CN116843361A - Power grid audit informatization management method and system - Google Patents
Power grid audit informatization management method and system Download PDFInfo
- Publication number
- CN116843361A CN116843361A CN202310789188.1A CN202310789188A CN116843361A CN 116843361 A CN116843361 A CN 116843361A CN 202310789188 A CN202310789188 A CN 202310789188A CN 116843361 A CN116843361 A CN 116843361A
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- evaluation coefficient
- energy
- consumption evaluation
- time period
- 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
- 238000012550 audit Methods 0.000 title claims abstract description 29
- 238000007726 management method Methods 0.000 title claims abstract description 24
- 238000005265 energy consumption Methods 0.000 claims abstract description 134
- 238000011156 evaluation Methods 0.000 claims abstract description 85
- 238000004519 manufacturing process Methods 0.000 claims abstract description 51
- 238000012545 processing Methods 0.000 claims abstract description 35
- 230000009466 transformation Effects 0.000 claims abstract description 33
- 238000000034 method Methods 0.000 claims abstract description 13
- 238000004891 communication Methods 0.000 claims abstract description 5
- 238000010248 power generation Methods 0.000 claims description 40
- 230000005611 electricity Effects 0.000 claims description 16
- 238000010606 normalization Methods 0.000 claims description 3
- 230000005856 abnormality Effects 0.000 abstract 1
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 10
- 239000002699 waste material Substances 0.000 description 9
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 7
- 229910052802 copper Inorganic materials 0.000 description 7
- 239000010949 copper Substances 0.000 description 7
- 238000003860 storage Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 229910052742 iron Inorganic materials 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
Classifications
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- 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
- 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The application discloses a method and a system for informationized management of power grid audit, in particular to the technical field of power grid audit, which are used for solving the problems that the energy consumption abnormality of power transformation equipment is difficult to discover in time and the normal operation of a power grid is influenced; the system comprises a data processing module, a data acquisition module, an energy consumption early warning module and a generator set prediction module, wherein the data acquisition module, the energy consumption early warning module and the generator set prediction module are in communication connection with the data processing module; the method comprises the steps of calculating an energy consumption evaluation coefficient by obtaining energy utilization information and energy efficiency information, evaluating the energy consumption condition of power transformation equipment, calculating predicted production electric quantity in each time period, and calculating the number of theoretical input generator sets according to the predicted production electric quantity in each time period; calculating actual predicted production electric quantity according to the energy consumption evaluation coefficient, and calculating the number of the generating sets to be increased; the application improves the auditing efficiency, can more accurately evaluate the energy consumption condition of the power transformation equipment, sends out a serious energy consumption early warning signal through the system, can discover problems in time and adopts effective measures.
Description
Technical Field
The application relates to the technical field of power grid audit, in particular to a power grid audit informationized management method and system.
Background
The informatization management of the power grid audit means that data in the running process of the power grid is collected, stored, processed and analyzed by informatization means so as to support the work of the power grid audit; the power grid energy consumption audit is used for evaluating the energy consumption condition of the power grid, identifying the problems and hidden dangers of energy waste, energy conservation and consumption reduction, and providing corresponding solutions and suggestions so as to ensure the safe and stable operation of the power grid.
In the power grid energy consumption audit, the current monitoring of the power grid energy consumption is mainly concentrated on parts such as a generator set, and the monitoring of the power consumption of power transformation equipment is relatively less, so that the abnormal energy consumption of the power transformation equipment is difficult to discover in time, thereby causing long-term energy consumption problems and further influencing the normal operation of the power grid; in addition, the input of the generator set is not flexibly adjusted according to the energy consumption condition of the power transformation equipment.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present application provide a method and a system for grid audit information management to solve the problems set forth in the background art.
In order to achieve the above purpose, the present application provides the following technical solutions:
the power grid audit informatization management method comprises the following steps:
step S1: acquiring energy utilization information and energy efficiency information, and calculating an energy consumption evaluation coefficient according to the energy utilization information and the energy efficiency information;
step S2: setting an energy consumption evaluation coefficient critical threshold, evaluating the energy consumption condition of the power transformation equipment through comparison of the energy consumption evaluation coefficient and the energy consumption evaluation coefficient critical threshold, and sending an energy consumption serious early warning signal by the system when the energy consumption evaluation coefficient is larger than the energy consumption evaluation coefficient critical threshold;
step S3: calculating predicted production electric quantity in each time period, and calculating the number of theoretical input generator sets according to the predicted production electric quantity in each time period;
step S4: and calculating actual predicted production electric quantity according to the energy consumption evaluation coefficient, and calculating the number of the generating sets which need to be increased according to the actual predicted production electric quantity.
In a preferred embodiment, in step S1, energy utilization information and energy efficiency information are acquired, the energy utilization information including transformer loss rate and switchgear conduction loss value; the energy efficiency information comprises a load rate deviation value and a combined electrical appliance energy efficiency value;
the transformer loss rate, the switching equipment conduction loss value, the load rate deviation value and the combined electrical appliance energy efficiency value are subjected to normalization processing, and an energy consumption evaluation coefficient is calculated, wherein the expression is as follows:
wherein, N is an energy consumption evaluation coefficient, tr, sv, lv, ea is a transformer loss rate, a switching device conduction loss value, a load rate deviation value and a combined electrical appliance energy efficiency value respectively; alpha 1 、α 2 、α 3 、α 4 Preset proportionality coefficients of transformer loss rate, switching equipment conduction loss value, load rate deviation value and combined electrical appliance energy efficiency value respectively, and alpha 1 ＞α 3 ＞α 2 ＞α 4 ＞0。
In a preferred embodiment, in step S2, an energy consumption evaluation coefficient critical threshold is set, which is marked as N 0 ；
When the energy consumption evaluation coefficient is larger than the energy consumption evaluation coefficient critical threshold, the system sends out an energy consumption serious early warning signal;
when the energy consumption evaluation coefficient is smaller than the energy consumption evaluation coefficient critical threshold, the system does not send out an early warning signal.
In a preferred embodiment, in step S3, the per-period power generation amount, the historical per-period power generation amount, and the historical power generation amount increase rate of each group of power generation sets are acquired;
the predicted production electricity quantity per time period is as follows: y=ts (1+eg);
the theoretical input generator sets are as follows:
wherein Y is the predicted power generation amount in each time period, F is the theoretical input number of the generator sets, and M is the power generation amount in each time period of each group of the generator sets; ts is the electricity quantity produced by the history in each time period, and Eg is the historical electricity generation rate.
In a preferred embodiment, in step S4, the actual predicted production electricity amount is calculated based on the energy consumption evaluation coefficient, expressed as:
wherein U is the actual predicted power generation, delta is the adjustment coefficient;
according to the actual predicted production electric quantity, the number of generating sets which need to be increased is calculated, and the expression is as follows:
wherein, C is the number of generating sets which need to be increased.
In a preferred embodiment, the grid audit informatization management system comprises a data processing module, a data acquisition module, an energy consumption early warning module and a generator set prediction module, wherein the data acquisition module, the energy consumption early warning module and the generator set prediction module are in communication connection with the data processing module;
the data acquisition module acquires energy utilization information and energy efficiency information, the energy utilization information and the energy efficiency information are sent to the data processing module, and the data processing module calculates an energy consumption evaluation coefficient;
the energy consumption early-warning module receives the energy consumption evaluation coefficient calculated by the data processing module, and the system sends out an energy consumption serious early-warning signal when the energy consumption evaluation coefficient is larger than the energy consumption evaluation coefficient critical threshold value according to the comparison between the energy consumption evaluation coefficient and the energy consumption evaluation coefficient critical threshold value;
transmitting the historical produced electric quantity and the historical power generation increase rate to a data processing module, and calculating the predicted produced electric quantity in each time period by the data processing module;
the energy consumption evaluation coefficient and the predicted production electric quantity in each time period are sent to a data processing module, the actual predicted production electric quantity is calculated and obtained, the generator set prediction module calculates the theoretical input generator set number and the number of the generator sets to be increased according to the actual predicted production electric quantity and the predicted production electric quantity in each time period of each group of generator sets by the data processing module, and the power generation amount in each time period and the predicted production electric quantity in each time period of each group of generator sets are calculated.
The method and the system for the informationized management of the power grid audit have the technical effects and advantages that:
1. by calculating the energy consumption evaluation coefficient and setting the energy consumption evaluation coefficient critical threshold, when the energy consumption evaluation coefficient exceeds the energy consumption evaluation coefficient critical threshold, a serious energy consumption early warning signal is timely sent out to remind professional technicians of optimizing and improving, so that the problems of energy waste, low efficiency, unstable operation and the like are avoided, the consumption of energy is reduced, the problem of power transformation equipment is timely found, and the safe and stable operation of the power transformation equipment is ensured.
2. The theoretical input of the number of the generator sets is calculated, so that a power grid company can be helped to more accurately predict future power demands, and the input number of the generator sets is arranged according to a prediction result, so that the operation efficiency of the power grid company is improved; the number of the generating sets which need to be increased is calculated through actual prediction of the production electric quantity, so that a power grid company can plan and manage the resources better, and the utilization efficiency of the resources is improved; reliability and stability of the power supply can be ensured.
Drawings
FIG. 1 is a schematic diagram of a method for informationized management of grid audit in accordance with the present application;
fig. 2 is a schematic structural diagram of the grid audit informationized management system of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
The application relates to an informatization management method and system for power grid audit, which are characterized in that energy consumption evaluation coefficients are calculated by acquiring energy utilization information and energy efficiency information, then energy consumption evaluation coefficient critical threshold values are set, the energy consumption condition of power transformation equipment is evaluated by comparing the energy consumption evaluation coefficients with the energy consumption evaluation coefficient critical threshold values, the production electric quantity is predicted by calculating each time period, and the quantity of power generation sets is calculated according to the prediction of the production electric quantity in each time period; calculating actual predicted production electric quantity according to the energy consumption evaluation coefficient, and calculating the number of generating sets which need to be increased according to the actual predicted production electric quantity; the auditing efficiency is improved, the data is automatically collected, analyzed and processed, and the time and the workload of manually processing the data are reduced, so that the auditing efficiency is improved; the energy consumption condition of the power transformation equipment can be estimated more accurately through the data analysis and the processing of the power grid audit informatization management system, so that the audit precision is further improved; the system sends out a serious energy consumption early warning signal, so that problems can be found in time, effective measures are taken, and the auditing effect is improved.
Fig. 1 shows a schematic diagram of the power grid audit informatization management method of the application, which comprises the following steps:
step S1: and acquiring energy utilization information and energy efficiency information, and calculating an energy consumption evaluation coefficient according to the energy utilization information and the energy efficiency information.
Step S2: setting an energy consumption evaluation coefficient critical threshold, evaluating the energy consumption condition of the power transformation equipment through comparison of the energy consumption evaluation coefficient and the energy consumption evaluation coefficient critical threshold, and sending an energy consumption serious early warning signal by the system when the energy consumption evaluation coefficient is larger than the energy consumption evaluation coefficient critical threshold.
Step S3: and calculating the predicted production electric quantity in each time period, and calculating the theoretical input generator set quantity according to the predicted production electric quantity in each time period.
Step S4: and calculating actual predicted production electric quantity according to the energy consumption evaluation coefficient, and calculating the number of the generating sets which need to be increased according to the actual predicted production electric quantity.
In step S1, energy utilization information and energy efficiency information are obtained, wherein the energy utilization information includes transformer loss rate and switching device conduction loss value; the energy efficiency information includes a load factor deviation value and a combined electrical appliance energy efficiency value.
The energy utilization information reflects the loss degree of a transformer in the power system and the loss degree of transformation equipment in the power system; the energy efficiency information reflects the condition of load management in the power system and the energy efficiency degree of the combined electrical appliance; in the auditing, the energy utilization information and the energy efficiency information can be acquired to help the auditor evaluate and analyze the energy utilization efficiency and the energy efficiency of the power system; through the energy utilization information and the energy efficiency information, auditors can find out the problems of energy waste and low energy efficiency.
The transformer loss rate, the switching device conduction loss value, the load rate deviation value and the combined electrical appliance energy efficiency value are described in detail as follows:
loss rate of transformer: transformer loss ratio= (copper loss + iron loss)/(input electric power); the higher the loss rate of the transformer is, the lower the energy utilization efficiency of the transformer is, namely, the more energy is wasted when the energy is converted by the transformer, so that the energy consumption condition of the power transformation equipment becomes more undesirable; the high loss rate may cause an increase in the heating value of the transformer, thereby increasing maintenance costs and operation costs of the transformer, and also possibly reducing the life of the transformer.
Copper losses are heat losses due to the flow of current in the copper wires of the transformer; the core loss is heat loss caused by eddy current generated in a magnetic field by a core of the transformer; input electric power refers to the product of the input voltage and the input current of the transformer; the calculation of copper loss, iron loss and input electric power is prior art and will not be described in detail here.
Switching device conduction loss value: the ratio of the total loss of the isolating switch and the circuit breaker to the transmitted electric energy; the higher the conduction loss value of the switching device, the lower the energy utilization rate, which can lead to an increase in energy consumption of the power system, further leading to an increase in energy waste and energy cost, and adversely affecting the energy consumption condition of the power transformation device.
The total loss and transfer electricity calculation method of the isolating switch and the circuit breaker is as follows:
the input voltage, output voltage, input current, output current and running time of the isolating switch and the circuit breaker are measured by means of voltmeters, ammeter, timers and the like.
Total loss of disconnector and circuit breaker = copper loss of disconnector and circuit breaker + iron loss of disconnector and circuit breaker + no-load loss of disconnector and circuit breaker.
The copper losses of the isolating switch and the circuit breaker are calculated according to the input current and the output current and the resistance values inside the isolating switch and the circuit breaker; the iron losses of the isolating switch and the circuit breaker can be calculated according to the input voltage and the output voltage, the inductance values of the isolating switch and the circuit breaker, hysteresis loss and other factors; the no-load loss of the isolating switch and the circuit breaker refers to the loss when the isolating switch and the circuit breaker do not transmit electric energy; the copper loss of the isolating switch and the circuit breaker, the iron loss of the isolating switch and the circuit breaker and the no-load loss of the isolating switch and the circuit breaker are obtained in the prior art and are mature, and the details are not repeated here.
The delivered electrical energy can be calculated by the following formula:
transfer power = input voltage x input current x run time.
Load factor deviation value: the load factor deviation value is the deviation value of the transformer load factor and the optimal threshold value of the transformer load factor; the greater the load factor deviation value, the lower the operating efficiency of the transformer, since the fixed part of the transformer loss occupies a relatively large area in this case, thus resulting in energy waste; the overload operation of the transformer can be caused, the electric and thermal loads in the transformer are increased, and the service life and the safety of the transformer are greatly influenced.
Transformer load factor: transformer load rate = actual load/rated capacity.
Wherein, the actual load refers to the load borne by the transformer and is usually expressed by the output power of the transformer; the rated capacity is the rated capacity of a transformer when it is designed, and is generally expressed in units of kilovolt amperes (kVA).
The optimal threshold for transformer loading rate may vary from transformer to transformer depending on the particular transformer design and use.
Energy efficiency value of combined electrical appliance: in the application, the combined electrical appliance refers to switching equipment, protection equipment and control equipment in a transformer substation, and plays a key role in a power transmission and distribution system; the automatic reclosing device comprises an isolating switch, a circuit breaker, a combined electrical cabinet, a grounding switch, a load switch, an automatic reclosing device, a lightning arrester and the like.
Energy efficiency value of combined electrical appliance: combined electrical energy efficiency value = output electrical power/input electrical power.
Wherein the output electric power is useful electric power provided by the combined electric appliance, namely, power supplied to a load; the input electric power is the total input electric power of the combined electric appliance, including the loss of the electric appliance itself and the electric power delivered to the load; and calculating the energy efficiency value of the combined electrical appliance by measuring parameters such as input current, input voltage, output current and the like of the combined electrical appliance.
The energy consumption of the power transformation equipment can be directly influenced by the energy efficiency value of the combined electrical appliance, and the higher the energy efficiency value of the combined electrical appliance is, the higher the energy utilization rate of the combined electrical appliance is, the less the waste of electric energy is, and correspondingly, the less the energy consumption of the power transformation equipment is; meanwhile, the high-energy-efficiency combined electrical appliance can be more stable and reliable in use, and the risks of faults and damage are reduced, so that the service life and the operation efficiency of the equipment are improved.
It is noted that in the present application, the transformation device includes, but is not limited to, a transformer, an isolating switch, and a circuit breaker.
The transformer loss rate, the switching equipment conduction loss value, the load rate deviation value and the combined electrical appliance energy efficiency value are subjected to normalization processing, and an energy consumption evaluation coefficient is calculated, wherein the expression is as follows:
wherein, N is an energy consumption evaluation coefficient, tr, sv, lv, ea is a transformer loss rate, a switching device conduction loss value, a load rate deviation value and a combined electrical appliance energy efficiency value respectively; alpha 1 、α 2 、α 3 、α 4 Preset proportionality coefficients of transformer loss rate, switching equipment conduction loss value, load rate deviation value and combined electrical appliance energy efficiency value respectively, and alpha 1 ＞α 3 ＞α 2 ＞α 4 ＞0。
In step S2, an energy consumption evaluation coefficient critical threshold is set, and marked as N 0 And (3) evaluating the energy consumption condition of the power transformation equipment through comparison of the energy consumption evaluation coefficient and the energy consumption evaluation coefficient critical threshold value.
When the energy consumption evaluation coefficient is larger than the energy consumption evaluation coefficient critical threshold, the system sends out an energy consumption serious early warning signal; at the moment, the energy consumption condition of the power transformation equipment is poor, which indicates that the energy consumption of the power transformation equipment exceeds the expected range, the risk of overhigh energy consumption exists, and the problems of energy waste, low efficiency, unstable operation and the like of the power transformation equipment exist; professional technicians need to take corresponding measures to optimize and improve according to the serious energy consumption early warning signals sent by the system, and the measures can comprise checking the running condition of the power transformation equipment, ensuring that the power transformation equipment runs normally and has no leakage phenomenon, and if the power transformation equipment has problems, maintaining or replacing the power transformation equipment in time; optimizing an operation strategy of the power transformation device, such as reducing unnecessary operation time or reducing load rate; energy efficiency modification or upgrading of the power transformation equipment, such as replacement of efficient motors or use of more advanced frequency converters, etc.
When the energy consumption evaluation coefficient is smaller than the energy consumption evaluation coefficient critical threshold, the energy utilization efficiency of the power transformation equipment is shown to be within a certain standard range, and the power transformation equipment can normally operate; the system does not send out an early warning signal.
By calculating the energy consumption evaluation coefficient and setting the energy consumption evaluation coefficient critical threshold, when the energy consumption evaluation coefficient exceeds the energy consumption evaluation coefficient critical threshold, a serious energy consumption early warning signal is timely sent out to remind professional technicians of optimizing and improving, so that the problems of energy waste, low efficiency, unstable operation and the like are avoided, the consumption of energy is reduced, the problems of unstable operation, low efficiency and the like of the power transformation equipment are timely found, and corresponding measures are adopted to optimize and improve, so that the safe and stable operation of the power transformation equipment is ensured.
In step S3, calculating predicted production electric quantity in each time period, and calculating the number of theoretical input generator sets according to the predicted production electric quantity in each time period; predicting the power generated by the generator set within a certain time period; the time period may be weekly, daily, etc., and the application analyzes daily.
And acquiring the power generation capacity of each group of generator sets in each time period, the historical power generation capacity in each time period and the historical power generation capacity growth rate.
The predicted production electricity quantity per time period is as follows: y=ts (1+eg).
The calculation formula of the theoretical input generator set number is as follows:
wherein Y is the predicted power generation amount in each time period, F is the theoretical input number of the generator sets, and M is the power generation amount in each time period of each group of the generator sets; ts is the electricity quantity produced by the history in each time period, and Eg is the historical electricity generation rate.
It is to be noted that the present application assumes that the power generation amount of each group of power generation sets is the same, and the setting of the power generation amount per time period of each group of power generation sets is set according to actual conditions, such as the case of the model of the power generator.
The historical electricity production per time period can collect electricity production in the same time period of the last year, for example, the electricity production is predicted per time period of 4 months and 2 days in the present year, and then the electricity production of the power grid of 4 months and 2 days in the last year is collected.
The historical power generation rate can be used for collecting the power generation rate of the last year period relative to the previous year period.
The calculation of the theoretical input of the number of the generator sets can help the power grid company to accurately predict the future power demand, and the input of the number of the generator sets is arranged according to the prediction result, so that the operation efficiency of the power grid company is improved.
In step S4, according to the energy consumption evaluation coefficient, the predicted produced electric quantity per time period is adjusted, so as to calculate an actual predicted produced electric quantity, where the expression of the actual predicted produced electric quantity is:
wherein U is the actual predicted power generation, and delta is the adjustment coefficient.
The larger the energy consumption evaluation coefficient is, the smaller the actual predicted produced electric quantity is, which means that the actual predicted produced electric quantity is smaller than the predicted produced electric quantity per time period, and the actual power generation amount of the power grid company cannot meet the actual power generation requirement, so that the power shortage is caused.
According to the actual predicted production electric quantity, the number of generating sets which need to be increased is calculated, and the expression is as follows:
wherein, C is the number of generating sets which need to be increased.
The power grid company can more accurately predict the future power demand by calculating the predicted power generation amount in each time period and obtaining the theoretical input power generation set quantity according to the predicted power generation amount in each time period, and the power grid company can arrange the input quantity of the power generation sets according to the prediction result, so that the situation of surplus or shortage is avoided; the actual predicted production electric quantity is adjusted according to the energy consumption evaluation coefficient, the actual predicted production electric quantity is obtained through calculation, the number of generating sets which need to be increased is calculated through the actual predicted production electric quantity, and a power grid company can plan and manage resources better, so that the resource utilization efficiency is improved; through accurate prediction and adjustment, the situation of excess or deficiency can be effectively avoided, the power waste and peak shaving cost are reduced, and the power generation cost is reduced; and the stability of the power grid is improved. Through accurate prediction and adjustment, the problems of unbalanced supply and demand of electric power and the like can be effectively avoided, the stability of a power grid is improved, and the reliability of electric power supply is ensured; by predicting future power demands and increasing the number of gensets, reliability and stability of the power supply may be ensured.
Example 2
The embodiment 2 of the present application is different from the embodiment 1 in that the present embodiment is introduced by a grid audit information management system.
Fig. 2 is a schematic structural diagram of the power grid audit information management system of the present application, which includes a data processing module, a data acquisition module, an energy consumption early warning module, and a generator set prediction module, which are communicatively connected with the data processing module.
The data acquisition module acquires the energy utilization information and the energy efficiency information, the energy utilization information and the energy efficiency information are sent to the data processing module, and the data processing module calculates an energy consumption evaluation coefficient.
The energy consumption early-warning module receives the energy consumption evaluation coefficient calculated by the data processing module, and the system sends out an energy consumption serious early-warning signal when the energy consumption evaluation coefficient is larger than the energy consumption evaluation coefficient critical threshold value according to the comparison between the energy consumption evaluation coefficient and the energy consumption evaluation coefficient critical threshold value.
And sending the historical produced electric quantity and the historical power generation increase rate to a data processing module, and calculating the predicted produced electric quantity in each time period by the data processing module.
The energy consumption evaluation coefficient and the predicted production electric quantity in each time period are sent to a data processing module, the actual predicted production electric quantity is calculated and obtained, the generator set prediction module calculates the theoretical input generator set number and the number of the generator sets to be increased according to the actual predicted production electric quantity and the predicted production electric quantity in each time period of each group of generator sets by the data processing module, and the power generation amount in each time period and the predicted production electric quantity in each time period of each group of generator sets are calculated.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (6)
1. The power grid audit informatization management method is characterized by comprising the following steps of:
step S1: acquiring energy utilization information and energy efficiency information, and calculating an energy consumption evaluation coefficient according to the energy utilization information and the energy efficiency information;
step S2: setting an energy consumption evaluation coefficient critical threshold, evaluating the energy consumption condition of the power transformation equipment through comparison of the energy consumption evaluation coefficient and the energy consumption evaluation coefficient critical threshold, and sending an energy consumption serious early warning signal by the system when the energy consumption evaluation coefficient is larger than the energy consumption evaluation coefficient critical threshold;
step S3: calculating predicted production electric quantity in each time period, and calculating the number of theoretical input generator sets according to the predicted production electric quantity in each time period;
step S4: and calculating actual predicted production electric quantity according to the energy consumption evaluation coefficient, and calculating the number of the generating sets which need to be increased according to the actual predicted production electric quantity.
2. The grid audit informatization management method according to claim 1, wherein: in step S1, energy utilization information and energy efficiency information are obtained, wherein the energy utilization information includes transformer loss rate and switching equipment conduction loss value; the energy efficiency information comprises a load rate deviation value and a combined electrical appliance energy efficiency value;
the transformer loss rate, the switching equipment conduction loss value, the load rate deviation value and the combined electrical appliance energy efficiency value are subjected to normalization processing, and an energy consumption evaluation coefficient is calculated, wherein the expression is as follows:
wherein, N is an energy consumption evaluation coefficient, tr, sv, lv, ea is a transformer loss rate, a switching device conduction loss value, a load rate deviation value and a combined electrical appliance energy efficiency value respectively; alpha 1 、α 2 、α 3 、α 4 Preset proportionality coefficients of transformer loss rate, switching equipment conduction loss value, load rate deviation value and combined electrical appliance energy efficiency value respectively, and alpha 1 ＞α 3 ＞α 2 ＞α 4 ＞0。
3. The grid audit informatization management method according to claim 2, wherein: in step S2, an energy consumption evaluation coefficient critical threshold is set, and marked as N 0 ；
When the energy consumption evaluation coefficient is larger than the energy consumption evaluation coefficient critical threshold, the system sends out an energy consumption serious early warning signal;
when the energy consumption evaluation coefficient is smaller than the energy consumption evaluation coefficient critical threshold, the system does not send out an early warning signal.
4. A method of grid audit informatization management according to claim 3 wherein: in step S3, the power generation amount per time period, the historical power generation amount per time period and the historical power generation amount increase rate of each group of power generation units are obtained;
the predicted production electricity quantity per time period is as follows: y=ts (1+eg);
the theoretical input generator sets are as follows:
wherein Y is the predicted power generation amount in each time period, F is the theoretical input number of the generator sets, and M is the power generation amount in each time period of each group of the generator sets; ts is the electricity quantity produced by the history in each time period, and Eg is the historical electricity generation rate.
5. The grid audit informatization management method according to claim 4, wherein: in step S4, according to the energy consumption evaluation coefficient, the actual predicted produced electricity is calculated, where the expression is:
wherein U is the actual predicted power generation, delta is the adjustment coefficient;
according to the actual predicted production electric quantity, the number of generating sets which need to be increased is calculated, and the expression is as follows:
wherein, C is the number of generating sets which need to be increased.
6. The power grid audit information management system is used for realizing the power grid audit information management method according to any one of claims 1-5, and is characterized in that: the system comprises a data processing module, a data acquisition module, an energy consumption early warning module and a generator set prediction module, wherein the data acquisition module, the energy consumption early warning module and the generator set prediction module are in communication connection with the data processing module;
the data acquisition module acquires energy utilization information and energy efficiency information, the energy utilization information and the energy efficiency information are sent to the data processing module, and the data processing module calculates an energy consumption evaluation coefficient;
the energy consumption early-warning module receives the energy consumption evaluation coefficient calculated by the data processing module, and the system sends out an energy consumption serious early-warning signal when the energy consumption evaluation coefficient is larger than the energy consumption evaluation coefficient critical threshold value according to the comparison between the energy consumption evaluation coefficient and the energy consumption evaluation coefficient critical threshold value;
transmitting the historical produced electric quantity and the historical power generation increase rate to a data processing module, and calculating the predicted produced electric quantity in each time period by the data processing module;
the energy consumption evaluation coefficient and the predicted production electric quantity in each time period are sent to a data processing module, the actual predicted production electric quantity is calculated and obtained, the generator set prediction module calculates the theoretical input generator set number and the number of the generator sets to be increased according to the actual predicted production electric quantity and the predicted production electric quantity in each time period of each group of generator sets by the data processing module, and the power generation amount in each time period and the predicted production electric quantity in each time period of each group of generator sets are calculated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310789188.1A CN116843361B (en) | 2023-06-30 | 2023-06-30 | Power grid audit informatization management method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310789188.1A CN116843361B (en) | 2023-06-30 | 2023-06-30 | Power grid audit informatization management method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116843361A true CN116843361A (en) | 2023-10-03 |
| CN116843361B CN116843361B (en) | 2024-02-06 |
Family
ID=88164636
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310789188.1A Active CN116843361B (en) | 2023-06-30 | 2023-06-30 | Power grid audit informatization management method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116843361B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103559576A (en) * | 2013-10-30 | 2014-02-05 | 广东工业大学 | Energy management system |
| CN105243600A (en) * | 2015-07-21 | 2016-01-13 | 河南行知专利服务有限公司 | Grid power generation adjustment method |
| CN105654393A (en) * | 2015-12-31 | 2016-06-08 | 国网电力科学研究院武汉南瑞有限责任公司 | Energy efficiency management service system of power distribution network area |
| US20160370771A1 (en) * | 2013-12-31 | 2016-12-22 | Universidad De Talca | System and method for monitoring and managing the energy efficiency of buildings |
| CN107392442A (en) * | 2017-07-03 | 2017-11-24 | 上海安悦节能技术有限公司 | Low pressure electric substation user's energy consumption evaluation method |
| CN108053109A (en) * | 2017-12-08 | 2018-05-18 | 长春工程学院 | A kind of distributed energy generating set energy efficiency evaluating method |
| CN109461093A (en) * | 2018-10-08 | 2019-03-12 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Electric quantity measuring system Accuracy Evaluation and station service energy consumption analysis system |
| CN110490405A (en) * | 2019-06-28 | 2019-11-22 | 贵州电网有限责任公司 | A kind of transformer energy efficiency evaluation analysis system |
| CN114372360A (en) * | 2021-12-31 | 2022-04-19 | 国网河北省电力有限公司营销服务中心 | Method, terminal and storage medium for power load prediction |
| CN116187518A (en) * | 2022-12-14 | 2023-05-30 | 中国铁塔股份有限公司青海省分公司 | Photovoltaic base station energy consumption prediction method and device |
| CN116307076A (en) * | 2023-02-02 | 2023-06-23 | 西安交通大学 | Industrial park energy efficiency management and control method based on Internet of things |
| CN116316635A (en) * | 2023-03-16 | 2023-06-23 | 国网信通亿力科技有限责任公司 | Electric power cooperative control method and system based on measurement information |
-
2023
- 2023-06-30 CN CN202310789188.1A patent/CN116843361B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103559576A (en) * | 2013-10-30 | 2014-02-05 | 广东工业大学 | Energy management system |
| US20160370771A1 (en) * | 2013-12-31 | 2016-12-22 | Universidad De Talca | System and method for monitoring and managing the energy efficiency of buildings |
| CN105243600A (en) * | 2015-07-21 | 2016-01-13 | 河南行知专利服务有限公司 | Grid power generation adjustment method |
| CN105654393A (en) * | 2015-12-31 | 2016-06-08 | 国网电力科学研究院武汉南瑞有限责任公司 | Energy efficiency management service system of power distribution network area |
| CN107392442A (en) * | 2017-07-03 | 2017-11-24 | 上海安悦节能技术有限公司 | Low pressure electric substation user's energy consumption evaluation method |
| CN108053109A (en) * | 2017-12-08 | 2018-05-18 | 长春工程学院 | A kind of distributed energy generating set energy efficiency evaluating method |
| CN109461093A (en) * | 2018-10-08 | 2019-03-12 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Electric quantity measuring system Accuracy Evaluation and station service energy consumption analysis system |
| CN110490405A (en) * | 2019-06-28 | 2019-11-22 | 贵州电网有限责任公司 | A kind of transformer energy efficiency evaluation analysis system |
| CN114372360A (en) * | 2021-12-31 | 2022-04-19 | 国网河北省电力有限公司营销服务中心 | Method, terminal and storage medium for power load prediction |
| CN116187518A (en) * | 2022-12-14 | 2023-05-30 | 中国铁塔股份有限公司青海省分公司 | Photovoltaic base station energy consumption prediction method and device |
| CN116307076A (en) * | 2023-02-02 | 2023-06-23 | 西安交通大学 | Industrial park energy efficiency management and control method based on Internet of things |
| CN116316635A (en) * | 2023-03-16 | 2023-06-23 | 国网信通亿力科技有限责任公司 | Electric power cooperative control method and system based on measurement information |
Non-Patent Citations (1)
| Title |
|---|
| 刘道新等: ""配电网同期线损监测系统的设计与实现"", 《电子设计工程》, pages 42 - 45 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116843361B (en) | 2024-02-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Diaz-Aguiló et al. | Field-validated load model for the analysis of CVR in distribution secondary networks: Energy conservation | |
| CN103647276A (en) | Electric energy quality early warning system and method thereof | |
| CN102879696B (en) | Automatic judgment method for over-load operational feasibility of transformer | |
| CN105243254A (en) | General line loss analysis method | |
| CN113095595A (en) | Energy efficiency optimization method and system based on power distribution operation and maintenance | |
| CN108701999B (en) | Power control of a microgrid | |
| CN115796406A (en) | Optimal adjustment method and system for virtual power plant | |
| CN112787398A (en) | Intelligent power distribution network exception handling method and system | |
| CN116882595B (en) | Comprehensive performance prediction method and system for transformer substation | |
| CN116843361B (en) | Power grid audit informatization management method and system | |
| Jalal | Case study: Implementation of dynamic rating for transformers in a distribution network | |
| CN201887554U (en) | Intelligent transformer monitoring system | |
| CN111049128B (en) | Power system reliability improving method considering load prediction power utilization influence | |
| CN113159717A (en) | Phase modulator state analysis early warning method and system based on weight evaluation mechanism | |
| CN112865323A (en) | Harmonic analysis and three-phase imbalance monitoring method based on intelligent CT | |
| Carneiro et al. | Impact of Distributed Energy Resources on Fault Location, Isolation, and Service Restoration | |
| CN117220356B (en) | Multi-time-scale-based annual loss reduction operation optimization method and system for power transmission network | |
| KR102664018B1 (en) | Voltage control device of two-level structure for conservation voltage reduction through voltage optimization control based on load prediction model | |
| CN112448478B (en) | Transformer load control method | |
| KR102664021B1 (en) | Voltage control device for conservation voltage reduction through voltage optimization control based on load prediction model | |
| CN203504005U (en) | Intelligent voltage-adjustable power transformation and distribution device | |
| Chacón et al. | Analysis of the impact of the electric vehicles charging on distribution transformers | |
| CN110912269B (en) | Electric energy dispatching and controlling system for offshore power grid | |
| Hunt et al. | Improving the operation of distribution substations | |
| CN109617064B (en) | Electric object state switching analysis method in dispatching system |
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 |