CN116739360A - Distributed wind-solar intelligent energy storage management system based on block chain - Google Patents
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Abstract
The invention discloses a distributed wind-light storage intelligent energy management system based on a block chain, relates to the technical field of new energy, and solves the technical problems of high energy storage cost and low energy management efficiency of a wind-light storage station in the prior art caused by low working efficiency of the wind-light storage station; the method predicts the predicted electricity consumption of the electricity consumption unit according to the basic electricity consumption and predicts the predicted electricity generation amount of each wind-light storage station according to the basic station information; the predicted electricity consumption of the surrounding electricity consumption units is comprehensively analyzed to realize the electric energy management of the wind-light storage station; the invention can reasonably control the generated energy and reduce the energy storage cost of the wind-light energy storage station; according to the invention, the electric energy management is carried out on each wind-solar energy storage station through the station control module on the basis of the optimal power transmission range and the credit score by combining the predicted power generation amount and the predicted power consumption amount; the method and the device can determine the power transmission coverage of the wind-solar storage station in advance, and supplement power transmission to the redundant units, so that the power generation income and the power utilization reliability are improved.
Description
Technical Field
The invention belongs to the field of new energy, relates to an energy management technology of a distributed wind-light storage station, and particularly relates to a distributed wind-light storage intelligent energy management system based on a block chain.
Background
With the development of the energy Internet, solar energy and wind energy with the advantages of few pollution products, high energy conversion rate, high reliability and the like gradually become main sources of electric power resources. However, wind and light storage sites generally need to be established in remote areas, and it is important how to perform centralized management on the distributed wind and light storage sites.
Wind and solar energy storage sites are generally provided with energy storage devices for storing generated electric energy and sending the stored electric energy to a power consumer in a network when electric power transaction is carried out. The existing wind-solar energy storage station needs to be provided with enough energy storage equipment, and electric energy is extracted from the energy storage equipment when the electricity demand is received; on one hand, the energy storage cost is increased, and on the other hand, the working efficiency of the wind-light storage station is influenced, so that the energy management efficiency of the wind-light storage station is low; therefore, there is a need for a distributed wind and solar energy storage intelligent energy management system based on a blockchain.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides a distributed wind-light storage intelligent energy management system based on a block chain, which is used for solving the technical problems of high energy storage cost and low working efficiency of a wind-light storage station in the prior art, and low energy management efficiency of the wind-light storage station.
In order to achieve the above object, a first aspect of the present invention provides a distributed wind-solar storage intelligent energy management system based on a blockchain, which comprises a blockchain module, a station control module and a plurality of intelligent electric meters, wherein the station control module is connected with the blockchain module, and the station control module performs association control on a plurality of wind-solar storage stations;
the block chain module acquires basic electricity utilization information of an electricity utilization unit through a plurality of intelligent electric meters, and analyzes historical electricity utilization data in the basic electricity utilization information to acquire unit characteristic data of the electricity utilization unit; the basic electricity consumption comprises historical electricity consumption data and unit position data, and the unit characteristic data comprises predicted electricity consumption and credit scores; and
basic station information of a plurality of wind-light storage stations is acquired through a station control module, and station characteristic data of each wind-light storage station are acquired by analyzing the basic station information; the station characteristic data comprises predicted power generation amount and power reserve amount;
the block chain module calculates the optimal power transmission range of each wind-solar storage station based on the unit position data and the station position data; and carrying out electric energy management on each wind-solar storage station through a station control module on the basis of the optimal power transmission range and the credit score by combining the predicted power generation amount and the predicted power consumption.
Preferably, the block chain module analyzes the historical electricity consumption data to obtain the predicted electricity consumption of the electricity consumption unit, including:
extracting historical electricity data from the basic electricity information; the historical electricity consumption data comprises electricity consumption and corresponding electricity consumption environment data, and the electricity consumption environment data comprises climate type, temperature or humidity;
integrating historical electricity consumption data according to a prediction period, and training an artificial intelligent model; then, the predicted electricity consumption is obtained by combining the predicted electricity consumption environment data; wherein the prediction period comprises one day or one week.
Preferably, the blockchain module analyzes the information of the base station to obtain the predicted power generation amount of each wind-solar energy storage station, and the method comprises the following steps:
extracting historical power generation data from the basic station information; the historical power generation data comprise power generation amount and corresponding power generation environment data, and the power generation environment data comprise climate types, wind power or light intensity;
integrating historical power generation data according to a prediction period, and training an artificial intelligent model; and then, acquiring the predicted power generation amount by combining the predicted power generation environment data.
Preferably, the artificial intelligent model is constructed based on a BP neural network model and an RBF neural network model; the artificial intelligent model is trained through model input data and corresponding model output data;
and integrating the electricity utilization environment data in the historical electricity utilization data into model input data, opening and closing the corresponding electricity utilization amount into model output data, or integrating the electricity generation environment data in the historical electricity generation data into model input data, and taking the corresponding electricity generation amount as model output data.
Preferably, the blockchain module is respectively in communication and/or electric connection with the station control module and a plurality of intelligent electric meters; the intelligent electric meters are connected with the electricity utilization units in a unique association mode and are responsible for collecting basic electricity utilization information;
the station control module is in communication and/or electrical connection with the wind-light storage stations, and is used for collecting information of the foundation stations of the wind-light storage stations and controlling electric energy management of the wind-light storage stations through control signals.
Preferably, the blockchain module calculates an optimal power transmission range of each wind-solar energy storage station based on unit position data and station position data, and the blockchain module comprises:
taking a wind-light storage station corresponding to station position data as a center, acquiring the length of a power transmission line from the wind-light storage station to each unit position pair application electric unit, and marking the power transmission line as SDC;
obtaining an optimal transmission length ZSC through a formula ZSC=alpha×YSS/DSS; when SDC is less than or equal to ZSC, the corresponding electricity utilization unit is brought into the optimal power transmission range of the wind-light storage station; wherein alpha is a proportionality coefficient larger than 0, YSS is the minimum power transmission loss allowed by the wind-solar energy storage station, and DSS is the power transmission loss of unit length.
Preferably, the electric energy management of each wind-solar energy storage station by the station control module based on the optimal power transmission range and the credit score by combining the predicted power generation amount and the predicted power consumption comprises:
sequencing a plurality of electricity utilization units according to credit scores in an optimal power transmission range to obtain a power transmission sequence;
the control station control module sequentially supplies electric energy for the electricity utilization units according to the corresponding power transmission sequences of the wind-solar storage stations, and the electric energy is transmitted by combining the predicted generated energy and the predicted used energy as redundant units.
Preferably, the method for transmitting electric energy by combining the predicted generated energy and the predicted used energy as a redundant unit includes:
marking the power consumption units which are not included in the optimal power transmission range of each wind-solar storage station as redundant units;
and (3) transmitting electric energy for a redundancy unit through the wind-solar storage station with the shortest transmission length on the basis of credit score.
Compared with the prior art, the invention has the beneficial effects that:
1. the method predicts the predicted electricity consumption of the electricity consumption unit according to the basic electricity consumption and predicts the predicted electricity generation amount of each wind-light storage station according to the basic station information; the predicted electricity consumption of the surrounding electricity consumption units is comprehensively analyzed to realize the electric energy management of the wind-light storage station; the invention can reasonably control the generated energy and reduce the energy storage cost of the wind-light energy storage station.
2. The method comprises the steps of calculating the optimal power transmission range of each wind-solar energy storage station based on unit position data and station position data; electric energy management is carried out on each wind-solar storage station through a station control module on the basis of the optimal power transmission range and credit score by combining the predicted power generation amount and the predicted power consumption amount; the method and the device can determine the power transmission coverage of the wind-solar storage station in advance, and supplement power transmission to the redundant units, so that the power generation income and the power utilization reliability are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the system principle of the present invention;
FIG. 2 is a schematic diagram of the working steps of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, an embodiment of a first aspect of the present invention provides a distributed wind-solar intelligent energy management system based on a blockchain, which includes a blockchain module, a station control module and a plurality of smart meters, wherein the station control module and the smart meters are connected with the blockchain module, and the station control module performs association control on a plurality of wind-solar storage stations; the block chain module acquires basic electricity utilization information of an electricity utilization unit through a plurality of intelligent electric meters, and analyzes historical electricity utilization data in the basic electricity utilization information to acquire unit characteristic data of the electricity utilization unit; the station control module is used for acquiring basic station information of a plurality of wind-light storage stations, analyzing the basic station information and acquiring station characteristic data of each wind-light storage station; the block chain module calculates the optimal power transmission range of each wind-solar storage station based on the unit position data and the station position data; and carrying out electric energy management on each wind-solar storage station through a station control module on the basis of the optimal power transmission range and the credit score by combining the predicted power generation amount and the predicted power consumption.
In the energy (electric energy) management process of a distributed wind-solar energy storage station, the following problems mainly exist: 1) The wind-solar energy storage station needs to store the electric energy after generating the electric energy, and the electricity utilization units need the electric energy to finish the electric energy transmission according to the requirement, so that obviously, the energy storage cost is higher; 2) The electric energy required by the electricity utilization unit has certain wave nature, the electric energy generated by the wind-light storage station also has certain wave nature, the wave nature and the electric energy cannot be completely consistent, and the wind-light storage station can be caused to do invalid work while the energy storage cost is increased. The two reasons mentioned above may lead to a decrease in the working efficiency of the distributed wind and solar energy storage station.
The block chain module is respectively communicated and/or electrically connected with the station control module and a plurality of intelligent electric meters; the intelligent electric meters are connected with the electricity utilization units in a unique association mode and are responsible for collecting basic electricity utilization information; the station control module is in communication and/or electrical connection with the wind-light storage stations, and is used for collecting information of basic stations of the wind-light storage stations and controlling electric energy management of the wind-light storage stations through control signals.
The intelligent electric meters are in one-to-one association with the electricity utilization units, and the collected data are sent according to the control signals of the block chain modules. The station control module is connected with a plurality of wind-solar storage stations and also transmits collected data according to control signals of the block chain module; the station control module is also used for controlling a plurality of wind-light storage stations, such as controlling which wind energy components or light energy components generate electric energy. The blockchain module processes the collected data, but the processing process and the processing result are stored through the blockchain encryption, and a control signal is sent to the station control module only according to the processing result so as to prevent the leakage of related data.
In order to dynamically control reasonable electric energy production and energy storage cost of the wind-solar energy storage stations, the electricity consumption of each electricity consumption unit and the electricity generation amount of each wind-solar energy storage station need to be predicted as much as possible.
In a preferred embodiment, the blockchain module analyzing the historical electricity usage data to obtain a predicted electricity usage of the electricity usage units includes: extracting historical electricity data from the basic electricity information; integrating historical electricity consumption data according to a prediction period, and training an artificial intelligent model; and then, acquiring the predicted electricity consumption by combining the predicted electricity consumption environment data.
The electricity consumption of the electricity consumption unit is mainly related to electric equipment and an electricity consumption environment, and the fluctuation of the electric equipment is not large, so that the electricity consumption prediction accuracy is affected. The historical electricity consumption data obtained in the embodiment includes electricity consumption and corresponding electricity consumption environment data, and the electricity consumption environment data includes climate type, temperature or humidity and the like. Dividing and integrating the power utilization environment data according to a prediction period to obtain model input data containing the power utilization environment data and model output data containing the power utilization environment data corresponding to the power utilization quantity; therefore, future electricity utilization environment data of electricity utilization units are obtained through the meteorological platform, corresponding predicted electricity utilization quantity can be obtained through the artificial intelligent model after training, and a data foundation is laid for work control of the wind-solar storage station.
Similarly, the block chain module analyzes the information of the basic station to obtain the predicted power generation amount of each wind-solar energy storage station, and the method comprises the following steps: extracting historical power generation data from the basic station information; integrating historical power generation data according to a prediction period, and training an artificial intelligent model; and then, acquiring the predicted power generation amount by combining the predicted power generation environment data.
The difference is that the historical power generation data includes power generation amount and corresponding power generation environment data, and the power generation environment data includes climate type, wind power or light intensity, etc. The wind-solar energy storage stations generate electric energy through wind power or sun, so that the wind power or light intensity is mainly used for influencing the generated energy of the wind-solar energy storage stations, the generated energy environment data are integrated into model input data, and the predicted generated energy of each wind-solar energy storage station can be predicted.
In the embodiment, the artificial intelligent model is constructed based on an RBF neural network model of the BP neural network model; the artificial intelligent model is trained through model input data and corresponding model output data; and integrating the electricity utilization environment data in the historical electricity utilization data into model input data, opening and closing the corresponding electricity utilization amount into model output data, or integrating the electricity generation environment data in the historical electricity generation data into model input data, and taking the corresponding electricity generation amount as model output data.
It should be noted that, in predicting the power generation amount, the wind energy component or the light energy component (power generation equipment) of the wind-light storage station is not considered, but is simplified into a unique identifier, namely, the number of the power generation equipment in the wind-light storage station is represented by a non-repeated number, and the unique identifier needs to be inserted into the model input data; the same applies when predicting the amount of electricity.
After the predicted generated energy and the predicted used energy are obtained through the artificial intelligent model, the responsible electricity utilization units are required to be configured for each wind-solar energy storage station, so that the electric energy utilization rate is improved, and the energy storage cost is reduced.
In a preferred embodiment, the blockchain module calculates an optimal power transmission range for each wind and solar energy storage station based on the unit location data and the station location data, and the blockchain module comprises: taking a wind-light storage station corresponding to station position data as a center, acquiring the length of a power transmission line from the wind-light storage station to each unit position pair application electric unit, and marking the power transmission line as SDC; obtaining an optimal transmission length ZSC through a formula ZSC=alpha×YSS/DSS; and when SDC is less than or equal to ZSC, the corresponding electricity utilization unit is brought into the optimal power transmission range of the wind-solar energy storage station.
The electric energy is lost to some extent during the transmission process, so that the transmission distance needs to be considered to reduce the cost. In the embodiment, a wind-light storage station is taken as a center, and the transmission distance between each wind-light storage station and each electricity utilization unit is obtained; and meanwhile, calculating the most suitable power transmission length of the corresponding wind-light storage station under the condition of ensuring the benefit, comparing the optimal power transmission length with the power transmission line length, and when the power transmission line length is smaller than the optimal power transmission length, bringing the corresponding power utilization unit into the optimal power transmission range of the wind-light storage station. It should be noted that the length of the power transmission line is not a straight line distance, but the length of the power transmission line between the wind-solar energy storage station and the electricity utilization unit is calculated; alpha is a proportionality coefficient larger than 0, and the influence of each power transformation device on the power transmission loss in the power transmission process is mainly considered.
Theoretically, the wind-solar energy storage station supplies power to the power utilization units in the optimal power transmission range preferentially. Electric energy management is carried out on each wind-solar energy storage station through a station control module on the basis of the optimal power transmission range and credit score by combining the predicted power generation amount and the predicted power consumption, and the method comprises the following steps: sequencing a plurality of electricity utilization units according to credit scores in an optimal power transmission range to obtain a power transmission sequence; the control station control module sequentially supplies electric energy for the electricity utilization units according to the corresponding power transmission sequences of the wind-solar storage stations, and the electric energy is transmitted by combining the predicted generated energy and the predicted used energy as redundant units.
The credit score mainly judges whether the electricity utilization unit has electricity utilization abnormality, and the electricity utilization abnormality comprises actions such as arrearage, electricity stealing and the like. And sequencing the electricity utilization units according to the credit scores in the optimal power transmission range to obtain a power transmission sequence. When the predicted power generation amount and the electric energy reserve amount of the wind-solar energy storage station can meet the predicted power consumption amount of all power consumption units in the power transmission sequence, the full-power generation is used for supplying power to all power consumption units; when the predicted power generation amount and the power reserve amount are insufficient to meet all power utilization units in the power transmission sequence, the power utilization units with high credit scores are preferentially met. In other preferred embodiments, the power transmission sequences may also be ordered by credit score and power transmission line length.
When the predicted power generation amount and the power reserve amount are insufficient to satisfy all the power units in the power transmission sequence, and the power supply of the power units with high credit scores or short transmission line length is preferentially satisfied, a part of the power units without power supply are in the optimal power transmission range, and the redundant units need additional processing. The method for transmitting the electric energy for the redundancy unit by combining the predicted generated energy and the predicted used energy comprises the following steps: marking the power consumption units which are not included in the optimal power transmission range of each wind-solar storage station as redundant units; and (3) transmitting electric energy for a redundancy unit through the wind-solar storage station with the shortest transmission length on the basis of credit score.
In short, when the redundancy units are powered, the wind-solar storage station is selected by taking the redundancy units as the center, and the power units with high credit scores are sequentially selected from the redundancy units as target units; and taking the target unit as a center, and selecting a wind-solar storage station with a short transmission line length and residual electric energy as a power supply. Therefore, the cost of each wind-solar energy storage station is reduced as much as possible while the electric energy of each electricity utilization unit is ensured to be sufficient.
The partial data in the formula are all obtained by removing dimension and taking the numerical value for calculation, and the formula is a formula closest to the real situation obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.
The working principle of the invention is as follows:
basic electricity consumption information of an electricity consumption unit is obtained, historical electricity consumption data in the basic electricity consumption information is analyzed, and unit characteristic data of the electricity consumption unit is obtained; and acquiring basic station information of a plurality of wind-light storage stations, and analyzing the basic station information to acquire station characteristic data of each wind-light storage station.
Calculating the optimal power transmission range of each wind-light storage station based on the unit position data and the station position data; and carrying out electric energy management on each wind-solar energy storage station on the basis of the optimal power transmission range and credit score by combining the predicted power generation amount and the predicted power consumption amount.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The distributed wind-solar intelligent energy storage management system based on the block chain comprises a block chain module, a station control module and a plurality of intelligent electric meters, wherein the station control module and the intelligent electric meters are connected with the block chain module, and the station control module carries out association control on a plurality of wind-solar storage stations; the method is characterized in that:
the block chain module acquires basic electricity utilization information of an electricity utilization unit through a plurality of intelligent electric meters, and analyzes historical electricity utilization data in the basic electricity utilization information to acquire unit characteristic data of the electricity utilization unit; the basic electricity consumption comprises historical electricity consumption data and unit position data, and the unit characteristic data comprises predicted electricity consumption and credit scores; and
basic station information of a plurality of wind-light storage stations is acquired through a station control module, and station characteristic data of each wind-light storage station are acquired by analyzing the basic station information; the station characteristic data comprises predicted power generation amount and power reserve amount;
the block chain module calculates the optimal power transmission range of each wind-solar storage station based on the unit position data and the station position data; and carrying out electric energy management on each wind-solar storage station through a station control module on the basis of the optimal power transmission range and the credit score by combining the predicted power generation amount and the predicted power consumption.
2. The blockchain-based distributed wind-solar energy storage intelligent energy management system of claim 1, wherein the blockchain module analyzes historical electricity usage data to obtain a predicted electricity usage of electricity usage units, comprising:
extracting historical electricity data from the basic electricity information; the historical electricity consumption data comprises electricity consumption and corresponding electricity consumption environment data, and the electricity consumption environment data comprises climate type, temperature or humidity;
integrating historical electricity consumption data according to a prediction period, and training an artificial intelligent model; then, the predicted electricity consumption is obtained by combining the predicted electricity consumption environment data; wherein the prediction period comprises one day or one week.
3. The blockchain-based distributed wind-solar energy storage intelligent energy management system of claim 1, wherein the blockchain module analyzes base station information to obtain predicted power generation of each wind-solar energy storage station, comprising:
extracting historical power generation data from the basic station information; the historical power generation data comprise power generation amount and corresponding power generation environment data, and the power generation environment data comprise climate types, wind power or light intensity;
integrating historical power generation data according to a prediction period, and training an artificial intelligent model; and then, acquiring the predicted power generation amount by combining the predicted power generation environment data.
4. The blockchain-based distributed wind-solar energy storage intelligent energy management system of claim 2 or 3, wherein the artificial intelligence model is constructed based on a BP neural network model RBF neural network model; the artificial intelligent model is trained through model input data and corresponding model output data;
and integrating the electricity utilization environment data in the historical electricity utilization data into model input data, opening and closing the corresponding electricity utilization amount into model output data, or integrating the electricity generation environment data in the historical electricity generation data into model input data, and taking the corresponding electricity generation amount as model output data.
5. The blockchain-based distributed wind-solar energy storage intelligent energy management system of claim 1, wherein the blockchain module is respectively in communication and/or electrical connection with a station control module and a plurality of intelligent electric meters; the intelligent electric meters are connected with the electricity utilization units in a unique association mode and are responsible for collecting basic electricity utilization information;
the station control module is in communication and/or electrical connection with the wind-light storage stations, and is used for collecting information of the foundation stations of the wind-light storage stations and controlling electric energy management of the wind-light storage stations through control signals.
6. The blockchain-based distributed wind-solar energy storage intelligent energy management system of claim 1, wherein the blockchain module calculates an optimal power transmission range for each wind-solar energy storage station based on unit location data, station location data, comprising:
taking a wind-light storage station corresponding to station position data as a center, acquiring the length of a power transmission line from the wind-light storage station to each unit position pair application electric unit, and marking the power transmission line as SDC;
obtaining an optimal transmission length ZSC through a formula ZSC=alpha×YSS/DSS; when SDC is less than or equal to ZSC, the corresponding electricity utilization unit is brought into the optimal power transmission range of the wind-light storage station; wherein alpha is a proportionality coefficient larger than 0, YSS is the minimum power transmission loss allowed by the wind-solar energy storage station, and DSS is the power transmission loss of unit length.
7. The blockchain-based distributed wind and solar energy storage intelligent energy management system of claim 6, wherein the combining of the predicted power generation and the predicted power consumption performs power management on each wind and solar energy storage station through a station control module based on an optimal power transmission range and a credit score, comprising:
sequencing a plurality of electricity utilization units according to credit scores in an optimal power transmission range to obtain a power transmission sequence;
the control station control module sequentially supplies electric energy for the electricity utilization units according to the corresponding power transmission sequences of the wind-solar storage stations, and the electric energy is transmitted by combining the predicted generated energy and the predicted used energy as redundant units.
8. The blockchain-based distributed wind-solar energy storage intelligent energy management system of claim 7, wherein the combining the predicted power generation and the predicted power consumption to deliver power as redundant units comprises:
marking the power consumption units which are not included in the optimal power transmission range of each wind-solar storage station as redundant units;
and (3) transmitting electric energy for a redundancy unit through the wind-solar storage station with the shortest transmission length on the basis of credit score.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117077978A (en) * | 2023-10-11 | 2023-11-17 | 浙江浙能能源服务有限公司 | Trans-regional new energy storage method and system |
| CN117273988A (en) * | 2023-11-23 | 2023-12-22 | 国网信通亿力科技有限责任公司 | Intelligent energy management system based on cross-business field |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117077978A (en) * | 2023-10-11 | 2023-11-17 | 浙江浙能能源服务有限公司 | Trans-regional new energy storage method and system |
| CN117077978B (en) * | 2023-10-11 | 2024-06-04 | 浙江浙能能源服务有限公司 | Trans-regional new energy storage method and system |
| CN117273988A (en) * | 2023-11-23 | 2023-12-22 | 国网信通亿力科技有限责任公司 | Intelligent energy management system based on cross-business field |
| CN117273988B (en) * | 2023-11-23 | 2024-02-02 | 国网信通亿力科技有限责任公司 | Intelligent energy management system based on cross-business areas |
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