Off-grid photovoltaic power supply base station energy saving method and device based on dormancy control

Abstract

The invention discloses an off-grid photovoltaic power supply base station energy saving method and device based on dormancy control. The method comprises the following steps: firstly, constructing a data model and a power consumption model of a base station; the state transition process of base station dormancy is represented by a Markov chain, and base station power consumption and service time delay are defined; the total overhead of the system is defined as the sum of the power consumption of the base station and the service time delay, the optimal strategy of the dormancy of the base station is made according to the illumination condition, and the total overhead of the system is minimized. According to the method and the device, by dynamically adjusting the dormancy control parameters, the energy consumption of the base station is effectively reduced, meanwhile, the communication service quality is ensured, the total overhead of the system can be remarkably reduced, and the economical efficiency and sustainability of the photovoltaic power supply base station are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power supply base station systems, and particularly to an energy-saving method and device for off-grid photovoltaic power supply base stations based on sleep control. Background Art

[0002] With the rapid development of wireless communication systems, the number of users and traffic volume of mobile communications have increased exponentially, and the problem of insufficient communication coverage has become prominent. Due to economic and technological limitations, more than 70% of land and 95% of the sea areas are still not effectively covered by mobile networks, which greatly restricts the resource development and economic development in remote, mountainous and deep-sea areas. The main reason for the insufficient mobile network coverage is the lack of power grid coverage. In off-grid areas, the cost of expanding mobile network coverage increases exponentially, so that the high initial and operating costs are unbearable for both operators and the government. To solve the energy supply problem of off-grid base stations, it is particularly urgent to introduce green energy to power off-grid base stations. Solar energy is regarded as an ideal power supply for off-grid base stations due to its clean, renewable and widely available characteristics. However, the instability of solar energy resources poses new technical challenges. Due to the discontinuity of sunlight and the influence of weather changes, the collected solar energy has significant randomness and volatility in terms of time and magnitude. This instability may lead to unstable power supply, affecting the normal operation and service quality of base stations. Therefore, how to make full use of the advantages of solar energy while reducing the negative impact of its instability and enabling off-grid photovoltaic power supply base stations to operate stably for a long time has become a key issue.

[0003] Base station dormancy is a method for controlling base stations to enter a low-power state, aiming to reduce energy consumption while ensuring the quality and reliability of communication services. In the research on base station dormancy control mechanisms, existing technologies have developed various base station energy-saving technologies. The core of the base station dormancy technology lies in adjusting the working state of the base station according to specific strategies while ensuring the continuity and quality of user services, and achieving a large-scale energy saving without affecting the user experience. Existing base station dormancy strategies can be roughly divided into three types: the first is the random strategy, which independently shuts down each base station with a certain probability; the second is the distance-aware strategy, which designs a more intelligent base station shutdown strategy for network deployment of a homogeneous network based on the distances between users and their associated base stations and between base stations; the third is the load-aware strategy, which takes advantage of the uneven traffic distribution in different geographical regions and shuts down underutilized base stations. The above base station dormancy methods often focus on reducing energy consumption, without fully considering the time delay required for the base station to wake up from the sleep mode to the fully operational state, nor considering the different power consumption requirements of different lighting conditions in the off-grid scenario for the base station. Different dormancy depths will result in different base station power consumption and different degrees of wake-up delay, which may affect the QoS. Therefore, how to balance the energy-saving effect and the operation of off-grid base stations to achieve the efficient and green operation of communication networks has become a key problem to be solved urgently at present. Summary of the Invention

[0004] The object of the present invention is to provide an energy-saving method and device for an off-grid photovoltaic-powered base station based on dormancy control. The method and device effectively reduce the energy consumption of the base station by dynamically adjusting the dormancy control parameters while ensuring the quality of communication services.

[0005] The object of the present invention is achieved by the following technical solutions:

[0006] An energy-saving method for an off-grid photovoltaic-powered base station based on dormancy control, the method comprising:

[0007] Step 1, constructing a data model and a power consumption model of the base station;

[0008] Step 2, representing the state transition process of base station dormancy by a Markov chain, and defining the base station power consumption and service delay;

[0009] Step 3, defining the total system overhead as the sum of the base station power consumption and the service delay, and formulating an optimal strategy for base station dormancy according to the lighting conditions to minimize the total system overhead.

[0010] An energy-saving device for an off-grid photovoltaic-powered base station based on dormancy control, the device comprising:

[0011] A power consumption model construction unit for constructing a data model and a power consumption model of the base station;

[0012] A state transition process definition unit, which is used to represent the state transition process of the base station in the sleep state by a Markov chain, and define the power consumption and service delay of the base station.

[0013] An optimal base station sleep strategy formulation unit, which is used to define the total system overhead as the sum of the base station power consumption and service delay, formulate the optimal base station sleep strategy according to the light condition, and minimize the total system overhead.

[0014] An electronic device includes a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method.

[0015] A computer storage medium stores multiple instructions, and the instructions are suitable for being loaded and executed by a processor to execute the method.

[0016] It can be seen from the technical solutions provided by the present invention above that the above method and device effectively reduce the energy consumption of the base station by dynamically adjusting the sleep control parameters, while ensuring the communication service quality, and can significantly reduce the overall system overhead, improving the economy and sustainability of the off-grid photovoltaic-powered base station. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flowchart of the energy-saving method for an off-grid photovoltaic-powered base station based on sleep control provided by an embodiment of the present invention;

[0019] Figure 2 It is a schematic structural diagram of the device according to an embodiment of the present invention;

[0020] Figure 3 It is a schematic diagram of the energy efficiency change during the training process of different methods in the examples given in the present invention. Detailed Embodiments

[0021] Next, in combination with the drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments, which do not constitute a limitation to the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0022] Such as Figure 1The following is a schematic flow chart of an energy-saving method for an off-grid photovoltaic power supply base station based on sleep control provided by an embodiment of the present invention. The method includes:

[0023] Step 1: Construct a data model and a power consumption model for the base station;

[0024] In this step, when the base station is in the sleep state, the basic power consumption is defined as P sleep ; when in the on state, the power consumption of the base station is divided into the basic power consumption P 0 and the transmission power P t that is linearly related to the base station load. t is the abbreviation of transmit, that is:

[0025]

[0026] where P BS is the power consumption of the base station, and Δp is the base station load parameter;

[0027] Based on the single base station scenario, the arrival of data packets follows a Poisson process with an intensity of λ. The average length of each data packet is l. The base station adopts the first-come-first-served principle and follows the M / G / 1 service queue model. The data packets arrive at the base station and queue up at the end of the queue waiting for service. When the service is completed, they leave the queue. Assuming that the transmission rate of the base station is x and is shared by all users, the departure rate of the data packets

[0028] When the service queue is empty, the base station switches to the sleep mode. When the queue length accumulates to N in the sleep mode, the base station is turned on again. N is the sleep control threshold, and there is a decision delay T re when the base station switches between the on and sleep states; the power consumption when the base station switches between the on and sleep states is P sw , then the energy consumption E sw =P sw T re ;

[0029] The transmission rate x of the base station satisfies:

[0030]

[0031] where B is the transmission bandwidth of the base station; G is the channel gain; N 0 is the noise power density; P t is the transmission power linearly related to the base station load.

[0032] Step 2: Represent the state transition process of the base station sleep with a Markov chain, and define the base station power consumption and service delay;

[0033] In this step, the base station sleep is determined by the sleep control threshold N and the transmission power P tControlled by two parameters, the state transition process of base station dormancy is represented by a Markov chain, and the state space of the base station is expressed as:

[0034] {(i,j):i=0,j=0,1,...,N-1;i=1,j=1,2,...}

[0035] Among them, i = 0 represents that the base station is in the dormant state, and i = 1 represents that the base station is in the active state; j represents the current queue length;

[0036] Define π(i,j) to represent the probability that the base station is in the state (i,j). When the base station is in the active state (1,j), within each time slot, the probability that a new service request joins the queue is λ, that is, the probability that the base station transfers from the state (1,j) to the state (1,j + 1) is λ; at the same time, the probability that the service is completed and leaves the queue is μ, that is, the probability that the base station transfers from the state (1,j) to the state (1,j - 1) is μ; the remaining probability of 1 - λ - μ means that the base station maintains the state (1,j) unchanged; when the base station is in the dormant state (0,j), new service requests join the queue with the same probability of λ. At this time, the base station does not provide services, so the probability that the service leaves the queue is 0, that is, the base station has a probability of λ to transfer from the state (0,j) to the state (0,j + 1), and a probability of 1 - λ to maintain the state (0,j) unchanged;

[0037] When the base station is in the state (0,N - 1), that is, the base station is dormant and the current queue length is N - 1, if another service request arrives at the queue, the base station will restart from the dormant state to the active state. This restart process is the decision delay, and the duration is T re , during this restart process, the traffic volume k that newly arrives at the queue follows a Poisson distribution with a mean of λT re . After the decision delay ends, the base station enters the active state. At this time, the queue length is N + k, and the base station is in the state (1,N + l). Then the state transition probability that the base station transfers from the state (0,N - 1) to the state (1,N + l) is λp l , where p l represents the probability that l services arrive during the decision delay time, that is:

[0038]

[0039] The global balance equations of the Markov chain are expressed as:

[0040]

[0041] where p 0 represents the probability that no service arrives during the decision delay time;

[0042] From this, the probability π(i,j) of each state is expressed as:

[0043] π(0, i) = π(0, 0), i ≤ N - 1 (5)

[0044]

[0045] Assume that the probability that the base station is restarting at a certain moment is Pr sw , then the probability that the base station is in the on state is 1 - Pr sw , and

[0046] Pr sw = λ(1 - Pr sw )π(0, N - 1) (7)

[0047] Then we have:

[0048] Therefore, the probability Pr ac that the base station is in the on state at a certain moment is:

[0049]

[0050] The probability Pr sl that the base station is in the sleep state at a certain moment is:

[0051]

[0052] Then the power consumption of the base station is expressed as:

[0053]

[0054] When the base station restarts from the sleep state, there are already N data packets queuing in the queue, and the data packets arriving within the decision delay T re follow a Poisson arrival model with intensity λ. The average queue length of the data packets arriving within the decision delay time is According to Bernoulli's theorem, combining formulas (9) and (10), the average queue length of the base station is derived as:

[0055]

[0056] where

[0057]

[0058] Therefore, the service delay is:

[0059]

[0060] Step 3: Define the total system overhead as the sum of base station power consumption and service delay, formulate the optimal strategy for base station sleep according to the lighting conditions, and minimize the total system overhead.

[0061] In this step, the total system overhead is defined as the sum of base station power consumption and service delay. The optimization goal is to find a trade-off between power consumption and delay to adapt to the volatility of photovoltaic power generation. The function is defined as:

[0062]

[0063] Where β is defined as a positive weight factor, which indicates the importance of service delay relative to base station power consumption in system evaluation;

[0064] In the photovoltaic power supply scenario, during the day when photovoltaic power supply is sufficient, β is reduced in exchange for lower latency; while at night when there is no photovoltaic power supply, β is increased, sacrificing a certain latency in exchange for lower energy consumption. Therefore, the positive weight factor β is expressed as:

[0065]

[0066] Among them, P pv represents the photovoltaic power generation at the current moment, so the optimal strategy for base station sleep is defined as the following optimization problem:

[0067]

[0068] The objective function of the optimization problem represented by formula 17 is the total cost of the base station that combines the power consumption term with the delay term. The constraint condition is used to ensure that the service delay does not exceed the preset service quality threshold. By solving the optimization problem, the optimal sleep threshold N that minimizes the total cost of the base station is obtained. * , thus the base station power under the optimal strategy of base station sleep is P tot (P t ,N * ).

[0069] It can be seen from the above scheme that when the photovoltaic power supply is sufficient, the user experience can be improved by reducing the sleep depth of the base station; and when the photovoltaic power supply is insufficient, the sleep depth can be appropriately increased to ensure that the base station can operate stably for a long time.

[0070] Based on the above method embodiment, the embodiment of the present invention also provides an off-grid photovoltaic power supply base station energy-saving device based on sleep control, such as Figure 2 FIG. 1 is a schematic diagram of the structure of a device according to an embodiment of the present invention, wherein the device comprises:

[0071] A power consumption model building unit, used to build a data model and a power consumption model of a base station;

[0072] A state transition process definition unit, which is used to represent the state transition process of the base station in the sleep state with a Markov chain, and define the power consumption and service delay of the base station;

[0073] An optimal base station sleep strategy formulation unit, which is used to define the total system overhead as the sum of the base station power consumption and service delay, formulate the optimal base station sleep strategy according to the light condition, and minimize the total system overhead.

[0074] The specific implementation process of each unit in the above device can be seen in the method embodiment.

[0075] An embodiment of the present invention also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method.

[0076] An embodiment of the present invention also provides a computer storage medium, which stores multiple instructions, and the instructions are suitable for being loaded and executed by the processor to execute the method.

[0077] It should be noted that the content not described in detail in the embodiments of the present invention belongs to the prior art well known to those skilled in the art.

[0078] The following uses a specific example to illustrate the effect of the method described in the present invention. This example applies the present invention to the base stations in Tibet, calculates the power consumption of the base stations under different methods according to the light condition and user requirements within a day. In the embodiment, the method of the present invention is compared with the traditional base station sleep method respectively, and the power consumption changes of different methods are recorded respectively, as Figure 3 Shown is the schematic diagram of the energy efficiency change during the training process of different methods in the example of the present invention. It can be seen from the result diagram that: during the period when the base station load is low at night, the energy-saving effect of this strategy is more significant. When the photovoltaic power generation is sufficient during the day, the base station operates in a shallow sleep mode to ensure service requirements; while when the photovoltaic power supply is insufficient at night, the base station operates in a deeper sleep mode to minimize energy consumption.

[0079] In summary, the method and device described in the embodiments of the present invention are based on the sleep control strategy according to the light condition, can make full use of solar energy resources, flexibly adjust the working state of the base station under different light conditions, achieve the best balance between energy consumption and service quality, reduce the energy consumption of the base station, ensure the long-term stable operation of off-grid base stations, and provide an effective solution for off-grid photovoltaic-powered base stations.

[0080] In addition, the method of the present invention can be extended to other types of base stations powered by renewable energy, such as wind energy or water energy powered base stations, providing new ideas for the development of green communication infrastructure. According to the actual deployment situation, the energy-saving strategy of the present invention can be further optimized to adapt to different environmental and service requirements, thus being more feasible and flexible in practical applications.

[0081] Based on the method and device of the present invention, the base station can operate independently without being restricted by grid power supply, and is applicable to off-grid scenarios such as remote areas and mountainous areas, providing stable communication services for users. At the same time, through the innovative sleep control method, the energy consumption can be effectively reduced, and the economy and sustainability of the system can be improved.

[0082] As described above, only the preferred specific embodiments of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims. The information disclosed in the background art part of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or any form of implication that this information constitutes the prior art known to those skilled in the art.

Claims

1. An off-grid photovoltaic power supply base station energy saving method based on sleep control, characterized in that: The method comprises: Step 1: Build a data model and power consumption model of the base station; Step 2: The state transition process of the base station sleep is represented by a Markov chain, and the base station power consumption and service delay are defined; Step 3: Define the total system overhead as the sum of base station power consumption and service delay, formulate the optimal strategy for base station sleep according to the lighting conditions, and minimize the total system overhead.

2. The off-grid photovoltaic power supply base station energy saving method based on sleep control according to claim 1 is characterized in that: In step 1, the base station is in sleep mode, and the basic power consumption is defined as P sleep ; In the on state, the power consumption of the base station is divided into basic power consumption P0 and transmission power P which is linearly related to the base station load t , t is the abbreviation of transmit, that is: Where P BS is the power consumption of the base station, Δp is the base station load parameter; Based on a single base station scenario, the arrival of data packets follows a Poisson process with an intensity of λ and an average length of each data packet of l. The base station adopts a first-come, first-served principle. Assuming that the transmission rate of the base station is x and is shared by all users, the departure rate of the data packet is When the service queue is empty, the base station enters sleep mode. When the queue length accumulates to N in sleep mode, the base station is turned on again. N is the sleep control threshold. There is a decision delay T each time the base station sleeps or turns on. re ; The power consumption when the base station switches from on to sleep state is P sw , then the energy consumption of each switch is E sw =P sw T re ; The transmission rate x of the base station satisfies: Where B is the transmission bandwidth of the base station; G is the channel gain; N0 is the noise power density; P t is the transmission power which is linearly related to the base station load.

3. The off-grid photovoltaic power supply base station energy saving method based on sleep control according to claim 1 is characterized in that: In step 2, the base station sleeps according to the sleep control threshold N and the transmission power P t The two parameters control the state transition process of the base station sleep state using a Markov chain, and the state space of the base station is expressed as: {(i,j):i=0,j=0,1,...,N-1;i=1,j=1,2,...} Where i=0 means the base station is in sleep state, i=1 means the base station is in start state; j represents the current queue length; Define π(i,j) to represent the probability that the base station is in the (i,j) state. When the base station is in the on state (1,j), in each time slot, the probability that a new service request joins the queue is λ, that is, the probability that the base station transfers from the state (1,j) to the state (1,j+1) is λ; at the same time, the probability that the service leaves the queue after the service is completed is μ, that is, the probability that the base station transfers from the state (1,j) to the state (1,j-1) is μ; the remaining 1-λ-μ probability that the base station maintains the (1,j) state unchanged; when the base station is in the dormant state (0,j), new service requests join the queue with the same probability λ. At this time, the base station does not provide service, so the probability that the service leaves the queue is 0, that is, the base station has a probability of λ to transfer from the state (0,j) to the state (0,j+1), and a probability of 1-λ to maintain the (0,j) state unchanged; When the base station is in the (0, N-1) state, that is, the base station is dormant and the current queue length is N-1, if another service request arrives at the queue, the base station will restart from the dormant state to the on state. This restart process is the decision delay, and the duration is T re During this restart process, the traffic volume k of the newly arrived queue follows the mean value λT re Poisson distribution, after the decision delay ends, the base station enters the open state. At this time, the queue length is N+k, and the base station is in state (1, N+l). Then the state transition probability of the base station from the (0, N-1) state to the (1, N+l) state is λpl, where p l represents the probability that l services arrive within the decision delay time, that is: The global equilibrium equations of the Markov chain are expressed as: Where p0 represents the probability that no business arrives within the decision delay time; The probability of each state π(i,j) is expressed as: π(0,i)=π(0,0),i≤N-1(5) Assume that the probability that a base station is restarting at a certain moment is Pr sw , then the probability that the base station is in the on state is 1-Pr sw , and Pr sw =λ(1-Pr sw )π(0,N-1) (7) Then we have: Therefore, the probability that the base station is in the on state at a certain moment is Pr ac for: The probability that the base station is in sleep state at a certain moment is Pr sl for: The power consumption of the base station It is expressed as: When the base station restarts from sleep mode, there are already N packets in the queue, and the decision delay is T re The data packets arriving within the decision delay time follow the Poisson arrival model with strength λ. The average queue length of the data packets arriving within the decision delay time is According to Bernoulli's theorem, combined with formula (9) and (10), the average queue length of the base station is derived for: in, Therefore, the service delay for:

4. The off-grid photovoltaic power supply base station energy saving method based on sleep control according to claim 3 is characterized in that: In step 3, the total system overhead is defined as the sum of the base station power consumption and the service delay. The optimization goal is to find a trade-off between power consumption and delay. The function is defined as: Where β is defined as a positive weight factor, which indicates the importance of service delay relative to base station power consumption in system evaluation; In the photovoltaic power supply scenario, during the day when photovoltaic power supply is sufficient, β is reduced in exchange for lower latency; while at night when there is no photovoltaic power supply, β is increased, sacrificing a certain latency in exchange for lower energy consumption. Therefore, the positive weight factor β is expressed as: Among them, P pv represents the photovoltaic power generation at the current moment, so the optimal strategy for base station sleep is defined as the following optimization problem: The objective function of the optimization problem represented by formula 17 is the total cost of the base station that combines the power consumption term with the delay term. The constraint condition is used to ensure that the service delay does not exceed the preset service quality threshold. By solving the optimization problem, the optimal sleep threshold N that minimizes the total cost of the base station is obtained. * , thus the base station power under the optimal strategy of base station sleep is P tot (P t ,N * ).

5. An off-grid photovoltaic power supply base station energy-saving device based on sleep control, characterized in that: The device comprises: A power consumption model building unit, used to build a data model and a power consumption model of a base station; A state transition process definition unit, used to represent the state transition process of the base station sleep with a Markov chain, and define the power consumption and service delay of the base station; The base station sleep optimal strategy formulation unit is used to define the total system overhead as the sum of the base station power consumption and the service delay, formulate the optimal strategy for base station sleep according to the lighting conditions, and minimize the total system overhead.

6. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

7. A computer storage medium, characterized in that: The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 4.

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