CN117634748B - Energy system multi-objective optimization method based on pollution reduction and carbon reduction - Google Patents
Energy system multi-objective optimization method based on pollution reduction and carbon reduction Download PDFInfo
- Publication number
- CN117634748B CN117634748B CN202410102768.3A CN202410102768A CN117634748B CN 117634748 B CN117634748 B CN 117634748B CN 202410102768 A CN202410102768 A CN 202410102768A CN 117634748 B CN117634748 B CN 117634748B
- Authority
- CN
- China
- Prior art keywords
- super capacitor
- energy storage
- collection efficiency
- smooth output
- charge collection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 81
- 230000009467 reduction Effects 0.000 title claims abstract description 39
- 238000005457 optimization Methods 0.000 title claims abstract description 33
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 25
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 25
- 239000003990 capacitor Substances 0.000 claims abstract description 177
- 230000002159 abnormal effect Effects 0.000 claims abstract description 81
- 230000008901 benefit Effects 0.000 claims abstract description 65
- 238000004458 analytical method Methods 0.000 claims abstract description 62
- 238000004146 energy storage Methods 0.000 claims description 130
- 230000008569 process Effects 0.000 claims description 59
- 230000005856 abnormality Effects 0.000 claims description 56
- 238000009499 grossing Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 230000007246 mechanism Effects 0.000 claims description 5
- 238000011217 control strategy Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 abstract description 5
- 238000007599 discharging Methods 0.000 description 10
- 230000008859 change Effects 0.000 description 7
- 230000004044 response Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 239000003792 electrolyte Substances 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
- 238000003860 storage Methods 0.000 description 4
- 238000010521 absorption reaction Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 230000010485 coping Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 230000020169 heat generation Effects 0.000 description 1
- 238000001453 impedance spectrum Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000002608 ionic liquid Substances 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Landscapes
- Testing Electric Properties And Detecting Electric Faults (AREA)
Abstract
The invention discloses a multi-objective optimization method of an energy system based on pollution reduction and carbon reduction, which relates to the technical field of pollution reduction and carbon reduction energy systems and comprises the following steps: deep analysis is carried out on the whole energy system, and a multi-objective optimization objective of the energy system is determined; and determining the configuration position of the super capacitor according to the system demand and the optimization result, and then determining the capacity of the super capacitor. According to the invention, the real-time monitoring of the running state of the super capacitor is realized by intelligently sensing and analyzing the dynamic point performance information and thermodynamic characteristic information of the super capacitor, the abnormal running of the super capacitor is found in time by setting the threshold value of the running benefit coefficient, and the early warning system is used for informing, so that the power quality is prevented from being reduced, and for sensitive equipment and systems, the method means more stable power supply, reduces the risks of equipment faults, errors or abnormal running, and improves the reliability of the whole power system.
Description
Technical Field
The invention relates to the technical field of pollution reduction and carbon reduction energy systems, in particular to a pollution reduction and carbon reduction-based energy system multi-objective optimization method.
Background
The multi-objective optimization of the pollution and carbon reduction energy system is to comprehensively consider a plurality of objectives of reducing environmental pollution, reducing carbon emission and the like in the planning and operation of the energy system, and realize various environmental protection and sustainable development objectives by reasonably configuring energy resources, technologies and strategies. The proposal of this concept reflects the urgent need of society for slowing down climate change and improving environmental quality while meeting energy demands. In this context, the multi-objective optimization approach becomes an effective decision tool that can simultaneously consider multiple interrelated objectives to drive the sustainable development of the energy system in a more comprehensive, coordinated manner.
The super capacitor plays an important role in multi-objective optimization of the pollution and carbon reduction energy system. Supercapacitors are a high energy density, high power density energy storage device, the characteristics of which make it an ideal choice for reducing carbon emissions, improving energy efficiency, and increasing renewable energy integration.
The super capacitor can be charged and discharged rapidly, and has the characteristics of high power density and short charging time. In energy systems, supercapacitors can be used to store renewable energy sources that are instantaneously produced, such as wind and solar energy. By smoothing the output, the supercapacitor helps to improve the stability of the energy system, reducing power system fluctuations due to renewable energy fluctuations, and thus reducing reliance on traditional, more polluting energy sources. The super capacitor can recover energy in the modes of recovering braking energy, descending an elevator and the like. In industrial and transportation systems, mechanical energy can be efficiently converted to electrical energy and released when needed, thereby reducing waste. Such an energy recovery mechanism helps to increase the overall energy utilization efficiency of the system while reducing the need for traditional energy sources. The super capacitor has higher peak power output capability and can provide a large amount of electric energy in a short time. Making them ideal choices for supporting peak power demands of the system. In multi-objective optimization of energy systems, supercapacitors can be used to provide additional power to meet the power demands of the system during high demand, avoiding starting up conventional generator sets, and thus reducing reliance on highly polluting energy sources.
The prior art has the following defects:
When energy storage and smooth output are carried out through the super capacitor, if abnormality is not perceived in the energy storage and smooth output process of the super capacitor, the charging and discharging processes of the super capacitor are possibly uncontrolled, so that voltage fluctuation of a power system is caused, the stability of the whole power system is affected, when the situation occurs, the problems of instability, frequency fluctuation and the like of the power system are caused, even the power system is crashed, meanwhile, the electric energy quality is reduced, and faults, errors or abnormal operation can be caused for sensitive equipment and systems needing high-quality electric energy, and normal production and operation are affected.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a multi-objective optimization method of an energy system based on pollution reduction and carbon reduction, which is used for realizing real-time monitoring of the operation state of a super capacitor by intelligently sensing and analyzing dynamic point performance information, thermodynamic characteristic information, particularly charge collection efficiency information, equivalent series resistance information and internal gas pressure information of the super capacitor, timely finding out abnormal operation of the super capacitor by setting a threshold value of an operation benefit coefficient and informing by an early warning system, thereby being beneficial to preventing the reduction of electric energy quality, and for sensitive equipment and systems, the method means more stable power supply, reduces the risks of equipment faults, errors or abnormal operation, and further improves the reliability of the whole power system so as to solve the problems in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: the multi-objective optimization method of the energy system based on pollution and carbon reduction comprises the following steps:
deep analysis is carried out on the whole energy system, and a multi-objective optimization objective of the energy system is determined;
Determining the configuration position of the super capacitor according to the system requirement and the optimization result, and then determining the capacity of the super capacitor to meet the peak power requirement and the energy storage requirement of the system on different time scales;
Designing an intelligent control strategy of the super capacitor, realizing multi-objective optimization of the system, and storing and smoothly outputting energy through the super capacitor;
acquiring multiple data information of the super capacitor during energy storage and smooth output, wherein the multiple data information comprises dynamic point performance information and thermodynamic characteristic information of the super capacitor, and processing the dynamic point performance information and the thermodynamic characteristic information after acquiring the multiple data information;
Comprehensively analyzing the processed dynamic point performance information and thermodynamic characteristic information when the super capacitor performs energy storage and smooth output, establishing an abnormal sensing mechanism, and intelligently sensing the abnormal state when the super capacitor performs energy storage and smooth output;
When the process of energy storage and smooth output of the super capacitor is perceived to be abnormal, the abnormality is subjected to deep analysis, and different types of abnormality are prompted through the mobile terminal.
Preferably, the dynamic point performance information of the super capacitor during energy storage and smooth output comprises charge collection efficiency information and equivalent series resistance information, after the information is obtained, the charge collection efficiency information is processed to generate a charge collection efficiency gain index, and the equivalent series resistance information is processed to generate an equivalent series resistance variation index; thermodynamic characteristic information of the super capacitor during energy storage and smooth output comprises internal gas pressure information of the capacitor, and after the thermodynamic characteristic information is obtained, the internal gas pressure information is processed to generate an internal gas pressure abnormality hiding index.
Preferably, the logic for the charge collection efficiency gain index acquisition is as follows:
acquiring the real-time charge collection efficiency of the super capacitor during energy storage and smooth output in a fixed duration window, and using the real-time charge collection efficiency The method comprises the following steps of representing, wherein x represents the number of real-time charge collection efficiency obtained in a fixed time window when the supercapacitor performs energy storage and smooth output, and x=1, 2, 3, 4, … … and p are positive integers;
Comparing and analyzing the real-time charge collection efficiency and the charge collection efficiency reference value obtained in a fixed time window when the super capacitor performs energy storage and smooth output, and calculating a charge collection efficiency gain index through the real-time charge collection efficiency and the charge collection efficiency reference value, wherein the calculated expression is as follows: In which, in the process, Representing the charge collection efficiency gain index,/>Representing the real-time charge collection efficiency greater than or equal to the charge collection efficiency reference value acquired in a fixed time window when the supercapacitor performs energy storage and smooth output,/>Number of real-time charge collection efficiency greater than or equal to charge collection efficiency reference value acquired in fixed duration window when super capacitor performs energy storage and smooth output,/>，/>Is a positive integer,/>Representing the real-time charge collection efficiency of the supercapacitor, which is less than a charge collection efficiency reference value and is acquired in a fixed time window when the supercapacitor performs energy storage and smooth output,/>A number representing the real-time charge collection efficiency less than the charge collection efficiency reference value acquired within a fixed time window when the supercapacitor is performing energy storage and smoothing output,，/>Is a positive integer.
Preferably, the logic for obtaining the equivalent series resistance variation index is as follows:
In a fixed time window, acquiring a real-time equivalent series resistance of the super capacitor for energy storage and smooth output, and using the real-time equivalent series resistance as a function according to a time sequence A representation;
After the real-time equivalent series resistance value of the supercapacitor during energy storage and smooth output is obtained, calculating the difference value between two adjacent real-time equivalent series resistances according to a time sequence, and calibrating the difference value between the two adjacent real-time equivalent series resistances as Wherein y represents the number of the difference between two adjacent real-time equivalent series resistances after being sequenced according to time sequence, y=1, 2, 3, 4, … …, q is a positive integer;
By the difference between two adjacent real-time equivalent series resistances Calculating an equivalent series resistance variation index, wherein the calculated expression is as follows: /(I)In the above, the ratio of/>Indicating the equivalent series resistance variation index.
Preferably, the logic for the internal gas pressure anomaly concealment index acquisition is as follows:
Acquiring an optimal internal gas pressure range of the super capacitor for energy storage and smooth output, and calibrating the optimal internal gas pressure range as ；
In a window with fixed duration, acquiring a real-time internal gas pressure value of the super capacitor for energy storage and smooth output, and using the real-time internal gas pressure value as a function according to a time sequenceA representation;
calculating an internal gas pressure anomaly hiding index, wherein the calculated expression is as follows: In which, in the process, Index indicating abnormality in internal gas pressure,/>Representing that the real-time internal gas pressure value is lower than/>, when the super capacitor performs energy storage and smooth outputTime period of/>，/>Representing that the real-time internal gas pressure value is higher than/>, when the super capacitor performs energy storage and smooth outputTime period of/>。
Preferably, the charge collection efficiency gain index generated by processing the supercapacitor during energy storage and smooth outputIndex of equivalent series resistance variation/>Index of internal gas pressure abnormality concealmentComprehensive analysis is carried out to generate the running benefit coefficient/>The formula according to is: In the above, the ratio of/> 、/>、/>Charge collection efficiency gain index/>, respectivelyIndex of equivalent series resistance variation/>Index of internal gas pressure abnormality concealmentAnd/>、/>、/>Are all greater than 0.
Preferably, the comparison analysis is performed on the operation benefit coefficient generated when the super capacitor performs energy storage and smooth output and the preset operation benefit coefficient reference threshold value, and the comparison analysis result is as follows:
if the operation benefit coefficient is greater than or equal to the operation benefit coefficient reference threshold, generating a normal signal, and not sending an early warning prompt to the normal signal;
if the operation benefit coefficient is smaller than the operation benefit coefficient reference threshold, generating an abnormal signal, and sending an early warning prompt to a normal signal, wherein the early warning prompt is known by related staff.
Preferably, when the super capacitor is perceived to generate an abnormal signal in the energy storage and smooth output process, an analysis set is established by the corresponding operation benefit coefficient and a plurality of operation benefit coefficients generated subsequently when the abnormal signal is generated, and the analysis set is calibrated as I, thenF represents the number of the running benefit coefficient in the analysis set, f=1, 2, 3, 4, … …, u being a positive integer;
Comparing the operation benefit coefficient collected in the analysis set with a preset operation benefit coefficient reference threshold value, and calculating a depth analysis index The calculated expression is: /(I)In which, in the process,Representing the running benefit coefficient reference threshold,/>Representing operation benefit coefficients less than an operation benefit coefficient reference threshold in the analysis set,/>Representing the running benefit coefficient reference threshold,/>Number representing an operation benefit coefficient within the analysis set that is less than the operation benefit coefficient reference threshold,/>，/>Is a positive integer, and。
Preferably, the depth analysis index generated within the analysis set is determinedWith a preset gradient reference threshold/>AndAlignment is performed,/>The results of the comparison are as follows:
If it is Generating a first-level abnormal signal, carrying out first-level prompt on the first-level abnormal signal through a mobile terminal, and indicating that sudden abnormality exists when the super capacitor carries out energy storage and smooth output when the super capacitor senses that abnormality exists in the energy storage and smooth output process and carries out deep analysis on the abnormality to generate the first-level abnormal signal;
If it is Generating a secondary abnormal signal, carrying out secondary prompt on the secondary abnormal signal through a mobile terminal, and indicating that the super capacitor has an abnormal operation instability when carrying out energy storage and smooth output when sensing that the super capacitor has an abnormality in the energy storage and smooth output process and carrying out deep analysis on the abnormality to generate the secondary abnormal signal;
If it is And generating a three-level abnormal signal, carrying out three-level prompt on the three-level abnormal signal through a mobile terminal, and indicating that continuous abnormality exists when the super capacitor carries out energy storage and smooth output when the super capacitor senses that abnormality exists in the energy storage and smooth output process and carries out deep analysis on the abnormality to generate the three-level abnormal signal.
In the technical scheme, the invention has the technical effects and advantages that:
According to the invention, through intelligent sensing and analysis of dynamic point performance information and thermodynamic characteristic information of the super capacitor, particularly charge collection efficiency information, equivalent series resistance information and internal gas pressure information, real-time monitoring of the operation state of the super capacitor is realized, the abnormal operation of the super capacitor is timely found through setting a threshold value of an operation benefit coefficient, and notification is carried out through an early warning system, so that the power quality is prevented from being reduced, for sensitive equipment and systems, more stable power supply is realized, the risks of equipment faults, errors or abnormal operation are reduced, and the reliability of the whole power system is improved;
When the super capacitor is perceived to generate an abnormal signal in the energy storage and smooth output process, an analysis set is established for the corresponding operation benefit coefficient and a plurality of operation benefit coefficients generated subsequently when the abnormal signal is generated, the abnormal state is subjected to deep research through the depth analysis index generated in the analysis set, the abnormal property and the severity can be accurately judged through the generation of the first-stage abnormal signal, the second-stage abnormal signal and the third-stage abnormal signal, the maintenance measures can be timely taken, and related staff can take different coping strategies according to the abnormal level through the multi-stage prompt of the mobile terminal, so that the reliability and maintainability of the system are improved, and the risk of the abnormality on the system operation is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
FIG. 1 is a flow chart of a method of the multi-objective optimization method of the energy system based on pollution reduction and carbon reduction.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides a multi-objective optimization method of an energy system based on pollution reduction and carbon reduction as shown in fig. 1, which comprises the following steps:
deep analysis is carried out on the whole energy system, and a multi-objective optimization objective of the energy system is determined;
The characteristics of the whole energy system, the energy input and output conditions and the working characteristics of each energy component are deeply analyzed, so that the operation mechanism of the system is comprehensively mastered. This includes understanding the spatiotemporal nature of power demand, examining the volatility of renewable energy sources, analyzing traditional energy supply scenarios, and in-depth understanding of the performance characteristics of energy storage devices, power generation devices, and traditional generator sets. The comprehensive analysis is helpful to determine an optimization strategy of the system operation, and realizes the efficient utilization of energy, the reduction of carbon emission and the reliable operation of the whole system. Meanwhile, the interaction of each energy component and the economic and environmental influence of the system are known in detail, so that the sustainable energy system is built, and the aim of pollution reduction and carbon reduction is promoted;
the multi-objective optimization objectives include reducing carbon emissions, improving energy efficiency, smoothing output fluctuations, etc.
Determining the configuration position of the super capacitor according to the system requirement and the optimization result, and then determining the capacity of the super capacitor to meet the peak power requirement and the energy storage requirement of the system on different time scales;
the configuration position of the super capacitor may be close to renewable energy power generation equipment, an electric load center or a power grid connection point and the like, and the capacity of the super capacitor is determined, so that the peak power requirement and the energy storage requirement of the system on different time scales are met, and factors such as system load, renewable energy fluctuation and the like need to be considered in capacity planning.
Designing an intelligent control strategy of the super capacitor, realizing multi-objective optimization of the system, and storing and smoothly outputting energy through the super capacitor;
The control strategy may include charge and discharge scheduling, voltage balancing, capacity management, and other aspects, and if the system further includes other energy storage devices, a collaborative operation strategy needs to be designed, so that the super capacitor and the other devices can complement advantages, and efficiency and performance of the overall energy storage system are improved.
Acquiring multiple data information of the super capacitor during energy storage and smooth output, wherein the multiple data information comprises dynamic point performance information and thermodynamic characteristic information of the super capacitor, and processing the dynamic point performance information and the thermodynamic characteristic information after acquiring the multiple data information;
The dynamic point performance information of the super capacitor during energy storage and smooth output comprises charge collection efficiency information and equivalent series resistance information, and after the information is obtained, the charge collection efficiency information is processed to generate a charge collection efficiency gain index, and the equivalent series resistance information is processed to generate an equivalent series resistance variation index.
The charge collection efficiency of the super capacitor during energy storage and smooth output refers to the charge collection efficiency of the internal electrode of the capacitor during the process of charging, when the capacitor absorbs external electric energy and stores the external electric energy and releases the stored electric energy during the discharging process, which relates to the design of the internal structure and materials of the capacitor, so as to ensure that the electric charge can be efficiently absorbed and released during the charging and discharging processes, the efficiency of energy storage and output is improved to the greatest extent, and the high charge collection efficiency means less electric charge loss, so that the super capacitor can more effectively perform the functions of energy storage and smooth output, and has important influence on the performance and stability of the whole energy system.
The low charge collection efficiency of the supercapacitor when performing energy storage and smooth output may cause a series of problems affecting the stability of the power system, the following being specific reasons:
Insufficient energy storage and release: the low charge collection efficiency means that part of the charge cannot be efficiently absorbed and released during the charge and discharge process, which results in insufficient energy stored by the supercapacitor, which makes it incapable of providing sufficient electric energy support when needed, affecting smooth output of energy, and causing fluctuation of the power system.
Capacitor charge rate limited: low charge collection efficiency can result in a limited rate of capacitor charge, i.e., a slower rate of capacitor absorption of electrical energy, which can cause problems when the system requires a fast response, such as when the power demand increases instantaneously, the capacitor cannot be charged quickly to cope with the high power demand.
The discharge process is unstable: the low charge collection efficiency may cause uneven discharge of the charge during discharge of the capacitor, generating an unstable current, which induces voltage fluctuation in the power system, adversely affecting the voltage stability of the system.
The system frequency response is insufficient: super capacitors act as energy storage devices in power systems to regulate and smooth the flow of electrical energy, and when the charge collection efficiency is low, their frequency response is limited, and the smooth output of electrical energy cannot be effectively regulated, resulting in system frequency fluctuations.
Affecting the overall reliability of the system: the low charge collection efficiency may cause excessive heat generation inside the capacitor, increasing the temperature of the components, thereby shortening the lifetime of the device, which not only affects the reliability of the capacitor itself, but also negatively affects the reliability of the entire power system.
Therefore, the charge collection efficiency of the super capacitor during energy storage and smooth output is monitored, and the hidden trouble that the energy storage and smooth output process of the super capacitor has abnormality due to low charge collection efficiency can be found out in time.
The logic for the charge collection efficiency gain index acquisition is as follows:
acquiring the real-time charge collection efficiency of the super capacitor during energy storage and smooth output in a fixed duration window, and using the real-time charge collection efficiency The method comprises the following steps of representing, wherein x represents the number of real-time charge collection efficiency obtained in a fixed time window when the supercapacitor performs energy storage and smooth output, and x=1, 2, 3, 4, … … and p are positive integers;
It should be noted that, by installing a current sensor and a voltage sensor at the input and output ports of the supercapacitor respectively, the current and the voltage during the charging and discharging processes of the supercapacitor can be measured in real time, and these measured values can be used to calculate the real-time power and the charge collection efficiency of the capacitor;
Comparing and analyzing the real-time charge collection efficiency and the charge collection efficiency reference value obtained in a fixed time window when the super capacitor performs energy storage and smooth output, and calculating a charge collection efficiency gain index through the real-time charge collection efficiency and the charge collection efficiency reference value, wherein the calculated expression is as follows: In which, in the process, Representing the charge collection efficiency gain index,/>Representing the real-time charge collection efficiency greater than or equal to the charge collection efficiency reference value acquired in a fixed time window when the supercapacitor performs energy storage and smooth output,/>Number of real-time charge collection efficiency greater than or equal to charge collection efficiency reference value acquired in fixed duration window when super capacitor performs energy storage and smooth output,/>，/>Is a positive integer,/>Representing the real-time charge collection efficiency of the supercapacitor, which is less than a charge collection efficiency reference value and is acquired in a fixed time window when the supercapacitor performs energy storage and smooth output,/>A number representing the real-time charge collection efficiency less than the charge collection efficiency reference value acquired within a fixed time window when the supercapacitor is performing energy storage and smoothing output,，/>Is a positive integer.
According to the calculation expression of the charge collection efficiency gain index, the larger the expression value of the charge collection efficiency gain index generated in a fixed time window when the supercapacitor performs energy storage and smooth output, the smaller the hidden danger of abnormality in the supercapacitor energy storage and smooth output process is indicated, and the larger the hidden danger of abnormality in the supercapacitor energy storage and smooth output process is indicated otherwise.
The equivalent series resistance of the super capacitor during energy storage and smooth output means that an analog resistor exists in the capacitor and is used for representing the energy loss or the effect of preventing current flow of the capacitor in the charging and discharging processes, the equivalent series resistance is not actually a resistor element, but is used for describing the non-ideality in the capacitor and the loss phenomenon in the energy conversion process, and the equivalent series resistance is a parameter reflecting the energy loss degree in the capacitor and affects the charging and discharging efficiency of the super capacitor and has an important effect on the performance of the super capacitor in an energy system.
The poor stability of the equivalent series resistance of the supercapacitor when performing energy storage and smooth output may cause a series of problems, affecting the stability of the power system. The following are specific reasons:
the energy conversion efficiency is reduced: poor stability of the equivalent series resistance can cause a change in current through the resistive element inside the capacitor, causing additional energy loss that reduces the energy conversion efficiency of the capacitor, resulting in reduced efficiency in storing and releasing energy.
Charge rate and discharge rate fluctuation: the change of the equivalent series resistance may cause fluctuation of the charging rate and the discharging rate of the capacitor, and instability of the charging rate and the discharging rate may cause current fluctuation of the super capacitor in the power system, thereby causing voltage fluctuation of the power system.
The system frequency response is poor: due to the instability of the equivalent series resistance, the frequency response of the super capacitor may be limited, and the poor frequency response may cause the capacitor to fail to quickly respond to the frequency variation in the power system, thereby affecting the stability of the power system.
The life of the capacitor is shortened: the change in the equivalent series resistance may cause an increase in temperature inside the supercapacitor, increasing the heat loss of the capacitor, which may shorten the life of the capacitor, reducing its reliability in the energy system.
Affecting the overall dynamics of the power system: the instability of the equivalent series resistance may make the charging and discharging behavior of the supercapacitor difficult to predict and control, which may affect the overall dynamic characteristics of the power system, increase uncertainty in the operation of the system, and thus pose challenges to the stability of the power system.
Therefore, the equivalent series resistance of the super capacitor during energy storage and smooth output is monitored, and the hidden trouble that the energy storage and smooth output process of the super capacitor has abnormality due to poor stability of the equivalent series resistance can be found out in time.
The logic for obtaining the equivalent series resistance variation index is as follows:
In a fixed time window, acquiring a real-time equivalent series resistance of the super capacitor for energy storage and smooth output, and using the real-time equivalent series resistance as a function according to a time sequence A representation;
By using an alternating current impedance analyzer, by applying alternating current signals with different frequencies in a certain frequency range and measuring the phase difference and amplitude of voltage and current, the impedance spectrum of the capacitor can be obtained, so as to calculate the equivalent series resistance;
After the real-time equivalent series resistance value of the supercapacitor during energy storage and smooth output is obtained, calculating the difference value between two adjacent real-time equivalent series resistances according to a time sequence, and calibrating the difference value between the two adjacent real-time equivalent series resistances as Wherein y represents the number of the difference between two adjacent real-time equivalent series resistances after being sequenced according to time sequence, y=1, 2, 3, 4, … …, q is a positive integer;
By the difference between two adjacent real-time equivalent series resistances Calculating an equivalent series resistance variation index, wherein the calculated expression is as follows: /(I)In the above, the ratio of/>Representing an equivalent series resistance variation index;
According to the calculation expression of the equivalent series resistance variation index, the larger the expression value of the equivalent series resistance variation index generated in a fixed time window when the supercapacitor performs energy storage and smooth output, the larger the hidden danger of abnormality in the energy storage and smooth output process of the supercapacitor is indicated, and the smaller the hidden danger of abnormality in the energy storage and smooth output process of the supercapacitor is indicated otherwise.
Thermodynamic characteristic information of the super capacitor during energy storage and smooth output comprises internal gas pressure information of the capacitor, and after the thermodynamic characteristic information is obtained, the internal gas pressure information is processed to generate an internal gas pressure abnormality hiding index.
In the multi-objective optimization of the energy system for pollution reduction and carbon reduction, the internal gas pressure when the supercapacitor performs energy storage and smooth output refers to the pressure condition of the gas existing between the internal electrode of the capacitor and the electrolyte, the supercapacitor uses the organic solution or the ionic liquid as the electrolyte, and the electrolyte may contain dissolved gas or gas generated in the electrolyte decomposition process, so the internal gas pressure is a parameter describing the existence state of the gas inside the capacitor, and the change of the internal gas pressure may affect the performance of the supercapacitor; firstly, the presence of gas can affect the electrical properties of the capacitor, such as capacitance and resistance, and thus directly affect the efficiency of energy storage and release, and secondly, the increase in internal gas pressure can lead to an increase in internal temperature of the capacitor, as the compression and expansion process of the gas can be accompanied by release or absorption of heat, which can affect the thermal properties of the capacitor, making challenges for its lifetime and performance stability, and in energy system optimisation, monitoring and managing the internal gas pressure of the supercapacitor becomes critical, helping to find potential problems in advance and taking corresponding maintenance measures.
The excessive or insufficient internal gas pressure of the supercapacitor during energy storage and smooth output may cause a series of problems, which affect the stability of the power system. The following are specific reasons:
The electrical performance of the capacitor is unstable: too much or too little internal gas pressure may change the electrical properties of the capacitor, resulting in changes in the critical parameters of capacitance, resistance, etc. This variation can cause the capacitor to appear unstable during charging and discharging, affecting its controllability in the power system.
Affecting the charge and discharge rate: too much or too little gas pressure may affect the charge and discharge rates of the capacitor, resulting in uncontrolled processes. Rapid charge and discharge rate fluctuations can cause severe changes in the current and voltage of the power system, which can pose a threat to system stability.
The thermal effect increases: abnormal changes in internal gas pressure may lead to a sharp rise in the internal temperature of the capacitor, as the compression and expansion process of the gas is accompanied by release or absorption of heat. Excessive temperatures can affect the insulating properties of the capacitor, increase thermal effects, and reduce the life of the capacitor.
Gas release causes failure: excessive internal gas pressure may cause gas release inside the capacitor, creating gas bubbles or gas explosions. These phenomena may damage the capacitor itself, and may also cause malfunction of other devices, affecting the normal operation of the whole power system.
Stability is reduced: too much or too little gas pressure can increase the uncertainty of the interaction between the gas inside the capacitor and the electrolyte, thus reducing the stability of the supercapacitor. This may lead to the difficulty of maintaining a stable operating state of the capacitor under system dynamic conditions, causing instability of the power system.
Therefore, the internal gas pressure during the energy storage and smooth output of the super capacitor is monitored, and the hidden trouble that the energy storage and smooth output process of the super capacitor is abnormal due to the fact that the internal gas pressure is too large or too small can be found out in time.
The logic for the internal gas pressure anomaly concealment index acquisition is as follows:
Acquiring an optimal internal gas pressure range of the super capacitor for energy storage and smooth output, and calibrating the optimal internal gas pressure range as ；
It should be noted that referring to specifications and instruction manuals provided by manufacturers of supercapacitors, manufacturers typically provide recommendations regarding optimal operating conditions, including recommended internal gas pressure ranges;
In a window with fixed duration, acquiring a real-time internal gas pressure value of the super capacitor for energy storage and smooth output, and using the real-time internal gas pressure value as a function according to a time sequence A representation;
it should be noted that, a special gas pressure sensor is deployed and installed in or near the supercapacitor to monitor the change of the gas pressure in real time, and the gas pressure sensor can generally provide measurement with high precision and high sensitivity, so as to accurately obtain the gas pressure value;
calculating an internal gas pressure anomaly hiding index, wherein the calculated expression is as follows: In which, in the process, Index indicating abnormality in internal gas pressure,/>Representing that the real-time internal gas pressure value is lower than/>, when the super capacitor performs energy storage and smooth outputTime period of/>，/>Representing that the real-time internal gas pressure value is higher than/>, when the super capacitor performs energy storage and smooth outputTime period of/>；
The calculation expression of the internal gas pressure abnormality hidden index shows that the larger the expression value of the equivalent series resistance variation index generated in a fixed time window when the supercapacitor performs energy storage and smooth output is, the larger the hidden danger of abnormality in the energy storage and smooth output process of the supercapacitor is indicated, and the smaller the hidden danger of abnormality in the energy storage and smooth output process of the supercapacitor is indicated otherwise.
Comprehensively analyzing the processed dynamic point performance information and thermodynamic characteristic information when the super capacitor performs energy storage and smooth output, establishing an abnormal sensing mechanism, and intelligently sensing the abnormal state when the super capacitor performs energy storage and smooth output;
charge collection efficiency gain index generated after processing of supercapacitor during energy storage and smoothing output Index of equivalent series resistance variation/>Index of internal gas pressure abnormality concealmentComprehensive analysis is carried out to generate the running benefit coefficient/>The formula according to is: In the above, the ratio of/> 、/>、/>Charge collection efficiency gain index/>, respectivelyIndex of equivalent series resistance variation/>Index of internal gas pressure abnormality concealmentAnd/>、/>、/>Are all greater than 0.
The calculation formula shows that the larger the charge collection efficiency gain index generated in the fixed time window, the smaller the equivalent series resistance variation index and the smaller the internal gas pressure abnormal hiding index are when the super capacitor performs energy storage and smooth output, namely the operation benefit coefficient generated in the fixed time window when the super capacitor performs energy storage and smooth outputThe larger the expression value of the super capacitor is, the smaller the hidden danger of abnormality in the energy storage and smooth output process of the super capacitor is, and the larger the hidden danger of abnormality in the energy storage and smooth output process of the super capacitor is.
Comparing and analyzing the operation benefit coefficient generated when the super capacitor performs energy storage and smooth output with a preset operation benefit coefficient reference threshold value, wherein the comparison and analysis result is as follows:
If the operation benefit coefficient is greater than or equal to the operation benefit coefficient reference threshold, generating a normal signal, and not giving an early warning prompt to the normal signal, wherein when the super capacitor generates the normal signal in the energy storage and smooth output process, the super capacitor can efficiently store energy and smoothly output;
If the operation benefit coefficient is smaller than the operation benefit coefficient reference threshold, an abnormal signal is generated, an early warning prompt is sent out to the normal signal, the abnormal signal is known by relevant staff, the situation is convenient to process by the relevant staff in time, when the super capacitor generates the abnormal signal in the energy storage and smooth output process, the abnormal hidden danger exists when the super capacitor stores energy and outputs smoothly, the super capacitor needs to be maintained and managed in time, and the super capacitor can be ensured to store energy and output smoothly.
When the process of energy storage and smooth output of the super capacitor is perceived to be abnormal, carrying out depth analysis on the abnormality, and prompting different types of abnormality through a mobile terminal;
When the super capacitor is perceived to generate an abnormal signal in the energy storage and smooth output process, an analysis set is established by the corresponding operation benefit coefficient and a plurality of operation benefit coefficients generated subsequently when the abnormal signal is generated, and the analysis set is calibrated as I, then F represents the number of the running benefit coefficient in the analysis set, f=1, 2, 3, 4, … …, u being a positive integer;
Comparing the operation benefit coefficient collected in the analysis set with a preset operation benefit coefficient reference threshold value, and calculating a depth analysis index The calculated expression is: /(I)In which, in the process,Representing the running benefit coefficient reference threshold,/>Representing operation benefit coefficients less than an operation benefit coefficient reference threshold in the analysis set,/>Representing the running benefit coefficient reference threshold,/>Number representing an operation benefit coefficient within the analysis set that is less than the operation benefit coefficient reference threshold,/>，/>Is a positive integer, and。
From the calculation formula, the depth analysis index generated in the analysis set can be obtainedThe larger the expression value of the super capacitor is, the more serious the abnormal hidden trouble exists in the energy storage and smooth output process of the super capacitor is, otherwise, the less serious the abnormal hidden trouble exists in the energy storage and smooth output process of the super capacitor is.
Depth analysis index generated in analysis setWith a preset gradient reference threshold/>And/>Alignment is performed,/>The results of the comparison are as follows:
If it is Generating a first-level abnormal signal, carrying out first-level prompt on the first-level abnormal signal through a mobile terminal, and indicating that sudden abnormality exists when the super capacitor carries out energy storage and smooth output when the super capacitor senses that abnormality exists in the energy storage and smooth output process and carries out deep analysis on the abnormality to generate the first-level abnormal signal;
If it is Generating a secondary abnormal signal, carrying out secondary prompt on the secondary abnormal signal through a mobile terminal, and indicating that the super capacitor has an abnormal operation instability when carrying out energy storage and smooth output when sensing that the super capacitor has an abnormality in the energy storage and smooth output process and carrying out deep analysis on the abnormality to generate the secondary abnormal signal;
If it is And generating a three-level abnormal signal, carrying out three-level prompt on the three-level abnormal signal through a mobile terminal, and indicating that continuous abnormality exists when the super capacitor carries out energy storage and smooth output when the super capacitor senses that abnormality exists in the energy storage and smooth output process and carries out deep analysis on the abnormality to generate the three-level abnormal signal.
According to the invention, through intelligent sensing and analysis of dynamic point performance information and thermodynamic characteristic information of the super capacitor, particularly charge collection efficiency information, equivalent series resistance information and internal gas pressure information, real-time monitoring of the operation state of the super capacitor is realized, the abnormal operation of the super capacitor is timely found through setting a threshold value of an operation benefit coefficient, and notification is carried out through an early warning system, so that the power quality is prevented from being reduced, for sensitive equipment and systems, more stable power supply is realized, the risks of equipment faults, errors or abnormal operation are reduced, and the reliability of the whole power system is improved;
When the super capacitor is perceived to generate an abnormal signal in the energy storage and smooth output process, an analysis set is established for the corresponding operation benefit coefficient and a plurality of operation benefit coefficients generated subsequently when the abnormal signal is generated, the abnormal state is subjected to deep research through the depth analysis index generated in the analysis set, the abnormal property and the severity can be accurately judged through the generation of the first-stage abnormal signal, the second-stage abnormal signal and the third-stage abnormal signal, the maintenance measures can be timely taken, and related staff can take different coping strategies according to the abnormal level through the multi-stage prompt of the mobile terminal, so that the reliability and maintainability of the system are improved, and the risk of the abnormality on the system operation is reduced.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided by the present application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. The energy system multi-objective optimization method based on pollution and carbon reduction is characterized by comprising the following steps of:
deep analysis is carried out on the whole energy system, and a multi-objective optimization objective of the energy system is determined;
Determining the configuration position of the super capacitor according to the system requirement and the optimization result, and then determining the capacity of the super capacitor to meet the peak power requirement and the energy storage requirement of the system on different time scales;
Designing an intelligent control strategy of the super capacitor, realizing multi-objective optimization of the system, and storing and smoothly outputting energy through the super capacitor;
acquiring multiple data information of the super capacitor during energy storage and smooth output, wherein the multiple data information comprises dynamic point performance information and thermodynamic characteristic information of the super capacitor, and processing the dynamic point performance information and the thermodynamic characteristic information after acquiring the multiple data information;
Comprehensively analyzing the processed dynamic point performance information and thermodynamic characteristic information when the super capacitor performs energy storage and smooth output, establishing an abnormal sensing mechanism, and intelligently sensing the abnormal state when the super capacitor performs energy storage and smooth output;
the dynamic point performance information of the super capacitor during energy storage and smooth output comprises charge collection efficiency information and equivalent series resistance information, the charge collection efficiency information is processed after the charge collection efficiency information is obtained to generate a charge collection efficiency gain index, and the equivalent series resistance information is processed to generate an equivalent series resistance variation index; thermodynamic characteristic information of the super capacitor during energy storage and smooth output comprises internal gas pressure information of the capacitor, and after the thermodynamic characteristic information is obtained, the internal gas pressure information is processed to generate an internal gas pressure abnormality hiding index;
charge collection efficiency gain index generated after processing of supercapacitor during energy storage and smoothing output Index of equivalent series resistance variation/>Index of hiding abnormality of internal gas pressure/>Comprehensive analysis is carried out to generate the running benefit coefficient/>The formula according to is: In the above, the ratio of/> 、/>、/>Charge collection efficiency gain index/>, respectivelyIndex of equivalent series resistance variation/>Index of internal gas pressure abnormality concealmentAnd/>、/>、/>Are all greater than 0;
When the process of energy storage and smooth output of the super capacitor is perceived to be abnormal, the abnormality is subjected to deep analysis, and different types of abnormality are prompted through the mobile terminal.
2. The energy system multi-objective optimization method based on pollution reduction and carbon reduction according to claim 1, wherein the logic for acquiring the charge collection efficiency gain index is as follows:
acquiring the real-time charge collection efficiency of the super capacitor during energy storage and smooth output in a fixed duration window, and using the real-time charge collection efficiency The method comprises the following steps of representing, wherein x represents the number of real-time charge collection efficiency obtained in a fixed time window when the supercapacitor performs energy storage and smooth output, and x=1, 2, 3, 4, … … and p are positive integers;
Comparing and analyzing the real-time charge collection efficiency and the charge collection efficiency reference value obtained in a fixed time window when the super capacitor performs energy storage and smooth output, and calculating a charge collection efficiency gain index through the real-time charge collection efficiency and the charge collection efficiency reference value, wherein the calculated expression is as follows: In which, in the process, Representing the charge collection efficiency gain index,/>Representing the real-time charge collection efficiency greater than or equal to the charge collection efficiency reference value acquired in a fixed time window when the supercapacitor performs energy storage and smooth output,/>Number of real-time charge collection efficiency greater than or equal to charge collection efficiency reference value acquired in fixed duration window when super capacitor performs energy storage and smooth output,/>，/>Is a positive integer,/>Representing the real-time charge collection efficiency of the supercapacitor, which is less than a charge collection efficiency reference value and is acquired in a fixed time window when the supercapacitor performs energy storage and smooth output,/>A number representing the real-time charge collection efficiency less than the charge collection efficiency reference value acquired within a fixed time window when the supercapacitor is performing energy storage and smoothing output,，/>Is a positive integer.
3. The energy system multi-objective optimization method based on pollution reduction and carbon reduction according to claim 2, wherein the logic for obtaining the equivalent series resistance variation index is as follows:
In a fixed time window, acquiring a real-time equivalent series resistance of the super capacitor for energy storage and smooth output, and using the real-time equivalent series resistance as a function according to a time sequence A representation;
After the real-time equivalent series resistance value of the supercapacitor during energy storage and smooth output is obtained, calculating the difference value between two adjacent real-time equivalent series resistances according to a time sequence, and calibrating the difference value between the two adjacent real-time equivalent series resistances as Wherein y represents the number of the difference between two adjacent real-time equivalent series resistances after being sequenced according to time sequence, y=1, 2, 3, 4, … …, q is a positive integer;
By the difference between two adjacent real-time equivalent series resistances Calculating an equivalent series resistance variation index, wherein the calculated expression is as follows: /(I)In the above, the ratio of/>Indicating the equivalent series resistance variation index.
4. The energy system multi-objective optimization method based on pollution abatement and carbon reduction according to claim 3, wherein the logic for obtaining the internal gas pressure anomaly hiding index is as follows:
Acquiring an optimal internal gas pressure range of the super capacitor for energy storage and smooth output, and calibrating the optimal internal gas pressure range as ；
In a window with fixed duration, acquiring a real-time internal gas pressure value of the super capacitor for energy storage and smooth output, and using the real-time internal gas pressure value as a function according to a time sequenceA representation;
calculating an internal gas pressure anomaly hiding index, wherein the calculated expression is as follows: In which, in the process, Index indicating abnormality in internal gas pressure,/>Representing that the real-time internal gas pressure value is lower than/>, when the super capacitor performs energy storage and smooth outputTime period of/>，/>Representing that the real-time internal gas pressure value is higher than/>, when the super capacitor performs energy storage and smooth outputTime period of/>。
5. The multi-objective optimization method of energy system based on pollution reduction and carbon reduction according to claim 4, wherein the comparison analysis is performed between the operation benefit coefficient generated when the super capacitor performs energy storage and smooth output and a preset operation benefit coefficient reference threshold value, and the comparison analysis results are as follows:
if the operation benefit coefficient is greater than or equal to the operation benefit coefficient reference threshold, generating a normal signal, and not sending an early warning prompt to the normal signal;
if the operation benefit coefficient is smaller than the operation benefit coefficient reference threshold, generating an abnormal signal, and sending an early warning prompt to a normal signal, wherein the early warning prompt is known by related staff.
6. The multi-objective optimization method of energy system based on pollution reduction and carbon reduction according to claim 5, wherein when the super capacitor is perceived to generate an abnormal signal in the energy storage and smooth output process, an analysis set is established by the corresponding operation benefit coefficient and a plurality of operation benefit coefficients generated subsequently when the abnormal signal is generated, and the analysis set is calibrated as I, thenF represents the number of the running benefit coefficient in the analysis set, f=1, 2, 3, 4, … …, u being a positive integer;
Comparing the operation benefit coefficient collected in the analysis set with a preset operation benefit coefficient reference threshold value, and calculating a depth analysis index The calculated expression is: /(I)In the above, the ratio of/>Representing the running benefit coefficient reference threshold,/>Representing operation benefit coefficients less than an operation benefit coefficient reference threshold in the analysis set,/>Representing the running benefit coefficient reference threshold,/>Number representing an operation benefit coefficient within the analysis set that is less than the operation benefit coefficient reference threshold,/>，/>Is a positive integer, and/>。
7. The energy system multi-objective optimization method based on pollution reduction and carbon reduction according to claim 6, wherein depth analysis indexes generated in analysis sets are calculatedWith a preset gradient reference threshold/>And/>The comparison is carried out,The results of the comparison are as follows:
If it is Generating a first-level abnormal signal, carrying out first-level prompt on the first-level abnormal signal through a mobile terminal, and indicating that sudden abnormality exists when the super capacitor carries out energy storage and smooth output when the super capacitor senses that abnormality exists in the energy storage and smooth output process and carries out deep analysis on the abnormality to generate the first-level abnormal signal;
If it is Generating a secondary abnormal signal, carrying out secondary prompt on the secondary abnormal signal through a mobile terminal, and indicating that the super capacitor has an abnormal operation instability when carrying out energy storage and smooth output when sensing that the super capacitor has an abnormality in the energy storage and smooth output process and carrying out deep analysis on the abnormality to generate the secondary abnormal signal;
If it is And generating a three-level abnormal signal, carrying out three-level prompt on the three-level abnormal signal through a mobile terminal, and indicating that continuous abnormality exists when the super capacitor carries out energy storage and smooth output when the super capacitor senses that abnormality exists in the energy storage and smooth output process and carries out deep analysis on the abnormality to generate the three-level abnormal signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410102768.3A CN117634748B (en) | 2024-01-25 | 2024-01-25 | Energy system multi-objective optimization method based on pollution reduction and carbon reduction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410102768.3A CN117634748B (en) | 2024-01-25 | 2024-01-25 | Energy system multi-objective optimization method based on pollution reduction and carbon reduction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117634748A CN117634748A (en) | 2024-03-01 |
| CN117634748B true CN117634748B (en) | 2024-04-30 |
Family
ID=90020296
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410102768.3A Active CN117634748B (en) | 2024-01-25 | 2024-01-25 | Energy system multi-objective optimization method based on pollution reduction and carbon reduction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117634748B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108512239A (en) * | 2018-05-10 | 2018-09-07 | 安徽大学 | Hybrid energy source system for electric vehicle and control strategy thereof |
| CN113610269A (en) * | 2021-06-28 | 2021-11-05 | 天津大学 | Multi-objective optimization-based rural residential building low-carbon energy system optimization method |
| CN117292515A (en) * | 2023-09-21 | 2023-12-26 | 国网江苏省电力有限公司信息通信分公司 | Power communication equipment management method and system based on power Internet of things |
| CN117439133A (en) * | 2023-12-21 | 2024-01-23 | 山东省科学院能源研究所 | Public building renewable energy source multi-energy complementary system based on load prediction algorithm |
-
2024
- 2024-01-25 CN CN202410102768.3A patent/CN117634748B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108512239A (en) * | 2018-05-10 | 2018-09-07 | 安徽大学 | Hybrid energy source system for electric vehicle and control strategy thereof |
| CN113610269A (en) * | 2021-06-28 | 2021-11-05 | 天津大学 | Multi-objective optimization-based rural residential building low-carbon energy system optimization method |
| CN117292515A (en) * | 2023-09-21 | 2023-12-26 | 国网江苏省电力有限公司信息通信分公司 | Power communication equipment management method and system based on power Internet of things |
| CN117439133A (en) * | 2023-12-21 | 2024-01-23 | 山东省科学院能源研究所 | Public building renewable energy source multi-energy complementary system based on load prediction algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117634748A (en) | 2024-03-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation | |
| CN103165201A (en) | Nuclear power station power supply detection system and method | |
| WO2014078830A2 (en) | Profiling transformer of power system | |
| CN117439133B (en) | Public building renewable energy source multi-energy complementary system based on load prediction algorithm | |
| CN115954989A (en) | Energy storage power station operation monitoring management system | |
| CN104297577A (en) | Super capacitor-based aging state estimation detection system and method | |
| CN111929579B (en) | Generator online fault diagnosis method and device and computer device | |
| CN103310296A (en) | Operation ticket security check method based on disturbance evaluation and trend analysis | |
| CN109559019A (en) | A kind of Electrical Power System Dynamic safety evaluation method based on risk index | |
| CN117474319A (en) | Risk assessment method and system for electric power system | |
| CN113746425B (en) | Photovoltaic inverter parameter anomaly analysis method and system | |
| CN117634748B (en) | Energy system multi-objective optimization method based on pollution reduction and carbon reduction | |
| CN111999608B (en) | Monitoring method and monitoring system of distribution transformer and intelligent terminal | |
| CN104714179A (en) | Method for estimating battery life based on alternating-current impedance spectrum | |
| CN116565354A (en) | Fault grading diagnosis early warning method, system and equipment for electrochemical energy storage system | |
| Atmaja et al. | Component degradation and system deterioration: An overview of early termination of PV-DG microgrid system | |
| CN109633329B (en) | Fault detection method and device for multi-loop energy storage equipment, terminal equipment and medium | |
| CN107346519B (en) | Failure calculation method for total consumption of renewable energy in integrated energy system | |
| Todeschini et al. | Forecasting the impact of renewable energy sources on transmission grid power quality | |
| Rajan et al. | Design and Simulation of Battery Management System for Electric Vehicles | |
| Chen et al. | A Collaborative Design and Modularized Assembly for Prefabricated Cabin Type Energy Storage System With Effective Safety Management | |
| CN117578464B (en) | Highway multi-source power supply system and method | |
| Wang et al. | A detection method for response time of overall primary frequency regulation of power grid | |
| Zhang et al. | Design and Application of Active Monitoring System for Secondary Equipment in Substation | |
| Yi et al. | Evaluation of Distribution Transformer Operation Status under Big Data Analysis Technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |