CN113311703A - Smart energy multi-energy interactive evaluation visualization system and evaluation method - Google Patents
Smart energy multi-energy interactive evaluation visualization system and evaluation method Download PDFInfo
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Abstract
The invention discloses a smart energy multi-energy interactive evaluation visualization system which comprises a basic equipment group, a data acquisition module, an energy consumption evaluation model and a data display module. And the data acquisition module acquires the operating parameters of different devices of the basic device group in real time and transmits the operating parameters to the energy consumption evaluation model. And the energy consumption evaluation model processes the acquired real-time operation parameters of the basic equipment group and then transmits the result to the data display module for visual display. The energy consumption evaluation model is based on historical data and is combined with actually acquired data to obtain an energy consumption trend analysis result. And the basic equipment group dynamically and intelligently adjusts the working state of the basic equipment group according to the energy consumption trend analysis result. The intelligent energy multi-energy interactive evaluation visualization system optimizes and designs the parameter values of the energy consumption evaluation model and visually displays the parameter values. Meanwhile, an evaluation method adopting the intelligent energy multi-energy interactive evaluation visualization system is also provided.
Description
Technical Field
The invention relates to the technical field of intelligent energy management, in particular to a smart energy multi-energy interactive evaluation visualization system and an evaluation method.
Background
With the rapid development of renewable energy industry in China, related technologies of intelligent energy and multi-energy systems are also developed to a certain extent. At present, for the realization of energy system resource optimization configuration and energy consumption management, how to improve the energy utilization rate, reduce the power consumption, and intelligently manage the modern energy system, the intelligent energy multi-energy complementary system is an important measure with profound significance.
For the popularization and development of the intelligent energy multi-energy complementary system, a standard monitoring and evaluation system and an evaluation platform are essential. At present, the existing method does not have universality, often carries out empirical evaluation aiming at a local single system, lacks evaluation comparison among a plurality of systems at the same time, and is relatively lack of a unified evaluation platform, so that the intelligent energy multi-energy complementary system lacks a unified and effective evaluation system platform under different time dimensions, scale sizes and energy supply modes, and the development of the intelligent energy multi-energy complementary system is restricted or hindered to a certain extent.
In addition, for the intelligent energy multi-energy complementary system, the energy metering management system and the evaluation system platform are designed to be basically independent and operate independently, which results in a great deal of resource waste. At present, the prior art in China has no visual system platform for online monitoring and comprehensive evaluation of the intelligent energy multi-energy complementary system.
Therefore, it is necessary to provide a system and a method for intelligent energy multi-energy interactive evaluation visualization to solve the above technical problems.
Disclosure of Invention
The invention aims to overcome the technical problems and provides an intelligent energy multi-energy interactive evaluation visualization system.
Meanwhile, an evaluation method adopting the intelligent energy multi-energy interactive evaluation visualization system is further provided.
The technical scheme of the invention is as follows: an intelligent energy multi-energy interactive evaluation visualization system comprises a basic equipment group, a data acquisition module, an energy consumption evaluation model and a data display module. And the data acquisition module acquires the operating parameters of the basic equipment group in real time. The energy consumption evaluation model receives real-time operation parameters from the data acquisition module, evaluates an energy consumption trend analysis result of the basic equipment group based on historical data prestored in the energy consumption evaluation model and the real-time operation parameters acquired by the data acquisition module, is based on a deep learning model for generating a countermeasure network, and comprises a generation module, a discrimination module and a loss function, wherein the loss function is a total loss function and meets the following formula:
L(GAB,GBA,DA,DB,X,Y)＝LGAN(GAB,DB,X,Y)+LGAN(GBA,DA,Y,X)
+λcycLcyc(GAB,GBA,X,Y)
+λperLper(GAB,GBA,X,Y)
wherein: gABIs an energy consumption generation module, X is the original operating parameter collected by the data collection module, GAB(x) The original operation parameters which represent the data acquisition module to acquire pass through the energy consumption generation module GABThe generated operating parameters;
GBAis a predicted energy consumption parameter generation module, y is a predicted energy consumption parameter, GBA(y) the predicted energy consumption parameter is generated by the predicted energy consumption parameter generation module GBAThe actual operating parameters generated;
DBthe operation parameter judging module is used for judging the truth of the generated actual operation parameter and the predicted operation parameter;
DAthe acquisition parameter judging module is used for judging the authenticity of the generated actual operation parameter and the originally acquired operation parameter;
log is a logarithmic operation, E represents a distribution function expected value;
phi j refers to a characteristic diagram of a j-th layer network generated by the convolutional network according to the input parameters, H represents the height of the characteristic diagram, and W represents the width of the characteristic diagram;
LGAN(GAB,DB,X,Y)，LGAN(GBA,DAy, X) represents the challenge loss;
Lcyc(GAB,GBAx, Y) represents a loss of cyclic consistency;
Lper(GAB,GBAx, Y) represents a loss of perception;
λ cyc is the coefficient of the cyclic consistency loss, λ per is the coefficient of the perceptual loss,
L(GAB,GBA,DA,DBx, Y) is the total loss function;
and the data display module is used for visually outputting the energy consumption trend analysis result of the energy consumption evaluation model. .
Preferably, the basic equipment group comprises a photovoltaic array system, a wind turbine generator, an energy storage converter, a storage battery pack, a load system, an intelligent meter and a meteorological system.
Preferably, the meteorological system provides historical meteorological data to be prestored in the energy consumption evaluation model.
Preferably, the energy consumption evaluation model prestores historical operating data of the basic equipment group.
Preferably, the key parameters and index logic factor parameters of the basic equipment group are prestored in the energy consumption evaluation model.
Preferably, the basic equipment group comprises a photovoltaic array system, and the energy consumption evaluation model at least prestores local historical sunshine data, climate characteristics and geographic characteristics.
Preferably, the basic equipment group comprises a wind turbine generator, and the energy consumption evaluation model at least prestores local climate characteristics and geographic characteristics.
An intelligent energy multi-energy interactive evaluation method comprises the following steps:
providing a basic equipment group, recording historical operation data of the basic equipment group and acquiring actual operation parameters of the basic equipment group in real time;
providing an energy consumption evaluation model, and pre-storing historical operation data, key parameters and index logic factor parameters of the basic equipment group;
the energy consumption evaluation model receives actual operation parameters from the basic equipment group and evaluates an energy consumption trend analysis result of the basic equipment group based on historical data prestored in the energy consumption evaluation model and real-time operation parameters acquired by the data acquisition module;
and providing a data display module, and visually outputting the energy consumption trend analysis result of the energy consumption evaluation model.
Preferably, the method further comprises the step of feeding back a control signal to the basic equipment group according to an energy consumption trend analysis result output by the data display module, and dynamically adjusting the working state of the basic equipment group to realize the input and output of the intelligent energy multi-energy interaction.
Preferably, the basic equipment group comprises a photovoltaic array system, a wind turbine generator, an energy storage converter, a storage battery pack, a load system, an intelligent meter and a meteorological system, and the energy consumption evaluation model initializes the operating environment of the energy consumption evaluation model according to pre-stored historical data.
Compared with the prior art, the intelligent energy multi-energy interactive evaluation visualization system and the evaluation method provided by the invention have the advantages that an energy consumption evaluation model is constructed on the basis of the existing historical data by combining the actual operation parameters of the relevant equipment, the historical data is initialized and prestored by the energy consumption evaluation model, and an energy consumption trend visualization graph is output after the energy consumption evaluation model is evaluated and analyzed according to the initial data and the actual operation data, so that the accurate prediction and management of the energy consumption trend are facilitated. Further, according to the evaluation result of the energy consumption evaluation model, the actual operation working state of the basic equipment group is dynamically adjusted, the integrated intelligent cooperation of an energy system is effectively managed, and the production potential of the basic equipment group is effectively and reasonably allocated.
Drawings
FIG. 1 is a schematic diagram of a framework of a smart energy multi-energy interactive evaluation visualization system according to the present invention;
FIG. 2 is a schematic flow chart of an evaluation method of the intelligent energy multi-energy interactive evaluation visualization system shown in FIG. 1;
fig. 3 is a schematic diagram illustrating the principle that the intelligent energy multi-energy interactive evaluation visualization system shown in fig. 1 is applied to the fields of wind power generation and photovoltaic power generation.
Detailed Description
The invention will be further explained with reference to the drawings and the embodiments.
Please refer to fig. 1, which is a schematic diagram of a framework of a smart energy multi-functional interactive evaluation visualization system according to the present invention. The intelligent energy multi-energy interactive evaluation visualization system 10 is an integrated energy consumption management platform integrating a plurality of energy providing systems, energy storage systems and energy consumption systems. The intelligent energy multi-energy interactive evaluation visualization system 10 is embedded into a green energy system to perform system modeling, analysis and feedback of the working operation condition of the current energy consumption system, performs real-time visual display, and controls the intelligent cooperative work of configuration space of energy supply, storage, control and utilization of the green energy system according to the requirements of actual scheduling and operation regulation and control by dynamically feeding back a control signal.
The intelligent energy multi-energy interactive evaluation visualization system 10 comprises a basic equipment group 11, a data acquisition module 13, a data transmission module 15, an energy consumption evaluation model 17, a data output module 19 and a data display module 20.
The basic equipment group 11 comprises distributed energy equipment, environment monitoring equipment and an energy metering terminal, and provides original data for the intelligent energy multi-energy interactive evaluation visualization system 10. The base device cluster 11 includes but is not limited to: the system comprises a photovoltaic array, a wind turbine generator, an energy storage converter, a storage battery pack, a load, a combined cooling heating and power supply, cold/heat accumulation, an intelligent meter, a meteorological station and a ground source heat pump system.
In the present embodiment, specifically, the infrastructure equipment group 11 includes a photovoltaic array system 111, a wind turbine generator 113, an energy storage converter 115, a battery pack 117, a load system 118, a smart meter 119, and a meteorological system 120. The basic device group 11 serves as actual hardware of an integrated energy consumption management platform, and the photovoltaic array system 111 and the wind turbine generator 113 are used for providing electric energy for a power generation system. The energy storage converter 113 and the storage battery pack 115 are used for storing distributed generation electric energy. The load system 118 and the smart meter 119 are used to detect, transmit and consume power. The meteorological system 120 serves as an external influence factor to provide sunshine data, climate characteristic parameters, geographic characteristic data and the like for the intelligent energy multi-energy interactive evaluation visualization system 10.
The data acquisition module 13 is a sensor, which monitors real-time operation data of each device in the basic device group 11 in real time. For example, it is necessary for the photovoltaic array system 111 to collect power generation amount, power supply amount, reserve energy, power consumption amount, and the like. For the weather station 120, sunshine data, weather characteristic parameters, and characteristics such as geographic dimension, altitude, calendar information need to be collected. If the load system 118 is a lighting device, the number of lamps to be turned on, the dimming brightness ratio, the lighting time period, and other parameters are correspondingly required.
The data transmission module 15 receives the acquisition result from the data acquisition module 13, and transmits the acquisition result to the energy consumption evaluation model 17 through ethernet.
The energy consumption evaluation model 17 is a simulation model established based on an actual hardware data source and an operation logic, and can realize the associated management of actual hardware, macroscopic scheduling and regulation. For example, the linkage of the structural functions of the multilevel topological relation of the lighting system, the linkage between the power generation system and the actual power utilization load, the regulation and control linkage between the energy storage system and the power utilization demand, and the linkage of the estimated trend analysis of the power utilization, the energy potential mining of the equipment and the economic investment are supported. The energy consumption evaluation model 17 prestores key parameters, external influence factors and index logics of the intelligent energy multi-energy interactive evaluation visualization system, initializes the energy consumption evaluation model 17 based on the key parameters, the external influence factors and the index logics, and simultaneously obtains a pre-estimated analysis result, namely energy consumption trend visualization data, by combining the received real-time operation parameters of each device in the basic device group 11 acquired by the data acquisition module 13 after analysis and operation.
Taking the lighting device as an example, the historical data may be index logic of the basic device registration data initialization and lighting operation management rule, and is correspondingly pre-stored in the energy consumption evaluation model 17.
The energy consumption evaluation model is a deep learning model based on a generated countermeasure network, and comprises a generation module, a discrimination module and a loss function, wherein the loss function is a total loss function and meets the following formula:
L(GAB,GBA,DA,DB,X,Y)＝LGAN(GAB,DB,X,Y)+LGAN(GBA,DA,Y,X)
+λcycLcyc(GAB,GBA,X,Y)
+λperLper(GAB,GBA,X,Y)
wherein: gABIs an energy consumption generation module, X is the original operating parameter collected by the data collection module, GAB(x) The original operation parameters which represent the data acquisition module to acquire pass through the energy consumption generation module GABThe generated operating parameters;
GBAis a predicted energy consumption parameter generation module, y is a predicted energy consumption parameter, GBA(y) the predicted energy consumption parameter is generated by the predicted energy consumption parameter generation module GBAThe actual operating parameters generated;
DBthe operation parameter judging module is used for judging the truth of the generated actual operation parameter and the predicted operation parameter;
DAthe acquisition parameter judging module is used for judging the authenticity of the generated actual operation parameter and the originally acquired operation parameter;
log is a logarithmic operation, E represents a distribution function expected value;
phi j refers to a characteristic diagram of a j-th layer network generated by the convolutional network according to the input parameters, H represents the height of the characteristic diagram, and W represents the width of the characteristic diagram;
LGAN(GAB,DB,X,Y)，LGAN(GBA,DAy, X) represents the challenge loss;
Lcyc(GAB,GBAx, Y) represents a loss of cyclic consistency;
Lper(GAB,GBAx, Y) represents a loss of perception;
λ cyc is the coefficient of the cyclic consistency loss, λ per is the coefficient of the perceptual loss,
L(GAB,GBA,DA,DBx, Y) is the total loss function.
Taking fan power generation as an example, the historical data is historical climate data provided by a third-party platform company, such as a meteorological part, and is pre-stored into the energy consumption evaluation model 17 corresponding to a cloud, so that a fan power generation model is initialized.
The data output module 19 transmits the energy consumption trend visualization data of the basic equipment group 11 to the data display module 20.
The data display module 20 correspondingly displays the energy consumption trend in the modes of icon curves and the like.
Also take lighting apparatus as an example, real-time data and historical data that data acquisition module 13 gathered construct lighting apparatus's visual figure, carry out software modeling to intelligent lighting system according to the actual illumination operation demand of project, show the relation between the logic configuration of anticipated, key parameter of equipment energy consumption and illumination control operation strategy, external influence factor.
Correspondingly, in the fan power generation equipment, the data acquisition module 13 acquires fan operation parameters and working curve characteristics in real time, and calculates the relation between the logic configurations of external influence factors by combining real-time wind speed and historical climate data.
Compared with the prior art, the intelligent energy multi-energy interactive evaluation visualization system 10 is additionally provided with the energy consumption evaluation model 17. The energy consumption evaluation model 17 initializes and prestores historical data of the basic equipment group 11, correspondingly obtains energy consumption trend visual data of the intelligent energy multi-energy interactive evaluation visual system 10 through physical model calculation analysis based on the prestored historical data of the basic equipment group 11 and by combining real-time operation data of the basic equipment group 11, more truly and accurately obtains operation trends of each basic equipment group 11 in the intelligent energy multi-energy interactive evaluation visual system 10, estimates subsequent trends thereof after evaluation, adjusts the operation condition of the basic equipment group 11 for linkage, excavates energy production potential of the basic equipment group 11, avoids energy surplus through macroscopic scheduling, reduces supersaturation of stored energy, avoids advance predictions such as overvoltage saturation of the storage battery group 117 and the like, and coordinates benign operation of the whole integrated energy consumption management platform.
Further, through data display module 20 is through various patterns visualization such as data chart, curve, heating power distribution the actual parameter of the different links of integration energy consumption management platform gives managers more audio-visual analysis foundation.
More importantly, the manager obtains a feedback control signal to control the normal operation of the integrated energy consumption management platform on the basis of the energy consumption trend visualization data output by the energy consumption evaluation model 17.
Referring to fig. 2, a schematic flow chart of an evaluation method of the intelligent energy multi-energy interactive evaluation visualization system shown in fig. 1 is shown. When the intelligent energy multi-energy interactive evaluation visualization system 10 works, the method comprises the following steps:
step S01, providing a basic equipment group 11, recording historical operation data of the basic equipment group 11 and acquiring actual operation data in real time;
step S02, providing an energy consumption evaluation model 17, and pre-storing historical operation data, key parameters and index logic factor parameters of the basic equipment group 11;
step S03, the energy consumption evaluation model 17 receives actual operation data from the basic equipment group 11, and evaluates an energy consumption trend analysis result of the basic equipment group 11 based on historical data pre-stored by the energy consumption evaluation model 17 and real-time data acquired by the data acquisition module 13;
and step S04, providing the data display module 20, and visually outputting the energy consumption trend analysis result of the energy consumption evaluation model 17.
Further, between the step S01 and the step S02, a data acquisition module 13 and a data transmission module 15 are provided, where the data acquisition module 13 acquires actual operation data of the basic equipment group 11 in real time and transmits the actual operation data to the energy consumption evaluation model 17 through the data transmission module 15. The data transmission module 15 may transmit data in a wired manner or in a wireless manner.
Furthermore, after the step S04, the method further includes the step of feeding back a control signal to the basic device group 11 according to the energy consumption trend analysis result output by the data display module 20, and dynamically adjusting the operating state of the basic device group 11 to realize the input and output of the smart energy.
As a further improvement of the above embodiment, the energy consumption evaluation model 17 further includes an economic benefit sub-model, and on the basis of the energy consumption trend evaluation result of the energy consumption evaluation model 17, an investment cost formula is added as a supplementary consideration factor, so as to quickly calculate the economic calculation model of the basic equipment group 11.
During the initialization process of the energy consumption evaluation model 17 in the actual operation process, it can gradually modify the initialization data according to the industry experience and the actual experience of the skilled person.
Please refer to fig. 3, which is a schematic diagram of the intelligent energy multi-energy interactive evaluation visualization system 10 shown in fig. 1 applied to interactive evaluation and economic prediction in the fields of wind power generation and photovoltaic power generation. The difference from the foregoing embodiment is that, in the present embodiment, the foundation equipment group 11 is a wind power generation equipment and a photovoltaic power generation equipment for distributed power generation. And the energy consumption evaluation model 17 correspondingly outputs the predicted generated energy of the wind power generation equipment and the photovoltaic power generation equipment. The battery efficiency characteristics of the battery pack 117 are prestored in the energy consumption evaluation model 17. The load system 118 is a lighting system, and the energy use plan and the on-schedule of the lighting system 118 are pre-stored in the energy consumption evaluation model 17.
The energy consumption evaluation model 17 constructs a model and an operating environment based on the predicted power generation amounts of the wind power generation equipment and the photovoltaic power generation equipment, the battery efficiency characteristics of the storage battery pack 117, the energy consumption plan and the start-up plan of the lighting system 118. The data acquisition module 13 monitors and acquires actual operation data of the wind power generation equipment, the photovoltaic power generation equipment, the storage battery pack 117 and the load system 118 in real time, analyzes the actual operation data to obtain an energy consumption trend result, and further calculates an energy gap, the economic benefit sub-model calculates and calculates installed capacities of the wind power generation equipment, the photovoltaic equipment and the storage battery pack according to the energy gap, and constructors organize investment according to characteristics of actual construction investment.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A wisdom energy multipotency interactive evaluation visualization system based on generate confrontation network, its characterized in that includes:
a group of base devices;
the data acquisition module is used for acquiring the operation parameters of the basic equipment group in real time;
the energy consumption evaluation model receives the real-time operation parameters from the data acquisition module, evaluates the energy consumption trend analysis result of the basic equipment group based on the historical data prestored by the energy consumption evaluation model and the real-time operation parameters acquired by the data acquisition module, is based on a deep learning model for generating a countermeasure network and comprises a generation module, a judgment module and a loss function, wherein the loss function is a total loss function and meets the following formula:
L(GAB,GBA,DA,DB,X,Y)＝LGAN(GAB,DB,X,Y)+LGAN(GBA,DA,Y,X)+λcycLcyc(GAB,GBA,X,Y)+λperLper(GAB,GBA,X,Y)
wherein: gABIs an energy consumption generation module, X is the original operating parameter collected by the data collection module, GAB(x) The original operation parameters which represent the data acquisition module to acquire pass through the energy consumption generation module GABThe generated operating parameters;
GBAis a predicted energy consumption parameter generation module, y is a predicted energy consumption parameter, GBA(y) the predicted energy consumption parameter is generated by the predicted energy consumption parameter generation module GBAThe actual operating parameters generated;
DBthe operation parameter judging module is used for judging the truth of the generated actual operation parameter and the predicted operation parameter;
DAthe acquisition parameter judging module is used for judging the authenticity of the generated actual operation parameter and the originally acquired operation parameter;
log is a logarithmic operation, E represents a distribution function expected value;
phi j refers to a characteristic diagram of a j-th layer network generated by the convolutional network according to the input parameters, H represents the height of the characteristic diagram, and W represents the width of the characteristic diagram;
LGAN(GAB,DB,X,Y)，LGAN(GBA,DAy, X) represents the challenge loss;
Lcyc(GAB,GBAx, Y) represents a loss of cyclic consistency;
Lper(GAB,GBAx, Y) represents a loss of perception;
λ cyc is the coefficient of the cyclic consistency loss, λ per is the coefficient of the perceptual loss,
L(GAB,GBA,DA,DBx, Y) is the total loss function;
and the data display module is used for visually outputting the energy consumption trend analysis result of the energy consumption evaluation model.
2. The intelligent energy multi-energy interactive evaluation visualization system according to claim 1, wherein the basic device group comprises a photovoltaic array system, a wind turbine generator, an energy storage converter, a storage battery, a load system, a smart meter and a meteorological system.
3. The intelligent energy-based multi-energy interactive evaluation visualization system according to claim 2, wherein the meteorological system provides historical meteorological data to the energy consumption evaluation model in advance.
4. The intelligent energy-based multi-energy interactive evaluation visualization system according to claim 1, wherein the energy consumption evaluation model prestores historical operating data of the basic equipment group.
5. The intelligent energy-based multi-energy interactive evaluation visualization system as claimed in claim 1, wherein key parameters and index logic factor parameters of the basic equipment group are pre-stored in the energy consumption evaluation model.
6. The intelligent energy multi-energy interactive evaluation visualization system according to claim 1, wherein the basic equipment group comprises a photovoltaic array system, and the energy consumption evaluation model at least prestores local historical sunshine data, climate characteristics and geographic characteristics.
7. The intelligent energy multi-energy interactive evaluation visualization system according to claim 1, wherein the basic equipment group comprises a wind turbine, and the energy consumption evaluation model at least prestores local climate characteristics and geographic characteristics.
8. An intelligent energy multi-energy interactive evaluation method is characterized by comprising the following steps:
providing a basic equipment group, recording historical operation data of the basic equipment group and acquiring actual operation data of the basic equipment group in real time;
providing an energy consumption evaluation model, and pre-storing historical operation data, key parameters and index logic factor parameters of the basic equipment group;
the energy consumption evaluation model receives actual operation data from the basic equipment group, and evaluates an energy consumption trend analysis result of the basic equipment group based on historical data prestored in the energy consumption evaluation model and real-time data acquired by the data acquisition module;
and providing a data display module, and visually outputting the energy consumption trend analysis result of the energy consumption evaluation model.
9. The method as claimed in claim 8, further comprising feeding back a control signal to the basic device group according to the energy consumption trend analysis result outputted from the data display module, and dynamically adjusting the operating status of the basic device group to realize the input and output of the smart energy.
10. The intelligent energy multi-energy interactive evaluation method according to claim 8, wherein the basic equipment group comprises a photovoltaic array system, a wind turbine generator, an energy storage converter, a storage battery pack, a load system, a smart meter and a meteorological system, and the energy consumption evaluation model initializes an operating environment of the energy consumption evaluation model according to pre-stored historical data.
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