CN102376026B - Industrial enterprise power utilization load optimizing system - Google Patents
Industrial enterprise power utilization load optimizing system Download PDFInfo
- Publication number
- CN102376026B CN102376026B CN201110337037.XA CN201110337037A CN102376026B CN 102376026 B CN102376026 B CN 102376026B CN 201110337037 A CN201110337037 A CN 201110337037A CN 102376026 B CN102376026 B CN 102376026B
- Authority
- CN
- China
- Prior art keywords
- module
- requirement
- data
- electricity
- timesharing
- 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.)
- Expired - Fee Related
Links
Classifications
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
A kind of industrial enterprise power utilization load optimizing system, belongs to enterprise's electric load scheduling field.A kind of industrial enterprise power utilization load optimizing system, it is characterized in that, hardware comprises relational database server, application server, client rs PC, live database server etc., application server is connected with live database server and carries out data interaction, application server be connected with relational database carry out data store with read, client rs PC is connected with application server carries out information interaction; Application module comprises data acquisition module, data processing and memory module, requirement computing module, parameter setting module, requirement control and prediction module, spatial load forecasting scheme and warning message administration module, requirement analysis module, timesharing electricity charge analysis optimization module.Advantage is, by requirement analysis and timesharing electricity charge analysis optimization, formulates the electricity consumption plan of the following minimum cost of enterprise, reaches the object of moving Pinggu, peak, balanced load, increase consumer life-span, reduction business electrical cost.
Description
Technical field
The invention belongs to enterprise's electric load scheduling field, a kind of industrial enterprise power utilization load optimizing system is particularly provided.
Background technology
The industries such as iron and steel, electrolytic aluminium, copper smelting are the industries that power consumption is large, the two-part rate system price of what enterprise customer's the charge of electricity of these industries performed is large commercial power, total electricity charge form primarily of power cost, basic charge as per installed capacity (also known as electrical capacity charge or demand charge) and the adjustment electricity charge.Power cost is according to the power consumption of user at spike Pinggu day part, the electricity charge calculated according to spike Pinggu rate; Basic charge as per installed capacity is with transformer station's expense that " maximum demand " is collected this month 40% expense collected of transformer capacity (or by); The adjustment electricity charge are the expenses of carrying out rewards and punishments according to of that month power factor size.
Requirement is the average power in the requirement cycle.
In the requirement cycle, the computation period of requirement, China is 15 minutes in the general provision requirement cycle.
Maximum demand is the maximal value of requirement in the time interval of specifying, and is generally a payment cycle.
Requirement account form, sliding, from any time, measure the method for requirement by the time recursion being less than the requirement cycle, recurrence time is the slippage time, and the slippage time can be 1,3,5,15 minute (or random time of 1 to 15 minutes); Interval type, from any time, measures the method for requirement by given requirement cycle recursion.
Impact load, some large-scale electric equipments of enterprise, as Recent Development for Large Scale Electric Arc, large-scale blooming mill, smelting furnace, electrolytic furnace, all kinds of shape rolling mill, continuous cold and hot-slabbing mill etc., single-machine capacity is large, amplitude, the frequency of the variation of its active power are all comparatively large, and this type load is referred to as impact load.
By the moon tracing analysis of business electrical load, finding electricity consumption high load capacity period very limited, is generally several hours to tens hours, and is exactly that the high load capacity of this limited period of time makes this month maximum demand exceed certain numerical value.The reason causing power load to raise is exactly the operation of impact load, and the superposition of impact load sometimes makes load higher.The short time impact load being less than 15 minutes can not cause requirement to raise; but these main equipments such as electric arc furnaces, smelting furnace, electrolytic furnace, milling train generally only can not open a few minutes; once start can not be shut down to reduce load, shutting down the loss caused can be larger.
The scheduling major part of current enterprise electric system is just according to information such as trend, load, warnings, carry out phone scheduling, not deep to the understanding of requirement and maximum demand, also corresponding means are lacked to observe the relation of current loads and requirement, load adjustment scheme cannot be made in time, reduce the impact on requirement; And a lot of local power supply department does not allow that enterprise directly reads the maximum demand information in Source of Gateway Meter, can only artificial timing meter reading, the moon maximum demand payment that enterprise can only provide according to power supply department, cannot know when, which workshop or equipment result in the increase of this month maximum demand, also just cannot provide useful information for the production adjustment of next month.
Visible, to the enterprise of pressing maximum demand payment, the level of detail grasped requirement information is the key factor affecting its electric cost.The computation period of requirement is 15 minutes, and dispatcher can not stare at many load curves for a long time, to the load changed greatly, is difficult to judge requirement value in time; One day of write by hand every day or class's requirement information not in detail in time, to optimization power scheduling, are optimized production scheduling, save demand charge, are reduced costs, just lacked very important information source.
Because each period electricity price level is different, the production schedule of enterprise is subject to the impact of spike Pinggu period, the production schedule do whether rationally and whether also have optimizable leeway, be need the detailed data of timesharing active electrical degree amount and production information to provide data supporting.A lot of enterprise often only analyzes the electricity consumption situation of a long period section, does not excavate useful information from the detailed timesharing electricity of every day and the yield data of correspondence, is also just not easy to find Problems existing and optimizable place.
In order to tackle twice world energy sources crisis and day by day serious environmental pressure; external beginning one's study from 20 century 70s has promoted demand Side Management (DSM), and wherein an important content is exactly peak load shifting, reduces maximum demand, steadily power load.The representational product of demand control is the VDLFHZK microcomputer power load central control system of Japanese technology and " the safe energy of PTE hundred " demand control system (having another name called power demand side's management system) of Germany technology respectively, and this two series products has application in the production technology such as the electric furnace of some iron and steel enterprises and the smelting furnace of copper metallurgy enterprise at home and abroad.
China also introduces dsm from early 1980s, and government and many large industrial enterprises begin one's study dsm and optimize measure and the technology of load, reach the object of balanced load, economize energy.
" demand control system and method thereof " (CN101697074A) that the Chinese Telecommunication Co Ltd (Taiwan) announced in " a kind of method of load management and device " (CN101640417) of Siemens Company's application that Patent Office of the People's Republic of China announces on February 3rd, 2010, on April 21st, 2010 applies for.The requirement that above patent or product are all some technique to enterprise, workshop or equipment carry out monitors or controls, and does not carry out overall requirement and load monitoring to enterprise; Only monitor for the one of requirement or timesharing load, do not take into consideration from the timesharing electricity charge and basic charge as per installed capacity two aspect, to seek total grid electricity fee cost minimum.
Summary of the invention
The object of the present invention is to provide a kind of industrial enterprise power utilization load optimizing system, from enterprise's overall situation, consider the load variations situation of all great electricity consumption workshops or equipment (determining which is great user according to the total load size of each enterprise and customer charge proportion), by the monitoring to enterprise's total requirement, search rule (load changing rate exceedes setting value or load exceedes setting value) is utilized to find the electricity consumption workshop or equipment causing total requirement to increase in time, and export corresponding control program, controling parameters in Utilization plan to be regulated customer charge by controller and reduces high load capacity in short-term, by requirement analysis and timesharing electricity charge analysis optimization, formulate the electricity consumption plan of the following minimum cost of enterprise, reach the object of moving Pinggu, peak, balanced load, increase consumer life-span, reduction business electrical cost.
The hardware of system of the present invention comprises relational database server, application server, client rs PC, live database server etc., application server is connected with live database server and carries out data interaction, application server be connected with relational database carry out data store with read, client rs PC is connected with application server carries out information interaction.Application module comprises data acquisition module, data processing and memory module, requirement computing module, parameter setting module, requirement control and prediction module, spatial load forecasting scheme and warning message administration module, requirement analysis module, timesharing electricity charge analysis optimization module; Wherein parameter setting module, requirement control and prediction module, spatial load forecasting scheme and warning message administration module, requirement analysis module, timesharing electricity charge analysis optimization module are deployed in client machine; Data processing and memory module, requirement computing module are deployed in application server, and relational database is deployed in relational database server, and data acquisition module is deployed in live database server;
Parameter setting module and relational database data acquisition module, data processing and memory module, requirement calculate model calling, transmit corresponding parameter; Except data acquisition, other modules are all connected with relational database, read or store data from database; Data processing is connected with data acquisition module with memory module, requirement computing module, obtains data from data acquisition module; Requirement control and prediction module, spatial load forecasting scheme and warning message administration module, requirement analysis module, timesharing electricity charge analysis optimization module and relational database, requirement calculate model calling, read information from database, read requirement related data by requirement computing module.
In the present invention, the requirement cycle adopts 15 minutes of national regulation, also can set arbitrarily according to actual needs (such as 30 minutes); Account form selects sliding, the slippage time is 1 minute (random times of 1 to 15 minutes), consistent with the computation period that most domestic demand meter designs, also can arrange the new slippage time as required, 1 minutes result of calculation is not only accurate but also little to the pressure of system.
Parameter setting module, is configured the information that system is used.The memory cycle of the different acquisition item (active power, active electrical degree amount etc.) of configuration enterprise each inlet wire, each main electricity consumption workshop or equipment inlet wire and storage mode, and the parameter such as the computation period of requirement, slippage time, want the maximum demand of operation circuit this month with every bar, configuration result is saved in relational database.
Data acquisition module, the data such as active power, active electrical degree amount, idle electricity of the electric system of real-time automated collection systems configuration.
Data processing and memory module, data are read from data acquisition module, after computing, automatically data are deposited into database, storage mode (mean value, aggregate-value, instantaneous value etc.) and memory cycle (5 seconds, 900 seconds, 3600 seconds etc.) arrange according to user's request.Store the data of the band timestamp that requirement computing module is sent.The regular Automatic clearance stale data of relational database.
Requirement computing module, utilizes the mean value of active power calculating per minute once in first 15 minutes (namely 1 minute calculating a requirement) collected, and result of calculation is issued data processing and memory module stores.
Requirement control and prediction module, to each circuit needing monitoring, requirement is carried out in line computation by requirement computing module, and application trend analytic approach carries out real-time requirement prediction, situation prediction requirement being exceeded to maximum demand setting value carries out Realtime Alerts, and store alarms information is to relational database, when the warning duration was more than 1 minute (1 to 3 minutes, but no more than 3 minutes, the time of adjustment will be incured loss through delay more than 3 minutes, significantly regulate equipment life in short-term, security all can have impact), the amount that prompting can regulate and remaining regulating time, and the workshop or equipment that cause requirement to increase is found by algorithm, provide the concrete spatial load forecasting scheme of each workshop that will regulate or equipment, then according to the load of priority automatic or manual adjustment portion consumer to reach balanced load, reduce maximum demand, reduce the object of electric cost.
Workshop or equipment are called as controlled device, priority must be protected (not allowing to regulate), adjustable (adjustable within certain limit) by being divided into successively from high to low according to the continuity of production technology and the coverage of start and stop, can stop (start and stop affect little on production and equipment), priority can adjust according to practical production status.Such as electrolytic furnace is the equipment that can not stop power supply suddenly in process of production that must protect, and priority is the highest.
Spatial load forecasting scheme and warning message administration module, warning message, the spatial load forecasting scheme of inquiry requirement control and prediction module record, comprise the information such as moment maximum demand of reporting to the police, prediction requirement, warning reason, residual accommodation time, spatial load forecasting object, spatial load forecasting amount.Situation about being occurred by summary warning message and the spatial load forecasting scheme of correspondence, understand the reason that requirement increases, for the production schedule and electricity consumption plan formulating future provides reference.
Requirement analysis module, by to (being generally a metering period in the past period, such as one month, also can be any a period of time) requirement trend analyze, find and cause requirement to increase and the time period and the reason that exceed setting value, be used to guide production and carry out adjusting to reduce fringe load increase and cause requirement to increase.
Timesharing electricity charge analysis optimization module, analyzing in the past period (is generally a metering period, such as one month, also can be any a period of time) the power consumption trend in spike Pinggu and electricity charge situation, in conjunction with the yield data of every day, under the prerequisite of maximum demand constraint, when providing bu by optimized algorithm, cost and reasonable, the most most economical time sharing electrical energy allocative decision of day ton product timesharing cost, reasonably adjust for the Instructing manufacture when not affecting output.
Timesharing electricity data in the optimized algorithm Corpus--based Method phase, under timesharing electricity numerical value hourly can not exceed the constraint condition of maximum demand numerical value, application optimization algorithm, obtains most economical time sharing electrical energy allocative decision, mainly comprises two steps:
Step one: optimum electric charge scheme during bu, the timesharing electricity of collection analysis cycle (being generally a metering period) interior every day, be located in the statistics phase and have n days, in every day, spike Pinggu has 24 electricity PP respectively
k, (k ∈ [0,23]) (electricity numerical value per hour), maximum demand MD.In i-th (∈ [1, n]) sky, timesharing electricity is PP
ik, (k ∈ [0,23]), the electricity price in spike Pinggu is P respectively
ji, P
fi, P
pi, P
gi, it is G respectively that spike Pinggu electricity gathers
ji, G
fi, G
pi, G
gi, electricity charge total value C
i=G
ji× P
ji+ G
fi× P
fi+ G
pi× P
pi+ G
gi× P
gi.
(wherein
) be optimum timesharing cost, C
mincorresponding power use by time shearing PP
ik, (k ∈ [0,23]) are optimum power use by time shearing scheme, can be used as the important references of following electricity consumption plan;
Step 2: the optimum electric charge scheme of day ton product timesharing, on the basis of step one, adds production information, in i-th (∈ [1, n]) sky, product yield F
i, (i ∈ [1, n]).
(wherein
) be the optimum timesharing cost of ton product, C
mincorresponding power use by time shearing PP
ik, (k ∈ [0,23]) are optimum ton product power use by time shearing scheme, can be used as the important reference of following electricity consumption plan.
The invention has the advantages that:
(1) establish enterprise's power system information database, record the detailed data of active power, requirement, electricity comprehensively, substitute write by hand ammeter completely, comparatively write by hand data accurately, in detail, avoids the mistake of data transmission, alleviates the burden of user;
(2) requirement control and prediction, proceed from the situation as a whole, consider the load variations situation of all consumers, the reason finding requirement to increase by analyses and prediction and searching algorithm, and the optimization control scheme exporting correspondence carries out spatial load forecasting, accomplishes holistic approach, Partial controll;
(3) provide history requirement analytic function, help customer analysis a period of time domestic demand amount reason of changes, combine the details and the information of spatial load forecasting scheme of reporting to the police, provide foundation for the formulation production schedule;
(4) provide the analysis optimization of the timesharing electricity charge accurately function in detail, utilize production information and help user to find optimum time sharing electrical energy allocative decision and the corresponding production schedule by optimizing to calculate.
Accompanying drawing explanation
Fig. 1 is present system module relation diagram.
Fig. 2 is requirement control and prediction process flow diagram of the present invention.
Fig. 3 is spatial load forecasting schematic diagram of the present invention.
Fig. 4 is requirement analysis process figure of the present invention.
Fig. 5 is time sharing electrical energy of the present invention and cost analysis process flow diagram.
Embodiment
The present invention adopts the framework of client/server, and service end is responsible for the function such as read-write of data acquisition, calculating and database, and client realizes concrete operation flow, and carries out information interaction with service end.
Serve end program of the present invention (comprising data acquisition module, data processing and memory module, requirement computing module) is installed on the server, the client-side program (comprising parameter setting module, requirement control and prediction module, spatial load forecasting scheme and warning message administration module, requirement analysis module, timesharing electricity charge analysis optimization module) of native system is installed on a client.Running client, user is configured the electricity consumption line information that will monitor as required, as memory cycle and the account form of different pieces of information (active power, active electrical degree amount etc.) on the inlet wire of the data of enterprise's inlet wire and each great electricity consumption workshop or equipment, each circuit, and the parameter information such as the computation period of requirement, slippage time, every bar inlet wire or want the maximum demand of operation circuit this month, configuration result is saved to database.
As the graph of a relation that Fig. 1 is each functional module, comprise the data acquisition that live database server runs, the data that application server runs calculate and process, the relational database that relational database server runs, the configuration of client operation, analysis & control.
After system starts, data acquisition module is connected with the communication module of electric system, gathers active power and the active electrical degree amount data of the circuit configured.
Data processing and memory module calculate according to configuration information active power and active electrical degree amount and store.The collection period of active power is 5 seconds, and real time data does not need to calculate direct storage; The collection period of active electrical degree amount is 900 seconds, and cumulative data stores after needing to do difference operation again; Meanwhile, this module also accepts the data of the band timestamp that requirement computing module calculates, and directly stores numerical value and time.
Requirement computing module reads the data of the active power in the current time previous requirement cycle (15 minutes) at interval of the slippage time (1 minute), calculate its time average, and result of calculation and current time are sent to data processing and memory module.
As Fig. 2 dispatcher opens requirement control and prediction module, the information such as maximum demand early warning value (setting value), current requirement, prediction requirement, residue power-adjustable can be seen, and warning message.When this period forecasting requirement is greater than setting value, system alarm.Reported to the police the duration more than 1 minute, and starting guide analytical algorithm, according to residue power-adjustable and selection of time, the minimum quick adjustment strategy not affecting again production is regulated on equipment, and export corresponding control program information, dispatcher can select to automatically perform control also manually can perform control, and actuator can regulate equipment according to the controlled quentity controlled variable exported and control strategy.Also control can be performed not in accordance with the control program exported.The requirement cycle (15 minutes) if at the end of this cycle requirement be greater than the value of current maximum demand, then upgrade maximum demand, otherwise the time pushes ahead the slippage time (1 minute) automatically, calculate the information such as new requirement, prediction requirement from new.
As Fig. 3 dispatcher opens spatial load forecasting scheme and warning message administration module, select time scope, inquiry warning message: moment maximum demand of reporting to the police, predict requirement, the line load information that causes requirement to increase, the residual accommodation time; And corresponding spatial load forecasting scheme of reporting to the police: spatial load forecasting object, spatial load forecasting amount.Situation about being occurred by summary warning message and the spatial load forecasting scheme of correspondence, the reason that better understanding requirement increases, the workshop increased often causing requirement or equipment, can be used as emphasis and consider object, reasonably allocate after formulating when the production schedule and electricity consumption plan.
As Fig. 4 dispatcher or managerial personnel open requirement analysis module, the selection analysis time period, such as a metering period is from 2011-05-01 0:00:00 to 2011-05-31 23:59:00, system searches the time period exceeding maximum demand setting value automatically, and the total requirement that the load increase that analysis is which workshop or equipment causes increases, and provide solution, such as: (1) 1# milling train on time superposed with the 1# electric furnace smelting start time, the suggestion start time staggers 15 minutes; (2) milling train reduces 50kW load in the spike period.Production scheduling personnel just can formulate more rationally in the production below, more energy-conservation plan.Also can analyze the requirement situation of random time section (as yesterday), so as dispatcher better grasp electric system ruuning situation and produce before relation, make to dispatch detailed data foundation.
As Fig. 5 dispatcher or managerial personnel open timesharing electricity charge analysis optimization module, the selection analysis time period, such as a metering period is from 2011-05-01 0:00:00 to 2011-05-31 23:59:00, in conjunction with the product yield data of every day, system passes through analytical calculation, the time sharing electrical energy allocative decision of cost minimization and day ton product timesharing cost minimization when providing most economical bu, dispatcher is used to guide to produce and reasonably adjusts the carrying out not affecting output.
System is at certain iron and steel enterprise's installation and operation, You Liangge transformer station of this enterprise, total volume 980000kVA, by real-time demand control, to reduce in production run the probability that high load capacity in short-term causes maximum demand to increase significantly, requirement analysis and timesharing electricity charge analysis tool are that power scheduling personnel and managerial personnel provide good analytical plan, the understanding be perfectly clear is had to maximum demand, time sharing electrical energy and the timesharing electricity charge, there is provided fine-grained management and scheduling in conjunction with detailed data, make summary afterwards become planned adjustment in advance.One year over, system achieves balanced load, the peak load that disappears, increase the object of equipment life, adds up, for this enterprise's power cost saving more than 800 ten thousand yuan, to achieve good economic and social benefit in on-the-spot stable operation.
Claims (2)
1. an industrial enterprise power utilization load optimizing system, is characterized in that, hardware comprises relational database server, application server, client rs PC, live database server; Application server is connected with live database server and carries out data interaction, and application server is connected with relational database and carries out data and store and read, and client rs PC is connected with application server carries out information interaction; Application module comprises data acquisition module, data processing and memory module, requirement computing module, parameter setting module, requirement control and prediction module, spatial load forecasting scheme and warning message administration module, requirement analysis module, timesharing electricity charge analysis optimization module; Wherein parameter setting module, requirement control and prediction module, spatial load forecasting scheme and warning message administration module, requirement analysis module, timesharing electricity charge analysis optimization module are deployed in client machine; Data processing and memory module, requirement computing module are deployed in application server, and relational database is deployed in relational database server, and data acquisition module is deployed in live database server;
Parameter setting module and data acquisition module, data processing and memory module, requirement calculate model calling, transmit corresponding parameter; Except data acquisition, other modules are all connected with relational database, read or store data from database; Data processing is connected with data acquisition module with memory module, requirement computing module, obtains data from data acquisition module; Requirement control and prediction module, spatial load forecasting scheme and warning message administration module, requirement analysis module, timesharing electricity charge analysis optimization module and relational database, requirement calculate model calling, read information from database, read requirement related data by requirement computing module;
Described parameter setting module, is configured the information that system is used; The memory cycle of the different acquisition item of configuration enterprise each inlet wire, each electricity consumption workshop or equipment inlet wire and storage mode, and the computation period of requirement, the slippage time, every bar wants the maximum demand of operation circuit this month, and configuration result is saved in relational database; Different acquisition item comprises active power, active electrical degree amount;
Data acquisition module, active power, active electrical degree amount, the idle electricity of the electric system of real-time automated collection systems configuration;
Data processing and memory module, read data from data acquisition module, after computing, automatically data are deposited into database, storage mode and memory cycle are arranged according to user's request; Store the data of the band timestamp that requirement computing module is sent, the regular Automatic clearance stale data of relational database; Storage mode comprises mean value, aggregate-value, instantaneous value;
Requirement computing module, utilizes the mean value of active power calculating per minute once in first 15 minutes that collects, and result of calculation is issued data processing and memory module stores;
Requirement control and prediction module, to each circuit needing monitoring, requirement is carried out in line computation by requirement computing module, and application trend analytic approach carries out real-time requirement prediction, situation prediction requirement being exceeded to maximum demand setting value carries out Realtime Alerts, and store alarms information is to relational database, when in 1 ~ 3 minute duration of warning, the amount that prompting can regulate and remaining regulating time, and the workshop or equipment that cause requirement to increase is found by algorithm, provide the concrete spatial load forecasting scheme of each workshop that will regulate or equipment, then according to the load of priority automatic or manual adjustment portion consumer to reach balanced load, reduce maximum demand, reduce the object of electric cost,
Spatial load forecasting scheme and warning message administration module, warning message, the spatial load forecasting scheme of inquiry requirement control and prediction module record, comprise moment maximum demand of reporting to the police, prediction requirement, warning reason, residual accommodation time, spatial load forecasting object, spatial load forecasting amount; Situation about being occurred by summary warning message and the spatial load forecasting scheme of correspondence, understand the reason that requirement increases, for the production schedule and electricity consumption plan formulating future provides reference;
Requirement analysis module, by analyzing the requirement trend in the past period, finding and causing requirement to increase and the time period and the reason that exceed setting value, is used to guide production and carries out adjusting to reduce fringe load increase and cause requirement to increase;
Timesharing electricity charge analysis optimization module, analyze power consumption trend and the electricity charge situation in the spike Pinggu in the past period, in conjunction with the yield data of every day, under the prerequisite of maximum demand constraint, when providing bu by optimized algorithm, cost and reasonable, the most most economical time sharing electrical energy allocative decision of day ton product timesharing cost, reasonably adjust for the Instructing manufacture when not affecting output.
2. system according to claim 1, is characterized in that, the timesharing electricity data in the described optimized algorithm Corpus--based Method phase, under timesharing electricity numerical value hourly can not exceed the constraint condition of maximum demand numerical value, application optimization algorithm, obtains most economical time sharing electrical energy allocative decision, comprises two steps, wherein variable i is number of days, scope is [1, n], and k is hourage, scope is [0,23]:
(1) optimum electric charge scheme during bu, in the collection analysis cycle, the timesharing electricity of every day, is located in the statistics phase and has n days, and in every day, spike Pinggu has 24 electricity PP
k, maximum demand MD, at i-th day, timesharing electricity was PP
ik, the electricity price in spike Pinggu is P respectively
ji, P
fi, P
pi, P
gi, it is G respectively that spike Pinggu electricity gathers
ji, G
fi, G
pi, G
gi, electricity charge total value C
i=G
ji× P
ji+ G
fi× P
fi+ G
pi× P
pi+ G
gi× P
gi,
be optimum timesharing cost, wherein
c
mincorresponding power use by time shearing PP
ik, be optimum power use by time shearing scheme, can be used as the important references of following electricity consumption plan;
(2) the optimum electric charge scheme of day ton product timesharing, on the basis of step (1), adds production information, at i-th day, and product yield F
i,
be the optimum timesharing cost of ton product, wherein
cP
mincorresponding power use by time shearing PP
ik, be optimum ton product power use by time shearing scheme, as the important reference of following electricity consumption plan.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110337037.XA CN102376026B (en) | 2011-10-31 | 2011-10-31 | Industrial enterprise power utilization load optimizing system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110337037.XA CN102376026B (en) | 2011-10-31 | 2011-10-31 | Industrial enterprise power utilization load optimizing system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102376026A CN102376026A (en) | 2012-03-14 |
| CN102376026B true CN102376026B (en) | 2015-08-05 |
Family
ID=45794590
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201110337037.XA Expired - Fee Related CN102376026B (en) | 2011-10-31 | 2011-10-31 | Industrial enterprise power utilization load optimizing system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102376026B (en) |
Families Citing this family (39)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930404A (en) * | 2012-11-14 | 2013-02-13 | 奉化市供电局 | Very important person (VIP) service management platform of customer service center |
| CN103136895B (en) * | 2013-01-25 | 2015-02-25 | 东北大学 | Furnace group energy consumption system and method in fused magnesia smelting process |
| CN103336910B (en) * | 2013-07-23 | 2016-06-29 | 上海申瑞继保电气有限公司 | Power consumer year contract Calculation of electric charge method |
| CN103489259B (en) * | 2013-08-16 | 2016-06-29 | 国家电网公司 | A kind of high-voltage user local cost control intelligent electric energy meter |
| CN103455852A (en) * | 2013-08-28 | 2013-12-18 | 西安交通大学 | Power transmission and distribution cost allocation method based on DEA cooperative game |
| CN104750052B (en) * | 2013-12-31 | 2017-10-31 | 华北电力大学 | A kind of power consumption control device and method |
| CN104200389A (en) * | 2014-02-21 | 2014-12-10 | 东华大学 | Product power consumption allocation method based on production process time and equipment working time |
| CN104297542B (en) * | 2014-09-30 | 2017-07-14 | 小米科技有限责任公司 | A kind of reminding method and device based on power consumption |
| CN104950738B (en) * | 2015-03-23 | 2017-06-16 | 石家庄科林电气股份有限公司 | The system and method that multiple-way supply circuit controls maximum demand |
| CN105006822A (en) * | 2015-07-30 | 2015-10-28 | 乔治费歇尔汽车产品(昆山)有限公司 | Energy optimization control system |
| KR102586745B1 (en) | 2015-09-01 | 2023-10-10 | 삼성전자 주식회사 | Method and apparatus of controlling energy consumption |
| CN112465669A (en) * | 2016-06-01 | 2021-03-09 | 李丽萍 | Enterprise power load self-checking and adjusting system and method |
| CN106097136A (en) * | 2016-06-01 | 2016-11-09 | 吕世全 | A kind of enterprise electric load is analyzed and the management system and method for maximum demand |
| CN107817700B (en) * | 2016-09-13 | 2020-09-15 | 北京易方通达科技有限公司 | Power consumption control device, power consumption control system, and power consumption control method |
| CN106355297B (en) * | 2016-10-11 | 2019-08-23 | 国电南瑞科技股份有限公司 | A kind of power grid decreasing loss optimization method based on electrolytic aluminium part throttle characteristics |
| US11238547B2 (en) | 2017-01-12 | 2022-02-01 | Johnson Controls Tyco IP Holdings LLP | Building energy cost optimization system with asset sizing |
| CN106872740A (en) * | 2017-04-17 | 2017-06-20 | 国网山东省电力公司青州市供电公司 | Requirement super-limit prewarning device and requirement transfinite determination methods |
| CN107203826A (en) * | 2017-05-19 | 2017-09-26 | 浙江大学 | A kind of power program optimization method of industrial user |
| CN107483408B (en) * | 2017-07-20 | 2020-04-07 | 宁波三星医疗电气股份有限公司 | Energy-saving monitoring system of electric equipment |
| EP3457513A1 (en) * | 2017-09-13 | 2019-03-20 | Johnson Controls Technology Company | Building energy system with load balancing |
| CN110245771B (en) * | 2018-03-09 | 2021-08-20 | 亿可能源科技（上海）有限公司 | Demand prediction method, demand control method and system |
| CN108761199A (en) * | 2018-05-21 | 2018-11-06 | 河南星火源科技有限公司 | Monitoring system and method is transported in the start and stop of contamination type enterprise |
| CN108665189A (en) * | 2018-05-24 | 2018-10-16 | 万洲电气股份有限公司 | A kind of industrial intelligent Optimization of Energy Saving system based on power structure model analysis |
| CN109190818B (en) * | 2018-08-28 | 2021-02-09 | 交叉信息核心技术研究院(西安)有限公司 | Power resource management method and system, server and computer readable storage medium |
| CN109217312B (en) * | 2018-11-16 | 2021-11-02 | 河南中原特钢装备制造有限公司 | Control system and control method for power load demand of heat treatment resistance furnace group |
| CN111832859A (en) * | 2019-04-18 | 2020-10-27 | 万洲电气股份有限公司 | Intelligent optimization energy-saving system based on industrial production line management synchronous optimization and accurate management and control |
| CN110059971A (en) * | 2019-04-24 | 2019-07-26 | 深圳市艾赛克科技有限公司 | Energy monitoring management method and device |
| CN110501946A (en) * | 2019-08-22 | 2019-11-26 | 万洲电气股份有限公司 | A kind of energy management system based on coal mill energy-saving analysis diagnosis |
| CN111582569A (en) * | 2020-04-29 | 2020-08-25 | 新奥数能科技有限公司 | Energy consumption optimization analysis method and device |
| CN112185071B (en) * | 2020-09-16 | 2023-02-17 | 山东莱钢永锋钢铁有限公司 | Electrical equipment load early warning method and device and storage equipment |
| CN113376456A (en) * | 2021-05-01 | 2021-09-10 | 辽宁能量云智能科技有限公司 | Cloud platform-based method for analyzing and managing electric quantity and electric charge of large electric power user |
| CN113422364B (en) * | 2021-05-06 | 2023-04-28 | 华翔翔能科技股份有限公司 | Full-buried variable load management method considering two electricity rates |
| CN113344367B (en) * | 2021-05-31 | 2023-04-07 | 青岛奥利普奇智智能工业技术有限公司 | Equipment load adjusting method, device, equipment, storage medium and product |
| CN113467265A (en) * | 2021-07-08 | 2021-10-01 | 仪征祥源动力供应有限公司 | Power consumption maximum demand control system and power consumption maximum demand control method |
| CN114255544A (en) * | 2021-11-24 | 2022-03-29 | 厦门中源能链科技有限公司 | Demand calculation method for different meters of same household of electric power charging system |
| CN114219538A (en) * | 2021-12-20 | 2022-03-22 | 青海绿能数据有限公司 | Method and system for cement plant mill production and electricity consumption analysis |
| CN114626609A (en) * | 2022-03-17 | 2022-06-14 | 山东莱钢永锋钢铁有限公司 | Intelligent management system for power demand |
| CN115800266B (en) * | 2022-12-16 | 2023-06-27 | 上海玫克生储能科技有限公司 | Power demand control method and device, electronic equipment and storage medium |
| CN116151129A (en) * | 2023-04-18 | 2023-05-23 | 石家庄科林电气股份有限公司 | Power dispatching system fault diagnosis method based on extreme learning machine |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101187813A (en) * | 2007-12-07 | 2008-05-28 | 冶金自动化研究设计院 | Integrative data source based automatic optimization scheduling system and method for steel, iron and energy source |
| CN101763105A (en) * | 2010-01-07 | 2010-06-30 | 冶金自动化研究设计院 | Self-adaptation selectable constrained gas optimizing dispatching system and method for steel enterprises |
| CN201576033U (en) * | 2009-12-25 | 2010-09-08 | 北京博纳电气有限公司 | Three-phase charge control intelligent electric energy meter |
| CN102193544A (en) * | 2011-03-25 | 2011-09-21 | 汉鼎信息科技股份有限公司 | Intelligent building energy management system |
-
2011
- 2011-10-31 CN CN201110337037.XA patent/CN102376026B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101187813A (en) * | 2007-12-07 | 2008-05-28 | 冶金自动化研究设计院 | Integrative data source based automatic optimization scheduling system and method for steel, iron and energy source |
| CN201576033U (en) * | 2009-12-25 | 2010-09-08 | 北京博纳电气有限公司 | Three-phase charge control intelligent electric energy meter |
| CN101763105A (en) * | 2010-01-07 | 2010-06-30 | 冶金自动化研究设计院 | Self-adaptation selectable constrained gas optimizing dispatching system and method for steel enterprises |
| CN102193544A (en) * | 2011-03-25 | 2011-09-21 | 汉鼎信息科技股份有限公司 | Intelligent building energy management system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102376026A (en) | 2012-03-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102376026B (en) | Industrial enterprise power utilization load optimizing system | |
| US20210181774A1 (en) | Voltage conservation using advanced metering infrastructure and substation centralized voltage control | |
| CN114243779B (en) | User adjustable load resource demand response method and system based on virtual power plant | |
| CN111966662B (en) | Multi-user-side comprehensive energy monitoring service application platform | |
| CN111966664A (en) | Multi-user-side comprehensive energy monitoring, tracking and analyzing system | |
| CN103337890A (en) | Orderly charging system and method for electric taxi charging station | |
| CN103208085A (en) | Analysis intelligence system for improving load rate and reducing maximum demand of power utilization of enterprises | |
| CN111614160A (en) | Low-voltage user load regulation service bearing platform | |
| CN112884459A (en) | Energy consumption process monitoring system and energy-saving analysis method thereof | |
| CN106251034A (en) | Wisdom energy saving electric meter monitoring system based on cloud computing technology | |
| CN103927694A (en) | Real-time analysis and decision system of regional loads of urban power grid and working method thereof | |
| CN103399562B (en) | A kind of production line real-time dynamic cost calculation signal conditioning package and method | |
| CN104638636A (en) | Power daily load characteristic indicator prediction method | |
| CA2905076A1 (en) | Management of energy on electric power systems | |
| CN103793756A (en) | Transformer economic operation characteristic analyzing method | |
| CN107453369B (en) | Intelligent power distribution network optimization power saving and loss reduction system | |
| CN104952235A (en) | Power transmission line monitoring and loss reducing method | |
| CN210776794U (en) | Device for carrying out load analysis and prediction aiming at temperature change | |
| CN104517160A (en) | Novel electricity market prediction system and method based on capacity utilization characteristics | |
| CN115995880A (en) | Comprehensive monitoring and analyzing method and system for multidimensional state of power distribution automation terminal | |
| CN210380927U (en) | Distributed data acquisition structure of electric quantity monitoring and early warning device in electric power system | |
| CN114219204A (en) | Intelligent management system for energy demand | |
| CN112308348A (en) | Intelligent analysis method for medium-voltage line loss abnormity | |
| Pauli et al. | An Analysis on the Use of IoT Devices in the Integrated Energy Resources Planning | |
| Zhang et al. | Fault detection method and application of electric energy metering device based on customer profile data portrait |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20150805 Termination date: 20191031 |