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CN112803495A - 5G base station microgrid optical storage system capacity optimal configuration method based on energy sharing - Google Patents

5G base station microgrid optical storage system capacity optimal configuration method based on energy sharing Download PDF

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CN112803495A
CN112803495A CN202110205508.5A CN202110205508A CN112803495A CN 112803495 A CN112803495 A CN 112803495A CN 202110205508 A CN202110205508 A CN 202110205508A CN 112803495 A CN112803495 A CN 112803495A
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base station
microgrid
photovoltaic
energy
micro
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CN112803495B (en
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麻秀范
段颖
孟祥玉
朱秋萍
王志
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North China Electric Power University
Information and Telecommunication Branch of State Grid Tianjin Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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  • Power Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Photovoltaic Devices (AREA)

Abstract

The invention discloses a capacity optimization configuration method of a 5G base station microgrid optical storage system based on energy sharing, and belongs to the technical field of capacity optimization of optical storage systems. Comprises the following steps of 1: analyzing the operation and power generation characteristics of the microgrid of the 5G base station; the method comprises a base station load time-space characteristic model, a base station microgrid operation characteristic based on a dormancy mechanism, a photovoltaic power generation model and a base station backup battery energy storage model; step 2: establishing an energy storage configuration based on energy sharing, wherein the energy storage configuration comprises an outer layer model and an inner layer model; and step 3: and (4) solving the double-layer model established in the step (2). The method can improve the utilization rate of photovoltaic and 5G base station standby batteries, improve the capacity of the 5G base station participating in peak clipping and valley filling of the power grid, reduce 5G electric power expenses and delay upgrading and reconstruction of the power grid, and achieve mutual profit and win-win between a 5G operator and the power grid.

Description

5G base station microgrid optical storage system capacity optimal configuration method based on energy sharing
Technical Field
The invention relates to the technical field of capacity optimization of an optical storage system, in particular to a capacity optimization configuration method of a 5G base station microgrid optical storage system based on energy sharing.
Background
In recent years, the investment of new information infrastructure represented by 5G is increased, and the power consumption of 5G base stations reaches about 3500 billion kilowatt-hours by 2025 years. At present, the reduction of equipment energy consumption and the reduction of electric charge are a great demand for 5G commercial use. In order to ensure that a 5G base station can be stably used, an operator generally configures a backup guarantee power supply for a macro base station according to the maximum energy utilization requirement, and the proportion of a micro base station with the backup guarantee power supply is 70%. Along with the strong construction of the power grid, the power supply reliability is continuously improved, the backup energy storage utilization rate of the 5G base station is low, the charging, discharging, operation and maintenance cost is high, and a large amount of resource waste is generated. The solar energy resource reserves are abundant, the cycle development period is short, but the output fluctuation is large under the influence of the environment, and the photovoltaic energy storage combined output can be stably output. The light storage microgrid technology provides a new idea for solving the problem of energy consumption of the 5G base station.
The existing method for optimizing and configuring the capacity of the microgrid optical storage system aims at reducing the configured photovoltaic capacity and the stored energy capacity as much as possible, an optical storage microgrid economic dispatching or optimized operation scheme is formulated, the consideration on the communication characteristics of a 5G base station and the requirement of a backup power supply for ensuring the reliable power utilization of the base station is lacked, the energy consumption is reduced on the premise that the 5G base station can not meet the user requirement, and the method is not suitable for the research on the optimized and configured capacity of the microgrid optical storage of the 5G base station. The existing research on energy conservation and consumption reduction of the 5G base station mainly takes the energy conservation technology of the base station and the interaction with renewable energy sources as main points, and a backup battery of the base station is not brought into the research range. The electricity consumption of the 5G base stations has a time-space characteristic, and the complementarity and the difference of the energy consumption exist among the 5G base station micro-grids, so that an optimal configuration method of the optical storage capacity of the 5G base station micro-grid based on energy sharing is urgently needed.
Disclosure of Invention
The invention aims to provide a capacity optimal configuration method of a 5G base station microgrid optical storage system based on energy sharing, which is characterized by comprising the following steps:
step 1: analyzing the operation and power generation characteristics of the microgrid of the 5G base station; the method comprises a base station load time-space characteristic model, a base station microgrid operation characteristic based on a dormancy mechanism, a photovoltaic power generation model and a base station backup battery energy storage model;
step 2: establishing an energy storage configuration based on energy sharing, wherein the energy storage configuration comprises an outer layer model and an inner layer model;
and step 3: and (4) solving the double-layer model established in the step (2).
The base station load space-time characteristic model in the step 1 specifically comprises the following steps:
the macro base station is provided with a photovoltaic panel and a storage battery for power supply and standby power supply, is connected to a power grid, and forms a 5G base station micro-grid structure with a group of micro base stations within the coverage range of the macro base station;
power consumption P of macro base stationmComprises the following steps:
Pm＝P0+ξ·Pout (1)
wherein P isoutIs the traffic load of the macro base station, ξ.PoutIs the energy consumption, P, generated by the traffic load0The basic energy consumption is generated when the macro base station has no flow load;
the power consumption of the micro base station is as follows:
Figure BDA0002950352250000021
wherein theta isk{0,1} represents the state of the micro base station k, 0 represents sleep, 1 represents active, PθactiveFor micro base station power consumption in active state, PθsleepThe power consumption of the micro base station in a dormant state;
thus with one macro base station BmWithin range of which there are k micro base stations Bs＝{B1,B2,……,BkThe method is a 5G base station microgrid, wherein the total energy consumption is as follows:
Figure BDA0002950352250000022
in terms of traffic load, the traffic load has dual characteristics in time and space; the base station time domain flow model in the same spatial domain is fitted by a sine curve superposition model, and the model is as follows:
Figure BDA0002950352250000023
wherein, Pout(t) is the total traffic of all base stations in the piconet, a0Is the average traffic, omega, over a period of timewIs the frequency component of traffic flow variation, awAnd
Figure BDA0002950352250000024
amplitude and phase, respectively, W is the number of frequency components; the frequency component is a change inflection point of the flow load, and the frequency components in different areas are different;
services in the base station microgrid are in non-uniform distribution and are expressed by lognormal distribution:
Figure BDA0002950352250000025
the mean parameter mu will change periodically with time; due to the social behavior of the user, the distribution of the user and the service flow is obviously related to the scene type, so that the values of the standard deviation sigma in different scenes are different, and the difference of the energy consumption of the 5G base station microgrid in different scenes is displayed;
in the 5G base station micro-grid, the flow of a macro base station and the flow of a micro base station have strong periodicity in time, and the ascending trend and the descending trend are in pace, so that the flow load of the macro base station is set to be X times of that of the micro base station; the change of the service flow of a single micro base station in the 5G base station micro grid has randomness, and then the flow load model of the kth micro base station at the time t is as follows:
Pk·out(t)＝lognrnd(μ(t),σ) (6)
wherein nrnd is a lognormal distribution function;
wherein the average traffic of a single micro base station is:
Figure BDA0002950352250000031
the mean μ of the log normal distribution model at each time is:
Figure BDA0002950352250000032
therefore, energy consumption characteristic curves of the 5G base station microgrid under different scenes are obtained; the energy consumption of the 5G base station is high, and the energy consumption of the 5G base station microgrid is optimized by utilizing a dormancy mechanism.
The operation characteristic of the base station microgrid based on the dormancy mechanism in the step 1 specifically comprises the following steps:
step A1: changing the traffic flow of the service in the micro-network at a certain time, if all users can find the base station meeting the requirement, performing the step A2, if all users can not find the base station meeting the requirement, waking up all the base stations, and performing the step A2 after mounting all the users according to the distance;
step A2: sequencing the traffic of base stations in the microgrid from small to large;
step A3: for users of a macro base station, sequencing the distance of micro base stations in the range from near to far;
step A4: according to the sequencing results of the step A2 and the step A3, if a certain user meets the service quality condition, transferring the user;
step A5: if the transfer condition is not met for a certain user, the user does not sleep and returns to the original connection state;
step A6: if all users in a certain base station are transferred, the base station sleeps;
the objective function and constraint conditions of the operation characteristics of the base station microgrid based on the dormancy mechanism are as follows:
an objective function:
Fsleep＝minPtotal
wherein, FsleepEnergy consumption of the 5G base station microgrid is based on a dormancy mechanism;
and ensuring user quality constraint conditions:
SNRi,j≥SNRmin (9)
SNRi,jrepresenting the signal-to-interference-and-noise ratio, SNR, of user i accessing base station jminMinimum signal to interference plus noise ratio (sinr) indicating guaranteed user quality of service:
Figure BDA0002950352250000041
wherein m represents a macro base station, K represents a micro base station set, and N0Which is representative of the thermal noise, is,
Figure BDA0002950352250000042
is the path loss from base station j to user i;
pl(d)＝69.55+26.16logfc-13.83lghte-α(hre)+(44.9-6.55loghte)logd (11)
where d represents the distance from base station j to user i, hteFor effective height of base station antenna, hreFor effective height of reception, fcIs the working frequency;
the macro base station receiving power constraint condition is as follows:
Figure BDA0002950352250000043
wherein
Figure BDA0002950352250000044
The maximum value of power is accepted for the macro base station.
The photovoltaic power generation model in the step 1 specifically comprises the following steps:
the relation between the photovoltaic power generation power and the rated capacity is as follows:
Figure BDA0002950352250000045
wherein P isPV,STCIs the unit area rated capacity of the photovoltaic panel; m is the area of the photovoltaic module, GCIs regional lightIntensity in kW/m2；GSTCIs the radiation intensity of the sun under STC condition; alpha is alphapThe power temperature coefficient of the solar panel; t isC,STCThe temperature of a panel in the photovoltaic array under the STC condition; t isCOperating temperature of the cell panel during the conversion of electrical energy to the photovoltaic array, and
Figure BDA0002950352250000046
where T is the ambient temperature of the area in which the photovoltaic array is located.
The energy storage model of the backup battery of the base station in the step 1 is specifically as follows:
the formula of the charging and discharging power of the backup energy storage battery of the 5G base station is as follows:
Figure BDA0002950352250000051
Figure BDA0002950352250000052
wherein E is the rated capacity of a backup energy storage battery of the 5G base station, and SOC (t) is the state of charge at the time t; dESSIs the self-discharge coefficient; pcha(t)、Pdisc(t) is the charge and discharge power within 1 h; p' (t) is power input by other 5G base station piconets, and if the power is output to other 5G base station piconets, the power is a negative value; etainvTo the inverter efficiency; etabatThe charge-discharge efficiency of the storage battery is obtained; Δ t is one hour.
The outer layer model in the step 2 is specifically as follows:
the minimum total cost of the microgrid with the 5G base stations in the whole life cycle is taken as an objective function:
Figure BDA0002950352250000053
investment costs for energy storage and photovoltaics:
Figure BDA0002950352250000054
wherein N is the number of the 5G base station light storage micro-grids participating in energy sharing, r is the discount rate,
Figure BDA0002950352250000055
for the configured capacity of the photovoltaic within the nth micro-grid,
Figure BDA0002950352250000056
unit investment cost for the photovoltaic system; l is the life span of the stored energy,
Figure BDA0002950352250000057
for the configured capacity of the energy storage of the l year in the nth microgrid,
Figure BDA0002950352250000058
the unit investment cost of the energy storage system is saved;
energy storage and photovoltaic maintenance costs:
Figure BDA0002950352250000059
wherein T issIs divided into seasons in one year,
Figure BDA00029503522500000510
for the configured capacity of the photovoltaic in the nth micro-grid in the s th season of the l year,
Figure BDA00029503522500000511
for the configured capacity of energy storage within the nth microgrid for the s-th season of the first year,
Figure BDA00029503522500000512
in order to reduce the maintenance cost per unit of photovoltaic,
Figure BDA00029503522500000513
maintenance cost for energy storage units;
the daily electricity purchasing cost of the optical storage micro-grid of the 5G base station to the power grid is as follows:
Figure BDA0002950352250000061
wherein
Figure BDA0002950352250000062
And for the electricity purchase cost of the power grid in the h-th period, obtaining the financial subsidy of the photovoltaic power generation by outer-layer planning as follows:
Figure BDA0002950352250000063
Figure BDA0002950352250000064
for the generated power of the photovoltaic system in the h period,
Figure BDA0002950352250000065
supplementing power for photovoltaic power generation in the first year;
the benefits of delaying the upgrading of the power grid are as follows:
Figure BDA0002950352250000066
wherein C isGinvFor the cost of power grid upgrading construction, delta n is the number of years of power grid upgrading delayed after 5G base station microgrid energy sharing:
Figure BDA0002950352250000067
wherein tau is the annual growth rate of the load, and lambda is the peak clipping rate of peak clipping and valley filling achieved by energy sharing of the 5G base station; wherein
Figure BDA0002950352250000068
And
Figure BDA0002950352250000069
decision variables of the outer layer model;
constraint conditions are as follows:
investment cost upper value:
Figure BDA00029503522500000610
wherein,
Figure BDA00029503522500000611
the maximum value of the investment cost;
photovoltaic site limitation:
M≤MPV max (25)
wherein M isPV maxThe maximum area in which the photovoltaic module can be installed.
The inner layer model in the step 2 is specifically as follows:
the lowest daily operation cost is taken as an objective function
Figure BDA0002950352250000071
The daily operation cost of each 5G base station light storage micro-grid is as follows:
Figure BDA0002950352250000072
Figure BDA0002950352250000073
wherein q isPV(t)，qdis(t)，qGrid(t) is the electricity price of the photovoltaic, the energy storage and the power grid at the moment t respectively, and t belongs to [1,24 ]]，PPV·j(t)，Pdis·j(t) the power q 'shared by the photovoltaic of the jth 5G base station optical storage microgrid and the stored energy at the time t to the ith 5G base station optical storage microgrid respectively'PV(t)、q′dis(t) the photovoltaic of the jth 5G base station optical storage microgrid and the electricity price of the stored energy at the time t are respectively;
constraint conditions are as follows:
power balance constraint in microgrid
Ptotal(t)+Pcha(t)＝PPV(t)+Pdist(t)+PGrid(t) (29)
Energy storage state of charge constraint:
SOCmin≤SOC≤SOCmax (30)
therein, SOCmin、SOCmaxRespectively a lowest value and a highest value of the allowable state of charge;
energy storage charge and discharge power constraint:
Figure BDA0002950352250000074
and (3) restraining the electric quantity of the energy storage period at the beginning and the end:
Figure BDA0002950352250000075
the electric quantity stored by E (0) and E (T) at the starting time and the ending time of the operation period respectively;
energy storage reserve capacity constraint:
in order to guarantee the reliable power consumption of the 5G base station, the requirement of energy storage under the condition of no commercial power is met:
Figure BDA0002950352250000081
wherein: pt+P(t)＝Ptotal(t)-PPVAnd (t), namely, when the commercial power is cut off, the local photovoltaic and the energy storage jointly ensure that the 5G base station operates for 3 hours without power failure.
The step 3 specifically comprises the following substeps:
step 31: initializing an algorithm; loading basic data, including operation duration, operation period, base station micro-grid load curve parameters, flow load curves of a macro base station and a micro base station, photovoltaic output curves, energy storage technical indexes, photovoltaic technical indexes and time-of-use electricity prices;
step 32: calculating a dormancy mechanism according to the flow load of the macro base station and the micro base station at each moment to obtain a load curve of each 5G base station micro-grid after executing a dormancy algorithm;
step 33: generating an initial population Q with each individual containing energy storage rated capacity and photovoltaic rated capacity information;
step 34: based on a load curve result of optimization calculation of a sleep mechanism, calling an fmincon function, and calculating by using an inner layer model to obtain an optimal charge-discharge curve result and an annual load peak clipping rate of each individual in an initial population Q after further optimization operation by using energy sharing of a base station optical storage system in an operation period;
step 35: calculating the lowest cost of the multiple 5G base station micro-grids and the power grid in the energy storage full life cycle according to the energy storage rated capacity, the photovoltaic rated capacity, the load curve after the energy sharing strategy among the micro-grids is optimized, the annual load peak clipping rate and the energy storage charging and discharging curve, and calculating the individual fitness value in the initial population Q;
step 36: subjecting the initial population Q to genetic manipulation including selection, crossing and mutation, wherein the crossing results in a progeny population Q0And replacing the initial population, and repeating the steps 34 to 35 until a termination condition is met, so as to obtain and output a final double-layer model optimization result.
The invention has the beneficial effects that:
1. Multi-5G base station microgrid operation strategy based on energy sharing
The method is characterized in that the space-time energy consumption characteristics and the dormancy mechanism of the 5G base stations are combined, photovoltaic and energy storage energy sharing is carried out on a plurality of 5G base station micro-grids with space domain energy consumption differences and complementarity at the same moment, and an energy sharing strategy is formulated based on time-of-use electricity prices. The utilization rate of photovoltaic and 5G base station standby batteries can be improved, the capability of the 5G base station participating in peak clipping and valley filling of a power grid is improved, 5G electric power charge is reduced, upgrading and reconstruction of the power grid are delayed, and mutual profit and win-win between a 5G operator and the power grid are achieved.
2. Energy storage sharing considering electricity reliability of 5G base station
When the regulation and control of the energy storage among the multiple 5G base station micro-grids are carried out, the electricity utilization reliability of the 5G base stations is calculated, the photovoltaic and self backup energy storage configured by the 5G base station micro-grids can support the stable operation of the 5G base stations under the condition that the power failure of the commercial power can be met, and local power backup is carried out. The microgrid energy sharing of multiple 5G base stations is met, the reliable power utilization function of the base stations can be guaranteed, and the service quality of users is met; and meanwhile, the benefits of both the 5G base station operator and the power grid are considered.
Drawings
Fig. 1 is a schematic diagram of a two-layer model of capacity optimization configuration of a 5G base station microgrid optical storage system based on energy sharing;
fig. 2 is a solving flow chart of a two-layer model for capacity optimization configuration of a 5G base station microgrid optical storage system based on energy sharing.
Detailed Description
The invention provides a capacity optimization configuration method of a 5G base station microgrid optical storage system based on energy sharing, and the invention is further explained with reference to the accompanying drawings and specific embodiments.
The method comprises the following steps: 5G base station microgrid operation and power generation characteristic analysis
(1) Model of time-space characteristics of base station load
The macro base station is provided with a Photovoltaic (PV) panel and a storage battery for power supply and standby power, and is connected to a power grid, so that the coverage range of a base line and the reliable connection of capacity and a processing process are ensured, and the macro base station and a group of micro base stations in the coverage range of the macro base station form a 5G base station micro-grid structure.
Power consumption P of macro base stationmComprises the following steps:
Pm＝P0+ξ·Pout (1)
wherein P isoutIs the traffic load of the macro base station, ξ.PoutIs generated by traffic loadEnergy consumption of P0Is the basic energy consumption of the macro base station when there is no traffic load.
The basic power consumption of the micro base station occupies most of the basic power consumption, the change of the basic power consumption is not big along with the change of the flow load, and the power consumption of the micro base station is as follows because the micro base station is in an active state and a dormant state:
Figure BDA0002950352250000091
wherein theta iskWith 0,1 indicating the state of the micro base station k, 0 indicating sleep and 1 indicating active.
Thus with one macro base station BmWithin range of which there are k micro base stations Bs＝{B1,B2,……,BkThe method is a 5G base station microgrid, wherein the total energy consumption is as follows:
Figure BDA0002950352250000101
in terms of traffic load, it has a dual characteristic in time and space. The main feature of traffic in the time domain is the tidal effect caused by the typical daytime-nighttime behavior patterns of the user. The main features in the spatial domain are closely related to the mobility of user behavior. The base station time domain flow model in the same spatial domain is fitted by a sine curve superposition model, and the model is as follows:
Figure BDA0002950352250000102
wherein, Pout(t) is the total traffic of all base stations in the piconet, a0Is the average traffic, omega, over a period of timewIs the frequency component of traffic flow variation, awAnd
Figure BDA0002950352250000103
amplitude and phase respectively, W being the number of frequency components. The frequency component is the change inflection point of the flow load, and different regionsThe frequency components are not identical.
Services in the base station microgrid are in non-uniform distribution and can be represented by lognormal distribution:
Figure BDA0002950352250000104
the mean parameter mu varies periodically with time. Due to the social behavior of users, the distribution of users and traffic flow is significantly related to the scene type, and thus the value of the standard deviation σ is different in different scenes. And displaying the difference of the energy consumption of the 5G base station microgrid in different scenes.
In the 5G base station micro-grid, the flow of the macro base station and the micro base station has strong periodicity in time, and the ascending trend is in pace with the descending trend, so that the flow load of the macro base station is set to be X times of that of the micro base station. The change of the service flow of a single micro base station in the 5G base station micro grid has randomness, and then the flow load model of the kth micro base station at the time t is as follows:
Pk·out(t)＝lognrnd(μ(t),σ) (6)
wherein the average traffic of a single micro base station is:
Figure BDA0002950352250000105
the mean μ of the log normal distribution model at each time is:
Figure BDA0002950352250000106
therefore, the energy consumption characteristic curve of the 5G base station microgrid under different scenes is obtained. The energy consumption of the 5G base station is high, the existing energy-saving and consumption-reducing research mainly adopts a dormancy mechanism, and the energy consumption of the 5G base station microgrid is optimized by utilizing the dormancy mechanism.
Base station microgrid operation characteristic based on dormancy mechanism
1) Service flow changes in the micro-network at a certain time, if all users can find the base station meeting the requirements, the step 2) is carried out, if all users can not find the base station meeting the requirements, all base stations are awakened, and after all users are mounted according to the distance, the step 2) is carried out;
2) sequencing the traffic of base stations in the microgrid from small to large;
3) for users of a macro base station, sequencing the distance of micro base stations in the range from near to far;
4) sorting according to 2) and 3), and transferring the users if a certain user meets the service quality condition;
5) if the transfer condition is not met for a certain user, the user does not sleep and returns to the original connection state;
6) if all users in a certain base station are transferred, the base station sleeps.
An objective function: fsleep＝minPtotal
FsleepAnd the energy consumption of the 5G base station microgrid is based on a dormancy mechanism.
And ensuring user quality constraint conditions:
SNRi,j≥SNRmin (9)
SNRi,jrepresenting the signal-to-interference-and-noise ratio, SNR, of user i accessing base station jminMinimum signal to interference plus noise ratio (sinr) indicating guaranteed user quality of service:
Figure BDA0002950352250000111
wherein m represents a macro base station, K represents a micro base station set, and N0Which is representative of the thermal noise, is,
Figure BDA0002950352250000112
is the path loss from base station j to user i.
pl(d)＝69.55+26.16logfc-13.83lghte-α(hre)+(44.9-6.55loghte)logd (11)
Where d represents the distance from base station j to user i, hteFor effective height of base station antenna, hreFor effective height of reception, fcIs the operating frequency.
The macro base station receiving power constraint condition is as follows:
Figure BDA0002950352250000113
Figure BDA0002950352250000114
the maximum value of power is accepted for the macro base station.
(2) Photovoltaic power generation model
The relation between the photovoltaic power generation power and the rated capacity is as follows:
Figure BDA0002950352250000115
wherein P isPV,STCIs the unit area rated capacity of the photovoltaic panel; m is the area of the photovoltaic module, GCIs the regional illumination intensity with the unit of kW/m2；GSTCIs the radiation intensity of the sun under STC condition; alpha is alphapIs the power temperature coefficient of the panel.
TCThe working temperature of the cell panel in the process of converting electric energy for the photovoltaic array is as follows:
Figure BDA0002950352250000121
where T is the ambient temperature of the area in which the photovoltaic array is located.
TC,STCIs the temperature of the panel in the photovoltaic array under STC conditions.
(3) Energy storage model of backup battery of base station
The formula of the charging and discharging power of the backup energy storage battery of the 5G base station is as follows:
Figure BDA0002950352250000122
Figure BDA0002950352250000123
wherein E is the rated capacity of a backup energy storage battery of the 5G base station, and SOC (t) is the state of charge at the time t; dESSIs the self-discharge coefficient; pcha(t)、Pdisc(t) is the charge and discharge power within 1 h; p' (t) is power input by other 5G base station piconets, and if the power is output to other 5G base station piconets, the power is a negative value; etainv-inverter efficiency; etabat-battery charge-discharge efficiency.
Step two: energy storage configuration double-layer model based on energy sharing
As shown in fig. 1, the concrete model is as follows:
(1) outer layer model
The minimum total cost of the microgrid with the 5G base stations in the whole life cycle is taken as an objective function:
Figure BDA0002950352250000124
investment costs for energy storage and photovoltaics:
Figure BDA0002950352250000125
wherein N is the number of the 5G base station light storage micro-grids participating in energy sharing, r is the discount rate,
Figure BDA0002950352250000126
for the configured capacity of the photovoltaic within the nth micro-grid,
Figure BDA0002950352250000127
unit investment cost for the photovoltaic system; l is the life span of the stored energy,
Figure BDA0002950352250000128
for the configured capacity of the energy storage of the l year in the nth microgrid,
Figure BDA0002950352250000129
the unit investment cost of the energy storage system is saved.
Energy storage and photovoltaic maintenance costs:
Figure BDA00029503522500001210
wherein T issIs divided into seasons in one year,
Figure BDA0002950352250000131
in order to reduce the maintenance cost per unit of photovoltaic,
Figure BDA0002950352250000132
the maintenance cost is the unit of energy storage.
The daily electricity purchasing cost of the optical storage micro-grid of the 5G base station to the power grid is as follows:
Figure BDA0002950352250000133
Figure BDA0002950352250000134
the electricity purchase cost of the power grid for the h-th period is obtained by outer layer planning
The financial subsidies of photovoltaic power generation are as follows:
Figure BDA0002950352250000135
Figure BDA0002950352250000136
for the generated power of the photovoltaic system in the h period,
Figure BDA0002950352250000137
for photovoltaic power generation within the first year of power supply subsidy, delta t is one hour.
The benefits of delaying the upgrading of the power grid are as follows:
Figure BDA0002950352250000138
wherein C isGinvFor the cost of power grid upgrading construction, delta n is the number of years of power grid upgrading delayed after 5G base station microgrid energy sharing:
Figure BDA0002950352250000139
wherein tau is the annual growth rate of the load, and lambda is the peak clipping rate of peak clipping and valley filling achieved by the energy sharing of the 5G base station.
Wherein
Figure BDA00029503522500001310
And
Figure BDA00029503522500001311
is a decision variable of the outer layer model.
Constraint conditions are as follows:
investment cost upper value:
Figure BDA00029503522500001312
Figure BDA00029503522500001313
is the maximum value of investment cost.
Photovoltaic site limitation:
M≤MPV max (25)
MPV maxthe maximum area in which the photovoltaic module can be installed.
(2) Inner layer model
An inner layer model objective function of a capacity double-layer model of the 5G base station microgrid optical storage system based on energy sharing is established, the inner layer model objective function takes the lowest daily operation cost as a target, the operation mode of the 5G base station microgrid optical storage system is optimized, an optimal operation strategy of the 5G base station microgrid optical storage system based on energy sharing is formulated, and the lowest daily operation cost is taken as an objective function
Figure BDA0002950352250000141
The daily operation cost of each 5G base station light storage micro-grid is as follows:
Figure BDA0002950352250000142
Figure BDA0002950352250000143
qPV(t)，qdis(t)，qGrid(t) is the electricity price of the photovoltaic, the energy storage and the power grid at the moment t respectively, and t belongs to [1,24 ]]，PPV·j(t)，Pdis·j(t) the power q 'shared by the photovoltaic of the jth 5G base station optical storage microgrid and the stored energy at the time t to the ith 5G base station optical storage microgrid respectively'PV(t)、q′disAnd (t) the photovoltaic of the jth 5G base station optical storage microgrid and the electricity price of the stored energy at the time t are respectively.
Constraint conditions are as follows:
power balance constraint in microgrid
Ptotal(t)+Pcha(t)＝PPV(t)+Pdist(t)+PGrid(t) (29)
Energy storage state of charge constraint:
SOCmin≤SOC≤SOCmax (30)
wherein the SOCmin、SOCmaxRespectively, lowest and highest allowable state of charge
Energy storage charge and discharge power constraint:
Figure BDA0002950352250000144
and (3) restraining the electric quantity of the energy storage period at the beginning and the end:
Figure BDA0002950352250000145
wherein E (0), E (T) are the electric quantity stored at the starting time and the ending time of the operation period respectively.
Energy storage reserve capacity constraint:
in order to guarantee the reliable power consumption of the 5G base station, the requirement of energy storage under the condition of no commercial power is met:
Figure BDA0002950352250000146
wherein: pt+P(t)＝Ptotal(t)-PPVAnd (t), namely, when the commercial power is cut off, the 5G base station can be ensured to operate for 3 hours without power failure by local photovoltaic and energy storage.
Step three: model solution
Solving the double-layer model established in the second step; as shown in fig. 2, the outer layer optimization configuration model adopts a genetic algorithm, and optimizes the rated capacity of photovoltaic and energy storage of the base station microgrid based on the lowest total cost of the outer layer target, namely the 5G base station microgrid in the whole life cycle; the inner-layer optimization model calls an fmincon function, the minimum daily operation cost of the base station microgrid is taken as a target, an optimal operation strategy of energy storage based on energy sharing under the rated capacity of corresponding photovoltaic and energy storage is obtained through one-year circular calculation and is transmitted to the outer layer, the target function value and the fitness value are calculated on the outer layer, optimization is carried out through genetic operations such as crossing and variation, and finally the optimal solution of the double-layer optimization model is obtained.
(1) Initializing an algorithm, and loading basic data, including operation duration, an operation period, base station micro-grid load curve parameters, macros, a flow load curve of a micro base station, a photovoltaic output curve, energy storage technical indexes, photovoltaic technical indexes, time-of-use electricity price and other data;
(2) calculating a dormancy mechanism according to the flow load of the macro base station and the micro base station at each moment to obtain a load curve of each 5G base station micro-grid after executing a dormancy algorithm;
(3) generating an initial population Q containing energy storage rated capacity and photovoltaic rated capacity information of each individual;
(4) based on a load curve result of optimization calculation of a sleep mechanism, calling an fmincon function, and calculating by using an inner layer model to obtain an optimal charge-discharge curve result and an annual load peak clipping rate of each individual in an initial population Q after further optimization operation by using energy sharing of a base station optical storage system in an operation period;
(5) calculating the lowest cost of the multiple 5G base station micro-grids and the power grid in the energy storage full life cycle according to the energy storage rated capacity, the photovoltaic rated capacity, the load curve after the energy sharing strategy among the micro-grids is optimized, the annual load peak clipping rate and the energy storage charging and discharging curve, and calculating the individual fitness value in the initial population Q;
(6) subjecting the initial population Q to genetic manipulation including selection, crossing and mutation, wherein the crossing results in a progeny population Q0And (5) replacing the initial population, and repeating the steps (4) to (5) until a termination condition is met to obtain and output a final double-layer model optimization result.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. A capacity optimization configuration method of a 5G base station microgrid optical storage system based on energy sharing is characterized by comprising the following steps:
step 1: analyzing the operation and power generation characteristics of the microgrid of the 5G base station; the method comprises a base station load time-space characteristic model, a base station microgrid operation characteristic based on a dormancy mechanism, a photovoltaic power generation model and a base station backup battery energy storage model;
step 2: establishing an energy storage configuration based on energy sharing, wherein the energy storage configuration comprises an outer layer model and an inner layer model;
and step 3: and (4) solving the double-layer model established in the step (2).
2. The capacity optimization configuration method for the 5G base station microgrid optical storage system based on energy sharing of claim 1, wherein the base station load spatiotemporal characteristic model in the step 1 is specifically as follows:
the macro base station is provided with a photovoltaic panel and a storage battery for power supply and standby power supply, is connected to a power grid, and forms a 5G base station micro-grid structure with a group of micro base stations within the coverage range of the macro base station;
power consumption P of macro base stationmComprises the following steps:
Pm＝P0+ξ·Pout (1)
wherein P isoutIs the traffic load of the macro base station, ξ.PoutIs the energy consumption, P, generated by the traffic load0The basic energy consumption is generated when the macro base station has no flow load;
the power consumption of the micro base station is as follows:
Figure FDA0002950352240000011
wherein theta isk{0,1} represents the state of the micro base station k, 0 represents sleep, 1 represents active, PθactiveFor micro base station power consumption in active state, PθsleepThe power consumption of the micro base station in a dormant state;
thus with one macro base station BmWithin range of which there are k micro base stations Bs＝{B1,B2,……,BkThe method is a 5G base station microgrid, wherein the total energy consumption is as follows:
Figure FDA0002950352240000012
in terms of traffic load, the traffic load has dual characteristics in time and space; the base station time domain flow model in the same spatial domain is fitted by a sine curve superposition model, and the model is as follows:
Figure FDA0002950352240000021
wherein, Pout(t) is the total traffic of all base stations in the piconet, a0Is the average traffic, omega, over a period of timewIs the frequency component of traffic flow variation, awAnd
Figure FDA0002950352240000025
amplitude and phase, respectively, W is the number of frequency components; the frequency component is a change inflection point of the flow load, and the frequency components in different areas are different;
services in the base station microgrid are in non-uniform distribution and are expressed by lognormal distribution:
Figure FDA0002950352240000022
the mean parameter mu will change periodically with time; due to the social behavior of the user, the distribution of the user and the service flow is obviously related to the scene type, so that the values of the standard deviation sigma in different scenes are different, and the difference of the energy consumption of the 5G base station microgrid in different scenes is displayed;
in the 5G base station micro-grid, the flow of a macro base station and the flow of a micro base station have strong periodicity in time, and the ascending trend and the descending trend are in pace, so that the flow load of the macro base station is set to be X times of that of the micro base station; the change of the service flow of a single micro base station in the 5G base station micro grid has randomness, and then the flow load model of the kth micro base station at the time t is as follows:
Pk·out(t)＝log nrnd(μ(t),σ) (6)
wherein nrnd is a lognormal distribution function;
wherein the average traffic of a single micro base station is:
Figure FDA0002950352240000023
the mean μ of the log normal distribution model at each time is:
Figure FDA0002950352240000024
therefore, energy consumption characteristic curves of the 5G base station microgrid under different scenes are obtained; the energy consumption of the 5G base station is high, and the energy consumption of the 5G base station microgrid is optimized by utilizing a dormancy mechanism.
3. The method for optimal configuration of capacity of the optical storage system of the 5G base station microgrid based on energy sharing of claim 1, wherein the operation characteristics of the base station microgrid based on the dormancy mechanism in the step 1 specifically include the following steps:
step A1: changing the traffic flow of the service in the micro-network at a certain time, if all users can find the base station meeting the requirement, performing the step A2, if all users can not find the base station meeting the requirement, waking up all the base stations, and performing the step A2 after mounting all the users according to the distance;
step A2: sequencing the traffic of base stations in the microgrid from small to large;
step A3: for users of a macro base station, sequencing the distance of micro base stations in the range from near to far;
step A4: according to the sequencing results of the step A2 and the step A3, if a certain user meets the service quality condition, transferring the user;
step A5: if the transfer condition is not met for a certain user, the user does not sleep and returns to the original connection state;
step A6: if all users in a certain base station are transferred, the base station sleeps;
the objective function and constraint conditions of the operation characteristics of the base station microgrid based on the dormancy mechanism are as follows:
an objective function:
Fsleep＝minPtotal
wherein, FsleepEnergy consumption of the 5G base station microgrid is based on a dormancy mechanism;
and ensuring user quality constraint conditions:
SNRi,j≥SNRmin (9)
SNRi,jrepresenting the signal-to-interference-and-noise ratio, SNR, of user i accessing base station jminMinimum signal to interference plus noise ratio (sinr) indicating guaranteed user quality of service:
Figure FDA0002950352240000031
wherein m represents a macro base station, K represents a micro base station set, and N0Which is representative of the thermal noise, is,
Figure FDA0002950352240000032
is the path loss from base station j to user i;
pl(d)＝69.55+26.16logfc-13.83lghte-α(hre)+(44.9-6.55loghte)logd (11)
where d represents the distance from base station j to user i, hteFor effective height of base station antenna, hreFor effective height of reception, fcIs the working frequency;
the macro base station receiving power constraint condition is as follows:
Figure FDA0002950352240000041
wherein
Figure FDA0002950352240000042
The maximum value of power is accepted for the macro base station.
4. The capacity optimization configuration method for the 5G base station microgrid optical storage system based on energy sharing of claim 1, wherein the photovoltaic power generation model in the step 1 is specifically as follows:
the relation between the photovoltaic power generation power and the rated capacity is as follows:
Figure FDA0002950352240000043
wherein P isPV,STCIs the unit area rated capacity of the photovoltaic panel; m is the area of the photovoltaic module, GCIs the regional illumination intensity with the unit of kW/m2；GSTCIs the radiation intensity of the sun under STC condition; alpha is alphapThe power temperature coefficient of the solar panel; t isC,STCThe temperature of a panel in the photovoltaic array under the STC condition; t isCOperating temperature of the cell panel during the conversion of electrical energy to the photovoltaic array, and
Figure FDA0002950352240000044
where T is the ambient temperature of the area in which the photovoltaic array is located.
5. The capacity optimal configuration method for the 5G base station microgrid optical storage system based on energy sharing of claim 1, wherein the base station backup battery energy storage model in the step 1 is specifically:
the formula of the charging and discharging power of the backup energy storage battery of the 5G base station is as follows:
Figure FDA0002950352240000045
Figure FDA0002950352240000046
wherein E is the rated capacity of a backup energy storage battery of the 5G base station, and SOC (t) is the state of charge at the time t; dESSIs the self-discharge coefficient; pcha(t)、Pdisc(t) is the charge and discharge power within 1 h; p' (t) is the power input by the microgrid of other 5G base stations, and if the microgrid is input to other 5G base stationsThe output of the station microgrid is a negative value; etainvTo the inverter efficiency; etabatThe charge-discharge efficiency of the storage battery is obtained; Δ t is one hour.
6. The capacity optimization configuration method for the 5G base station microgrid optical storage system based on energy sharing of claim 1, characterized in that the outer model in the step 2 is specifically:
the minimum total cost of the microgrid with the 5G base stations in the whole life cycle is taken as an objective function:
Figure FDA0002950352240000051
investment costs for energy storage and photovoltaics:
Figure FDA0002950352240000052
wherein N is the number of the 5G base station light storage micro-grids participating in energy sharing, r is the discount rate,
Figure FDA0002950352240000053
for the configured capacity of the photovoltaic within the nth micro-grid,
Figure FDA0002950352240000054
unit investment cost for the photovoltaic system; l is the life span of the stored energy,
Figure FDA0002950352240000055
for the configured capacity of the energy storage of the l year in the nth microgrid,
Figure FDA0002950352240000056
the unit investment cost of the energy storage system is saved;
energy storage and photovoltaic maintenance costs:
Figure FDA0002950352240000057
wherein T issIs divided into seasons in one year,
Figure FDA0002950352240000058
for the configured capacity of the photovoltaic in the nth micro-grid in the s th season of the l year,
Figure FDA0002950352240000059
for the configured capacity of energy storage within the nth microgrid for the s-th season of the first year,
Figure FDA00029503522400000510
in order to reduce the maintenance cost per unit of photovoltaic,
Figure FDA00029503522400000511
maintenance cost for energy storage units;
the daily electricity purchasing cost of the optical storage micro-grid of the 5G base station to the power grid is as follows:
Figure FDA00029503522400000512
wherein
Figure FDA0002950352240000061
And for the electricity purchase cost of the power grid in the h-th period, obtaining the financial subsidy of the photovoltaic power generation by outer-layer planning as follows:
Figure FDA0002950352240000062
Figure FDA0002950352240000063
for the generated power of the photovoltaic system in the h period,
Figure FDA0002950352240000064
for photovoltaic power generationC, electricity supplementing in one year;
the benefits of delaying the upgrading of the power grid are as follows:
Figure FDA0002950352240000065
wherein C isGinvFor the cost of power grid upgrading construction, delta n is the number of years of power grid upgrading delayed after 5G base station microgrid energy sharing:
Figure FDA0002950352240000066
wherein tau is the annual growth rate of the load, and lambda is the peak clipping rate of peak clipping and valley filling achieved by energy sharing of the 5G base station; wherein
Figure FDA0002950352240000067
And
Figure FDA0002950352240000068
decision variables of the outer layer model;
constraint conditions are as follows:
investment cost upper value:
Figure FDA0002950352240000069
wherein,
Figure FDA00029503522400000610
the maximum value of the investment cost;
photovoltaic site limitation:
M≤MPV max (25)
wherein M isPV maxThe maximum area in which the photovoltaic module can be installed.
7. The capacity optimization configuration method for the 5G base station microgrid optical storage system based on energy sharing of claim 1, wherein the inner layer model in the step 2 is specifically as follows:
the lowest daily operation cost is taken as an objective function
Figure FDA0002950352240000071
The daily operation cost of each 5G base station light storage micro-grid is as follows:
Figure FDA0002950352240000072
Figure FDA0002950352240000073
wherein q isPV(t)，qdis(t)，qGrid(t) is the electricity price of the photovoltaic, the energy storage and the power grid at the moment t respectively, and t belongs to [1,24 ]]，PPV·j(t)，Pdis·j(t) the power q 'shared by the photovoltaic of the jth 5G base station optical storage microgrid and the stored energy at the time t to the ith 5G base station optical storage microgrid respectively'PV(t)、q′dis(t) the photovoltaic of the jth 5G base station optical storage microgrid and the electricity price of the stored energy at the time t are respectively;
constraint conditions are as follows:
power balance constraint in microgrid
Ptotal(t)+Pcha(t)＝PPV(t)+Pdist(t)+PGrid(t) (29)
Energy storage state of charge constraint:
SOCmin≤SOC≤SOCmax (30)
therein, SOCmin、SOCmaxRespectively a lowest value and a highest value of the allowable state of charge;
energy storage charge and discharge power constraint:
Figure FDA0002950352240000074
and (3) restraining the electric quantity of the energy storage period at the beginning and the end:
Figure FDA0002950352240000081
the electric quantity stored by E (0) and E (T) at the starting time and the ending time of the operation period respectively;
energy storage reserve capacity constraint:
in order to guarantee the reliable power consumption of the 5G base station, the requirement of energy storage under the condition of no commercial power is met:
Figure FDA0002950352240000082
wherein: pt+P(t)＝Ptotal(t)-PPVAnd (t), namely, when the commercial power is cut off, the local photovoltaic and the energy storage jointly ensure that the 5G base station operates for 3 hours without power failure.
8. The energy-sharing-based optimal configuration method for the capacity of the 5G base station microgrid optical storage system according to claim 1, wherein the step 3 specifically comprises the following substeps:
step 31: initializing an algorithm; loading basic data, including operation duration, operation period, base station micro-grid load curve parameters, flow load curves of a macro base station and a micro base station, photovoltaic output curves, energy storage technical indexes, photovoltaic technical indexes and time-of-use electricity prices;
step 32: calculating a dormancy mechanism according to the flow load of the macro base station and the micro base station at each moment to obtain a load curve of each 5G base station micro-grid after executing a dormancy algorithm;
step 33: generating an initial population Q with each individual containing energy storage rated capacity and photovoltaic rated capacity information;
step 34: based on a load curve result of optimization calculation of a sleep mechanism, calling an fmincon function, and calculating by using an inner layer model to obtain an optimal charge-discharge curve result and an annual load peak clipping rate of each individual in an initial population Q after further optimization operation by using energy sharing of a base station optical storage system in an operation period;
step 35: calculating the lowest cost of the multiple 5G base station micro-grids and the power grid in the energy storage full life cycle according to the energy storage rated capacity, the photovoltaic rated capacity, the load curve after the energy sharing strategy among the micro-grids is optimized, the annual load peak clipping rate and the energy storage charging and discharging curve, and calculating the individual fitness value in the initial population Q;
step 36: subjecting the initial population Q to genetic manipulation including selection, crossing and mutation, wherein the crossing results in a progeny population Q0And replacing the initial population, and repeating the steps 34 to 35 until a termination condition is met, so as to obtain and output a final double-layer model optimization result.
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CN116365592A (en) * 2023-06-01 2023-06-30 湖南大学 Collaborative optimization method, device and medium for design and operation of optical storage flexible system
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