CN117439133A - Public building renewable energy source multi-energy complementary system based on load prediction algorithm - Google Patents
Public building renewable energy source multi-energy complementary system based on load prediction algorithm Download PDFInfo
- Publication number
- CN117439133A CN117439133A CN202311764292.1A CN202311764292A CN117439133A CN 117439133 A CN117439133 A CN 117439133A CN 202311764292 A CN202311764292 A CN 202311764292A CN 117439133 A CN117439133 A CN 117439133A
- Authority
- CN
- China
- Prior art keywords
- time
- hidden danger
- energy
- super capacitor
- value
- 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.)
- Granted
Links
- 230000000295 complement effect Effects 0.000 title claims abstract description 21
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 16
- 239000003990 capacitor Substances 0.000 claims abstract description 107
- 238000012423 maintenance Methods 0.000 claims abstract description 58
- 238000004146 energy storage Methods 0.000 claims abstract description 35
- 238000004458 analytical method Methods 0.000 claims abstract description 27
- 230000004044 response Effects 0.000 claims description 86
- 238000007726 management method Methods 0.000 claims description 55
- 238000011156 evaluation Methods 0.000 claims description 51
- 238000000034 method Methods 0.000 claims description 7
- 238000012163 sequencing technique Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000012502 risk assessment Methods 0.000 claims description 3
- 238000004148 unit process Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 abstract description 4
- 230000008859 change Effects 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 6
- 238000005259 measurement Methods 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000003792 electrolyte Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 206010000117 Abnormal behaviour Diseases 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J15/00—Systems for storing electric energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J2207/50—Charging of capacitors, supercapacitors, ultra-capacitors or double layer capacitors
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Emergency Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Human Computer Interaction (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a public building renewable energy source multi-energy complementary system based on a load prediction algorithm, which relates to the technical field of renewable energy source multi-energy complementation and comprises an energy storage module, an energy release module, a power grid frequency modulation module, an acquisition unit, a load prediction model building unit, an analysis unit and an alarm unit; and the energy storage module is used for storing the electric energy generated by the renewable energy source. According to the invention, through monitoring the operation state of the super capacitor, when the hidden danger that the super capacitor cannot effectively store and release electric energy exists during operation, an alarm prompt is sent out to inform relevant staff of the situation, and relevant maintenance management work is carried out on the super capacitor in advance, so that the system is ensured not to have problems in terms of energy balance due to the fact that the super capacitor cannot effectively store and release electric energy, and meanwhile, the stability of electric power supply is not influenced, and normal and efficient operation of key equipment in a building is ensured.
Description
Technical Field
The invention relates to the technical field of renewable energy source multi-energy complementation, in particular to a public building renewable energy source multi-energy complementation system based on a load prediction algorithm.
Background
The renewable energy source multi-energy complementary system of the public building is characterized in that a plurality of renewable energy source technologies are applied to the public building, and the aims of high-efficiency utilization and environmental protection of energy sources are achieved through complementation and integration. Such systems typically include multiple sources of energy, such as solar, wind, geothermal, etc., that complement the deficiencies of the various sources of energy in a complementary manner to improve the stability and sustainability of the energy.
Super capacitors play an important role in renewable energy source multi-energy complementary systems of public buildings. Firstly, the super capacitor can store and release electric energy rapidly, in a renewable energy system, energy generation is not necessarily matched with energy demand in real time, and the super capacitor can serve as energy storage equipment to store the electric energy generated in a high-capacity period for release when required, so that energy balance and peak shaving are realized. Second, supercapacitors have a relatively high power density, and are capable of providing high power output in situations where large amounts of electrical energy are instantaneously required. This is very important for handling sudden high energy demands or energy demands that cope with rapid changes in the system, for example in case of building equipment start-up, transient load fluctuations, etc. Finally, the super capacitor can be used for improving the electric energy quality between the building and the power grid, helping to smooth the fluctuation of the power grid and providing the stability of the electric energy quality, thereby ensuring the stable operation of the system under different working states.
The prior art has the following defects: when the super capacitor cannot effectively store and release electric energy during operation, the prior art cannot know the situation in time, when the situation happens, the situation can seriously influence the meeting of the system on energy requirements, particularly, under the condition that high power output is required instantaneously or the battery cannot meet the high energy requirements, the system can cause problems in terms of energy balance, the stability of power supply is influenced, key equipment in a building can possibly not normally operate, and the continuous serious influence can be caused on the key equipment in the building, so that the service life of the key equipment in the building is greatly reduced.
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 public building renewable energy source multifunctional complementary system based on a load prediction algorithm, by monitoring the operation state of a super capacitor, when hidden danger that electric energy cannot be effectively stored and released exists when the super capacitor operates, an alarm prompt is sent out to inform relevant staff of the situation, relevant maintenance management work is carried out on the super capacitor in advance, the system is ensured not to have problems in terms of energy balance due to the fact that the super capacitor cannot effectively store and release electric energy, meanwhile, the stability of power supply is not influenced, and normal and efficient operation of key equipment in a building is ensured, so that the problems in the background technology are solved.
In order to achieve the above object, the present invention provides the following technical solutions: the public building renewable energy source multi-energy complementary system based on the load prediction algorithm comprises an energy storage module, an energy release module, a power grid frequency modulation module, an acquisition unit, a load prediction model building unit, an analysis unit and an alarm unit;
the energy storage module is used for storing electric energy generated by renewable energy sources;
an energy release module for rapidly releasing stored electric energy to meet the demand when the system requires additional power, high power output is instantaneously required, or the battery cannot meet the high energy demand;
the power grid frequency modulation module is used for balancing power fluctuation on a power grid;
the collecting unit is used for collecting multiple information including energy storage release state information and frequency modulation dynamic response information when the super capacitor operates, and after the information is collected, the energy storage release state information and the frequency modulation dynamic response information are processed and then uploaded to the load prediction model building unit;
the load prediction model building unit is used for building a load prediction model from the energy storage release state information and the frequency modulation dynamic response information which are processed during the operation of the supercapacitor, generating a hidden danger assessment index and transmitting the hidden danger assessment index to the analysis unit;
the analysis unit is used for comparing and analyzing the hidden danger evaluation index generated during the operation of the supercapacitor with a preset hidden danger evaluation index reference threshold value, generating a high operation risk signal or a low operation risk signal, transmitting the signals to the alarm unit, and sending an alarm prompt to the high operation risk signal through the alarm unit.
Preferably, the energy storage release state information during the operation of the super capacitor comprises a capacitance value variation coefficient and a voltage and current limit exceeding coefficient, the frequency modulation dynamic response information during the operation of the super capacitor comprises a frequency response instability coefficient, and after the acquisition, the acquisition unit respectively calibrates the capacitance value variation coefficient and the voltage and current limit exceeding coefficient asAnd->The frequency response instability coefficient is calibrated to +.>。
Preferably, the logic for obtaining the capacitance value variation coefficient is as follows:
s101, acquiring a real-time capacitance value of the super capacitor in Q time during operation, and calibrating the real-time capacitance value as，kA number representing a number of real-time capacitance values acquired during Q time when the supercapacitor is running,，Pis a positive integer;
s102, establishing a data set of real-time capacitance values acquired in Q time when the super capacitor operates, sequencing the real-time capacitance values in the data set according to the sequence, and recalibrating the real-time capacitance values after sequencing into valuesThen->Numbers representing sequentially ordered real-time capacitance values in the data set, +.>，PIs a positive integer;
s103, meterCalculating a capacitance value variation coefficient, wherein the calculated expression is as follows:in which, in the process,Prepresenting the total number of real-time capacitance values acquired during Q time when the supercapacitor is running.
Preferably, the logic for the voltage current limit excess factor acquisition is as follows:
s201, acquiring a voltage limit value and a current limit value of the super capacitor during operation, and calibrating the voltage limit value and the current limit value as respectivelyAnd->；
S202, acquiring a real-time voltage value and a real-time current value of the super capacitor in Q time during operation, and respectively calibrating the real-time voltage value and the real-time current value asAnd->；
S203, calculating a voltage and current limit exceeding coefficient, wherein the calculated expression is as follows:wherein->Representing the period when the real-time voltage value is greater than the voltage limit value in Q time when the super capacitor is in operation, +.>Indicating a period of time when the real-time current value is greater than the current limit value within the Q time when the supercapacitor is operating, +.>，/>。
Preferably, the logic for frequency response instability coefficient acquisition is as follows:
s301, acquiring a plurality of actual frequency response durations generated in Q time when the super capacitor operates, and calibrating the actual frequency response durations as，xNumber representing a number of actual frequency response durations generated during Q time when the supercapacitor is in operation,/->，NIs a positive integer;
s302, calculating a frequency response time standard deviation and a frequency response time average value through a plurality of actual frequency response time acquired in Q time when the super capacitor is in operation, and respectively calibrating the frequency response time standard deviation and the frequency response time average value asAnd->Then: />Wherein->；
S303, calculating the standard deviation of the frequency response duration in the Q time when the super capacitor operatesAnd frequency response duration average +.>Calculating a frequency response duration variation coefficient, wherein the calculated expression is as follows: />The present invention relates to a method for manufacturing a semiconductor deviceIn (I)>Representing the frequency response duration variation coefficient;
s304, calculating a frequency response instability coefficient, wherein the calculated expression is as follows:。
preferably, the load prediction model building unit processes the capacitance value variation coefficient of the supercapacitor during operationVoltage-current limit excess factor->Frequency response instability coefficient +.>Then, a load prediction model is established, and a hidden danger assessment index is generated>The formula according to is: />Wherein->、/>、/>Capacitance value variation coefficients ∈ ->Voltage-current limit excess factor->Frequency response instability coefficient +.>Is a preset proportionality coefficient of>、/>、/>Are all greater than 0.
Preferably, if the hidden danger assessment index is greater than or equal to the hidden danger assessment index reference threshold, a high running risk signal is generated, the signal is transmitted to an alarm unit, an alarm prompt is sent out through the alarm unit, and relevant staff are informed of the situation;
if the hidden danger assessment index is smaller than the hidden danger assessment index reference threshold, a low running risk signal is generated and transmitted to the alarm unit, and an alarm prompt is not sent out through the alarm unit.
Preferably, the system further comprises a maintenance management module;
the maintenance management module comprehensively analyzes hidden danger assessment indexes output in real time through the load prediction model building unit during maintenance management of the super capacitor, prompts information fed back during maintenance management through the mobile terminal, and ensures that the super capacitor is successfully maintained and managed.
Preferably, the maintenance management module establishes an analysis set for hidden danger assessment indexes output in real time by the load prediction model establishment unit during maintenance management of the supercapacitor, and calibrates the analysis set asIThen，/>Number indicating risk assessment index in analysis set, +.>，uIs a positive integer;
calculating a standard deviation of the hidden danger evaluation index and an average value of the hidden danger evaluation index through analyzing hidden danger evaluation indexes in a set, and respectively comparing the standard deviation of the hidden danger evaluation index and the average value of the hidden danger evaluation index with a preset standard deviation reference threshold value and a preset hidden danger evaluation index reference threshold value to obtain the following comparison analysis results:
if the average value of the hidden danger evaluation indexes is larger than or equal to the reference threshold value of the hidden danger evaluation indexes, generating a maintenance management failure signal, transmitting the signal to the mobile terminal, and prompting through the mobile terminal;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is larger than or equal to the reference threshold value of the standard deviation, generating a maintenance management unstable signal, transmitting the signal to the mobile terminal, and prompting through the mobile terminal;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is smaller than the reference threshold value of the standard deviation, a maintenance management success signal is generated, the signal is transmitted to the mobile terminal, and prompt is carried out through the mobile terminal.
In the technical scheme, the invention has the technical effects and advantages that:
according to the invention, through monitoring the operation state of the super capacitor, when the hidden danger that electric energy cannot be effectively stored and released exists during the operation of the super capacitor, an alarm prompt is sent out to inform relevant staff of knowing the situation, and relevant maintenance management work is carried out on the super capacitor in advance, so that the system is ensured not to have problems in terms of energy balance due to the fact that the super capacitor cannot effectively store and release electric energy, and meanwhile, the stability of electric power supply is not influenced, and normal and efficient operation of key equipment in a building is ensured;
according to the invention, the running state of the supercapacitor during maintenance management is comprehensively analyzed, so that the real-time maintenance management condition of the supercapacitor is judged, when a signal of failure in maintenance management or unstable maintenance management is generated during the maintenance management of the supercapacitor, the supercapacitor is continuously maintained, the success of the maintenance management of the supercapacitor is ensured, and the situation that the running state of the supercapacitor is abnormal again after the maintenance management of the supercapacitor is judged through manual experience can be avoided.
Drawings
For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.
FIG. 1 is a schematic diagram of a public building renewable energy multi-energy complementary system based on a load prediction algorithm.
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 public building renewable energy source multi-energy complementary system based on a load prediction algorithm as shown in fig. 1, which comprises an energy storage module, an energy release module, a power grid frequency modulation module, an acquisition unit, a load prediction model building unit, an analysis unit and an alarm unit;
the energy storage module is used for storing electric energy generated by renewable energy sources (such as solar energy, wind energy and the like);
this is a critical step in the system to store the energy when it is generated in excess, for future use.
An energy release module for rapidly releasing stored electric energy to meet the demand when the system requires additional power, high power output is instantaneously required, or the battery cannot meet the high energy demand;
the stored electric energy is released rapidly, so that the system can respond to the high-energy requirement rapidly, and the requirements of various devices in the building are met.
The power grid frequency modulation module is used for balancing power fluctuation on a power grid;
balancing power fluctuations on the grid may increase the stability of the grid, which is important to increase the overall permeability of the renewable energy source and the sustainability of the renewable energy source to generate electricity.
The collecting unit is used for collecting multiple information (including energy storage and release state information and frequency modulation dynamic response information) of the supercapacitor during operation (energy storage, release and power grid frequency modulation), and after the information is collected, the energy storage and release state information and the frequency modulation dynamic response information are processed and then uploaded to the load prediction model building unit;
the energy storage release state information of the super capacitor during operation comprises a capacitance value variation coefficient and a voltage and current limit excess coefficient, and after acquisition, the acquisition unit respectively calibrates the capacitance value variation coefficient and the voltage and current limit excess coefficient asAnd->；
The capacitance value of a supercapacitor refers to the capacity of the supercapacitor to store charges, usually in faraday (F for short), and describes the amount of charge that the supercapacitor can store at a unit voltage, and is a key parameter of the performance of the supercapacitor, unlike a traditional battery, the supercapacitor stores energy in the form of an electric field, rather than through chemical reaction as in a battery, and the capacitance value of the supercapacitor determines the energy stored and released, so that the capacitance value is an important index for evaluating the performance of the supercapacitor, and the supercapacitor can store more charges as the capacity value is larger, so that more electric energy can be released when needed.
When the fluctuation of the capacitance value in the supercapacitor is large, the effective storage and release of electric energy can be affected, and the following are some causes which can cause problems:
capacitance value fluctuations lead to instability: the performance of the super capacitor is influenced by the capacitance value, when the capacitance value fluctuates greatly, instability of the system can occur in the aspects of electric energy storage and release, and the instability can be represented as problems of reduced energy storage efficiency, inconsistent electric energy release rate and the like;
the system is difficult to control: the fluctuation of the capacitance value of the super capacitor can cause the increase of the control difficulty of the system, the control system generally needs to adjust the storage and release of energy according to the actual capacitance value of the capacitor, if the fluctuation of the capacitance value is large, the system needs to adjust more accurately and timely, otherwise, the expected performance can not be achieved;
unstable energy storage: the capacity value of the super capacitor is directly related to the capacity of energy storage, the fluctuation of the capacity value is large, the unstable storage of energy can be caused, and if the capacity value is suddenly high or suddenly low, the system can not accurately estimate the current energy storage state of the super capacitor, so that a reasonable energy storage strategy is influenced;
the energy storage efficiency is reduced: when the super capacitor is charged and discharged, the fluctuation of the capacitance value can cause the reduction of energy storage efficiency, the energy storage and release efficiency is directly related to the stability of the capacitance value, and if the fluctuation of the capacitance value is large, the system can not realize optimal energy transmission, so that the energy loss is increased;
therefore, the capacitor value of the supercapacitor during operation is monitored, and the potential hazard that the supercapacitor cannot effectively store and release electric energy can be found out in time due to the fact that the capacitor value of the supercapacitor is changed greatly.
The logic for obtaining the capacitance value variation coefficient is as follows:
s101, acquiring a real-time capacitance value of the super capacitor in Q time during operation, and calibrating the real-time capacitance value as，kA number representing a number of real-time capacitance values acquired during Q time when the supercapacitor is running,，Pis a positive integer;
it should be noted that, by applying a small ac current to the supercapacitor and then measuring the voltage response, the capacitance value can be calculated using a current-voltage method, which obtains the capacitance value by measuring the phase difference and amplitude of the voltage and the current, and real-time current and voltage measurement can be accomplished by specially designed current sensors and voltage sensors;
s102, establishing a data set of real-time capacitance values acquired in Q time when the super capacitor operates, sequencing the real-time capacitance values in the data set according to the sequence, and recalibrating the real-time capacitance values after sequencing into valuesThen->Numbers representing sequentially ordered real-time capacitance values in the data set, +.>，PIs a positive integer;
s103, calculating a capacitance value variation coefficient, wherein the calculated expression is as follows:in which, in the process,Prepresenting the total number of real-time capacitance values acquired during Q time when the supercapacitor is running.
According to the calculation expression of the capacitance value variation coefficient, the larger the expression value of the capacitance value variation coefficient generated in the Q time when the supercapacitor operates, the larger the hidden danger that the supercapacitor cannot effectively store and release electric energy is indicated, and the smaller the hidden danger that the supercapacitor cannot effectively store and release electric energy is indicated.
Supercapacitors are electronic devices capable of efficiently storing and releasing electrical energy, and are often used in renewable energy multi-energy complementary systems, however, during use, if the voltage or current exceeds the design limits of the supercapacitors, they may not be able to efficiently store and release electrical energy, and the effects of these two conditions are described in detail below:
voltage limit exceeded: supercapacitors have a certain voltage limit, exceeding which can lead to reduced performance or even damage to the capacitor, and when the voltage exceeds the limit, the dielectric inside the capacitor can suffer from breakdown, resulting in the capacitor losing a part or all of its energy storage capacity, and exceeding the voltage limit can also cause instability of the electrolyte, resulting in a change in the internal structure of the capacitor, reducing its lifetime;
the current limit is exceeded: the super capacitor also has a limit of maximum allowable current, when the current exceeds the limit, heat in the capacitor is caused to rise sharply, the internal structure of the capacitor is possibly damaged, the capacitor is further caused to lose part or all of energy storage capacity, and in addition, the current exceeds the limit, the interface between the electrode and the electrolyte is possibly caused to have a problem, so that the performance of the capacitor is influenced;
therefore, the voltage and the current of the super capacitor during operation are monitored, and the potential hazards that the super capacitor cannot effectively store and release electric energy can be found out when the voltage and the current limit of the super capacitor are exceeded.
The logic for obtaining the voltage current limit exceeding coefficient is as follows:
s201, acquiring a voltage limit value and a current limit value of the super capacitor during operation, and calibrating the voltage limit value and the current limit value as respectivelyAnd->；
It should be noted that manufacturers of supercapacitors typically provide detailed specifications manuals, including voltage and current limits, which typically contain detailed information about product design, operating conditions, performance parameters, etc.;
s202, acquiring a real-time voltage value and a real-time current value of the super capacitor in Q time during operation, and respectively calibrating the real-time voltage value and the real-time current value asAnd->；
It should be noted that, voltage and current sensors installed in the supercapacitor circuit are the most common real-time measurement modes, and these sensors can directly measure the real-time voltage value and the real-time current value in the circuit and transmit these data to the acquisition unit;
s203, calculating a voltage and current limit exceeding coefficient, wherein the calculated expression is as follows:wherein->Representing the period when the real-time voltage value is greater than the voltage limit value in Q time when the super capacitor is in operation, +.>Indicating a period of time when the real-time current value is greater than the current limit value within the Q time when the supercapacitor is operating, +.>，/>。
According to the calculation expression of the voltage and current limit exceeding coefficient, the larger the expression value of the voltage and current limit exceeding coefficient generated in the Q time when the super capacitor operates, the larger the hidden danger that the super capacitor cannot effectively store and release electric energy is indicated, and the smaller the hidden danger that the super capacitor cannot effectively store and release electric energy is indicated.
The frequency modulation dynamic response information of the supercapacitor in operation comprises a frequency response instability coefficient, and after acquisition, the acquisition unit calibrates the frequency response instability coefficient as；
In a renewable energy source multi-energy complementary system of a public building, the frequency response refers to the rapid adjustment and response capability of a super capacitor to the frequency change of a power grid, the frequency modulation of the power grid is usually used for coping with transient load change, renewable energy source fluctuation or other system disturbance so as to maintain the stability of the frequency of the power grid, the frequency modulation of the power grid is used for maintaining the adjustment of the frequency of alternating current in a power system at a standard value (usually 50 hertz or 60 hertz), the super capacitor plays an important role in the process, and the frequency response characteristic of the super capacitor influences the adaptability of the system to the frequency change of the power grid;
if the super capacitor has poor frequency response stability when performing grid frequency modulation, the super capacitor can be influenced to effectively store and release electric energy, and the following is the reason for elaborating this:
frequency instability affects electrical energy storage and release: the poor stability of the frequency response of the super capacitor may cause the super capacitor to be unable to effectively follow the change of the frequency of the power grid, in the power system, the rapid fluctuation of the frequency may be caused by load change, renewable energy fluctuation and other reasons, if the super capacitor cannot be adjusted in time and stably to adapt to the frequency changes, the super capacitor may be unable to store and release electric energy at the right moment;
frequency deviation may affect system stability: the unstable frequency response can cause deviation of the super capacitor during frequency adjustment, which can further influence the stability of the whole power system, and the unstable frequency can cause abnormal behaviors of other components in the system, and finally the system is unstable;
the system requirements cannot be satisfied: in an electric power system, the requirement on frequency is usually very strict, particularly in the frequency modulation range of the electric power system, the unstable frequency response may cause that the supercapacitor cannot meet the actual requirement of the system on frequency adjustment, so that the effectiveness of the supercapacitor in the system is reduced;
lose frequency regulation function: the function of the super capacitor in grid frequency modulation is to provide rapid electric energy storage and release to maintain the stability of the grid frequency, if the frequency response is unstable, the super capacitor may not be capable of adjusting the electric energy in an expected manner, so that the system cannot effectively cope with the frequency change;
therefore, the capacitor value of the supercapacitor during operation is monitored, and the potential hazard that the supercapacitor cannot effectively store and release electric energy can be found out in time due to the fact that the capacitor value of the supercapacitor is changed greatly.
The logic for obtaining the frequency response instability coefficients is as follows:
s301, acquiring a plurality of actual frequency response durations generated in Q time when the super capacitor operates, and calibrating the actual frequency response durations as，xNumber representing a number of actual frequency response durations generated during Q time when the supercapacitor is in operation,/->，NIs a positive integer;
it should be noted that, in the prior art, frequency measurement devices are usually installed in the supercapacitor, and these frequency measurement devices are the most direct means for obtaining the actual frequency response time, and by using these frequency measurement devices, the change of the grid frequency can be measured, and the response of the supercapacitor system to these changes can be recorded;
s302, calculating a frequency response time standard deviation and a frequency response time average value through a plurality of actual frequency response time acquired in Q time when the super capacitor is in operation, and respectively calibrating the frequency response time standard deviation and the frequency response time average value asAnd->Then: />Wherein->；
S303, operating through the super capacitor at Q timeStandard deviation of frequency response time length obtained by internal calculationAnd frequency response duration average +.>Calculating a frequency response duration variation coefficient, wherein the calculated expression is as follows: />Wherein->Representing the frequency response duration variation coefficient;
coefficient of variation from frequency response durationThe calculated expression of (2) shows that the coefficient of variation of the frequency response durationThe larger the expression value of the (2) is, the worse the stability of a plurality of actual frequency response time durations generated in the Q time when the super capacitor operates is indicated, otherwise, the better the stability of a plurality of actual frequency response time durations generated in the Q time when the super capacitor operates is indicated;
s304, calculating a frequency response instability coefficient, wherein the calculated expression is as follows:；
according to the calculation expression of the frequency response instability coefficient, the larger the expression value of the frequency response instability coefficient generated in the Q time when the supercapacitor operates, the larger the hidden danger that the supercapacitor cannot effectively store and release electric energy is indicated, and the smaller the hidden danger that the supercapacitor cannot effectively store and release electric energy is indicated.
The load prediction model building unit is used for building a load prediction model from the energy storage release state information and the frequency modulation dynamic response information which are processed during the operation of the supercapacitor, generating a hidden danger assessment index and transmitting the hidden danger assessment index to the analysis unit;
the load prediction model building unit processes the capacitance value variation coefficient of the supercapacitor during operationVoltage-current limit excess factor->Frequency response instability coefficient +.>Then, a load prediction model is established, and a hidden danger assessment index is generated>The formula according to is: />Wherein->、/>、Capacitance value variation coefficients ∈ ->Voltage-current limit excess factor->Frequency response instability coefficientIs a preset proportionality coefficient of>、/>、/>Are all greater than 0;
as can be seen from the calculation formula, the larger the capacitance value variation coefficient generated in the Q time when the super capacitor operates, the larger the voltage and current limit exceeding coefficient and the larger the frequency response instability coefficient, namely the hidden danger assessment index generated in the Q time when the super capacitor operatesThe larger the expression value of the super capacitor is, the larger the hidden danger that the super capacitor cannot effectively store and release electric energy is, otherwise, the smaller the hidden danger that the super capacitor cannot effectively store and release electric energy is;
it should be noted that, the above-mentioned Q time is selected as a time period with a relatively short time, and the time in the time period is not limited herein specifically, and may be set according to practical situations, so as to monitor the operation situation of the supercapacitor in the Q time when the supercapacitor is in operation, so as to monitor the operation situation of the supercapacitor in different time periods (in Q time) when the supercapacitor is in operation in real time, and evaluate the hidden trouble situations of effectively storing and releasing electric energy of the supercapacitor.
The analysis unit is used for comparing and analyzing the hidden danger evaluation index generated during the operation of the supercapacitor with a preset hidden danger evaluation index reference threshold value, generating a high operation risk signal or a low operation risk signal, transmitting the signal to the alarm unit, and sending an alarm prompt to the high operation risk signal through the alarm unit;
the analysis unit compares the hidden danger evaluation index generated during the operation of the supercapacitor with a preset hidden danger evaluation index reference threshold value, and the comparison analysis results are as follows:
if the hidden danger assessment index is larger than or equal to the hidden danger assessment index reference threshold, a high operation risk signal is generated, the signal is transmitted to an alarm unit, an alarm prompt is sent out through the alarm unit, relevant staff is informed of the situation, when the super capacitor is operated, the high operation risk signal is generated, the hidden danger that the super capacitor cannot effectively store and release electric energy is large is shown, and the super capacitor needs to be maintained and managed in time;
if the hidden danger assessment index is smaller than the hidden danger assessment index reference threshold, a low operation risk signal is generated and transmitted to the alarm unit, an alarm prompt is not sent out through the alarm unit, and when the low operation risk signal is generated when the supercapacitor is in operation, the supercapacitor is indicated to have a good operation state, and the supercapacitor can effectively store and release electric energy.
The system also comprises a maintenance management module;
the maintenance management module is used for comprehensively analyzing hidden danger assessment indexes output in real time through the load prediction model building unit during maintenance management of the super capacitor, prompting information fed back during maintenance management through the mobile terminal and ensuring successful maintenance management of the super capacitor;
the maintenance management module establishes an analysis set for hidden danger assessment indexes output in real time through the load prediction model establishment unit during maintenance management of the supercapacitor, and marks the analysis set asIThen，/>Number indicating risk assessment index in analysis set, +.>，uIs a positive integer;
calculating a hidden danger evaluation index standard deviation and a hidden danger evaluation index average value (the calculation process of the hidden danger evaluation index standard deviation and the hidden danger evaluation index average value is not specifically described herein, and refer to the frequency response time length standard deviation and the frequency response time length average value), and comparing the hidden danger evaluation index standard deviation and the hidden danger evaluation index average value with a preset standard deviation reference threshold value and a preset hidden danger evaluation index reference threshold value respectively for analysis, wherein the comparison analysis results are as follows:
if the average value of the hidden danger evaluation indexes is larger than or equal to the reference threshold value of the hidden danger evaluation indexes, a maintenance management failure signal is generated, the signal is transmitted to the mobile terminal, the mobile terminal prompts, and when the maintenance management failure signal is generated during the maintenance management of the supercapacitor, the failure of the maintenance management during the maintenance management of the supercapacitor is indicated, and the maintenance needs to be continued;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is larger than or equal to the reference threshold value of the standard deviation, generating a maintenance management unstable signal, transmitting the signal to a mobile terminal, prompting through the mobile terminal, and indicating that the maintenance management is unstable when the super capacitor is maintained and managed when the maintenance management unstable signal is generated when the super capacitor is maintained and managed, and also needing to continue maintenance;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is smaller than the reference threshold value of the standard deviation, a maintenance management success signal is generated, the signal is transmitted to the mobile terminal, the mobile terminal is used for prompting, and when the maintenance management success signal is generated during the maintenance management of the supercapacitor, the successful maintenance management during the maintenance management of the supercapacitor is indicated.
According to the invention, through monitoring the operation state of the super capacitor, when the hidden danger that electric energy cannot be effectively stored and released exists during the operation of the super capacitor, an alarm prompt is sent out to inform relevant staff of knowing the situation, and relevant maintenance management work is carried out on the super capacitor in advance, so that the system is ensured not to have problems in terms of energy balance due to the fact that the super capacitor cannot effectively store and release electric energy, and meanwhile, the stability of electric power supply is not influenced, and normal and efficient operation of key equipment in a building is ensured;
according to the invention, the running state of the supercapacitor during maintenance management is comprehensively analyzed, so that the real-time maintenance management condition of the supercapacitor is judged, when a signal of failure in maintenance management or unstable maintenance management is generated during the maintenance management of the supercapacitor, the supercapacitor is continuously maintained, the success of the maintenance management of the supercapacitor is ensured, and the situation that the running state of the supercapacitor is abnormal again after the maintenance management of the supercapacitor is judged through manual experience can be avoided.
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 foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by 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 (9)
1. The public building renewable energy source multi-energy complementary system based on the load prediction algorithm is characterized by comprising an energy storage module, an energy release module, a power grid frequency modulation module, an acquisition unit, a load prediction model building unit, an analysis unit and an alarm unit;
the energy storage module is used for storing electric energy generated by renewable energy sources;
an energy release module for rapidly releasing stored electric energy to meet the demand when the system requires additional power, high power output is instantaneously required, or the battery cannot meet the high energy demand;
the power grid frequency modulation module is used for balancing power fluctuation on a power grid;
the collecting unit is used for collecting multiple information including energy storage release state information and frequency modulation dynamic response information when the super capacitor operates, and after the information is collected, the energy storage release state information and the frequency modulation dynamic response information are processed and then uploaded to the load prediction model building unit;
the load prediction model building unit is used for building a load prediction model from the energy storage release state information and the frequency modulation dynamic response information which are processed during the operation of the supercapacitor, generating a hidden danger assessment index and transmitting the hidden danger assessment index to the analysis unit;
the analysis unit is used for comparing and analyzing the hidden danger evaluation index generated during the operation of the supercapacitor with a preset hidden danger evaluation index reference threshold value, generating a high operation risk signal or a low operation risk signal, transmitting the signals to the alarm unit, and sending an alarm prompt to the high operation risk signal through the alarm unit.
2. The system of claim 1, wherein the energy storage and release state information of the super capacitor during operation comprises a capacitance value variation coefficient and a voltage and current limit exceeding coefficient, the frequency modulation dynamic response information of the super capacitor during operation comprises a frequency response instability coefficient, and the acquisition unit respectively calibrates the capacitance value variation coefficient and the voltage and current limit exceeding coefficient asAnd->The frequency response instability coefficient is calibrated to +.>。
3. The load prediction algorithm-based public building renewable energy multi-energy complementary system according to claim 2, wherein the logic for obtaining the capacitance value variation coefficient is as follows:
s101, acquiring a real-time capacitance value of the super capacitor in Q time during operation, and calibrating the real-time capacitance value as，kA number representing a number of real-time capacitance values acquired during Q time when the supercapacitor is running,，Pis a positive integer;
S102establishing a data set of real-time capacitance values acquired in Q time during the operation of the super capacitor, sequencing the real-time capacitance values in the data set according to the sequence, and recalibrating the real-time capacitance values after sequencing as followsThen->Numbers representing sequentially ordered real-time capacitance values in the data set, +.>，PIs a positive integer;
s103, calculating a capacitance value variation coefficient, wherein the calculated expression is as follows:
in which, in the process,Prepresenting the total number of real-time capacitance values acquired during Q time when the supercapacitor is running.
4. A utility renewable energy complementary system based on a load prediction algorithm according to claim 3, characterized in that the logic for the voltage current limit excess coefficient acquisition is as follows:
s201, acquiring a voltage limit value and a current limit value of the super capacitor during operation, and calibrating the voltage limit value and the current limit value as respectivelyAnd->；
S202, acquiring a real-time voltage value and a real-time current value of the super capacitor in Q time during operation, and respectively calibrating the real-time voltage value and the real-time current value asAnd->；
S203, calculating a voltage and current limit exceeding coefficient, wherein the calculated expression is as follows:wherein->Representing the period when the real-time voltage value is greater than the voltage limit value in Q time when the super capacitor is in operation, +.>Indicating a period of time when the real-time current value is greater than the current limit value within the Q time when the supercapacitor is operating, +.>，/>。
5. The load prediction algorithm-based public building renewable energy multi-energy complementary system according to claim 4, wherein the logic for obtaining the frequency response instability coefficients is as follows:
s301, acquiring a plurality of actual frequency response durations generated in Q time when the super capacitor operates, and calibrating the actual frequency response durations as，xNumber representing a number of actual frequency response durations generated during Q time when the supercapacitor is in operation,/->，NIs a positive integer;
s302, through super capacitorCalculating a frequency response time standard deviation and a frequency response time average value of a plurality of actual frequency response time acquired in the Q time during operation, and respectively calibrating the frequency response time standard deviation and the frequency response time average value asAnd->Then: />Wherein->；
S303, calculating the standard deviation of the frequency response duration in the Q time when the super capacitor operatesAnd frequency response duration average +.>Calculating a frequency response duration variation coefficient, wherein the calculated expression is as follows: />Wherein->Representing the frequency response duration variation coefficient;
s304, calculating a frequency response instability coefficient, wherein the calculated expression is as follows:。
6. the load prediction algorithm-based public building renewable energy multi-energy complementary system according to claim 5, wherein the load prediction model isThe building unit processes the capacitance value variation coefficient of the super capacitor during operationVoltage-current limit excess factor->Frequency response instability coefficient +.>Then, a load prediction model is established, and a hidden danger assessment index is generated>The formula according to is: />Wherein->、/>、/>Capacitance value variation coefficients ∈ ->Voltage-current limit excess factor->Frequency response instability coefficientIs a preset proportionality coefficient of>、/>、/>Are all greater than 0.
7. The public building renewable energy multi-energy complementary system based on the load prediction algorithm according to claim 6, wherein the analysis unit compares the hidden danger assessment index generated during the operation of the supercapacitor with a preset hidden danger assessment index reference threshold value, and the comparison analysis results are as follows:
if the hidden danger assessment index is greater than or equal to the hidden danger assessment index reference threshold, a high running risk signal is generated, the signal is transmitted to an alarm unit, an alarm prompt is sent out through the alarm unit, and relevant staff are informed of the situation;
if the hidden danger assessment index is smaller than the hidden danger assessment index reference threshold, a low running risk signal is generated and transmitted to the alarm unit, and an alarm prompt is not sent out through the alarm unit.
8. The load prediction algorithm-based public building renewable energy multi-energy complementary system according to claim 7, further comprising a maintenance management module;
the maintenance management module comprehensively analyzes hidden danger assessment indexes output in real time through the load prediction model building unit during maintenance management of the super capacitor, prompts information fed back during maintenance management through the mobile terminal, and ensures that the super capacitor is successfully maintained and managed.
9. The public building renewable energy multi-energy complementary system based on the load prediction algorithm according to claim 8, wherein the maintenance management module establishes an analysis set for hidden danger assessment indexes output in real time by the load prediction model establishment unit during maintenance management of the supercapacitor, and marks the analysis set asIThen，/>Number indicating risk assessment index in analysis set, +.>，uIs a positive integer;
calculating a standard deviation of the hidden danger evaluation index and an average value of the hidden danger evaluation index through analyzing hidden danger evaluation indexes in a set, and respectively comparing the standard deviation of the hidden danger evaluation index and the average value of the hidden danger evaluation index with a preset standard deviation reference threshold value and a preset hidden danger evaluation index reference threshold value to obtain the following comparison analysis results:
if the average value of the hidden danger evaluation indexes is larger than or equal to the reference threshold value of the hidden danger evaluation indexes, generating a maintenance management failure signal, transmitting the signal to the mobile terminal, and prompting through the mobile terminal;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is larger than or equal to the reference threshold value of the standard deviation, generating a maintenance management unstable signal, transmitting the signal to the mobile terminal, and prompting through the mobile terminal;
if the average value of the hidden danger evaluation indexes is smaller than the reference threshold value of the hidden danger evaluation indexes and the standard deviation of the hidden danger evaluation indexes is smaller than the reference threshold value of the standard deviation, a maintenance management success signal is generated, the signal is transmitted to the mobile terminal, and prompt is carried out through the mobile terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311764292.1A CN117439133B (en) | 2023-12-21 | 2023-12-21 | Public building renewable energy source multi-energy complementary system based on load prediction algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311764292.1A CN117439133B (en) | 2023-12-21 | 2023-12-21 | Public building renewable energy source multi-energy complementary system based on load prediction algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117439133A true CN117439133A (en) | 2024-01-23 |
| CN117439133B CN117439133B (en) | 2024-05-03 |
Family
ID=89546435
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311764292.1A Active CN117439133B (en) | 2023-12-21 | 2023-12-21 | Public building renewable energy source multi-energy complementary system based on load prediction algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117439133B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117634748A (en) * | 2024-01-25 | 2024-03-01 | 中国环境科学研究院 | Energy system multi-objective optimization method based on pollution reduction and carbon reduction |
| CN117952569A (en) * | 2024-03-27 | 2024-04-30 | 山东省科学院能源研究所 | Public building collaborative energy supply management system based on multisource renewable energy sources |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115144680A (en) * | 2022-09-02 | 2022-10-04 | 深圳市今朝时代股份有限公司 | Super-capacitor energy storage system and method for auxiliary frequency modulation |
| WO2023108384A1 (en) * | 2021-12-14 | 2023-06-22 | 浙江大学台州研究院 | Comprehensive identification index-based energy storage multi-scenario application switching control method |
| CN116418119A (en) * | 2023-04-25 | 2023-07-11 | 江苏安世朗智能科技有限公司 | Three-phase capacitance intelligent monitoring control system |
| CN116599151A (en) * | 2023-05-15 | 2023-08-15 | 国网湖北省电力有限公司孝感供电公司 | Source network storage safety management method based on multi-source data |
| CN117171366A (en) * | 2023-11-03 | 2023-12-05 | 国网信息通信产业集团有限公司 | Knowledge graph construction method and system for power grid dispatching operation situation |
-
2023
- 2023-12-21 CN CN202311764292.1A patent/CN117439133B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023108384A1 (en) * | 2021-12-14 | 2023-06-22 | 浙江大学台州研究院 | Comprehensive identification index-based energy storage multi-scenario application switching control method |
| CN115144680A (en) * | 2022-09-02 | 2022-10-04 | 深圳市今朝时代股份有限公司 | Super-capacitor energy storage system and method for auxiliary frequency modulation |
| CN116418119A (en) * | 2023-04-25 | 2023-07-11 | 江苏安世朗智能科技有限公司 | Three-phase capacitance intelligent monitoring control system |
| CN116599151A (en) * | 2023-05-15 | 2023-08-15 | 国网湖北省电力有限公司孝感供电公司 | Source network storage safety management method based on multi-source data |
| CN117171366A (en) * | 2023-11-03 | 2023-12-05 | 国网信息通信产业集团有限公司 | Knowledge graph construction method and system for power grid dispatching operation situation |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117634748A (en) * | 2024-01-25 | 2024-03-01 | 中国环境科学研究院 | Energy system multi-objective optimization method based on pollution reduction and carbon reduction |
| CN117634748B (en) * | 2024-01-25 | 2024-04-30 | 中国环境科学研究院 | Energy system multi-objective optimization method based on pollution reduction and carbon reduction |
| CN117952569A (en) * | 2024-03-27 | 2024-04-30 | 山东省科学院能源研究所 | Public building collaborative energy supply management system based on multisource renewable energy sources |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117439133B (en) | 2024-05-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117439133B (en) | Public building renewable energy source multi-energy complementary system based on load prediction algorithm | |
| AU2017202918B2 (en) | Management of battery capacity | |
| US9106104B2 (en) | Power control device, power control method, and power supply system | |
| Panhwar et al. | Mitigating power fluctuations for energy storage in wind energy conversion system using supercapacitors | |
| JP5520365B2 (en) | System stabilization system, power supply system, centralized management device control method, and centralized management device program | |
| US20110077792A1 (en) | Method for controlling distributed power sources | |
| KR101566296B1 (en) | Frequency Control System in Power System | |
| KR20190000992A (en) | Energy storage system battery enable module management and control of battery | |
| JP6614010B2 (en) | Battery system | |
| WO2011078215A1 (en) | Electric-power supply method, computer-readable recording medium, and electric-power generating system | |
| CN104505907A (en) | Monitoring device of battery energy storage system with reactive adjusting function | |
| CN110661250B (en) | Reliability evaluation method and system for wind-solar energy storage and power generation power transmission system | |
| KR101464411B1 (en) | System and method for managementing electric power | |
| JP2013219941A (en) | Control method and control device of power generation system using renewable energy | |
| CN114384432A (en) | Energy storage system and detection method thereof | |
| KR20200144726A (en) | Method for estimating the capacity of an energy storage system associated with renewable energy and apparatus therefor | |
| CN104538981A (en) | Monitoring method for battery energy storage system with reactive power regulation function | |
| JP2018007323A (en) | Power variation control device and method | |
| JP6768571B2 (en) | Power controller, method and power generation system | |
| Jusoh et al. | Control strategy of a grid-connected photovoltaic with battery energy storage system for hourly power dispatch | |
| CN113381496A (en) | Hybrid energy storage system limit value management control method | |
| CN107069821B (en) | Optimized smooth operation method for micro-grid | |
| TW202226701A (en) | Power regulation method and power regulation system having the discharge decision machine learning model indicating that the power of the stored electrical energy is inversely proportional to the system frequency | |
| Jia et al. | Modeling and Control of Wind Storage Microgrid in Grid-connected and Off-grid Mode | |
| Rezaei et al. | Reliability Calculation Improvement of Electrolytic Capacitor-Banks Used in Energy Storage Applications Based on Internal Capacitor Faults and Degradation |
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 |