CN116633265A - Solar photovoltaic electric energy storage supervisory systems based on artificial intelligence - Google Patents
Solar photovoltaic electric energy storage supervisory systems based on artificial intelligence Download PDFInfo
- Publication number
- CN116633265A CN116633265A CN202310648440.7A CN202310648440A CN116633265A CN 116633265 A CN116633265 A CN 116633265A CN 202310648440 A CN202310648440 A CN 202310648440A CN 116633265 A CN116633265 A CN 116633265A
- Authority
- CN
- China
- Prior art keywords
- storage
- power generation
- solar photovoltaic
- electricity
- analysis
- 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.)
- Pending
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 22
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 16
- 238000003860 storage Methods 0.000 claims abstract description 132
- 238000010248 power generation Methods 0.000 claims abstract description 105
- 238000004458 analytical method Methods 0.000 claims abstract description 62
- 238000000034 method Methods 0.000 claims abstract description 48
- 230000005611 electricity Effects 0.000 claims description 97
- 238000007667 floating Methods 0.000 claims description 50
- 238000012544 monitoring process Methods 0.000 claims description 35
- 230000007613 environmental effect Effects 0.000 claims description 17
- 230000005540 biological transmission Effects 0.000 claims description 7
- 230000000630 rising effect Effects 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 238000005286 illumination Methods 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 241001270131 Agaricus moelleri Species 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000003891 environmental analysis Methods 0.000 description 2
- 230000002349 favourable effect Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 238000013211 curve analysis Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas 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
- 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
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S10/00—PV power plants; Combinations of PV energy systems with other systems for the generation of electric power
- H02S10/20—Systems characterised by their energy storage means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S40/00—Components or accessories in combination with PV modules, not provided for in groups H02S10/00 - H02S30/00
- H02S40/30—Electrical components
-
- 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
Abstract
The application discloses a solar photovoltaic electric energy storage supervision system based on artificial intelligence, which relates to the technical field of electric energy storage supervision, solves the technical problem that in the prior art, solar photovoltaic electric energy storage process cannot be supervised according to three stages before power generation, during storage and after storage, a pre-storage analysis module analyzes the solar photovoltaic power generation before operation, and after the analysis of real-time power generation before storage is completed, determines that the solar photovoltaic power generation operation can be performed after the storage is completed, and stores the real-time generated electric energy after the electric energy is generated by the solar photovoltaic power generation.
Description
Technical Field
The application relates to the technical field of electric energy storage supervision, in particular to a solar photovoltaic electric energy storage supervision system based on artificial intelligence.
Background
Solar photovoltaic systems, also known as photovoltaic, for short, refer to facilities that utilize the photovoltaic effect of photovoltaic semiconductor materials to convert solar energy into direct current electrical energy. The core of the photovoltaic facility is a solar panel; the energy storage technology is a technology for effectively utilizing energy and improving the overall efficiency of energy production systems in the process from energy production to consumption, and is one of the main links of energy saving technology.
However, in the prior art, solar photovoltaic power generation cannot be compared with current environmental analysis according to historical environmental analysis of a current area before execution, so that whether power generation is executed in a current period cannot be accurately judged, and power generation stability of power generation equipment cannot be guaranteed;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The application aims to provide a solar photovoltaic electric energy storage monitoring system based on artificial intelligence so as to solve the technical problems.
In order to achieve the above purpose, the application adopts the following technical scheme:
the solar photovoltaic electric energy storage monitoring system based on artificial intelligence comprises a pre-storage analysis module, a during-storage monitoring module and a post-storage prediction module; the pre-storage analysis module is used for analyzing the solar photovoltaic power generation before operation and determining whether to perform power generation operation after the pre-storage real-time power generation analysis is completed; the solar photovoltaic power generation operation is carried out when the storage can be carried out, the real-time generated electricity is stored after the electricity is generated by the solar photovoltaic power generation, and the storage time monitoring module is used for monitoring the real-time electricity storage in the process of rising the electricity storage amount; and on the premise of monitoring and managing the real-time electric quantity storage, the real-time storage quantity is predicted by a post-storage prediction module.
Further, the pre-storage analysis module analyzes data of the power generation area, sets a threshold time from the starting time of the current operation period at the starting time of the operation period of each solar photovoltaic power generation, takes the corresponding threshold time as an analysis period, divides the current operation period and the historical operation period according to the analysis period, and acquires environmental parameters of the solar photovoltaic power generation according to the historical power generation process of the current solar photovoltaic power generation area in the historical operation period; the environment parameters of solar photovoltaic power generation are represented as environment parameters such as regional illumination time, illumination intensity, illumination longest continuous time and the like.
Further, the number of the historical operation periods analyzed in the analysis period is not unique, analysis is carried out according to the ratio of the actual generated energy to the expected generated energy in the historical operation periods, and if the ratio of the actual generated energy to the expected generated energy exceeds a ratio threshold, the power generation process corresponding to the historical operation periods is regarded as a qualified process; otherwise, if the duty ratio of the completion amount does not exceed the duty ratio threshold, the power generation process corresponding to the historical operation period is regarded as a non-qualified process, the environment parameter is screened out by comparing the environment parameter values in the comparison object according to different qualified processes or the qualified process and the non-qualified process, and after the acquisition of the parameter to be managed is completed in the analysis period, the current solar photovoltaic power generation area is judged to execute power generation or area reselection according to the comparison of the parameter to be managed.
Further, when the solar photovoltaic power generation area executes power generation, the use and storage stage of the stored electric quantity is set as a scheduling stage, the power consumption demand analysis and the power storage demand analysis are carried out in the scheduling stage, the current demand intensity and the power supply performance are evaluated through the power consumption demand analysis and the power storage demand analysis, the power consumption demand information and the power storage demand information are acquired in the scheduling stage, whether the high-intensity demand risk exists is detected and judged according to the power consumption demand information and the power storage demand information, the management and the control are carried out when the high-intensity demand risk exists, and the time interval analysis is carried out in the scheduling stage when the high-intensity demand risk does not exist.
Further, the power consumption demand information is expressed as a transient increase span of the power consumption in the adaptive power consumption area and a duration of the power consumption increase, which are acquired in real time; the electricity storage demand information is expressed as a reduction span of the generated energy corresponding to the solar photovoltaic power generation area and a delay time of the generated energy transmission node, which are acquired in real time.
Further, taking a time node as an X axis, taking electricity consumption as a Y1 axis and electricity storage as a Y2 axis, collecting and substituting an electricity quantity value into a coordinate system in a scheduling stage, constructing an electricity quantity floating curve, analyzing corresponding periods of each period in the scheduling stage, analyzing corresponding duration spans of curve coverage areas in the electricity quantity floating curve in the coordinate system, monitoring execution intensity of an applied electricity region, and if adjacent electricity quantity floating regions are all executed with high intensity, and the duration span difference value floating quantity is increased in real time, performing electricity quantity floating management and control on current electricity consumption.
Further, the electric quantity floating curve coverage area is divided into an electric quantity floating area and an electric quantity storage floating area, and the electric quantity storage supervision is achieved by controlling the electric quantity floating area and the electric quantity generation area according to curve analysis corresponding to the electric quantity floating area and the electric quantity storage floating area.
Further, after the real-time supervision of the stored electricity quantity, the real-time stored electricity quantity is predicted, analysis is carried out according to the peak value floating quantity of the stored electricity quantity in the continuous operation period and the maximum span of the stored electricity quantity in the current operation period, and if any value of the data does not exceed the corresponding floating quantity threshold value or span threshold value, the electricity quantity storage is predicted to be normal; otherwise, if any value of the data exceeds the corresponding floating quantity threshold value or span threshold value, the electric quantity storage is predicted to be abnormal, and the monitoring module continuously monitors and controls the storage and allocation of the electric quantity.
Compared with the prior art, the application has the beneficial effects that:
1. according to the application, the monitoring module monitors the real-time electric quantity storage during storage, ensures the real-time electric quantity storage efficiency, is convenient to improve the high efficiency of electric quantity storage and the rationality of use of the stored electric quantity, and predicts the real-time storage quantity after the stored electric quantity is used for electric equipment by the storage post-prediction module; and after the influence degree of the environmental parameters is clarified, the solar photovoltaic power generation in the current operation period is subjected to targeted analysis, so that the photovoltaic power generation can be executed in the current operation period, the condition that the power generation equipment in the operation period operates but cannot reach the expected output is avoided, and unnecessary cost waste is caused.
2. According to the application, the operation period is compared with the historical operation period in environmental parameters, and the comparison is carried out according to different types of operation periods, so that the influence of the operation period on the environmental parameters is accurately obtained, the environmental parameters are conveniently monitored before the photovoltaic power generation is executed, and the execution efficiency of the photovoltaic power generation is ensured; and in the environment parameter monitoring process, if numerical value abnormality exists, the environment parameter monitoring system can be adjusted in time.
3. In the application, the electricity demand analysis is to analyze the electricity demand of the solar photovoltaic power generation area, so as to ensure the real-time supervision of the electricity demand, and the electricity demand analysis is to supervise the electricity storage capacity of the solar photovoltaic power generation area, so that the control efficiency of electricity storage and distribution is ensured according to the analysis of the electricity storage direction and the electricity consumption direction.
Drawings
FIG. 1 is a schematic block diagram of an artificial intelligence based solar photovoltaic electrical energy storage monitoring system of the present application;
FIG. 2 is a schematic flow chart of the pre-storage analysis module of the present application;
FIG. 3 is a schematic flow chart of the supervision module in storage according to the present application;
FIG. 4 is a schematic diagram of a coordinate system used for the time period analysis of the present application.
Description of the embodiments
In order to make the present application better understood by those skilled in the art, the technical solution of the present application will be clearly and completely described with reference to the accompanying drawings and specific embodiments. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are intended to be within the scope of the present application based on the embodiments herein.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, the solar photovoltaic electric energy storage monitoring system based on artificial intelligence in this embodiment includes a pre-storage analysis module, a during-storage monitoring module, and a post-storage prediction module, where the pre-storage analysis module, the during-storage monitoring module, and the post-storage prediction module are sequenced according to a photovoltaic electric energy storage flow sequence.
According to the embodiment, when the solar photovoltaic is put into use, the pre-storage analysis module analyzes the solar photovoltaic power generation before operation, and after the pre-storage real-time power generation analysis is completed, the solar photovoltaic power generation operation is carried out after the storage can be determined, the real-time generated power is stored after the generated power is generated by the solar photovoltaic power generation, and in the power storage amount rising process, the real-time power storage is monitored by the monitoring module during storage, so that the real-time power generation storage efficiency is ensured, the high efficiency of power storage and the rationality of power storage are improved, and the real-time storage amount is predicted by the post-storage prediction module after the power storage is used for electric equipment; it is to be noted that, in this embodiment, the modules are all hinges for signal transmission, where, when a certain module is specified, the existing data acquisition and data transmission are all sensors or data acquisition means that are publicly known in the prior art, and the data that need to be acquired in the whole electrical energy storage monitoring system are all available in the prior art.
As a preferred embodiment of the present application, referring to fig. 2, when a current solar photovoltaic power generation area needs to operate, the pre-storage analysis module performs data analysis on the current power generation area, obtains the influence of environmental parameters in the current power generation area according to the operation period adjacent to the history of the solar photovoltaic power generation area, performs targeted analysis on solar photovoltaic power generation in the current operation period after determining the influence degree of the environmental parameters, ensures that photovoltaic power generation can be performed in the current operation period, and avoids the unnecessary cost waste caused by that power generation equipment cannot reach the expected output while operating in the operation period.
Therefore, at the starting time of the operation period of each solar photovoltaic power generation, setting a threshold time from the starting time of the current operation period, taking the corresponding threshold time as an analysis period, dividing the current operation period and the historical operation period according to the analysis period, and acquiring the environmental parameters of the solar photovoltaic power generation according to the historical power generation process of the current solar photovoltaic power generation area in the historical operation period, wherein the environmental parameters of the solar photovoltaic power generation are represented as the environmental parameters such as area illumination time, illumination intensity, illumination longest continuous time and the like, and the environmental parameters can be monitored by the sensor and can be used as data for transmission.
Analyzing the number of the historical operation periods analyzed in the analysis period according to the ratio of the actual power generation amount to the predicted power generation amount in the historical operation periods, and if the ratio of the completed power generation amount exceeds a ratio threshold, taking the power generation process corresponding to the historical operation period as a qualified process; otherwise, if the duty ratio of the completion amount does not exceed the duty ratio threshold, the power generation process corresponding to the historical operation period is a non-qualified process.
Setting a preset feasible parameter for the real-time environment parameter in the qualified process, analyzing the real-time environment parameters according to a plurality of qualified processes, taking different qualified processes as comparison objects, and marking the environment parameter with a high completion amount duty ratio of the qualified process and a high corresponding value of the qualified process with a low completion amount duty ratio as a preset beneficial factor; and marking the environment parameter with low corresponding value of the qualified process with high completion amount ratio relative to the qualified process with low completion amount ratio as a preset influence-free factor.
Then, taking the qualified process and the unqualified process as comparison objects, if the data of the corresponding preset beneficial factors in the qualified process is high relative to the unqualified process, setting the preset beneficial factors as parameters to be controlled, and setting the preset beneficial factors in proportion; otherwise, if the data of the corresponding preset favorable factors in the qualified process is lower than that in the unqualified process, the preset favorable factors are set as parameters to be controlled, and the set proportion is inversely proportional; if the values of the qualified process and the unqualified process corresponding to the preset influence-free factors are alternately high, determining the corresponding preset influence-free factors as influence-free parameters; otherwise, if the value of the qualified process is continuously high or continuously low, determining the corresponding preset influence-free factor as the parameter to be controlled.
After the acquisition of the parameters to be controlled is completed in the analysis period, analyzing the environmental parameters of the solar photovoltaic power generation area in the current operation period, and when the parameters to be controlled in the power generation area in the current operation period are in direct proportion, if the current operation period is increased relative to the numerical value of the historical adjacent operation period and the parameters to be controlled in the current operation period are not in the numerical threshold of the same parameter corresponding to the historical unqualified process, analyzing the environmental parameters of the current operation period, namely, the operation period of the solar photovoltaic power generation area can perform power generation; similarly, when the parameter to be controlled is inversely proportional, if the current operation period is reduced relative to the value of the historical adjacent operation period and the parameter to be controlled in the current operation period is not in the value threshold of the same parameter corresponding to the historical unqualified process, power generation can be executed; if the current operation period does not float in proportion to the value of the historical adjacent operation period or the parameter to be managed and controlled in the current operation period is in the value threshold value of the same parameter corresponding to the historical unqualified process, the current operation period cannot execute power generation; it is understood that non-proportional float is indicated as a decrease in the float when the value is proportional.
And (4) performing power generation region re-addressing if the operation period of the solar photovoltaic power generation region is continuous and power generation cannot be performed.
According to the technical scheme of the embodiment, the operation period is compared with the historical operation period according to the environmental parameters, and the influence of the operation period on the environmental parameters is accurately acquired according to the comparison of different types of operation periods, so that the environmental parameters are conveniently monitored before the photovoltaic power generation is executed, and the execution efficiency of the photovoltaic power generation is ensured; and in the environment parameter monitoring process, if numerical value abnormality exists, the environment parameter monitoring system can be adjusted in time.
As a preferred embodiment of the present application, referring to fig. 3, after the analysis is completed by the pre-storage analysis module, and the current photovoltaic power generation area performs power generation, the photovoltaic power generation area is monitored in real time, the real-time stored power generation is monitored in the power generation storage and rising process, after the power generation rising value is set to a magnitude, the current power generation storage device can be used as a power storage device or a power transmission device, and it is to be noted that in the photovoltaic power generation scene in the prior art, the power storage and the power supply can be achieved through devices such as an inverter, that is, the ac/dc change technical means.
The method comprises the steps of setting a use and storage phase of stored electricity as a scheduling phase, carrying out electricity demand analysis and electricity storage demand analysis in the scheduling phase, evaluating current demand intensity and power supply performance through the electricity demand analysis and the electricity storage demand analysis, ensuring no instantaneous high-intensity demand in the scheduling phase, further ensuring the reasonability of electricity storage and distribution in the scheduling phase, simultaneously carrying out execution control in time when the instantaneous high-intensity demand exists, and carrying out demand prevention through timely scheduling and incremental means in the prior art.
Therefore, the power consumption demand information is obtained in a scheduling stage, wherein the power consumption demand information is represented as an instantaneous increase span of the power consumption in the adaptive power consumption area and a duration of the power consumption increase, which are obtained in real time, and if any parameter in the power consumption demand information exceeds a corresponding numerical threshold, the power consumption demand information is judged to have a direct instantaneous high-strength demand risk, and the power consumption area is controlled, namely, peak clipping is performed on the power consumption area and peak staggering is performed on a power consumption increase period; otherwise, if any parameter in the electricity demand information does not exceed the corresponding numerical threshold, judging that the electricity demand information has no direct instantaneous high-strength demand risk, namely the electricity demand analysis is normal.
Acquiring electricity storage demand information in a scheduling stage, wherein the electricity storage demand information is represented by a reduction span of an electric energy generation amount corresponding to a solar photovoltaic power generation area and a delay time of an electric energy generation amount transmission node, which are acquired in real time, if any parameter in the electricity storage demand information exceeds a corresponding numerical threshold, judging that the electricity storage demand information has indirect instant high-strength demand risk, performing execution control on the corresponding power generation area, stabilizing the real-time standby stored electric quantity in the power generation area, and performing real-time operation and detection on an electric quantity transmission route; it should be noted that, the risk of the instantaneous high-intensity demand is represented by a large demand of electricity in a short time, which may be a sudden increase in demand of electricity or a sudden decrease in supply of electricity; otherwise, if any parameter in the electricity storage demand information does not exceed the corresponding numerical threshold, judging that the electricity storage demand information has no risk of the instant high-strength demand, namely the electricity storage demand analysis is normal.
After the electricity storage demand analysis is normal and the electricity consumption demand analysis is normal, the scheduling stage is subjected to time period analysis, a time node is taken as an X axis, the electricity consumption is taken as a Y1 axis, the electricity storage amount is taken as a Y2 axis, the electricity floating curve is constructed according to an electricity numerical collection substitution coordinate system in the scheduling stage, as can be seen from fig. 4, S1, S2, S3 and S4 are all area areas of four stages of the curve, it is to be noted that fig. 4 is a schematic diagram of only one period, any period number can exist in the scheduling stage, and any completion period can be a curve closed loop.
Analyzing the corresponding period of each period in the scheduling stage, marking S1 as an electricity utilization increasing region, S2 as an electricity utilization reducing region, S3 as an electricity storage increasing region and S4 as an electricity storage reducing region; uniformly marking each area in the electric quantity floating curve as an electric quantity floating area, periodically monitoring the duration spans of each electric quantity floating area, and judging that the execution intensity of the corresponding electric quantity floating area is high if the duration span difference value of the adjacent electric quantity floating areas exceeds the average value of the difference values of the historical adjacent spans, wherein the execution can be electricity storage or electricity use; otherwise, determining that the execution intensity of the corresponding electric quantity floating area is small; after the intensity analysis is carried out on the electric quantity floating areas, if the adjacent electric quantity floating areas are all carried out with high intensity and the time span difference value floating quantity is increased in real time, electric quantity floating management and control are carried out on the current power consumption, so that the power consumption is prevented from being large and the power storage is prevented from being large; the electric quantity floating control is a technical means such as electric quantity allocation in the prior art, and is not further disclosed again.
Then, marking S1 and S2 as electricity utilization floating areas, marking S3 and S4 as electricity storage floating areas, analyzing the peak values of the electricity utilization floating areas and the electricity storage floating areas, and expanding the electricity storage capacity of the application electricity area if the peak values of a plurality of continuous electricity utilization floating areas exceed opposite peak value thresholds and the corresponding duration of the peak flat-top length of the electricity utilization floating areas exceeds corresponding duration thresholds; if the peak value of the continuous multiple electricity storage floating areas exceeds the corresponding peak value threshold value, and the peak flat-top length corresponding time length of the electricity storage floating areas exceeds the corresponding time length threshold value, the electricity storage quantity of the photovoltaic power generation areas matched with the applied electricity areas is reduced, and meanwhile the storage period of the electricity storage quantity is shortened.
After the stored electricity quantity is monitored in real time, a prediction module predicts the real-time stored electricity quantity, analyzes according to the peak value floating quantity of the stored electricity quantity in a continuous operation period and the maximum span of the stored electricity quantity in the current operation period, and predicts the electricity quantity storage as normal if any value of the data does not exceed a corresponding floating quantity threshold or span threshold; otherwise, if any value of the data exceeds the corresponding floating quantity threshold value or span threshold value, the electric quantity storage is predicted to be abnormal, and the monitoring module continuously monitors and controls the storage and allocation of the electric quantity.
When the solar photovoltaic power generation system is used, the analysis module before storage analyzes the solar photovoltaic power generation operation, and determines whether to perform power generation operation after the real-time power generation analysis before storage is completed; when the storage capacity is determined to be capable of being stored, solar photovoltaic power generation operation is carried out, the real-time generated electricity is stored after the electricity is generated by the solar photovoltaic power generation, and in the process of rising the electricity storage capacity, the storage-time monitoring module monitors the real-time electricity storage; and on the premise of monitoring and managing the real-time electric quantity storage, the real-time storage quantity is predicted by a post-storage prediction module.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The utility model provides a solar photovoltaic electric energy storage supervisory systems based on artificial intelligence, includes analysis module before the storage, supervision module and prediction module after the storage during the storage, its characterized in that: the pre-storage analysis module is used for analyzing the solar photovoltaic power generation before operation and determining whether to perform power generation operation after the real-time power generation analysis before storage is completed; when the storage is determined to be possible, solar photovoltaic power generation operation is performed, and the real-time generated electricity is stored after the electricity generated by the solar photovoltaic power generation is performed; in the process of rising the electric quantity storage quantity, the monitoring module is used for monitoring the electric quantity storage in real time during storage; and on the premise of monitoring and managing the real-time electric quantity storage, the real-time storage quantity is predicted by a post-storage prediction module.
2. An artificial intelligence based solar photovoltaic electrical energy storage monitoring system as claimed in claim 1 wherein: the pre-storage analysis module analyzes data of the power generation area, sets a threshold time from the starting time of the current operation period at the starting time of the operation period of each solar photovoltaic power generation, takes the corresponding threshold time as an analysis period, divides the current operation period and the historical operation period according to the analysis period, and acquires environmental parameters of the solar photovoltaic power generation according to the historical power generation process of the current solar photovoltaic power generation area in the historical operation period.
3. An artificial intelligence based solar photovoltaic electrical energy storage monitoring system as claimed in claim 2 wherein: analyzing the number of the historical operation periods analyzed in the analysis period according to the ratio of the actual power generation amount to the predicted power generation amount in the historical operation periods, and if the ratio of the completed power generation amount exceeds a ratio threshold, taking the power generation process corresponding to the historical operation period as a qualified process; otherwise, if the duty ratio of the completion amount does not exceed the duty ratio threshold, the power generation process corresponding to the historical operation period is regarded as a non-qualified process, the environment parameter is screened out by comparing the environment parameter values in the comparison object according to different qualified processes or the qualified process and the non-qualified process, and after the acquisition of the parameter to be managed is completed in the analysis period, the current solar photovoltaic power generation area is judged to execute power generation or area reselection according to the comparison of the parameter to be managed.
4. An artificial intelligence based solar photovoltaic electrical energy storage monitoring system according to claim 3 and wherein: when the solar photovoltaic power generation area executes power generation, the use and storage stage of the stored electric quantity is set as a scheduling stage, the power consumption demand analysis and the power storage demand analysis are carried out in the scheduling stage, the current demand intensity and the power supply performance are evaluated through the power consumption demand analysis and the power storage demand analysis, the power consumption demand information and the power storage demand information are acquired in the scheduling stage, whether the high-intensity demand risk exists is detected and judged according to the period of the power consumption demand information and the power storage demand information, the management and the control are carried out if the high-intensity demand risk exists, and the time interval analysis is carried out in the scheduling stage if the high-intensity demand risk does not exist.
5. An artificial intelligence based solar photovoltaic electrical energy storage monitoring system as claimed in claim 4 wherein: the power consumption demand information is expressed as the instantaneous increase span of the power consumption in the adaptive power consumption area obtained in real time and the duration of the power consumption increase; the electricity storage demand information is expressed as a reduction span of the generated energy corresponding to the solar photovoltaic power generation area and a delay time of the generated energy transmission node, which are acquired in real time.
6. An artificial intelligence based solar photovoltaic electrical energy storage monitoring system as claimed in claim 4 wherein: and taking a time node as an X axis, taking electricity consumption as a Y1 axis and electricity storage as a Y2 axis, acquiring and substituting an electricity floating curve according to an electricity numerical value in a scheduling stage, analyzing corresponding periods of each period in the scheduling stage, monitoring the execution intensity of an applied electricity area according to corresponding duration span analysis of a curve coverage area in the electricity floating curve in the coordinate system, and if the adjacent electricity floating areas are all executed with high intensity and the duration span difference value floating quantity is increased in real time, performing electricity floating management and control on the current electricity.
7. An artificial intelligence based solar photovoltaic electrical energy storage monitoring system as claimed in claim 6 wherein: dividing the electric quantity floating curve coverage area into an electric quantity floating area and an electric quantity storage floating area, and controlling the electric quantity storage and supervision by analyzing the corresponding curves of the electric quantity floating area and the electric quantity storage floating area.
8. An artificial intelligence based solar photovoltaic electrical energy storage monitoring system as claimed in claim 7 wherein: after the real-time supervision of the electricity storage quantity, the real-time electricity storage quantity is predicted, analysis is carried out according to the peak value floating quantity of the electricity storage quantity in the continuous operation period and the maximum span of the electricity storage quantity in the current operation period, and if any value of the data does not exceed the corresponding floating quantity threshold value or span threshold value, the electricity storage is predicted to be normal; otherwise, if any value of the data exceeds the corresponding floating quantity threshold value or span threshold value, the electric quantity storage is predicted to be abnormal, and the monitoring module continuously monitors and controls the storage and allocation of the electric quantity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310648440.7A CN116633265A (en) | 2023-06-02 | 2023-06-02 | Solar photovoltaic electric energy storage supervisory systems based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310648440.7A CN116633265A (en) | 2023-06-02 | 2023-06-02 | Solar photovoltaic electric energy storage supervisory systems based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116633265A true CN116633265A (en) | 2023-08-22 |
Family
ID=87597157
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310648440.7A Pending CN116633265A (en) | 2023-06-02 | 2023-06-02 | Solar photovoltaic electric energy storage supervisory systems based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116633265A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116910520A (en) * | 2023-09-12 | 2023-10-20 | 北京长和信泰能源技术有限公司 | Intelligent storage method based on generated energy of photovoltaic building integrated system |
| CN117239893A (en) * | 2023-09-20 | 2023-12-15 | 山东探越物联网技术有限公司 | Charging and discharging control method for solar power supply system |
-
2023
- 2023-06-02 CN CN202310648440.7A patent/CN116633265A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116910520A (en) * | 2023-09-12 | 2023-10-20 | 北京长和信泰能源技术有限公司 | Intelligent storage method based on generated energy of photovoltaic building integrated system |
| CN116910520B (en) * | 2023-09-12 | 2023-12-01 | 北京长和信泰能源技术有限公司 | Intelligent storage method based on generated energy of photovoltaic building integrated system |
| CN117239893A (en) * | 2023-09-20 | 2023-12-15 | 山东探越物联网技术有限公司 | Charging and discharging control method for solar power supply system |
| CN117239893B (en) * | 2023-09-20 | 2024-04-09 | 山东探越物联网技术有限公司 | Charging and discharging control method for solar power supply system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116633265A (en) | Solar photovoltaic electric energy storage supervisory systems based on artificial intelligence | |
| CN104538999A (en) | Photovoltaic generating energy storage system and energy scheduling method thereof | |
| KR101298500B1 (en) | Micro-Grid Simulation Apparatus and Power Management System | |
| KR20190000992A (en) | Energy storage system battery enable module management and control of battery | |
| CN110783959A (en) | New forms of energy power generation system's steady state control system | |
| CN111917113B (en) | Power grid load allowance calculation system and method and charging pile access power distribution method | |
| CN106953356A (en) | Under a kind of island mode without energy storage wind-solar-diesel complementary electric power system and method | |
| CN115603453A (en) | Take intelligent monitoring system's direct current generating line group control device | |
| CN108449051A (en) | A kind of monitoring device of photovoltaic power station, system and method | |
| CN104184145B (en) | A kind of building intelligent distribution system | |
| CN117394403B (en) | Big data analysis-based intelligent control system for charging and discharging of energy storage power supply | |
| CN104467198A (en) | Electric energy storage system based on online distribution | |
| CN114977224B (en) | Three-phase imbalance time-period treatment method considering distributed power supply access residential area | |
| CN216530731U (en) | Synthesize green energy efficiency intelligence management and control system | |
| CN116404669A (en) | Optimized operation method, system, equipment and medium of reconfigurable battery energy storage system | |
| CN114893873A (en) | Air conditioner control method and device and air conditioner | |
| CN103236727A (en) | Intelligent equipment for controlling sequential charging and discharging of storage battery according to power generation capacity of new energy sources | |
| CN104009489A (en) | Micro-grid management system and management method thereof | |
| CN117239893B (en) | Charging and discharging control method for solar power supply system | |
| CN204271759U (en) | A kind of electric energy storage device based on distribution on line formula | |
| CN105021884A (en) | Power utilization device standby power consumption calculation method | |
| CN113991744B (en) | Intelligent energy supply system | |
| CN117578597B (en) | Energy-saving control method and system for photovoltaic inverter system | |
| CN219227283U (en) | Distribution area autonomous management system | |
| CN113270897B (en) | Intelligent power grid control system based on terminal capacity and control method thereof |
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 |