CN117350507A - Virtual power plant scheduling system - Google Patents
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Abstract
The invention relates to the technical field of virtual power plants, and discloses a virtual power plant scheduling system. The virtual power plant dispatching system is composed of a data acquisition module, a data processing module, an execution module, a prediction module, a data storage module, a renewable energy management module, an energy storage management module and a self-adaptive control module, wherein the output end of the data acquisition module is electrically connected with the input end of the data processing module, the output end of the data processing module is electrically connected with the input end of the data storage module, the output end of the data storage module is electrically connected with the input end of the prediction module, and the prediction module, the data processing module, the renewable energy management module and the energy storage management module. The invention can predict the renewable energy power generation, adjust the scheduling of the electric quantity according to the generated energy, and greatly reduce the electric quantity consumption of the traditional power generation.
Description
Technical Field
The invention relates to the technical field of virtual power plants, in particular to a virtual power plant scheduling system.
Background
The virtual power plant is a flexible energy system based on distributed energy resources, integrates various energy resources, and realizes coordination management and optimized operation. Unlike a conventional single power plant, a virtual power plant virtually combines multiple discrete energy devices and load devices into a single unit through advanced information technology and intelligent control to provide a more efficient, reliable, and sustainable power supply.
The accuracy of the existing virtual power plant dispatching system is highly dependent on the accuracy of data, if errors or deletions exist in the data acquisition process, the inaccuracy of system calculation and decision may be caused, meanwhile, the virtual power plant dispatching system depends on equipment and a communication network of a power system, if equipment faults or communication breaks, the normal operation of the system may be affected, often, workers cannot find the faults timely, the timeliness of solving the faults is reduced, and normal dispatching and use are affected.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a virtual power plant scheduling system, which solves the problems mentioned in the background art.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the utility model provides a virtual power plant dispatching system, a virtual power plant dispatching system constitute by data acquisition module, data processing module, execution module, prediction module, data storage module, renewable energy management module, energy storage management module, self-adaptation control module, the output of data acquisition module and the input electric connection of data processing module, the output of data processing module and the input electric connection of data storage module, the output of data storage module and the input electric connection of prediction module, data processing module, renewable energy management module, energy storage management module, self-adaptation control module's output all with the input electric connection of execution module.
Preferably, the output end of the data storage module is electrically connected with a capacity identification module and a data arrangement module, the data processed by the data processing module is uploaded to the data storage module for storage through a hard disk, the capacity of the hard disk is 1TB, the data arrangement module sorts the obtained data according to time, and meanwhile, when the capacity of the hard disk reaches the upper limit through the capacity identification module, the data is automatically uploaded to a cloud server for storage, and meanwhile, the uploaded data is automatically deleted.
Preferably, the prediction module comprises a load prediction module, wherein the load prediction module utilizes historical load data and weather data to construct a load prediction model by using a machine learning algorithm, so as to help the system to accurately predict future load demands.
Preferably, the prediction module further includes:
the power generation capacity prediction module is used for constructing a prediction model according to weather forecast data, equipment state monitoring and historical power generation data, so that a system is helped to accurately predict future power generation capacity;
the renewable energy power generation prediction module is used for constructing a prediction model according to the real-time weather data, the equipment state and the historical power generation data so as to accurately predict the output of renewable energy sources;
the fault prediction module is used for constructing a fault prediction model based on historical equipment operation data, equipment state monitoring and vibration sensor information so as to identify potential fault risks in advance.
Preferably, the execution module comprises an optimization scheduling sub-module, a control sub-module, a network communication sub-module and an execution test module.
Preferably, the optimization scheduling submodule can schedule and optimize power generation equipment, load and energy storage equipment in the power system through mathematical models, optimization algorithms and rule formulation; the control submodule is responsible for monitoring the state of the power system in real time, including a gas turbine, a wind driven generator and a photovoltaic battery pack, and monitoring and controlling load equipment of the power plant according to the optimal scheduling result; and the execution test module is used for regularly sending corresponding reasonable instructions to verify whether the system can execute or not and whether the execution is effective or not, and simultaneously, the execution test module is used for testing the data input, output, transmission and storage of the system.
Preferably, the renewable energy management module is specially used for managing and optimizing the power generation and storage of renewable energy.
Preferably, the re-energy management module further includes:
the wind energy and solar energy prediction submodule predicts the changes of the future wind energy and solar energy resources by using indexes of weather data, wind power and solar radiation and combining historical data and a machine learning algorithm;
the renewable energy power generation optimizing sub-module is formulated according to a predicted renewable energy power generation amount and power demand through an optimizing algorithm and a rule, manages and maximally utilizes renewable energy, and needs to consider factors including capacity, technical characteristics and power market rules of power generation equipment;
and the energy storage system management sub-module is responsible for effectively managing and scheduling the energy storage equipment so as to realize the balance of energy and the flexibility of scheduling.
Preferably, the adaptive control module is configured to adjust and optimize an operation parameter and a control policy of the power system in real time, and adapt to an operation environment and a requirement that change continuously, where the adaptive control module further includes:
the parameter identification and adjustment module is used for adaptively adjusting control parameters of the system by analyzing real-time data and performance indexes of the power system and applying a parameter estimation and identification technology;
and the model prediction and optimization sub-module predicts the future state and the change trend of the power system based on the model and the optimization algorithm of the system and performs optimization adjustment.
Preferably, the adaptive module further comprises a fault detection and recovery sub-module, the fault detection and recovery sub-module can adaptively detect and diagnose faults and take corresponding measures to recover, once the main equipment fails, the automatic switching mechanism can detect and judge the type and degree of the faults, and by setting a fault detection threshold value and a fault code matching mode, if the faults exceed the set threshold value or meet specific fault conditions, the switching action is triggered
(III) beneficial effects
The invention provides a virtual power plant scheduling system. The beneficial effects are as follows:
(1) When the virtual power plant scheduling system is used, the scheduling algorithm is continuously optimized by utilizing the self-adaptive algorithm, the deep learning technology and the like through the prediction module so as to adapt to the change of the running condition of the power system, the accuracy and the flexibility of the system are improved by introducing the real-time learning and self-adaptive optimization algorithm, the scheduling of the electric quantity can be adjusted according to the power generation quantity through the prediction of the renewable energy source power generation, and the electric quantity consumption of the traditional power generation is reduced.
(2) When the virtual power plant scheduling system is used, the system can periodically send corresponding reasonable instructions through the execution module to verify whether the system can execute or not and whether the execution is effective, and meanwhile, the execution test module can test the data input, output, transmission and storage of the system, simulate different data scenes, including real-time data, historical data, market data and the like, verify the accuracy, the integrity and the consistency of the data and ensure the accuracy of the execution module.
(3) When the virtual power plant scheduling system is used, the virtual power plant scheduling system can better manage and utilize renewable energy sources through the effective renewable energy source management module, sustainable power supply is realized, dependence on traditional energy sources is reduced, energy utilization efficiency is improved, and development and application of clean energy sources are promoted.
(4) When the virtual power plant scheduling system is used, the control parameters and the operation strategy of the power system are adjusted and optimized in real time through the intelligent algorithm and the decision strategy by arranging the self-adaptive control module so as to adapt to the continuously changing environment and requirements, and the stability, the reliability and the performance of the power system are improved.
Drawings
FIG. 1 is a schematic diagram of a system according to the present invention;
FIG. 2 is a schematic diagram of a data storage module according to the present invention;
FIG. 3 is a schematic diagram of a prediction module according to the present invention;
FIG. 4 is a schematic diagram of an implementation module of the present invention;
FIG. 5 is a schematic diagram of a renewable energy management module according to the present invention;
FIG. 6 is a schematic diagram of an energy storage management module according to the present invention;
fig. 7 is a schematic diagram of an adaptive control module according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but 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. 1-7, the present invention provides a virtual power plant scheduling system, which includes a data acquisition module, a data processing module, an execution module, a prediction module, a data storage module, a renewable energy management module, an energy storage management module, and an adaptive control module, wherein an output end of the data acquisition module is electrically connected with an input end of the data processing module, an output end of the data processing module is electrically connected with an input end of the data storage module, an output end of the data storage module is electrically connected with an input end of the prediction module, and output ends of the prediction module, the data processing module, the renewable energy management module, the energy storage management module, and the adaptive control module are all electrically connected with an input end of the execution module.
The data acquisition module is responsible for collecting, processing and managing various data related to the power system, connecting different devices, sensors and systems, ensuring that the system can acquire required real-time and historical data, installing different sensors in the power system, wherein various sensors and devices (such as power generation equipment, load equipment and wind power measuring instruments) in the power system are important sources for data acquisition, the sensors and the devices are connected in an interfacing way, data are acquired in real time, including power output, load requirements and environmental parameters, and data transmission and communication are carried out among different devices, sensors and systems, and the data transmission is ensured through Ethernet connection;
the data processing module is used for processing, filtering, compressing and storing the data acquired by the data acquisition module in real time, and can apply a data compression algorithm and an analysis method to reduce the cost of storage and transmission, ensure the integrity and accuracy of the data, monitor and evaluate the quality and accuracy of the acquired data, detect abnormal values, missing values and noise in the data, provide a corresponding data cleaning and correcting method to ensure the credibility and usability of the data, and visually display and analyze the finally obtained data. The method can generate a real-time visual report, a trend chart and a statistical chart to help a user understand and analyze the operation condition of the power system, support decision and prediction, ensure that a virtual power plant dispatching system obtains accurate real-time data and historical data, provide powerful support for dispatching, monitoring and optimizing the system, and further improve the efficiency and operation reliability of the power system;
the data storage module comprises a capacity identification module and a data arrangement module, wherein the data processed by the data processing module is uploaded to the data storage module and stored through a hard disk, the capacity of the hard disk is 1TB, the obtained data is ordered according to time through the data arrangement module, a time sequence database is built, the data can be distributed on different nodes by adopting distributed storage, parallel storage and inquiry of the data are realized, throughput and response performance of the system are improved, the data can be conveniently searched according to time as a unit, corresponding data can be quickly found, meanwhile, the data can be automatically uploaded to a cloud server for storage after the capacity of the hard disk reaches the upper limit through the capacity identification module, and meanwhile, the uploaded data is automatically deleted to ensure that the problem of data loss caused by the storage space of the hard disk is avoided;
the prediction module is used for predicting the power generation capacity of various power generation resources (such as coal, natural gas, wind energy and solar energy) in the future, constructing a prediction model according to weather forecast data, equipment state monitoring and historical power generation data, helping the system to accurately predict the power generation capacity in the future, predicting the power generation capacity in the future, adjusting power dispatching in a targeted manner by predicting the power generation capacity, wherein the emphasis comprises the renewable energy power generation prediction module, the module can predict the generating capacity of renewable energy sources, constructs a prediction model according to real-time weather data, equipment state and historical generating data to realize accurate prediction of the generating capacity of renewable energy sources, makes corresponding optimization decisions in power dispatching, can adjust the dispatching of electric quantity according to the generating capacity of renewable energy sources, greatly reduces the electric quantity consumption of traditional power generation, achieves the effect of more environmental protection, and aims at predicting the failure probability and the possible failure types of power equipment, constructs the failure prediction model based on the historical equipment operation data, equipment state monitoring and the information of vibration sensors to identify potential failure risks in advance, provides basis for maintenance and repair plans of a system, can predict the failure of submodules contained in the prediction module in advance, the problem of power scheduling errors due to errors in the predicted data is avoided to some extent.
The execution module is responsible for carrying out actual scheduling and control on the power system according to a prediction result and an optimization strategy of the prediction module, wherein the execution module further comprises an optimization scheduling sub-module, and schedules and optimizes power generation equipment, load and energy storage equipment in the power system through mathematical models, optimization algorithms and rule formulation, and the sub-module takes data provided by the prediction module and the data storage module into consideration so as to minimize cost, maximize renewable energy utilization rate or balance supply and demand targets, generate an optimal power scheduling scheme and execute the optimal power scheduling scheme; and the control submodule is responsible for monitoring the state of the power system in real time, including power generation equipment such as a gas turbine, a wind driven generator and a photovoltaic battery pack, monitoring and controlling load equipment of the power plant such as electric automobiles, heating and ventilation equipment and industrial production equipment according to the optimal scheduling result, monitoring the state and energy storage of energy storage equipment, and carrying out charge and discharge scheduling according to requirements so as to provide reliable power regulation and emergency standby and control the power generation equipment, the load and the energy storage equipment. The system can comprise an automatic control algorithm, a remote measuring and controlling system and intelligent equipment monitoring and adjusting functions, so that the power system is ensured to run according to a plan; the execution module needs to communicate and exchange data with various devices, sensors and other modules in the power system, and the network communication sub-module is responsible for realizing network communication functions, including data acquisition, information transmission, instruction issuing and state monitoring; and the execution test module is used for simulating different data scenes, including real-time data, historical data and market data, verifying the accuracy, integrity and consistency of the data and ensuring the accuracy of the execution module.
The renewable energy management module is specially used for managing and optimizing the power generation, storage and utilization of renewable energy, and comprises a wind energy and solar energy prediction sub-module, wherein the wind energy and solar energy prediction sub-module predicts the change of future wind energy and solar energy resources by utilizing indexes of weather data, wind power and solar radiation and combining historical data and a machine learning algorithm so as to help the system to reasonably allocate and optimize power; the renewable energy power generation optimizing sub-module is used for preparing, managing and utilizing renewable energy to the maximum extent through an optimizing algorithm and a rule according to the predicted renewable energy power generation amount and the power demand, and generating an optimal renewable energy power generation scheme by considering factors such as capacity, technical characteristics, power market rules and the like of power generation equipment; the energy storage system management submodule plays a key role in renewable energy management, and the submodule is responsible for effectively managing and scheduling energy storage equipment so as to realize the balance of energy and the flexibility of scheduling. The energy storage and release efficiency, the state and capacity of the energy storage equipment and other factors are considered, and the operation and the utilization of the energy storage system are optimized; and the cooperative control submodule is responsible for coordinating and controlling the power generation behaviors of a plurality of renewable energy power generation devices to work together and realize the optimal performance of the system, and the cooperative control submodule comprises time sequence adjustment, power output coordination and the like among the power generation devices so as to ensure stable supply of renewable energy and balance of a power system, and the virtual power plant dispatching system can better manage and utilize the renewable energy through the effective renewable energy management module, realize sustainable power supply, reduce the dependence on traditional energy, improve the energy utilization efficiency and promote development and application of clean energy.
The energy storage management module is specially used for managing and optimizing the operation and utilization of the energy storage equipment, and comprises an energy storage demand prediction submodule, wherein the submodule predicts the future energy storage demand according to historical data, load demand and a prediction model, considers factors such as load change of a power system, fluctuation of renewable energy sources, market price and the like to determine the optimal charge and discharge strategy of the energy storage equipment, and simultaneously considers the capacity, efficiency and state of the equipment, market operation rules and power system operation requirements to realize optimal energy storage equipment scheduling and energy flow management; and the energy storage device state monitoring and managing sub-module is used for monitoring the state and the performance of the energy storage device in real time. The system can collect and analyze real-time data, including information such as charge and discharge efficiency, capacity, temperature, health state and the like of the energy storage equipment, so as to carry out fault diagnosis, equipment maintenance and performance evaluation, and ensure stable operation and reliability of the energy storage equipment.
The self-adaptive control module is used for adjusting and optimizing the operation parameters and the control strategy of the power system in real time so as to adapt to the continuously-changing operation environment and requirements, and also comprises a parameter identification and adjustment module which is used for adaptively adjusting the control parameters of the system by analyzing the real-time data and the performance indexes of the power system and applying the parameter estimation and identification technology and updating the control parameters in real time according to the environmental change, the change of the load requirements and the change of the equipment state so as to improve the stability and the performance of the system; the model prediction and optimization sub-module can predict the future state and the change trend of the power system based on a system model and an optimization algorithm, perform optimization adjustment, predict load demands, renewable energy supply and market price, optimize energy scheduling, power generation strategies and energy storage decisions so as to improve the efficiency and the sustainability of the power system, analyze the power scheduling by utilizing artificial intelligence according to the obtained prediction result and perform uninterrupted advanced adjustment on the power scheduling; the fault detection and recovery sub-module can adaptively detect and diagnose faults and take corresponding measures for recovery when the power system has faults or abnormal conditions, a fault diagnosis algorithm, an intelligent fault detection technology and an automatic switching mechanism of standby equipment are used for coping with the faults of the system, once the main equipment has faults, the automatic switching mechanism can detect and judge the types and the degrees of the faults, a switching action can be triggered by setting a fault detection threshold value and a fault code matching mode, and a control strategy can be automatically adjusted to maintain the reliability and the stability of the system if the faults exceed the set threshold value or meet specific fault conditions, and in general, the self-adaptive control module adjusts and optimizes the control parameters and the operation strategy of the power system in real time through an intelligent algorithm and a decision strategy so as to adapt to the continuously changing environment and requirements and improve the stability, the reliability and the performance of the power system.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A virtual power plant scheduling system, characterized by: the virtual power plant scheduling system is composed of a data acquisition module, a data processing module, an execution module, a prediction module, a data storage module, a renewable energy management module, an energy storage management module and a self-adaptive control module, wherein the output end of the data acquisition module is electrically connected with the input end of the data processing module, the output end of the data processing module is electrically connected with the input end of the data storage module, the output end of the data storage module is electrically connected with the input end of the prediction module, and the output ends of the prediction module, the data processing module, the renewable energy management module, the energy storage management module and the self-adaptive control module are electrically connected with the input end of the execution module.
2. A virtual power plant scheduling system according to claim 1, wherein: the output end of the data storage module is electrically connected with a capacity identification module and a data arrangement module, data processed by the data processing module is uploaded to the data storage module to be stored through a hard disk, the capacity of the hard disk is 1TB, the data arrangement module sorts the obtained data according to time, and meanwhile, the data is automatically uploaded to a cloud server to be stored after the capacity of the hard disk reaches the upper limit through the capacity identification module, and meanwhile, the uploaded data is automatically deleted.
3. A virtual power plant scheduling system according to claim 1, wherein: the prediction module comprises a load prediction module, wherein the load prediction module utilizes historical load data and weather data, and a machine learning algorithm is used for constructing a load prediction model to help the system to accurately predict future load demands.
4. A virtual power plant scheduling system according to claim 1, wherein: the prediction module further comprises:
the power generation capacity prediction module is used for constructing a prediction model according to weather forecast data, equipment state monitoring and historical power generation data, so that a system is helped to accurately predict future power generation capacity;
the renewable energy power generation prediction module is used for constructing a prediction model according to the real-time weather data, the equipment state and the historical power generation data so as to accurately predict the output of renewable energy sources;
the fault prediction module is used for constructing a fault prediction model based on historical equipment operation data, equipment state monitoring and vibration sensor information so as to identify potential fault risks in advance.
5. A virtual power plant scheduling system according to claim 1, wherein: the execution module comprises an optimization scheduling sub-module, a control sub-module, a network communication sub-module and an execution test module.
6. A virtual power plant scheduling system in accordance with claim 5, wherein: the optimal scheduling submodule can schedule and optimize power generation equipment, load and energy storage equipment in the power system through mathematical models, optimization algorithms and rule formulation; the control submodule is responsible for monitoring the state of the power system in real time, including a gas turbine, a wind driven generator and a photovoltaic battery pack, and monitoring and controlling load equipment of the power plant according to the optimal scheduling result; and the execution test module is used for regularly sending corresponding reasonable instructions to verify whether the system can execute or not and whether the execution is effective or not, and simultaneously, the execution test module is used for testing the data input, output, transmission and storage of the system.
7. A virtual power plant scheduling system according to claim 1, wherein: the renewable energy management module is specially used for managing and optimizing the power generation and storage of renewable energy.
8. A virtual power plant scheduling system according to claim 1, wherein: the re-energy management module further comprises:
the wind energy and solar energy prediction submodule predicts the changes of the future wind energy and solar energy resources by using indexes of weather data, wind power and solar radiation and combining historical data and a machine learning algorithm;
the renewable energy power generation optimizing sub-module is formulated according to a predicted renewable energy power generation amount and power demand through an optimizing algorithm and a rule, manages and maximally utilizes renewable energy, and needs to consider factors including capacity, technical characteristics and power market rules of power generation equipment;
and the energy storage system management sub-module is responsible for effectively managing and scheduling the energy storage equipment so as to realize the balance of energy and the flexibility of scheduling.
9. A virtual power plant scheduling system according to claim 1, wherein: the self-adaptive control module is used for adjusting and optimizing the operation parameters and the control strategy of the power system in real time and adapting to the continuously changing operation environment and requirements, wherein the self-adaptive control module further comprises:
the parameter identification and adjustment module is used for adaptively adjusting control parameters of the system by analyzing real-time data and performance indexes of the power system and applying a parameter estimation and identification technology;
and the model prediction and optimization sub-module predicts the future state and the change trend of the power system based on the model and the optimization algorithm of the system and performs optimization adjustment.
10. A virtual power plant scheduling system in accordance with claim 9, wherein: the self-adaptive module also comprises a fault detection and recovery sub-module which can adaptively detect and diagnose faults and take corresponding measures to recover, once the main equipment breaks down, the automatic switching mechanism can detect and judge the type and degree of the faults, and by setting a fault detection threshold and a fault code matching mode, if the faults exceed the set threshold or meet specific fault conditions, the switching action is triggered.
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