CN106203702B - Cold machine load automatic control method based on power demand prediction - Google Patents
Cold machine load automatic control method based on power demand prediction Download PDFInfo
- Publication number
- CN106203702B CN106203702B CN201610543905.2A CN201610543905A CN106203702B CN 106203702 B CN106203702 B CN 106203702B CN 201610543905 A CN201610543905 A CN 201610543905A CN 106203702 B CN106203702 B CN 106203702B
- Authority
- CN
- China
- Prior art keywords
- demand
- real
- limit
- load
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000012544 monitoring process Methods 0.000 claims abstract description 9
- 230000035945 sensitivity Effects 0.000 claims 1
- 230000005611 electricity Effects 0.000 abstract description 3
- 238000001816 cooling Methods 0.000 description 5
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 238000005485 electric heating Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Devices That Are Associated With Refrigeration Equipment (AREA)
- Air Conditioning Control Device (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a cold machine load automatic control method based on power demand prediction, which comprises the following steps: firstly, setting an enterprise power demand limit value and an enterprise cold supply tail end temperature limit value, and configuring rated power and minimum current percentage of n coolers; secondly, monitoring real-time load and real-time demand of an enterprise; thirdly, judging whether the current real-time demand is greater than the power demand limit value, and if so, correcting the power demand limit value; if not, proceeding to the fourth step; fourthly, whether the current implementation load is larger than the limit value of the power demand or not is judged, and if yes, the fifth step is carried out; and fifthly, predicting the future demand according to the historical demand data and the current air temperature, and if the power demand limit value is not met, adjusting the load of the cold machine. The invention achieves the purpose of reducing the electricity consumption cost of the client without influencing the comfortable experience of the user.
Description
Technical Field
The invention relates to an automatic load control method of a refrigerator, in particular to an automatic control method based on enterprise power demand prediction.
Background
At present, two power rates, namely, a power rate and a basic power rate, are implemented for large industrial and commercial businesses in China, the basic power rate is based on the fact that the capacity or the maximum demand (namely the maximum value of the average load every 15 minutes in each month) of a transformer of an enterprise is used as the basis for calculating the power rate, the enterprise signs a contract with a power supply department, the limit is determined, and the power is collected fixedly every month without transferring the actual power consumption. Due to various reasons such as management, process, technology and the like, many large-scale enterprises have low power utilization efficiency, high power consumption cost occupying the operation cost of the enterprises, and great space saving.
For a large enterprise with centralized cooling, the load adjusting space is large, the maximum demand is used as a basic electric charge charging mode, the peak demand of the enterprise can be reduced by adjusting the load of the cooling machine, the basic electric charge expenditure of the enterprise is reduced, and therefore the electricity utilization cost of the enterprise is reduced.
According to the invention, the load of the refrigerator is dynamically and automatically adjusted according to the historical demand data and the air temperature prediction, and the temperature and humidity of the cold supply tail end are taken into consideration, so that the purposes of reducing the electricity consumption cost of customers and not influencing the comfortable experience of users are achieved.
Disclosure of Invention
The invention aims to automatically control the load of the refrigerating machine, improve the running efficiency of the refrigerating machine and effectively control the power consumption cost of an enterprise according to the condition that the power demand report value of the enterprise is an initial limit value under the premise of not influencing the comfortable experience of a user.
The invention provides a cold machine load automatic control method based on power demand prediction, which comprises the following steps:
firstly, setting a limit value D of the power demand of an enterpriselimitEnterprise cold supply end temperature limit TlimitThe rated power of n refrigerators is set as { P }chiller1，Pchiller2，...，PchillernThe percentage of the lowest current is { I } respectivelymin1，Imin2，...，Iminn}；
Secondly, monitoring the real-time load P of the enterpriserealReal time demand Dreal；
Thirdly, judging the current real-time demand DrealWhether it is greater than the power demand limit DlimitIf so, the power demand limit is corrected, i.e. Dlimit＝DrealAnd returning to the second step; if not, proceeding to the fourth step;
the fourth step, judge the present implementation load PrealWhether it is greater than the power demand limit DlimitIf yes, carrying out the fifth step; if not, returning to the second step;
fifthly, predicting future demand D according to historical demand data and current air temperaturepredictJudgment of DpredictWhether or not it is greater than DlimitIf yes, adjusting the load of the cold machine, and performing the sixth step; if not, returning to the second step.
Sixthly, adjusting the load of the refrigerator, wherein the method comprises the following steps:
step one, calculating a demand difference s-Dpredict-DlimitFor each cold machine I, the configured cold machine minimum current percentage IminiFor constraint, the demand difference s is distributed to each cold machine which can participate in control;
step two, the 1 st ginseng is addedIssuing a current percentage control value of the demand-controlled cooler, delaying for 1 minute, and reading a temperature value T at the cold supply tail endrealJudgment of TrealWhether or not greater than TlimitIf yes, stopping the control of the cold machine and sending an alarm signal without regulation measures; if not, continuing issuing the current percentage control values of other participating quantity control refrigerators.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention provides a cold machine load automatic control method based on power demand prediction, which comprises the following steps:
(1) setting a contract demand declaration value signed with a power supply department as an initial limit value D of the enterprise power demandlimitEnterprise cold supply end temperature limit TlimitAnd the rated power { P of the refrigerator is configuredchiller1，Pchiller2，...，Pchillern}, percent of lowest current { Imin1，Imin2，...，Iminn}；
(2) Monitoring enterprise real-time load PrealReal time demand Dreal，DrealIs a 15 minute average power statistic;
(3) judging the current DrealWhether it is greater than the power demand limit DlimitIf yes, correcting the power demand limit Dlimit＝DrealAnd returning to the step (2); if not, performing the step (4);
(4) judging the current PrealWhether it is greater than the power demand limit DlimitIf yes, performing the step (5); if not, returning to the step (2);
(5) predicting future 15-minute demand D according to historical demand data and current air temperaturepredictJudgment of DpredictWhether or not it is greater than DlimitIf yes, adjusting the load of the refrigerator, and performing the step (6); if not, returning to the step (2);
(6) calculating demand difference s ═ Dpredict-DlimitFor each refrigerator i, the lowest power of the configured refrigeratorPercentage of flow IminiAnd (3) for constraint, distributing the s value of the demand difference to each cold machine which can participate in demand control, and assuming that m cold machines can participate in control, reducing the I electric current percentage of each cold machine by s/m/Pchilleri；
(7) Issuing the current percentage control value of the 1 st parameter control refrigerator and the demand control refrigerator, delaying for 1 minute, and reading the temperature value T of the cold supply tail endrealJudgment of TrealWhether or not greater than TlimitIf yes, stopping the control of the cold machine and sending an alarm signal without regulation measures; if not, the current percentage control values of other participating quantity control refrigerators are continuously issued.
In the above method for automatically controlling a cold machine load based on power demand control, the historical data refers to a historical demand value of a previous working day or a previous week, and is determined by the following method:
① if the monitoring day is Monday workday, the historical data is the demand D of the same time as the last Monday workdayhis；
② if the monitoring day is a non-Monday working day, the historical data is the demand D of the previous working day at the same timehis。
In the method for automatically controlling the load of the cold machine based on the power demand control, the predicted value D of the demand in the future 15 minutespredictThe method is determined by the following load prediction model, and the specific implementation method is as follows:
Dpredict＝Dreal+W(T)+V(t)
wherein DrealW (T) is the temperature sensitive load component, and V (t) is the random load component at the moment t; w (T) ═ Ks(T-Tw)，KsThe value is different for different enterprises, T is the actual temperature, TwThe critical temperature of electric heating, the Shenzhen region is 26 ℃; v (t) is calculated by using a regression calculation model (AR), and V (t) [ ]1[V(t-1)]+Φ2[V(t-2)]+...+Φp[V(t-p)]The order p and the coefficient Φ (i ═ 1,2, …, p) are determined from the history values by model identification and parameter estimation.
The following description will further describe the present invention with reference to fig. 1.
Fig. 1 reflects a specific flow of an automatic control method based on enterprise power demand prediction. Take an example where an enterprise user has 2 chillers of 360kW, a power demand declaration of 3950 kW.
(1) Setting the initial limit Dlimit of the power demand of an enterprise to 3950kW and the final temperature limit T of the cooling end of the enterpriselimitConfiguring rated power of the refrigerator {360kW, 360kW }, and lowest current percentage { 30%, 30% } at 29 ℃;
(2) monitoring enterprise real-time load Preal4002kW, real-time demand Dreal＝3875kW；
(3) Current Dreal<DlimitThe power demand limit value does not need to be corrected;
(4) current Preal>DlimitAnd (5) performing the step;
(5) predicting future 15-minute demand D according to historical demand data and current temperaturepredict＝4010kW>DlimitAdjusting the load of the cold machine, and performing the step (6);
(6) calculating demand difference s ═ Dpredict–DlimitWhen the temperature is 4010-;
(7) sending a current percentage control value of the 1 st cold machine: 85% -8.3% ═ 76.7%, delay time 1 minute, read cooling end temperature value Treal＝26℃，Treal<TlimitAnd continuously issuing a current percentage control value of the 2 nd cooler: 80% -8.3% ═ 71.7%, delay 1 minute; reading a cooling end temperature value Treal＝27℃，Treal<Tlimit. And (5) repeating the step (2) in this way, and the description is omitted.
The above examples are illustrative of the preferred embodiments of the present invention, but the present invention is not limited to the above examples, and any other modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and they should be included in the scope of the present invention.
Claims (1)
1. A cold machine load automatic control method based on power demand prediction comprises the following steps:
firstly, setting a limit value D of the power demand of an enterpriselimitEnterprise cold supply end temperature limit TlimitThe rated power of n refrigerators is set as { P }chiller1，Pchiller2，...，PchillernThe percentage of the lowest current is { I } respectivelymin1，Imin2，...，Iminn}; setting a contract demand declaration value signed with a power supply department as an enterprise power demand limit value DlimitAn initial value of (1); real time demand DrealIs a 15 minute average power statistic;
secondly, monitoring the real-time load P of the enterpriserealReal time demand Dreal；
Thirdly, judging the current real-time demand DrealWhether it is greater than the power demand limit DlimitIf so, the power demand limit is corrected, i.e. Dlimit＝DrealAnd returning to the second step; if not, proceeding to the fourth step;
the fourth step, judge the present real-time load PrealWhether it is greater than the power demand limit DlimitIf yes, carrying out the fifth step; if not, returning to the second step;
fifthly, predicting a future demand predicted value D according to the historical demand data and the current air temperaturepredictJudgment of DpredictWhether or not it is greater than DlimitIf yes, adjusting the load of the cold machine; if not, returning to the second step; the historical demand data refers to the historical demand value of the previous working day or the previous week; the method comprises the following steps: if the monitoring day is a Monday working day, the historical demand data takes the demand value D of the same time of the last Monday working dayhis(ii) a If the monitoring day is a non-Monday working day, the historical demand data takes the demand value D of the same moment of the previous working dayhis；
Wherein, the adjusting the cold load in the fifth step includes:
step one, calculating a demand difference s-Dpredict-DlimitFor each cold machine I, the configured cold machine minimum current percentage IminiFor constraint, the demand difference s is distributed to each cold machine which can participate in control;
step two, issuing the current percentage control value of the 1 st refrigerating machine which is controlled by the parameter and the required quantity, delaying for 1 minute, and reading the temperature value T of the cold supply tail endrealJudgment of TrealWhether or not greater than TlimitIf yes, stopping the control of the cold machine and sending an alarm signal without regulation measures; if not, continuing issuing current percentage control values of other participating quantity control refrigerators;
wherein, in the fifth step, the predicted value D of the future demand ispredictThe method is determined by the following load prediction model, and the specific implementation method is as follows:
Dpredict＝Dreal+W(T)+V(t)
wherein DrealW (T) is the temperature sensitive load component, and V (t) is the random load component at the moment t;
wherein W (T) ═ Ks(T-Tw)，KsIs the load air temperature sensitivity coefficient, T is the actual temperature, TwIs the electrothermal critical temperature;
v (t) is a regression calculation model (AR), V (t) ═ phi1[V(t-1)]+Φ2[V(t-2)]+...+Φp[V(t-p)]Order p and coefficient phii(i ═ 1,2, …, p) is determined from historical values by model identification and parameter estimation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610543905.2A CN106203702B (en) | 2016-07-11 | 2016-07-11 | Cold machine load automatic control method based on power demand prediction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610543905.2A CN106203702B (en) | 2016-07-11 | 2016-07-11 | Cold machine load automatic control method based on power demand prediction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106203702A CN106203702A (en) | 2016-12-07 |
| CN106203702B true CN106203702B (en) | 2020-07-28 |
Family
ID=57476505
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610543905.2A Active CN106203702B (en) | 2016-07-11 | 2016-07-11 | Cold machine load automatic control method based on power demand prediction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106203702B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107392442B (en) * | 2017-07-03 | 2020-08-14 | 上海安悦节能技术有限公司 | Low-voltage substation user energy consumption evaluation method |
| CN108771951B (en) * | 2018-06-09 | 2021-06-08 | 深圳库博能源科技有限公司 | Temperature and humidity control-based automatic regeneration control method and device for rotary dehumidifier |
| CN110148957B (en) * | 2019-05-27 | 2022-09-16 | 阳光新能源开发股份有限公司 | Demand control method, device and system based on energy storage system |
| 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 |
| TWI823183B (en) * | 2021-11-12 | 2023-11-21 | 台泥儲能科技股份有限公司 | Power demand controlling system and method thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104269940A (en) * | 2014-10-29 | 2015-01-07 | 四川慧盈科技有限责任公司 | Building air conditioning equipment load monitoring system |
| CN104573858A (en) * | 2014-12-26 | 2015-04-29 | 国家电网公司 | Prediction, regulation and control method for electric network loads |
| CN105590174A (en) * | 2015-12-29 | 2016-05-18 | 南京因泰莱电器股份有限公司 | Enterprise power consumption load prediction method based on K-means clustering RBF neural network |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8556188B2 (en) * | 2010-05-26 | 2013-10-15 | Ecofactor, Inc. | System and method for using a mobile electronic device to optimize an energy management system |
-
2016
- 2016-07-11 CN CN201610543905.2A patent/CN106203702B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104269940A (en) * | 2014-10-29 | 2015-01-07 | 四川慧盈科技有限责任公司 | Building air conditioning equipment load monitoring system |
| CN104573858A (en) * | 2014-12-26 | 2015-04-29 | 国家电网公司 | Prediction, regulation and control method for electric network loads |
| CN105590174A (en) * | 2015-12-29 | 2016-05-18 | 南京因泰莱电器股份有限公司 | Enterprise power consumption load prediction method based on K-means clustering RBF neural network |
Non-Patent Citations (1)
| Title |
|---|
| 巴彦淖尔市配网自动化规划与效益分析;李海清;《万方数据 学位首页》;20121231;第1-56页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106203702A (en) | 2016-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106203702B (en) | Cold machine load automatic control method based on power demand prediction | |
| US8041467B2 (en) | Optimal dispatch of demand side electricity resources | |
| CN110425706B (en) | Power grid peak clipping-oriented aggregated air conditioner load regulation and control method | |
| CN102025150B (en) | Method and system for demand response in a distribution network | |
| JP4600235B2 (en) | Cogeneration facility control system and cogeneration facility control method | |
| CN106127337B (en) | Unit combination method based on variable frequency air conditioner virtual unit modeling | |
| US9310815B2 (en) | Setpoint adjustment-based duty cycling | |
| CN108361885B (en) | Dynamic planning method for ice storage air conditioning system | |
| CN109059195B (en) | Control method and control system for central air conditioner for reducing load peak value of power grid | |
| US20100023786A1 (en) | System and method for reduction of electricity production and demand | |
| WO2015183678A1 (en) | Method and apparatus for selective componentized thermostatic controllable loads | |
| CN104456845B (en) | The computational methods of utilities building central air-conditioning pre- opening time | |
| JP6839277B2 (en) | Management method and management device | |
| EP3732401B1 (en) | Responsive power steering and redundancy | |
| CN106765911B (en) | A kind of control method, device and the controller of central air-conditioning cold | |
| Verrilli et al. | Demand Side Management for heating controls in Microgrids | |
| Ramdaspalli et al. | Transactive control for efficient operation of commercial buildings | |
| TWI358516B (en) | Method for managing air conditioning power consump | |
| CN103218693A (en) | Proportion distribution method and system of energy supplying equipment of energy station | |
| CN103471178B (en) | Heat supply energy-saving control system and energy-saving control method | |
| US20140246909A1 (en) | System and method for balancing supply and demand of energy on an electrical grid | |
| CN117267910A (en) | Method, device, equipment and medium for flexibly adjusting load of central air-conditioning refrigeration system | |
| US9964983B2 (en) | Micro demand response scheduling | |
| CN105976047B (en) | Resident load demand response potential evaluation method based on flexibility index | |
| Li et al. | Optimization for the chilled water system of HVAC systems in an intelligent building |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Zheng Xi Inventor after: Guo Zijian Inventor after: Menkun Inventor after: Wu Junyang Inventor before: Zheng Xi Inventor before: Menkun Inventor before: Guo Zijian |
|
| CB03 | Change of inventor or designer information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 518000, 2nd Floor, Building 2, Tongchan New Materials Industrial Park, No. 28 Langshan Road, Songpingshan Community, Xili Street, Nanshan District, Shenzhen, Guangdong Province Patentee after: Shenzhen Kubo Energy Co.,Ltd. Address before: Room 908, International Student Entrepreneurship Building, No. 29 Xinnanhuan Road, Nanshan District, Shenzhen City, Guangdong Province, 518057 Patentee before: SHENZHEN KUBO ENERGY SCIENCE & TECHNOLOGY Co.,Ltd. |
|
| CP03 | Change of name, title or address |