CN116456379B

CN116456379B - 5G base station and power grid cooperative control method considering dormancy and energy storage adjustment capacity

Info

Publication number: CN116456379B
Application number: CN202310231142.8A
Authority: CN
Inventors: 穆云飞; 马晓燕; 姜欣阳; 靳小龙; 贾宏杰; 余晓丹; 侯恺; 张嘉睿
Original assignee: Tianjin University
Current assignee: Tianjin University
Priority date: 2023-03-11
Filing date: 2023-03-11
Publication date: 2024-03-29
Anticipated expiration: 2043-03-11
Also published as: CN116456379A

Abstract

The invention discloses a 5G base station and power grid cooperative control method considering dormancy and energy storage regulation capacity, which comprises the following steps: establishing a system model comprising a 5G base station power consumption model and a communication model, constructing a 5G base station energy consumption optimization model considering communication load by using the system model, and providing a multi-base station cooperative dormancy strategy considering communication load transfer; then, a 5G base station energy storage minimum standby power time model is established, a communication load state and energy storage minimum standby power time after base station energy consumption are optimized based on a multi-base station cooperative dormancy strategy, a 5G base station energy storage regulation capacity quantification model is established, and then the energy storage regulation capacity of the base stations is calculated and aggregated to determine the charging and discharging feasible region boundaries of the energy storage of each base station; and finally, constructing an optimization model of the 5G base station participating in the cooperative regulation and control of the power grid by using the energy storage regulation capacity of the 5G base station and the aggregation result thereof as constraint conditions, and interacting with the power grid by controlling the charging and discharging modes of each BS energy storage, thereby reducing the operation cost of the base station and supporting peak clipping and valley filling of the power grid.

Description

5G base station and power grid cooperative control method considering dormancy and energy storage adjustment capacity

Technical Field

The invention relates to the field of cooperative control of 5G base stations and power grids, in particular to a cooperative control method of the 5G base stations and the power grids, which takes dormancy and energy storage adjustment capacity into account.

Background

As one of the basic stones for new infrastructure construction, the number of 5G Base Stations (BS) is exponentially increasing ^[1] BS power consumption has dynamic adjustable characteristics, while BS typically configures energy storage batteries to ensure continuous power supply itself ^[2-3] If available, is expected to provide a regulatory flexibility potential of about 78.6 GW.h ^[4] . However, the large-scale dense deployment of the current 5G BS causes the energy consumption of the BS to be multiplied, the electricity cost is increased greatly, and the power consumption of the BS is difficult to be regulated effectively under the dynamic change of the communication load; under the normal power supply state, the BS energy storage resources are largely idle, so that the flexible regulation potential of the BS is underutilized ^[5-7] . How to effectively optimize the 5G BS energy consumption, and simultaneously aggregate a large amount of BS idle energy storage resources to participate in the power grid operation regulation, further support the peak clipping and valley filling of the power grid on the basis of reducing the BS operation cost, and the related problems are needed to be solved.

The 5G BS energy consumption management and control technology and the introduction of BS standby energy storage directly influence the flexible regulation and control potential of the BS to the power grid ^[2,8] . At present, students at home and abroad pay attention to a BS dormancy technology capable of being flexibly designed, and energy consumption is reduced by dormancy of idle load/light load BSs ^[9-10] . Meanwhile, uninterrupted power supply of the BS is ensured when the power grid fails by configuring energy storage priority, and the residual energy storage capacity is used for participating in cooperative regulation and control of the power grid ^[2] . At present, the BS energy storage is mainly in a static standby mode, namely the energy storage capacity is configured according to the BS peak power consumption, and the continuous operation of the power grid in 3 hours is usually ensured ^[11] The BS energy storage capacity has a great amount of redundancy and poor power preparation flexibility, and is particularly prominent particularly when the power grid reliably supplies power ^[2] . Therefore, how to utilize an effective BS dormancy strategy and combine BS energy storage charge and discharge to further mine the cooperative regulation and control flexible potential of the BS and the power grid is significant.

There have been many advances in research on the utilization of 5G BS sleep strategies and BS energy storage to participate in grid cooperative control. Document [12] proposes a cooperative optimization scheduling method of BS communication equipment, an air conditioner and energy storage, wherein the working state of the BS is adjusted through a dormancy strategy, and the energy consumption cost of the BS is reduced through the cooperation of the thermal inertia of a machine room and the energy storage capacity of the communication equipment; literature [8, 13] proposes a BS space-time energy management strategy, and adjusts the association state between the BS and a Mobile User (MUs) according to the electricity price difference between the BSs, so as to participate in the grid demand response, and reduce the BS electricity purchasing cost. However, the above documents do not fully exploit the controllable potential of BS energy storage, resulting in waste of BS energy storage flexibility resources. In order to effectively utilize BS idle energy storage, documents [2, 14] evaluate the controllable capacity of BS standby energy storage and apply it to operation optimization and frequency modulation response of the power system; the literature [15] divides the 5G BS into three states of zero load, normal load and heavy load, proposes an energy storage adjustable potential calculation method considering the BS load state, further aggregates the BS energy storage resources to participate in the power grid adjustment and maximizes the system adjustment income, but the method does not consider the BS energy storage adjustable potential in the load migration and heavy load state of the BS under the dormancy strategy, and limits the capacity of the BS energy storage to participate in the power grid cooperative adjustment; document [16] proposes a 5G BS communication load migration rule, and a BS energy storage charging and discharging strategy is utilized to obtain a matching result of communication load and photovoltaic power generation, so that BS operation cost is reduced, but a BS dormancy strategy of MUs unordered migration weakens MUs service quality; document [17] is to reduce BS electricity cost, optimize BS power consumption by using a fixed threshold dormancy strategy, and further establish an energy storage spare capacity model for guaranteeing BS 3 hours power supply according to BS load state to participate in cooperative regulation of a power grid, however, the dormancy threshold cannot dynamically change along with communication load, and BS energy storage static electricity standby time is conservative, which is easy to cause waste of energy storage resources, and the combination of the two is unfavorable for effective excavation of BS flexibility. Although the documents [12-17] utilize a certain 5G BS wireless resource, the organic synergic consideration between the BS dormancy strategy under the real-time change of the communication load and the BS energy storage for guaranteeing the power supply reliability is insufficient, so that the interaction flexibility potential of the BS and the energy storage resource thereof and the power grid is not fully utilized, and the reduction of the BS operation cost and the peak clipping and valley filling level of the power grid are limited.

Therefore, considering the dynamic change of the BS communication load and the adjustability of the energy storage standby time, on the basis of the work of the existing 5G BS and grid cooperative control research, it is highly necessary to propose a 5G base station and grid cooperative control method which takes into account the sleep and energy storage adjustment capacity, so as to explore the flexibility adjustment potential of the BS by considering the BS energy consumption optimization and the effective utilization of energy storage resources.

Disclosure of Invention

In order to solve the problems that the BS and the energy storage resources thereof cannot be fully utilized, the BS operation cost is reduced and the peak load cut level of the power grid is limited due to the insufficient organic synergic consideration between the BS dormancy strategy under the real-time change of the communication load and the BS energy storage guaranteeing the power supply reliability, the invention provides a 5G base station and power grid synergic control method considering dormancy and energy storage adjustment capacity, which aims at discovering the flexibility control potential of a 5G BS, reducing the BS energy consumption, improving the controllable capacity of the BS energy storage, reducing the BS operation cost and supporting the peak load cut of the power grid by considering the BS energy consumption optimization and the effective utilization of the energy storage resources, and is described in detail below:

A5G base station and power grid cooperative control method considering dormancy and energy storage adjustment capacity comprises the following steps:

Establishing a system model comprising a 5G BS power consumption model and a communication model, constructing a 5G BS energy consumption optimization model considering communication load by using the system model, and providing a multi-BS cooperative dormancy strategy considering communication load transfer;

establishing a 5G BS energy storage minimum standby time model, optimizing a communication load state and energy storage minimum standby time after BS energy consumption based on a multi-BS cooperative dormancy strategy, establishing a 5G BS energy storage regulation capacity quantization model, and further determining a charging and discharging feasible region boundary of each BS energy storage by calculating and aggregating BS energy storage regulation capacity;

and constructing an optimization model of 5G BS participating in grid collaborative regulation by using the 5G BS energy storage regulation capacity and the aggregation result thereof as constraint conditions, and interacting with the grid by controlling the charging and discharging modes of each BS energy storage, so that the BS operation cost is reduced, and meanwhile, peak clipping and valley filling of the grid are supported.

The technical scheme provided by the invention has the beneficial effects that:

(1) According to the multi-BS cooperative dormancy strategy considering communication load transfer, on the premise of ensuring MUs service quality, the idle/light-load BS is dormant to the maximum extent according to the dynamic change of the communication load, and users served by the dormant BS are reasonably transferred, so that the energy consumption of a 5G BS can be reduced, and the operation cost of the BS can be indirectly reduced;

(2) According to the 5G BS energy storage regulation capacity quantization model, a multi-BS cooperative dormancy strategy is utilized to optimize the communication load state after BS energy consumption and a 5G BS energy storage minimum standby time model considering the system power supply reliability, so that the BS energy storage standby capacity is calculated, and the adjustable capacity of the BS energy storage can be improved to a certain extent;

(3) The invention can realize the reduction of BS operation cost and peak clipping and valley filling of the power grid and realize the mutual win-win of the BS operator and the power grid by aggregating the adjustable capacity of the 5G BS energy storage cluster to participate in the cooperative control of the power grid.

Drawings

Fig. 1 is a block diagram of an implementation process of a 5G base station and grid cooperative control method for accounting for sleep and energy storage adjustment capacity provided by the invention;

fig. 2 is a schematic diagram of a multi-base station cooperative sleep strategy provided in the present invention;

fig. 3 is a schematic diagram of dynamic division of energy storage capacity of a 5G base station according to the present invention;

FIG. 4 is a graph of typical daily electrical load and communication load for an office area provided by the present invention;

FIG. 5 is a diagram of a distribution diagram of a base station and a mobile subscriber provided by the present invention;

fig. 6 is a schematic diagram of dynamic change of sleep threshold percentage of a 5G base station according to the present invention;

fig. 7 is a base station-mobile user connection topology diagram provided by the present invention;

Fig. 8 is a graph of power consumption/transmission rate-communication load contrast of an office area 5G base station in the whole day according to the present invention;

fig. 9 is a diagram of an aggregation result of the capacity of the energy storage of the office base station provided by the invention;

fig. 10 is a graph of a Pareto front of an office area provided by the invention;

fig. 11 is a diagram of an energy storage charging and discharging optimization result of an office area 5G base station provided by the invention;

fig. 12 is a graph of the result of optimizing the power load of the office area provided by the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail below.

In the prior art, the problems of insufficient organic synergistic consideration between a BS dormancy strategy under the real-time change of communication load and BS energy storage for guaranteeing power supply reliability, insufficient utilization of interaction flexibility potential of the BS and energy storage resources thereof and a power grid, limitation of reduction of BS operation cost, peak clipping and valley filling level of the power grid and the like are caused. In order to solve the above problems, the embodiment of the present invention provides the following technical solutions.

Example 1

In order to solve the problems, the embodiment of the invention provides a 5G base station and power grid cooperative control method considering dormancy and energy storage adjustment capacity. Fig. 1 is a block diagram of an implementation process of the method according to the present invention, which mainly includes the following steps:

101: establishing a system model comprising a 5G BS power consumption model and a communication model, constructing a 5G BS energy consumption optimization model considering communication load by using the system model, and providing a multi-BS cooperative dormancy strategy considering communication load transfer;

the cooperative dormancy strategy is used for dormancy and migration of MU to the idle/light-load BS to the greatest extent, and reduces connection between the MU and the MBS, so as to minimize BS energy consumption.

102: establishing a 5G BS energy storage minimum standby time model, optimizing a communication load state and energy storage minimum standby time after BS energy consumption based on a multi-BS cooperative dormancy strategy, establishing a 5G BS energy storage regulation capacity quantization model, and further determining a charging and discharging feasible region boundary of each BS energy storage by calculating and aggregating BS energy storage regulation capacity;

103: and constructing an optimization model of 5G BS participating in grid collaborative regulation by using the 5G BS energy storage regulation capacity and the aggregation result thereof as constraint conditions, and interacting with the grid by controlling the charging and discharging modes of each BS energy storage, so that the BS operation cost is reduced, and meanwhile, peak clipping and valley filling of the grid are supported.

In summary, through the steps 101-103, the embodiment of the invention considers the BS energy consumption optimization and the effective utilization of the energy storage resources, so as to discover the flexibility regulation potential of the 5G BS, reduce the BS energy consumption, improve the adjustable capacity of the BS energy storage, reduce the BS operation cost, and support the peak clipping and valley filling of the power grid.

Example 2

The scheme of example 1 is further described below in conjunction with specific examples, figures 1-2, and is described in detail below:

the communication network researched by the embodiment of the invention is a heterogeneous cellular network in which macro base stations (Macro Base Station, MBS) and small base stations (Small Base Station, SBS) are mixed and deployed ^[18] . Because the power grid is difficult to directly perform direct control on a plurality of 5G BSs, the embodiment of the invention introduces an Energy-information virtual aggregation center (EIVAC) of the 5G BS as an intermediate operator to aggregate and regulate BS information and Energy ^[15] . The BS-power grid cooperative control process specifically comprises the following steps:

1) The massive BSs are used as demand response resources to access the EIVAC, by implementing a BS dormancy strategy, dormancy idle load/light load BSs migrate MUs of dormancy BS service on the premise of ensuring MUs service quality, the BS energy consumption is reduced, information transmission and energy transfer among the BSs are realized, and the BS operation cost is indirectly reduced;

2) The EIVAC calculates and aggregates available energy storage resources configured under each BS communication load after dormancy optimization, and determines the boundary of each BS energy storage charging and discharging feasible region to participate in power grid operation on the premise of guaranteeing the BS power supply reliability ^[14] ；

3) The power grid makes a reasonable power plan according to BS energy storage aggregation information provided by the EIVAC, and transmits the information to the EIVAC, and then the EIVAC makes each BS energy storage charging and discharging plan and issues the plan, and the BS energy storage adjusts the charging and discharging power response plan ^[14,15] Thereby realizing the cooperative control of the BS-power grid.

201: a system model comprising a 5G BS power consumption model and a communication model is established, a 5G BS energy consumption optimization model which takes into account communication load is established by utilizing the system model, and a multi-BS cooperative dormancy strategy which takes into account communication load transfer is provided, so that idle/light-load BSs are dormant and migrate MUs to the greatest extent, the connection between MUs and MBS is reduced, and the minimization of BS energy consumption is realized;

202: establishing a 5G BS energy storage minimum standby time model, optimizing a communication load state and energy storage minimum standby time after BS energy consumption based on a multi-BS cooperative dormancy strategy, establishing a 5G BS energy storage regulation capacity quantization model, and further determining a charging and discharging feasible region boundary of each BS energy storage by calculating and aggregating BS energy storage regulation capacity;

203: and constructing an optimization model of 5G BS participating in grid collaborative regulation by using the 5G BS energy storage regulation capacity and the aggregation result thereof as constraint conditions, and interacting with the grid by controlling the charging and discharging modes of each BS energy storage, so that the BS operation cost is reduced, and meanwhile, peak clipping and valley filling of the grid are supported.

In step 201, a multi-BS cooperative dormancy policy considering communication load transfer is proposed to dormant and migrate MU to the idle/light-load BS to the greatest extent, reduce connection between MU and MBS, and achieve minimization of BS energy consumption specifically as follows:

BS in the hypothetical model _i For i [ i E (0, M)]BS, i=0 denotes MBS, i+.0 denotes ith SBS; MU (MU) _j For j [ j E (1, N)]A plurality of MUs; t [ t E (0, T)]Is the t-th regulation time. Wherein M and N are the numbers of BS and MU respectively; t is the regulation period.

Assuming that MBS is always in operation to ensure network coverage (s ₀ (t) =1), the SBS can be switched between active and dormant states to accommodate different communication loads ^[9] . In order to optimize BS energy consumption on the premise of ensuring MUs service quality, the embodiment of the present invention proposes a multi-BS cooperative dormancy strategy, and fig. 2 is taken as an example to illustrate that the multi-BS cooperative dormancy strategy includes 1 MBS and 8 SBSs. Specifically comprises two stages:

1) BS sleep phase

At time t, according to the dynamically-changed communication load, calculating and comparing different dormancy thresholds based on the BS energy consumption optimization modelWherein the dormancy threshold percentage w is [0, 100 ]]，/>To round down) BS power consumption and energy consumption, and further setting a sleep Threshold according to the BS power consumption minimum principle, thereby determining the best w and Threshold at time t, and determining an idle/light load BS below Threshold _i Dormancy (e.g. BS ₃ 、BS ₆ And BS ₈ )；

2) Load migration phase

To ensure MUs quality of service, a lightly loaded dormant BS is used as shown by the load migration communication link in fig. 2 (b) _i Connected MU _j At a transmission rate R _i,j (t) transfer in ascending order and correlate MU _j To transmission rate R in the coverage overlap region _i,j (t) suboptimal BS to be selected _k(k≠i) If the candidate SBS does not exist, MU _j Associated to MBS. Specifically, if MU _j Optional BS of (c) _k(k≠i) Full capacity or super capacity, and BS _k(k≠i) Service MU _p Can be transferred to the non-full capacity and non-empty BS to be selected _k1(k1≠i) MU then _j Can still migrate to BS _k(k≠i) : such as dormant BS ₆ Associated one MU _j Is to be selected BS of (c) ₅ Full capacity, but BS ₅ The medium MU can be transferred to BS according to the communication link (1) ₄ MU then _j Can be transferred to BS according to communication link (2) ₅ 。

By using 1) and 2) two-stage dormancy idle load/light load BS and establishing the pairing connection between BS and MU according to the cooperation among a plurality of BSs, the BS can be dormant to the greatest extent, the connection between MU and MBS is reduced, and the BS energy consumption is effectively reduced.

In summary, through the steps 201-203, the embodiment of the invention considers BS energy consumption optimization and effective utilization of energy storage resources to discover the flexibility regulation potential of the 5G BS, reduce BS energy consumption, improve the adjustable capacity of BS energy storage, reduce BS operation cost, and support peak clipping and valley filling of the power grid.

Example 3

The schemes of embodiments 1 and 2 are further described below in conjunction with specific formulas, figures, and examples, and are described in detail below:

step 301: establishing a system model comprising a 5G BS power consumption model and a communication model;

1) 5G base station power consumption model

The 5G BS power consumption mainly comes from an active antenna unit and a baseband unit and is divided into dynamic power consumption and static power consumption in a working state and power consumption in a dormant state ^[2,16] . Wherein, the dynamic power consumption in the working state and the communication load form a linear relation; static power consumption independent of communication load for maintaining normal operation of BS ^[8] . The 5G BS power consumption model is shown as (1) ^[2] ：

Wherein,

wherein: p (P) _i (t) is time t BS _i Power consumption (W);and->BS respectively _i A static power consumption and a dynamic power consumption maximum (W); t (T) _i (t) is BS _i Is a communication load rate of (a); l (L) _i (t) is time t BS _i MUs number of services; MU (MU) _i,max For BS _i The maximum number of accessible MUs; p (P) _i,sleep (t) is time t BS _i Sleep power consumption (W); defining the operation state matrix of the BS as s= [ S ] _i ] _1×(M+1) ，s _i (t) =1, i.e. time t BS _i Is in working state s _i (t) =0, BS _i Sleep. The invention is thatExample assume T _i (t)∈[0，20％]Is a low communication load level; t (T) _i (t)∈(20％，60％]Is a normal communication load level; t (T) _i (t)∈(60％，100％]Is a high communication load level.

2) 5G base station and mobile user communication model

In order to enable the MU to effectively access the BS on the premise of guaranteeing the MUs service quality, a communication model is introduced. The signal-to-interference-plus-noise ratio (Signal to Interference Plus Noise Ratio, SINR) is a common indicator for establishing a pairing connection between a BS and an MU in the communication field, when the SINR is not less than delta ^th (△ ^th A SINR threshold), the MU may be effectively served by the accessed BS ^[19] . Defining the association state matrix of the BS and the MU as A= [ a ] _i,j ] _(M+1)×N ，a _i,j (t) =1, i.e. time t BS _i With MU (multi-user) _j Establishing a communication connection, a _i,j (t) =0, BS _i With MU (multi-user) _j And cannot be connected. The SINR between BS and MU is shown in equation (3):

wherein: SINR (Signal to interference plus noise ratio) _i,j (t) and g _i,j (t) are respectively t time BS _i With MU (multi-user) _j SINR (dB) and channel gain (dB); p is p _i,j (t) is time t BS _i Assigning to MUs _j Is provided (W); a, a _k,j (t) and g _k,j (t) are respectively t time BS _k With MU (multi-user) _j Correlation state values and channel gains (dB) between; p is p _k,j (t) is time t BS _k For MU _j The interference power (W) generated; sigma (sigma) ² Is noise power (dBm/Hz); w (W) _i For time t BS _i Channel bandwidth (MHz).

According to shannon's theorem ^[20] The information transmission rate is shown in formula (4):

wherein: r is R _i,j (t) is time t BS _i With MU (multi-user) _j Transmission rate (Mbit/s) between.

Step 302: constructing a 5G BS energy consumption optimization model considering communication load by using a system model;

The energy consumption optimization model mainly comprises the following steps: objective functions and constraints.

1) Objective function

Based on the 5G BS power consumption model and the communication model in step 301, a 5G BS energy consumption optimization model considering the communication load is constructed, and the objective function of the constructed energy consumption optimization model is as follows:

wherein,

wherein:total energy consumption (kW.h) for BS throughout the day; />BS total power consumption (kW) at time t; Δt is the control time interval, and Δt=0.5h is set in the embodiment of the present invention. Since BS communication load varies with time and load time interval is fixed, BS power consumption is minimized so that energy consumption is minimized.

2) Constraint conditions

(1) BS running state and MU connection state constraints:

this equation illustrates that the BS has only dormant and active states and the BS has only disconnected and connected states with the MU.

(2) BS and MU connection constraint:

the time (8) represents the time (t) MU _j Only one BS can be connected; formula (9) represents BS _i The number of associations MUs must not exceed the upper limit.

(3) Communication network quality of service constraints:

wherein: r is R _j,min For MU _j Lower information transfer rate requirements.

Step 303: a multi-BS cooperative dormancy strategy is proposed that considers traffic load transfer, the strategy diagram of which is shown in fig. 2. Assuming that MBS is always in operation to ensure network coverage (s ₀ (t) =1), the SBS can be switched between active and dormant states to accommodate different communication loads ^[9] . In order to optimize BS energy consumption on the premise of ensuring MUs service quality, the embodiment of the present invention proposes a multi-BS cooperative dormancy strategy, and fig. 2 is taken as an example to illustrate that the multi-BS cooperative dormancy strategy includes 1 MBS and 8 SBSs. Specifically comprises two stages:

1) BS sleep phase

At time t, different dormancy thresholds are calculated and compared according to the dynamically changed communication load and based on formulas (5) - (6) in the BS energy consumption optimization modelWherein the dormancy threshold percentage w is [0,100 ]]，/>To round down) BS power consumption and energy consumption, and further setting the sleep Threshold according to the BS power consumption minimum principle, thereby determining the best w and Threshold at time t, and making the power consumption lower than ThresholdIdle/light load BS of ld _i Dormancy (e.g. BS ₃ 、BS ₆ And BS ₈ )。

2) Load migration phase

To sum up, by using 1) and 2) two-stage dormancy idle load/light load BS and establishing pairing connection between BS and MU according to cooperation among a plurality of BSs, BS can be dormant to the greatest extent, connection between MU and MBS can be reduced, and BS energy consumption can be effectively reduced.

Multiple BS cooperative dormancy strategy is achieved by combining the multiple BSs in different t (t E [0, T)]) Different Threshold (Threshold E [0, MU) _i,max ]) When the BS dormancy and load migration phases are utilized to carry out dormancy and migration of MU on the idle/light-load BS, thereby regulating the BS communication load and determining the communication load rate T _i (t) association status value a with BS-MU _i,j (t); further, calculating power consumption P of the BS by using the energy consumption optimization model _i (t) and energy consumptionAnd further selecting w when the power consumption of the BS is lowest at the moment t as the optimal dormancy threshold percentage at the moment t through a dormancy strategy; when t=t, the corresponding scheme is the optimal BS sleep and BS-MU association scheme (i.e., the 5G BS energy consumption optimal scheme), and the obtained energy consumption optimal result is the subsequent calculation of BS storageThe capacity providing model and data support can be adjusted.

Step 304: establishing a 5G BS energy storage minimum standby time model, optimizing a communication load state and energy storage minimum standby time after BS energy consumption based on a multi-BS cooperative dormancy strategy, establishing a 5G BS energy storage regulation capacity quantization model, and further determining a charging and discharging feasible region boundary of each BS energy storage by calculating and aggregating BS energy storage regulation capacity;

1) 5G base station energy storage minimum standby power time model

When no external power source is used for supplying power, if the storage reserve capacity of the BS can ensure that the continuous power supply time of the area is longer than the single maximum power failure time, the continuous power supply of the BS can be realized all the year round, so the BS _i Minimum standby time T of energy storage _i,spa Namely BS _i Single maximum power failure time T of region r _i,r,max . To ensure the power supply reliability of 5G BS while quantifying T with uncertainty _i,r,max The embodiment of the invention firstly introduces the BS power supply reliability factor f to ensure T _i,r,max Associated with power supply reliability:

T _i,r,max ＝[(1-R _r,s )·8760]·(1-f)，f∈[0,1] (11)

wherein R is _r,s The power supply reliability (%) of the power grid of the region r is obtained from the average power supply reliability of the region users provided by urban power supply enterprises ^[21] The method comprises the steps of carrying out a first treatment on the surface of the In a physical sense, (1-R _r,s ) 8760 is the annual average outage time (h) for region r, 1-f represents the single maximum outage time T _i,r,max Probability of taking average outage time throughout the year.

Further, in order to realize the dynamic electricity-standby effect of 5G BS energy storage, the method is different from the traditional principle of '3 hours standby' of BS energy storage " ^[11] According to the formula (11), a 5G BS energy storage minimum standby time model considering the power supply reliability of the system can be established, as shown in the formula (12):

T _i,spa ＝T _i,r,max ＝[(1-R _r,s )·8760]·(1-f) (12)

wherein when R is _s,r When the power is raised, the annual average power failure time is reduced, T _i,spa With a consequent reduction in the damage to the stored energy life.

2) 5G base station energy storage adjustment capacity quantization model

A schematic diagram of dynamic partitioning of the energy storage capacity of a 5G BS under power supply reliability and communication load conditions is shown in fig. 3. E (E) _max And E is _min Upper/lower limit (kW.h) of available capacity of BS energy storage respectively, which is limited by overcharge and overdischarge of energy storage ^[15] ；E _i For BS _i The actual stored energy (kW.h); e (E) _i,spa To consider BS _i Energy storage dynamic standby capacity (kW.h) of standby power function; e (E) _i,disp For BS _i Energy storage dynamic adjustable capacity (kW.h).

The difference of the BS access to the distribution network enables the power supply reliability R of different areas _s,r Different; optimizing BSs using multi-BS cooperative sleep strategy _i Energy consumption causes a traffic load rate T _i (t) change. At different R _s,r And T _i At (t), time BS _i Reserve capacity of energy storage E' _0,spa (t) is:

wherein: p (P) _i And (t) a BS power consumption result obtained after optimizing the 5G BS energy consumption optimization model by adopting a multi-BS cooperative dormancy strategy.

Further, considering the upper limit and lower limit constraint of the energy storage capacity, finally taking the BS _i The energy storage dynamic standby capacity and the adjustable capacity are respectively as follows:

E _i,spa (t)＝max{E _min ,E' _i,spa (t)} (14)

E _i,disp (t)＝E _i (t)-E _i,spa (t) (15)

in a high-reliability or low-communication load area, the probability of occurrence of faults of a power grid is small, the standby capacity of BS energy storage is low, and the dynamic adjustable capacity is high; in the low-reliability or high-communication load area, the probability of power grid faults is high, the standby capacity of BS energy storage is high, and the dynamic adjustable capacity is low. When multi-BS cooperation in step 303 is employed Sleep policy changing BS _i T of (2) _i (t) to reduce BS Power consumption P _i (t), or lifting R _s,r To reduce BS _i Minimum standby time T of energy storage _i,spa And in the process, the standby capacity of the BS energy storage in the formula (13) is reduced to a certain extent, and the dynamic adjustable capacity of the energy storage in the formula (15) is further improved.

Suppose BS _i The upper limit of the capacity of the stored energy is E _max BS then _i The energy storage chargeable and dischargeable capacity is:

wherein:and->Respectively t time BS _i Chargeable capacity and dischargeable capacity of stored energy (kw.h).

From (13) - (16), the BS can be further deduced _i The maximum charge and discharge power of the stored energy is as follows:

wherein:and->Respectively t time BS _i Adjustable maximum charge power and discharge power (kW) of the stored energy; />And->Respectively isMaximum charge power and discharge power limits (kW) of the stored energy; Δt is the control time interval, and Δt=0.5h is set in the embodiment of the present invention.

And the EIVAC determines the charging and discharging feasible domain boundaries of each BS energy storage by calculating and aggregating the generated 5G BS energy storage regulation capacity, and provides data support for the follow-up 5G BS to participate in the cooperative regulation and control of the power grid. The corresponding BS energy storage polymerization results are:

wherein:and->Respectively adjusting maximum charging power and discharging power (kW) for the BS energy storage cluster at the moment t; m is M _ch (t) and M _dis (t) the charge and discharge quantity of the BS energy storage clusters at the moment t respectively; / >And->Respectively t time BS _i Energy storage charge state and discharge state, < >>I.e. in a charged state->I.e. in a discharge state.

Step 305: utilizing the 5G BS energy storage regulation capacity and the aggregation result thereof as constraint conditions to construct an optimization model of the 5G BS participating in the cooperative regulation and control of the power grid;

the 5G BS participates in the optimization model of the grid collaborative regulation mainly comprises the following steps: objective functions and constraints.

1) Objective function

In order to realize effective cooperative regulation and control of the 5G BS and the power grid, the embodiment of the invention respectively establishes the following two objective functions from the perspective of BS operators and the power grid:

objective function one: minimizing 5G BS operating costs

minC _ope ＝min(C _grid +C _deg -C _gain ) (19)

Wherein: c (C) _ope Operating cost (yuan) for 5G BS throughout the day; c (C) _grid Cost (yuan) of electricity purchase for BS from the grid; c (C) _deg Cost of energy storage loss for BS (basic element) ^[22] ；C _gain And storing energy for the BS to participate in peak clipping and valley filling.

Wherein: e, e _grid (t) and e _bat (t) the time-sharing electricity price of the power grid at the moment t and the charging and discharging loss cost (yuan/kW.h) of the BS energy storage unit;for time t BS _i Purchasing power (kW) from the grid; />And->Respectively t time BS _i Charging power and discharging power (kW) of the stored energy; η (eta) _ch And eta _dis BS respectively _i Energy storage and charging efficiencyAnd discharge efficiency (%); lambda (lambda) _bat Price per unit energy storage capacity (yuan/kW.h); q is BS _i Maximum Charge and discharge times of energy storage in a State of Charge (SOC) range; SOC (State of Charge) _max And SOC (System on chip) _min The upper and lower limits of the SOC of the energy storage battery, respectively.

Objective function two: minimizing grid load curve variance

Wherein: c (C) _var For the power network load curve variance (kW) ² )；P _L (t) is the grid load (kW) at time t; p (P) _Avg Is the average value (kW) of the total load of the power grid in a day of the area.

2) Constraint conditions

(1) BS power supply and demand balance constraint

P _i ^grid (t)+P _i ^dis (t)＝P _i (t)+P _i ^ch (t) (25)

This description BS _i The power supply of (2) is required to satisfy the power balance condition.

(2) BS and power grid interaction power constraint

P _i ^grid (t)≥0 (26)

P _i (t)≥P _i ^dis (t) (27)

BS (26) _i Power purchasing constraints from the grid; BS avoidance of (27) _i The stored energy is overdischarged, which impairs battery life.

(3) BS energy storage cluster and power grid interaction power constraint

Wherein:and->And respectively obtaining an upper limit and a lower limit (kW) of interaction power of the BS energy storage cluster and the power grid at the moment t. Equation (28) illustrates that the interaction power of the BS energy storage cluster with the grid must not exceed a limit.

(4) BS energy storage charge-discharge state constraint

This description is at time t BS _i Is unable to both charge and discharge.

(5) BS energy storage cluster charge-discharge power constraint

Wherein:and->Correlating to equation (18) in the 5G BS energy storage adjustment capacity quantization model in step 304. Equation (30) illustrates that the charging and discharging power of the BS energy storage clusters must not exceed a limit.

(6) BS energy storage charging and discharging energy balance constraint

Wherein: e (E) _i (t) and E _i (t- Δt) is t and (t- Δt) period BS, respectively _i Energy storage capacity (kw·h); delta _leak Is an energy storage leakage factor. (31)) Description of BS _i The actual stored energy of the energy storage is determined by the preamble energy and the charge and discharge power.

(7) BS standby energy storage capacity constraint

E _i,spa (t)≤E _i (t)≤E _max (32)

Wherein: e (E) _i,spa (t) is related to equation (14) in the 5G BS energy storage adjustment capacity quantization model in step 304. This description BS _i The stored energy must not exceed the limit.

(8) BS energy storage start-end state capacity balance constraint

E _i (0)＝E _i (T) (33)

Wherein: e (E) _i (0) And E is _i (T) initial and final days of the day BS respectively _i The capacity of the stored energy. This constraint avoids BS of the current period _i The energy storage remaining capacity influences the energy storage regulation flexibility of the next period.

(9) BS energy storage state of charge constraints

Wherein: SOC (State of Charge) _i (t) is time t BS _i A state of charge of the stored energy; SOC (State of Charge) _max (t) and SOC _i,min (t) respectively considering the upper limit and the lower limit of the state of charge of the BS after the standby capacity is provided for the energy storage of the BS at the moment t; e (E) _rate Is the rated capacity of energy storage.

3) Multi-objective optimization model transformation:

the embodiment of the invention adopts a min-max standardization method ^[16] And carrying out normalization processing on different objective functions to eliminate dimensions, and further converting the multiple objectives into a single objective optimization model by adopting a weight coefficient method. The transformation process is as follows:

(1) Normalization of different objective functions

Wherein: c'. _ope (t) and C' _var (t) respectively normalizing the BS operation cost and the power grid load curve variance objective function value; c (C) _ope,max (C _var,max )、C _ope,min (C _var,min ) The maximum and minimum of the objective function, respectively.

(2) Multi-objective conversion to single-objective optimization model

minC _ope&var ＝min(w ₁ ·C' _ope +w ₂ ·C' _var ) (37)

Wherein: c (C) _ope&var To consider the overall objective function of BS operating cost and grid load curve variance; w (w) ₁ And w ₂ Is a weight coefficient, and w ₁ +w ₂ ＝1。

In summary, the optimization problem of the 5G BS participating in the grid collaborative regulation is as shown in formula (38):

example 4

The protocols in examples 1-3 were validated in conjunction with specific experimental data as described in detail below:

to verify the effectiveness of the method of the present invention, consider an office area of a city as an example, and assume an area of 500m ² The regional power supply reliability factor f takes 0.5, and the regional power supply reliability rate R is simplified _s,r City average power supply reliability 99.971% (assuming that the power supply reliability of the city power distribution network accessed by the 5G BS is the same) ^[21] . The invention has the regulation period T=24h, and the regional typical daily power load and communication load curves are shown in figure 4 ^[5,15] The general industrial and commercial time-of-use electricity prices in the region are shown in Table 1 ^[23] 。

Assume that the region has a total of 1The MBS with fixed positions and the SBSs with the same model are distributed uniformly, participate in the cooperative regulation of the power grid, and the area contains at most 1000 MUs which are distributed randomly and dynamically in different time intervals throughout the day. Office area 8: the BS-MU location distribution at time 00 is shown in fig. 5. The simulation parameters of the 5G communication network used in the invention are shown in table 2 ^[24] The parameters of the 5G BS configured energy storage lithium ion battery are shown in Table 3 ^[16,22] 。

Table 1 general industrial and commercial user time-of-use electricity prices

Table 2 main simulation parameters in 5g heterogeneous cellular network

Table 3 5g base station energy storage battery parameters

(1) Multi-base station cooperative dormancy strategy result analysis

In order to analyze the effectiveness of regulating 5GBS power consumption and optimizing 5G BS energy consumption under the premise of ensuring MUs service quality of a multi-BS cooperative dormancy strategy considering communication load transfer, the invention sets 3 scenes:

scene I: a BS-free sleep strategy; scene II: BS dormancy using fixed threshold dormancy strategy ^[17] That is, when the number of SBS services MUs is below a fixed threshold, the SBS goes into a dormant state, with its service MUs transitioning to other non-full capacity, non-empty SBS or MBS; scene III: BS dormancy employs the dormancy strategy proposed by the present invention.

1) 5G base station-user connection topology result analysis

The dynamic selection process of the BS sleep threshold percentage w when the 5G BS is at the low communication load (e.g., time 2:00, communication load rate 2%), normal communication load (e.g., time 8:00, communication load rate 60%) and high communication load (e.g., time 16:00, communication load rate 90%) level in the office area is analyzed by simulation, as shown in fig. 6.

As can be taken from fig. 6, compared to scene I (threshold=0) and scene Scenario III may dynamically select w and ++that minimizes BS power consumption based on changes in communication load> Finally, the optimal w range of the system under the conditions of low communication load, normal communication load and high communication load of the 5G BS cluster is [25%,100%]、[34％，41％]And [34%,41 ]]The best Threshold is [3, 12 respectively]Power consumption was reduced by 2.19%/0.056%, 0.52%/0.40% and 0.13%/0.13%, respectively, for 4 and 4.

To explore the influence of different sleep strategies on the BS-MU connection relationship, the following are respectively referred to as BS ₃₂ And BS ₃₄ Analysis for example t=8: the transfer of dormant SBS service MUs at 00 results are shown in fig. 7. Wherein the light gray solid line is the BS-MU association condition under the condition of ensuring MUs service quality; black dot-dash is a transition case of dormant SBS service MUs; the black solid line is the dormant BS _i Service MU _j Is to be selected BS of (c) _k(k≠i) MU when full capacity or super capacity _j Is a transition condition of (2).

As can be seen from fig. 7 (a, b), the sleep BS in scenario II ₃₂ MUs of service transfer to candidate neighbor less than full BS ₂₃ And MBS; as can be seen from fig. 7 (c, d), the sleeping BS in scenario III ₃₄ MUs of service transfer to candidate neighbor less than full BS ₃₃ Sum of full capacity BS ₃₅ The remaining MUs, where no SBS candidate exists, is transferred to MBS, where the BS is full ₃₅ Presence of transferable to pending less than full capacity BS ₂₆ As indicated by the solid black line. Finally, compared to scenario II (sleep 4 SBSs), the multi-BS cooperative sleep strategy in scenario III may sleep 21 SBSss, decreasing the activated SBS quantity 22.37%.

In summary, the dormancy strategy provided by the invention dynamically selects the dormancy Threshold according to the change of the communication load, so as to dormant SBS to the maximum extent, and reasonably migrate MUs of dormant SBS service, thereby reducing the power consumption of the BS.

2) Analysis of power consumption and rate optimization results of 5G base stations in different areas

To further verify the effectiveness of the multi-BS cooperative sleep strategy, the results of the power consumption and transmission rate adjustment for all days in scenario I, scenario II and scenario III are shown in fig. 8, and the power consumption/energy consumption and transmission rate comparison results are shown in table 4.

Table 4 comparison of power consumption/energy consumption and transmission Rate throughout the day in different scenarios

As can be seen from fig. 8: (1) the changes in power consumption and transmission rate of the 5G BS in the office approximately follow the dynamic changes in communication load at different times throughout the day. This is due to: MUs in the area moves along with time change, so that the communication load and the BS-MU communication process in the area also change, and finally the system power consumption and the transmission rate change correspondingly at different moments; (2) the overall power consumption and transmission rate (i.e., MU quality of service) of the system is reduced in scenario II and scenario III, and the reduction effect is more pronounced in scenario III, compared to scenario I. This is due to: different dormancy strategies are dormant by a certain quantity of SBSs, so that the probability that MUs of dormant SBSs service selects SBSs with suboptimal or worse transmission rates for association is increased, and therefore system power consumption and transmission rates are reduced, but in a scene III, multiple BSs are cooperated with the dormancy strategy to dynamically select the best Threshold according to communication load changes, the SBSs are dormant to the greatest extent, and therefore BS power consumption and MUs service quality are reduced to a greater extent. However, the proposed dormancy strategy migrates MUs of dormant SBS services according to the ascending transmission rate, so that the quality of service MUs in the office area is relatively minimum, but still increased by about 14.61 times compared with the minimum transmission rate, i.e. the proposed dormancy strategy can ensure MUs higher quality of service. As can be seen from table 4, under the premise of ensuring MUs service quality, the multi-BS cooperative dormancy strategy in the scenario III can reduce the average power consumption/energy consumption of 81 BSs in the office area by about 0.41% throughout the day, respectively, compared with the fixed threshold dormancy strategy in the scenario II; compared with the non-dormancy strategy in the scene I, the average power consumption/energy consumption of 81 BSs in the office area in the whole day can be reduced by about 2.24 percent respectively.

In conclusion, the multi-BS cooperative dormancy strategy can fully utilize the SBS wireless resource on the premise of ensuring MUs service quality, and effectively regulate and reduce BS power consumption and energy consumption.

(2) Analysis of aggregation results of dynamic energy storage capacity adjustment of 5G base station

When the area is supplied with power, the reliability rate R _s,r When= 99.971%, the energy storage minimum standby time T is obtained by the formula (12) _i,spa =1.27 h. In order to verify the effectiveness of the 5G BS energy storage adjustment capacity quantization model considering the power supply reliability of the system, under the conditions of scene I, scene II and scene III, the EIVAC calculates and aggregates the idle energy storage resources of the BS according to the BS energy storage adjustment capacity quantization model. The comparison result of the BS energy storage dynamic reserve capacity and the adjustable capacity under 1.27h and the conventional 3h standby time is shown in fig. 9.

As can be seen from fig. 9: taking office area 14:00 as an example, (1) when determining the energy storage and standby time (such as 1.27 h), compared with scene I, the office area can respectively reduce the BS energy storage dynamic standby capacity by 0.18% and 1.42% in scene II and scene III, and the adjustable capacity is respectively increased by 0.073% and 0.57%, that is, the BS energy storage dynamic standby capacity can be reduced to a greater extent by adopting the multi-BS cooperative dormancy strategy, and the adjustable capacity is improved. At low loads (e.g., 1:00-6:00 periods), E 'under sleep policy' _i,spa ＜E _min Taking the BS energy storage dynamic standby capacity E _i,spa ＝E _min The rest period approximately follows the dynamic change of the communication load, and the change of the adjustable capacity is opposite to the change trend of the communication load. This is due to: the BS energy storage dynamic spare capacity is related to the BS power consumption, MUs in different areas moves along with time, so that the communication load is changed along with the change, and the communication load is reasonably changed through a multi-BS cooperative dormancy strategy, so that the BS power consumption can be effectively reduced, the BS energy storage dynamic spare capacity is reduced, and the adjustable capacity is improved; (2) compared with the traditional 3h power-on time, when the power-on time is 1.27h, the office areaThe BS energy storage dynamic reserve capacity can be reduced by 57.41%, 57.46% and 57.90% respectively under the scene I, the scene II and the scene III, the adjustable capacity can be increased by 117.39%, 117.28% and 117.46% respectively, namely the BS energy storage dynamic reserve capacity is lower under the 1.27h standby time, and the adjustable capacity is higher. This is due to: when the same dormancy strategy is adopted, the power consumption of the BS is the same, and at the moment, the dynamic standby capacity of the BS energy storage is determined by the standby time, and the smaller the standby time is, the lower the standby capacity is.

In conclusion, by adopting a multi-BS cooperative dormancy strategy and a 5G BS energy storage regulation capacity quantification model considering the power supply reliability of the system, the dynamic standby capacity of BS energy storage can be reduced to a greater extent, and the adjustable capacity can be improved.

(3) 5G base station and power grid day-ahead cooperative regulation and control result analysis considering dormancy strategy and energy storage regulation capacity

1) Pareto optimal solution outcome analysis

The dynamic Pareto front obtained by solving the multi-objective optimization model form (38) using the min-max normalization method is shown in fig. 10.

As can be seen from fig. 10, the 5G BS operation cost in the office area has a negative correlation with the power grid load curve variance. This is due to: the flexibility regulation potential of the 5G BS is utilized to reduce the peak-valley difference of the power grid load, so that the cost of BS energy storage loss is increased, and the system economy is further affected. Therefore, the weight coefficient is selected according to the actual regulation and control requirement, and then a certain optimal solution on the Pareto front is selected as a 5G BS-power grid cooperative regulation and control scheme. Embodiments of the invention will take w ₁ ＝0.7，w ₂ Solution explanation is given by taking 0.3 as an example.

2) 5G base station optimization regulation result analysis under different scenes

In order to study the influence of a multi-BS cooperative dormancy strategy and the setting of 5G BS energy storage regulation capacity considering power supply reliability on the participation of the BS in the cooperative regulation of the power grid, taking an office area as an example, 6 example scenes shown in a table 5 are designed for comparison analysis, wherein the BS energy storage static electricity standby mode is to configure the BS energy storage capacity according to the peak power consumption of the BS in peak communication load, and the corresponding optimization result is shown in the table 6.

TABLE 5 setting cases of example scenes 1 to 6

TABLE 6 comparison of System Regulation results under different scenarios

As can be seen from table 6: (1) compared with Case 1/3 and Case 2/4, case 5 and Case6 introduce a multi-BS cooperative dormancy strategy, so that 5G BS power consumption is reduced to the greatest extent, and electricity purchasing cost is reduced by 2.17%/0.43% and 2.85%/0.69% respectively; (2) compared with Case 1/3/5, case 2/4/6 introduces 5G BS energy storage dynamic standby power considering power supply reliability, and increases the dynamic adjustable capacity of BS energy storage, so that 5G BS energy storage charging and discharging power is increased, the energy storage loss cost is increased, the peak clipping and valley filling benefits are increased, and the electricity purchasing cost is reduced; (3) compared with cases 1-5, case6 considers the multi-BS cooperative dormancy strategy and the standby power function of the 5G BS energy storage dynamic capacity, so that the BS power consumption is effectively regulated, the adjustable capacity of the BS energy storage is improved, the energy storage loss cost is increased, and peak clipping and valley filling benefits are brought. Compared with Case 5, the BS energy storage low-storage high-discharge form of Case6 reduces the BS operation cost by 1.47%, and reduces the power grid load curve variance by 0.20%.

3) 5G base station energy storage charge and discharge regulation result analysis

The regulation and control result of the 5G BS energy storage charge and discharge of the office area in the calculation case6 is shown in fig. 11, and the power load optimization result is shown in fig. 12.

As can be seen from fig. 11: considering from the BS operator side, (1) purchasing electricity from a power grid in an office area at a low electricity price in a period of 00:00-10:00, and charging the BS energy storage until the upper energy storage limit is reached while meeting the low communication load power requirement of the BS; (2) in the peak time of 10:00-15:00 electricity price, the office area discharges by using the stored electric energy to reduce electricity purchasing to the power grid, so that peak clipping of the electric load is realized; (3) in the period of 15:00-16:00, the electricity price is lower, the office area purchases electricity from the power grid to charge the BS energy storage, so that the BS energy storage is discharged in the period of 16:00-23:00 with higher power load, peak clipping and valley filling benefits are increased, and the BS energy storage is charged in the period of 23:00-24:00 so as to achieve equal energy storage capacity.

As can be seen from fig. 12: considering from the side of a power system, the 5G base station and power grid cooperative control method taking account of dormancy and energy storage adjustment capacity can effectively utilize BS energy storage resources to realize peak clipping and valley filling of the power grid, wherein the peak-valley difference of an office area is reduced by 3.66%.

In conclusion, the 5G BS energy storage fully utilizes the electric energy stored in low electricity price according to regional power load characteristics so as to release the electric energy in high electricity price or power load peak, thereby obtaining peak clipping and valley filling benefits, improving a power load curve and realizing win-win of a BS operator and a power grid.

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Those skilled in the art will appreciate that the drawings are schematic representations of only one preferred embodiment, and that the above-described embodiment numbers are merely for illustration purposes and do not represent advantages or disadvantages of the embodiments.

The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims

1. A 5G base station and grid cooperative control method that accounts for sleep and energy storage regulation capacity, the method comprising:

establishing a system model comprising a 5G Base Station (BS) power consumption model and a communication model, constructing a 5G BS energy consumption optimization model considering communication load by using the system model, and providing a multi-BS cooperative dormancy strategy considering communication load transfer;

utilizing the 5G BS energy storage regulation capacity and the aggregation result thereof as constraint conditions, constructing an optimization model of 5G BS participating in the cooperative regulation of the power grid, and supporting peak clipping and valley filling of the power grid by controlling the charge and discharge modes of each BS energy storage to interact with the power grid;

the objective function in the 5G BS energy consumption optimization model which is constructed by using the system model and takes the communication load into account is as follows:

wherein,

wherein: t [ t E (0, T)]The t-th regulation time; t is a regulation period; m is the number of BS;total energy consumption for BS throughout the day; The total power consumption of the BS at the t moment; p (P) _i (t) is time t BS _i Is a power consumption of (1); Δt is the regulation time interval; />And->I [ i E (0, M) respectively]Each BS _i Static power consumption and dynamic power consumption maximum values of (a); p (P) _i,sleep (t) is time t BS _i Is a sleep power consumption of (1); s is(s) _i (t) =1, i.e. time t BS _i Is in working state s _i (t) =0, BS _i Dormancy; t (T) _i (t) is the communication load factor of the BS;

the multi-BS cooperative dormancy strategy considering the communication load transfer specifically comprises the following steps:

BS in the hypothetical model _i For i [ i E (0, M)]The BS, i=0 represents macro BS MBS, i noteq 0 represents i-th BS SBS; MU (MU) _j For j [ j E (1, N)]A user MU;

BS sleep phase: according to the dynamically-changed communication load at the time t, calculating and comparing the BS power consumption and the energy consumption under the sleep Threshold corresponding to different sleep Threshold percentages w based on a BS energy consumption optimization model, setting the sleep Threshold according to the minimum BS power consumption principle, determining the optimal w and Threshold at the time t, and carrying out no-load/light-load BS lower than the Threshold _i Dormancy;

load migration phase: will lightly load dormant BS _i Connected MU _j At a transmission rate R _i,j (t) transfer in ascending order and correlate MU _j To transmission rate R in the coverage overlap region _i,j (t) suboptimal BS to be selected _k(k≠i) If the candidate SBS does not exist, MU _j Is associated to MBS; if MU _j Optional BS of (c) _k(k≠i) Full capacity or super capacity, and BS _k(k≠i) Service MU _p Can be transferred to the non-full capacity and non-empty BS to be selected _k1(k1≠i) MU then _j Migration to BS _k(k≠i) ；

The 5G BS energy storage minimum standby power time model is as follows:

T _i,spa ＝T _i,r,max ＝[(1-R _r,s )·8760]·(1-f)

wherein T is _i,spa For BS _i Minimum standby time of energy storage, R _r,s The power supply reliability of the power grid of the region r is represented by f which is the BS power supply reliability factor, and 1-f represents the single maximum power failure time T _i,r,max Probability of occupying annual average outage time;

the 5G BS energy storage regulation capacity quantization model is as follows:

BS _i the maximum charge and discharge power of the stored energy, namely the boundary of the charge and discharge feasible regions of each BS stored energy is:

wherein:and->Respectively t time BS _i An adjustable maximum charge/discharge power of the stored energy; />And->Maximum charge/discharge power limits for the stored energy, respectively; />And->Respectively t time BS _i Chargeable/dischargeable energy storageCapacity; Δt is the regulation time interval;

the boundary of the charging and discharging feasible domain of the BS energy storage aggregation result, namely the BS energy storage cluster is as follows:

wherein:and->Respectively the adjustable maximum charge/discharge power of the BS energy storage cluster at the t moment; m is M _ch (t) and M _dis (t) the charge/discharge quantity of the BS energy storage clusters at the moment t respectively; />And->Respectively t time BS _i And an energy storage charge/discharge state.