CN112803400A - Energy consumption regulation and control method for 5G base station - Google Patents
Energy consumption regulation and control method for 5G base station Download PDFInfo
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Abstract
The invention provides an energy consumption regulation and control method of a 5G base station, which comprises the following steps: s1, establishing an electricity price prediction model, and predicting the electricity price of each hour in one day by using the electricity price prediction model; s2, dividing the corresponding time interval into a charging time interval, a discharging time interval and a cut-off time interval according to the predicted electricity price; s3, charging the energy storage battery in the charging time period, and supplying power to the 5G base station by using the energy storage battery; the energy storage battery is only used for supplying power to the 5G base station in a discharging period; and the energy storage battery stops working in the cut-off time period, and the photovoltaic power supply is used for supplying power to the 5G base station. According to the solar photovoltaic energy storage system, the operation logic is optimized, the energy storage battery is charged preferentially when the electricity price is low, the energy storage battery is used for discharging when the electricity price is high, and the photovoltaic power supply is used in a matched mode to convert solar energy into electric energy to supplement a power supply channel, so that the energy consumption cost is reduced.
Description
Technical Field
The invention relates to the technical field of energy consumption regulation, in particular to an energy consumption regulation method of a 5G base station.
Background
To realize 5G mobile communication, a large number of 5G base stations including a macro base station and a small base station need to be deployed, the energy consumption of the base stations mainly takes electricity as a main part, along with the increase of the electric power cost, the expansion of a mobile network and the water rise of the electricity expense of a base station machine room, so that the situation that China Unicom needs to turn on and off the 5G base stations regularly can occur.
At present, the energy consumption of the 5G base station is mainly concentrated on four parts, namely the base station, transmission, a power supply and a machine room air conditioner, and the electricity charge expenditure of the base station accounts for more than 80% of the energy consumption of the whole network. In the energy consumption of the base station, the power consumption of the baseband unit (BBU) responsible for processing the signal coding is relatively small, and the power consumption of the radio frequency unit (RRU/AAU) is relatively large.
According to the white paper of 5G power supply published in the last year, although the power consumption of unit flow is greatly reduced from 4G to 5G, the total power consumption of 5G is still greatly increased compared with 4G. In the 5G era, the maximum power consumption of the 64T64R AAU is estimated to reach 1000-1400W, and the maximum power consumption of the BBU is estimated to reach about 2000W.
In the 5G era, one station multiple frequency would be a typical configuration, with a predicted over 5 frequency site occupancy that would increase from 3% in 2016 to 45% in 2023. The maximum power consumption of the whole station exceeds 10kW due to one station of multiple frequencies, the power consumption of the station at 10 frequencies and above 10 frequencies exceeds 20kW, and the power consumption is doubled under the sharing scene of multiple operators.
The architecture and morphology of the base station directly affect how the 5G network is deployed. In the technical standard, the frequency band of 5G is much higher than that of 2G, 3G and 4G networks, and the 5G network mainly works in the frequency band of 3000-5000MHz at present. The base station density of a 5G network will be higher since the higher the frequency, the greater the attenuation in the signal propagation. By 5 months in 2020, more than 20 ten thousand 5G base stations have been opened in China.
At present, the power consumption of the 5G base station is improved by more than 3 times compared with the 4G base station, and the required amount of the 5G base station is increased by times due to the attenuation of the coverage area, so for operators, the high power consumption of the 5G base station becomes the primary reason for restricting 5G network establishment.
Disclosure of Invention
The invention solves the problem of high operation cost of operators caused by huge energy consumption of a 5G base station, and provides an energy consumption regulation and control method of the 5G base station.
In order to realize the purpose, the following technical scheme is provided:
an energy consumption regulation and control method of a 5G base station comprises the following steps:
s1, establishing an electricity price prediction model, and predicting the electricity price of each hour in one day by using the electricity price prediction model;
s2, dividing the corresponding time interval into a charging time interval, a discharging time interval and a cut-off time interval according to the predicted electricity price;
s3, charging the energy storage battery in the charging time period, and supplying power to the 5G base station by using the energy storage battery; the energy storage battery is only used for supplying power to the 5G base station in a discharging period; and the energy storage battery stops working in the cut-off time period, and the photovoltaic power supply is used for supplying power to the 5G base station.
The operation logic of the energy storage battery and the photovoltaic power supply in the 5G base station is optimized through the electricity price prediction model, the charging time period, the discharging time period and the cut-off time period are set, the energy storage battery is charged when the electricity price is low, the photovoltaic power supply is preferentially used for supplying power to the 5G base station when the electricity price is particularly high, the energy storage battery is started until the photovoltaic power supply is reduced to a threshold value, the energy consumption of the 5G base station is reduced through the optimized operation logic, the basic configuration of the existing 5G base station is utilized for improvement, the cost is low, and the energy consumption cost is effectively reduced from the electricity price angle.
Preferably, the step S1 specifically includes the following steps:
s101, acquiring the electricity price A of each hour within n days before the forecast datein，i＝1、2、…、23、24；
S102, calculating the average electricity price of the corresponding hour in n days
Ai＝(Ai1+Ai2+…+Ai(n-1)+Ain)/n；
S103, averaging the electricity price AiAverage electricity rate as predicted dateAnd predicting the electricity price.
Preferably, the step S102 further includes a noise removing step:
s121, obtaining the median electrovalence A of the corresponding hour in n daysimM is the date corresponding to the median price;
s122, calculating the electricity price deviation T ═ a per hour for n daysim-AinIf the deviation value T is larger than or equal to the deviation threshold value, the electricity price of the corresponding time period does not participate in the step of calculating the average electricity price, and the electricity price of the corresponding time period is recorded into a deviation set;
s123, calculating the average electricity price of the corresponding hour in n days
Ai＝(Ai1+Ai2+…+Ai(n-1)+Ain) And/(n-s), s is the number of electricity prices removed for the corresponding time period.
Within n days, due to the influence of the special factors, the electricity price of a certain hour of a certain day is greatly different from the electricity price of the corresponding hour of the previous day, and the noise removing step is arranged to remove the influence of the special factors, so that the predicted electricity price is more accurate.
Preferably, the step S2 specifically includes the following steps: according to dividing a period in which the predicted electricity price is less than or equal to the charging electricity price threshold value into a charging period, dividing a period in which the predicted electricity price is greater than the charging electricity price threshold value and less than or equal to the discharging electricity price threshold value into a discharging period, and dividing a period in which the predicted electricity price is greater than the discharging electricity price threshold value into an off period.
Preferably, the invention calculates the lowest electricity price reference value by using the deviation set, and solves all the electricity prices A which are smaller than the median in the deviation setimThe method further includes a step of obtaining the current electricity price in real time in step S3, and if the current electricity price is less than or equal to the minimum electricity price reference value, the energy storage battery is charged at any time interval.
No matter what kind of special factor influences, the electricity price of a certain hour of a certain day is greatly different from the electricity price of the corresponding hour of the previous day, if the electricity price is over, the probability that the electricity price still appears is shown, and all the electricity prices less than the median A in the deviation set are solvedimThe average value of the electricity prices of the batteries is used as the lowest electricity price reference value, and if the current electricity price is less than or equal to the lowest electricity price reference value, the energy storage battery is charged in any time period unless the energy storage battery is fully charged. The purpose of doing so is not to pass the opportunity of charging at a lower price of electricity, further reducing the cost of charging and thus the cost of energy consumption of the 5G base station.
Preferably, the step S3 further includes an air conditioning control step in the 5G base station:
s301, monitoring the temperature in the 5G base station in real time, and controlling the temperature in the 5G base station to be a set temperature when the air conditioner works normally in a charging period and a discharging period;
s302, when the time is in the cut-off time period, the air conditioner is turned off;
s303, starting an air conditioner until the temperature in the 5G base station is greater than or equal to a starting threshold value;
s304, when the temperature in the 5G base station is less than or equal to the closing threshold value, the air conditioner is closed, and the step S301 is returned.
When the electricity price is higher, the air conditioner is reasonably closed for a period of time, so that more unnecessary energy consumption cost is avoided, and the electricity utilization efficiency is improved.
Preferably, the method also comprises the following energy storage battery health management steps:
sa, monitoring the electric quantity of the energy storage battery in real time, and when the electric quantity of the energy storage battery is larger than a first charging threshold, the energy storage battery is not charged and only the energy storage battery supplies power to the 5G base station;
sb, when the electric quantity of the energy storage battery is smaller than or equal to the first charging threshold value and larger than the second charging threshold value, the energy storage battery can be charged, and the energy storage battery and the photovoltaic power supply alternately supply power to the 5G base station;
and Sc, when the electric quantity of the energy storage battery is smaller than or equal to a second charging threshold, the energy storage battery is only charged, and the 5G base station is only supplied with power by the photovoltaic power supply.
The invention has the beneficial effects that: the operation logic of the energy storage battery and the photovoltaic power supply in the 5G base station is optimized through the electricity price prediction model, the charging time period, the discharging time period and the cut-off time period are set, the energy storage battery is charged when the electricity price is low, the photovoltaic power supply is preferentially used for supplying power to the 5G base station when the electricity price is particularly high, the energy storage battery is started until the photovoltaic power supply is reduced to a threshold value, the energy consumption of the 5G base station is reduced through the optimized operation logic, the basic configuration of the existing 5G base station is utilized for improvement, the cost is low, and the energy consumption cost is effectively reduced from the electricity price angle.
Drawings
FIG. 1 is a flow chart of the method of the present embodiment;
Detailed Description
Example (b):
the embodiment provides an energy consumption regulation method for a 5G base station, which, with reference to fig. 1, includes the following steps:
s1, establishing an electricity price prediction model, and predicting the electricity price of each hour in one day by using the electricity price prediction model;
step S1 specifically includes the following steps:
s101, acquiring the electricity price A of each hour within n days before the forecast datein，i＝1、2、…、23、24；
S102, calculating the average electricity price of the corresponding hour in n days
Ai＝(Ai1+Ai2+…+Ai(n-1)+Ain)/n；
Step S102 further includes a noise removal step:
s121, obtaining the median electrovalence A of the corresponding hour in n daysimM is the date corresponding to the median price;
s122, calculating the electricity price deviation T ═ a per hour for n daysim-AinIf the deviation value T is larger than or equal to the deviation threshold value, the electricity price of the corresponding time period does not participate in the step of calculating the average electricity price, and the electricity price of the corresponding time period is recorded into a deviation set;
s123, calculating the average electricity price of the corresponding hour in n days
Ai＝(Ai1+Ai2+…+Ai(n-1)+Ain) And/(n-s), s is the number of electricity prices removed for the corresponding time period.
Within n days due toThe influence of a special factor is that the electricity price of a certain hour of a certain day is greatly different from the electricity price of the corresponding hour of the previous day, and the noise removing step is arranged to remove the influence of the special factor, so that the predicted electricity price is more accurate. The invention utilizes the deviation set to calculate the lowest electricity price reference value, and all the electricity prices A which are smaller than the median in the deviation set are solvedimThe method further includes a step of obtaining the current electricity price in real time in step S3, and if the current electricity price is less than or equal to the minimum electricity price reference value, the energy storage battery is charged at any time interval.
No matter what kind of special factor influences, the electricity price of a certain hour of a certain day is greatly different from the electricity price of the corresponding hour of the previous day, if the electricity price is over, the probability that the electricity price still appears is shown, and all the electricity prices less than the median A in the deviation set are solvedimThe average value of the electricity prices of the batteries is used as the lowest electricity price reference value, and if the current electricity price is less than or equal to the lowest electricity price reference value, the energy storage battery is charged in any time period unless the energy storage battery is fully charged. The purpose of doing so is not to pass the opportunity of charging at a lower price of electricity, further reducing the cost of charging and thus the cost of energy consumption of the 5G base station.
S103, averaging the electricity price AiThe average electricity rate is used as the predicted electricity rate of the predicted date.
S2, dividing the corresponding time interval into a charging time interval, a discharging time interval and a cut-off time interval according to the predicted electricity price; step S2 specifically includes the following steps: according to dividing a period in which the predicted electricity price is less than or equal to the charging electricity price threshold value into a charging period, dividing a period in which the predicted electricity price is greater than the charging electricity price threshold value and less than or equal to the discharging electricity price threshold value into a discharging period, and dividing a period in which the predicted electricity price is greater than the discharging electricity price threshold value into an off period.
S3, charging the energy storage battery in the charging time period, and supplying power to the 5G base station by using the energy storage battery; the energy storage battery is only used for supplying power to the 5G base station in a discharging period; the energy storage battery stops working in the cut-off time period, and a photovoltaic power supply is used for supplying power to the 5G base station;
step S3 further includes an air conditioning control step in the 5G base station:
s301, monitoring the temperature in the 5G base station in real time, and controlling the temperature in the 5G base station to be a set temperature when the air conditioner works normally in a charging period and a discharging period;
s302, when the time is in the cut-off time period, the air conditioner is turned off;
s303, starting an air conditioner until the temperature in the 5G base station is greater than or equal to a starting threshold value;
s304, when the temperature in the 5G base station is less than or equal to the closing threshold value, the air conditioner is closed, and the step S301 is returned. When the electricity price is higher, the air conditioner is reasonably closed for a period of time, so that more unnecessary energy consumption cost is avoided, and the electricity utilization efficiency is improved.
The invention also comprises the following steps of energy storage battery health management:
sa, monitoring the electric quantity of the energy storage battery in real time, and when the electric quantity of the energy storage battery is larger than a first charging threshold, the energy storage battery is not charged and only the energy storage battery supplies power to the 5G base station;
sb, when the electric quantity of the energy storage battery is smaller than or equal to the first charging threshold value and larger than the second charging threshold value, the energy storage battery can be charged, and the energy storage battery and the photovoltaic power supply alternately supply power to the 5G base station;
and Sc, when the electric quantity of the energy storage battery is smaller than or equal to a second charging threshold, the energy storage battery is only charged, and the 5G base station is only supplied with power by the photovoltaic power supply.
The operation logic of the energy storage battery and the photovoltaic power supply in the 5G base station is optimized through the electricity price prediction model, the charging time period, the discharging time period and the cut-off time period are set, the energy storage battery is charged when the electricity price is low, the photovoltaic power supply is preferentially used for supplying power to the 5G base station when the electricity price is particularly high, the energy storage battery is started until the photovoltaic power supply is reduced to a threshold value, the energy consumption of the 5G base station is reduced through the optimized operation logic, the basic configuration of the existing 5G base station is utilized for improvement, the cost is low, and the energy consumption cost is effectively reduced from the electricity price angle.
Claims (7)
1. An energy consumption regulation and control method of a 5G base station is characterized by comprising the following steps:
s1, establishing an electricity price prediction model, and predicting the electricity price of each hour in one day by using the electricity price prediction model;
s2, dividing the corresponding time interval into a charging time interval, a discharging time interval and a cut-off time interval according to the predicted electricity price;
s3, charging the energy storage battery in the charging time period, and supplying power to the 5G base station by using the energy storage battery; the energy storage battery is only used for supplying power to the 5G base station in a discharging period; and the energy storage battery stops working in the cut-off time period, and the photovoltaic power supply is used for supplying power to the 5G base station.
2. The method as claimed in claim 1, wherein the step S1 specifically includes the following steps:
s101, acquiring the electricity price A of each hour within n days before the forecast datein，i＝1、2、…、23、24；
S102, calculating the average electricity price of the corresponding hour in n days
Ai＝(Ai1+Ai2+…+Ai(n-1)+Ain)/n；
S103, averaging the electricity price AiThe average electricity rate is used as the predicted electricity rate of the predicted date.
3. The method as claimed in claim 2, wherein the step S102 further comprises a noise removing step:
s121, obtaining the median electrovalence A of the corresponding hour in n daysimM is the date corresponding to the median price;
s122, calculating the electricity price deviation T ═ a per hour for n daysim-AinIf the deviation value T is larger than or equal to the deviation threshold value, the electricity price of the corresponding time period does not participate in the step of calculating the average electricity price, and the electricity price of the corresponding time period is recorded into a deviation set;
s123, calculating the average electricity price of the corresponding hour in n days
Ai＝(Ai1+Ai2+…+Ai(n-1)+Ain) V (n-s), s isThe electricity rate quantity of the corresponding period is removed.
4. The method as claimed in claim 1, wherein the step S2 specifically includes the following steps: according to dividing a period in which the predicted electricity price is less than or equal to the charging electricity price threshold value into a charging period, dividing a period in which the predicted electricity price is greater than the charging electricity price threshold value and less than or equal to the discharging electricity price threshold value into a discharging period, and dividing a period in which the predicted electricity price is greater than the discharging electricity price threshold value into an off period.
5. The method as claimed in claim 3, wherein the deviation set is used to calculate the lowest electricity price reference value, and all the electricity prices A less than the median in the deviation set are calculatedimThe method further includes a step of obtaining the current electricity price in real time in step S3, and if the current electricity price is less than or equal to the minimum electricity price reference value, the energy storage battery is charged at any time interval.
6. The method for regulating and controlling energy consumption of the 5G base station as claimed in claim 1, wherein the step S3 further comprises an air conditioner control step in the 5G base station:
s301, monitoring the temperature in the 5G base station in real time, and controlling the temperature in the 5G base station to be a set temperature when the air conditioner works normally in a charging period and a discharging period;
s302, when the time is in the cut-off time period, the air conditioner is turned off;
s303, starting an air conditioner until the temperature in the 5G base station is greater than or equal to a starting threshold value;
s304, when the temperature in the 5G base station is less than or equal to the closing threshold value, the air conditioner is closed, and the step S301 is returned.
7. The method for regulating and controlling energy consumption of the 5G base station according to any one of claims 1 to 6, further comprising the step of managing the health of the energy storage battery:
sa, monitoring the electric quantity of the energy storage battery in real time, and when the electric quantity of the energy storage battery is larger than a first charging threshold, the energy storage battery is not charged and only the energy storage battery supplies power to the 5G base station;
sb, when the electric quantity of the energy storage battery is smaller than or equal to the first charging threshold value and larger than the second charging threshold value, the energy storage battery can be charged, and the energy storage battery and the photovoltaic power supply alternately supply power to the 5G base station;
and Sc, when the electric quantity of the energy storage battery is smaller than or equal to a second charging threshold, the energy storage battery is only charged, and the 5G base station is only supplied with power by the photovoltaic power supply.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116260231A (en) * | 2022-09-29 | 2023-06-13 | 中兴通讯股份有限公司 | Communication station, power control method and device thereof and computer storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105514512A (en) * | 2015-12-31 | 2016-04-20 | 浙江南都电源动力股份有限公司 | Energy saving method for communication base station |
| CN106602585A (en) * | 2017-02-14 | 2017-04-26 | 中塔新兴通讯技术集团有限公司 | Power supply system and operation method of intelligent peak shifting and valley filling superimposed photovoltaic power generation |
| CN108899923A (en) * | 2018-08-13 | 2018-11-27 | 深圳康普盾科技股份有限公司 | Communication base station retired power battery energy resource system and its control method |
| CN110120667A (en) * | 2019-05-07 | 2019-08-13 | 华北电力大学（保定） | For reducing the renewable energy of energy cost and the distribution method of traditional energy |
-
2021
- 2021-01-29 CN CN202110129104.2A patent/CN112803400A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105514512A (en) * | 2015-12-31 | 2016-04-20 | 浙江南都电源动力股份有限公司 | Energy saving method for communication base station |
| CN106602585A (en) * | 2017-02-14 | 2017-04-26 | 中塔新兴通讯技术集团有限公司 | Power supply system and operation method of intelligent peak shifting and valley filling superimposed photovoltaic power generation |
| CN108899923A (en) * | 2018-08-13 | 2018-11-27 | 深圳康普盾科技股份有限公司 | Communication base station retired power battery energy resource system and its control method |
| CN110120667A (en) * | 2019-05-07 | 2019-08-13 | 华北电力大学（保定） | For reducing the renewable energy of energy cost and the distribution method of traditional energy |
Non-Patent Citations (1)
| Title |
|---|
| 夏威: "通信基站电源系统中的节能技术研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116260231A (en) * | 2022-09-29 | 2023-06-13 | 中兴通讯股份有限公司 | Communication station, power control method and device thereof and computer storage medium |
| CN116260231B (en) * | 2022-09-29 | 2024-04-12 | 中兴通讯股份有限公司 | Communication station, power control method and device thereof and computer storage medium |
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