field of energy coordination and optimization.
Background
In the current energy field, with rapid development of renewable energy and new energy technologies, integration and control of multiple types of power systems become an important research field. These systems typically include conventional energy sources (e.g., fossil fuel power stations) and various forms of new energy sources (e.g., solar energy, wind energy, geothermal energy, etc.). However, this varied combination of energy sources presents significant management and control challenges, particularly in terms of maintaining energy supply stability and improving energy efficiency.
Conventional energy management methods often have difficulty coping with complex new energy systems. For example, the instability and unpredictability of renewable energy sources, such as wind and solar energy volatility, requires energy management systems with a high degree of adaptability and quick response capabilities. In addition, interaction and fusion of different energy forms in the multi-type power supply system, how to optimize the optimal configuration of energy sources, so that the cost, the stability and the carbon emission are considered, and the method is an urgent problem to be solved in the prior art.
Disclosure of Invention
The invention aims to solve the technical problems that: a multi-type power supply and new energy integrated control method based on a self-organizing map network is provided, so as to overcome the defects of the prior art.
The technical scheme of the invention is as follows: a multi-type power supply and new energy integrated control method based on a self-organizing map network comprises the following steps:
collecting real-time data from new energy plants, including capacity, consumption, cost and environmental impact;
determining a network structure;
the weight value of the winning node and the neighbor nodes thereof is adjusted;
dynamically adjusting the capacity and scheduling of each power supply according to the output of the self-organizing map network;
evaluating stability and efficiency of the configuration scheme;
monitoring the running state of the whole power supply system in real time;
and adjusting the control strategy according to the real-time data and the prediction model.
Further, the determining the network structure includes:
determining the number of nodes, wherein the number of nodes is thatM is the number of samples;
and selecting a topological structure, wherein the topological structure is a hexagonal network.
Further, the method for adjusting the weights of the winning node and the neighbor nodes thereof comprises the following steps:
for each input vector, calculating the distance between the input vector and all node weight vectors;
selecting a node with the smallest distance from the input vector as a winning node;
calculating a neighborhood function, wherein the neighborhood function is as follows:
wherein r is c And r i The positions of winning nodes and nodes i in the grid, respectively, σ (t) represents the neighborhood radius over time,σ 0 is the initial neighborhood radius;
for the winning node and the neighbor nodes thereof, the weight is adjusted according to the neighborhood function, and the adjustment method is as follows:
W i (t+1)＝W i (t)+α(t)h ci (t)[X(t)-W i (t)]
wherein W is i (t) represents the weight of node i at time t, wi (t+1) represents the weight of node i at time t+1, X (t) represents the input vector at time t, alpha (t) is the learning rate decreasing with time, α 0 is the initial learning rate, and T is the total number of iterations.
Further, the new energy power station comprises a wind power station.
Further, the new energy power station comprises a photovoltaic power station.
Further, the new energy power station comprises a geothermal power station.
Further, the real-time data includes time, date, solar irradiance, light Fu Banmian and photovoltaic panel conversion efficiency for the solar power station.
Further, the real-time data comprises time, date, air density, wind wheel sweeping area, wind speed and power coefficient for the wind power station.
Further, the real-time data includes time, date, geothermal well temperature, geothermal well pressure, and geothermal well flow for the geothermal power plant.
The beneficial effects of the invention are as follows: compared with the prior art, the method can reflect and process the instability and unpredictability of renewable energy sources more accurately by collecting the real-time data (such as productivity, cost, environmental impact and the like) of the new energy power station. Secondly, the application of the self-organizing map network provides an efficient way to dynamically adjust the capacity and scheduling of the power supply, and improves the control capability and adaptability to the complex power supply system. In addition, the method is helpful for optimizing the configuration of energy sources by evaluating the stability and efficiency of the configuration scheme, and balancing the factors such as cost, stability, carbon emission and the like.
Drawings
Fig. 1 is a flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings of the present specification.
Example 1:
as shown in fig. 1, the method for controlling integration of multiple types of power supplies and new energy sources based on the self-organizing map network comprises the following steps:
collecting real-time data from each power source, including capacity, consumption, cost, and environmental impact;
determining a network structure;
the weight value of the winning node and the neighbor nodes thereof is adjusted;
dynamically adjusting the capacity and scheduling of each power supply according to the output of the self-organizing map network;
evaluating stability and efficiency of the configuration scheme;
monitoring the running state of the whole power supply system in real time;
and adjusting the control strategy according to the real-time data and the prediction model.
Further, the determining the network structure includes:
determining the number of nodes, wherein the number of nodes is thatM is the number of samples;
and selecting a topological structure, wherein the topological structure is a hexagonal network.
Further, the method for adjusting the weights of the winning node and the neighbor nodes thereof comprises the following steps:
for each input vector, calculating the distance between the input vector and all node weight vectors;
selecting a node with the smallest distance from the input vector as a winning node;
calculating a neighborhood function, wherein the neighborhood function is as follows:
wherein r is c And r i The positions of winning nodes and nodes i in the grid, respectively, σ (t) represents the neighborhood radius over time,σ 0 is the initial neighborhood radius;
for the winning node and the neighbor nodes thereof, the weight is adjusted according to the neighborhood function, and the adjustment method is as follows:
W i (t+1)＝W i (t)+α(t)h ci (t)[X(t)-W i (t)]
wherein W is i (t) represents the weight of node i at time t, W i (t+1) represents the weight of node i at time t+1, X (t) represents the input vector at time t, alpha (t) is the learning rate decreasing with time, α 0 initial learning rate, T is total number of iterations.
Further, the new energy source comprises a wind power plant.
Further, the new energy source comprises a photovoltaic power station.
Further, the new energy source comprises a geothermal power plant.
Further, the new energy productivity predicted value is obtained by the following method:
collecting historical data of wind power plants, solar power plants and geothermal power plants;
inputting the historical data into a neural network training prediction model;
collecting real-time data of a wind power station, a solar power station and a geothermal power station;
and inputting the real-time data into a prediction model to obtain a new energy productivity prediction value.
Further, the real-time data and historical data of the solar power plant include time, date, solar irradiance, light Fu Banmian and photovoltaic panel conversion efficiency.
Further, the real-time data and the historical data of the wind power plant comprise time, date, air density, wind swept area, wind speed and power coefficient.
Further, the real-time data and historical data of the geothermal power plant include time, date, geothermal well temperature, geothermal well pressure, and geothermal well flow.
The present invention is not described in detail in the present application, and is well known to those skilled in the art. Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered by the scope of the claims of the present invention.
Claims (9)
1. The method for integrated control of the multi-type power supply and the new energy based on the self-organizing map network is characterized by comprising the following steps:
collecting real-time data from new energy plants, including capacity, consumption, cost and environmental impact;
determining a network structure;
the weight value of the winning node and the neighbor nodes thereof is adjusted;
dynamically adjusting the capacity and scheduling of each power supply according to the output of the self-organizing map network;
evaluating stability and efficiency of the configuration scheme;
monitoring the running state of the whole power supply system in real time;
and adjusting the control strategy according to the real-time data and the prediction model.
2. The method for integrated control of multiple types of power sources and new energy sources based on the self-organizing map network according to claim 1, wherein the determining the network structure comprises:
determining the number of nodes, wherein the number of nodes is thatM is the number of samples;
and selecting a topological structure, wherein the topological structure is a hexagonal network.
3. The method for integrated control of multiple types of power sources and new energy sources based on self-organizing map network according to claim 1, wherein the method for adjusting weights of winning nodes and neighbor nodes thereof is as follows:
for each input vector, calculating the distance between the input vector and all node weight vectors;
selecting a node with the smallest distance from the input vector as a winning node;
calculating a neighborhood function, wherein the neighborhood function is as follows:
wherein r is c And r i The positions of winning nodes and nodes i in the grid, respectively, σ (t) represents the neighborhood radius over time,σ 0 is the initial neighborhood radius;
for the winning node and the neighbor nodes thereof, the weight is adjusted according to the neighborhood function, and the adjustment method is as follows:
W i (t+1)＝W i (t)+α(t)h ci (t)[X(t)-W i (t)]
wherein W is i (t) represents the weight of node i at time t, W i (t+1) represents the weight of node i at time t+1, X (t) represents the input vector at time t, alpha (t) is the learning rate decreasing with time, α 0 is the initial learning rate, and T is the total number of iterations.
4. The self-organizing map network-based multi-type power supply and new energy integrated control method according to claim 1, wherein the new energy power station comprises a wind power station.
5. The method for integrated control of multiple types of power sources and new energy sources based on self-organizing map network according to claim 4, wherein the new energy power station comprises a photovoltaic power station.
6. The integrated control method for multiple types of power sources and new energy sources based on the self-organizing map network according to claim 5, wherein the new energy source power station comprises a geothermal power station.
7. The method for integrated control of multiple types of power sources and new energy sources based on the self-organizing map network according to claim 6, wherein the real-time data pair solar power station comprises time, date, solar irradiance, light Fu Banmian and photovoltaic panel conversion efficiency.
8. The method for integrated control of multiple types of power sources and new energy sources based on the self-organizing map network according to claim 7, wherein the real-time data comprises time, date, air density, wind sweeping area, wind speed and power coefficient for the wind power station.
9. The integrated control method for multiple types of power sources and new energy sources based on the self-organizing map network according to claim 7, wherein the real-time data pair geothermal power station comprises time, date, geothermal well temperature, geothermal well pressure and geothermal well flow.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311543168.2A CN117559426A (en) | 2023-11-20 | 2023-11-20 | Multi-type power supply and new energy integrated control method based on self-organizing map network |
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| CN202311543168.2A CN117559426A (en) | 2023-11-20 | 2023-11-20 | Multi-type power supply and new energy integrated control method based on self-organizing map network |
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- 2023-11-20 CN CN202311543168.2A patent/CN117559426A/en not_active Withdrawn
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Application publication date: 20240213 |