5G Base Station Demand Response Method and System Considering New Energy Consumption and Unit Commitment

Abstract

The present invention discloses a 5G base station demand response method and system considering new energy consumption and unit commitment, belonging to the field of power system dispatching and optimization. The 5G base station accesses the power system node through the 5G base station resource aggregation point, and the communication tasks of the 5G base station can be migrated in space and time. The method includes: aiming at minimizing the operating cost of the generating units, the new energy abandonment cost, and the operating cost of the 5G base station, establishing a 5G base station demand response model and solving it under preset constraints to obtain the 5G base station working strategy, the generating unit working strategy, and the new energy consumption strategy. The constraints include the constraints based on the space-time migration characteristics of the 5G base station communication tasks and the constraints on the correlation between the energy consumption of the 5G base station and the energy storage of the energy storage battery. The present invention can fully consider the characteristics of the communication tasks in the 5G base station during the process of the 5G base station participating in the demand response, combine with the operation of the power grid side power system, realize the overall optimal dispatching of the 5G base station communication network and the power grid, and improve the comprehensive benefit.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatching and optimization, and more specifically, relates to a 5G base station demand response method and system considering new energy consumption and unit commitment. Background Art

[0002] When communication operators carry out the laying and coverage of 5G networks, they are faced with the double tests of the investment cost of base station construction and the power consumption cost of base station operation. Therefore, studying how to reduce the operation cost of 5G base stations plays a crucial role in the construction and coverage of 5G networks.

[0003] In recent years, with the development of renewable energy, the scale of renewable energy represented by wind power is expanding rapidly. However, the volatility and uncertainty of renewable energy output always hinder its consumption. Therefore, studying how to effectively improve the consumption capacity of renewable energy in the power system is an urgent problem to be solved.

[0004] Demand Response (DR) regards the adjustable electricity load in the power system as a flexible response resource for dynamic dispatching, incorporates it into the management scope, and guides the electricity consumption behavior of this part of power users through methods such as price compensation, economic incentives, and policy preferences, playing a role in regulating resources on the power generation side and the user side and optimizing the operation status of the power system. Due to the certain flexibility in adjusting the energy consumption of 5G base stations and the equipped energy storage batteries for energy storage and supply, it is possible for them to participate in demand response. Therefore, studying the method for 5G base stations to participate in demand response is a feasible solution to achieve reducing the operation cost of base stations and improving the consumption capacity of renewable energy in the power system.

[0005] Currently, there have been many studies on 5G base stations participating in demand response. However, most of the current studies focus on a certain characteristic of 5G base stations, do not fully consider the correlation between base station energy storage and energy consumption, and the research scope is somewhat limited. At the same time, most studies focus on reducing the operation cost of base stations through demand response, but do not fully consider the characteristics of communication tasks in 5G base stations. Moreover, there are few studies on the impact on the power grid side, such as the operation cost of generating units and the consumption of renewable energy. Therefore, aiming at these problems, incorporating the consumption of renewable energy and the unit commitment of the power system into the scope of investigation and formulating a reasonable and effective method for 5G base stations to participate in demand response is a difficult problem. Summary of the Invention

[0006] In view of the deficiencies of the existing technologies and the improvement requirements, the present invention provides a 5G base station demand response method and system considering new energy consumption and unit commitment, aiming to fully consider the characteristics of communication tasks in 5G base stations during the process of 5G base stations participating in demand response, combine with the operation of the power grid-side power system, realize the overall optimal scheduling of the 5G base station communication network and the power grid, and improve the comprehensive benefits.

[0007] To achieve the above object, according to one aspect of the present invention, there is provided a 5G base station demand response method considering new energy consumption and unit commitment. The 5G base station network is divided into multiple 5G base station resource aggregation points, and the 5G base station resource aggregation points are connected to the power system nodes. Communication tasks can be migrated among the 5G base stations within the same 5G base station resource aggregation point, and the non-real-time communication tasks in the 5G base stations can be migrated backward within the same scheduling period.

[0008] The 5G base station demand response method provided by the present invention includes: taking the minimum of the operating cost of the generating units, the new energy abandonment cost, and the operating cost of the 5G base stations as the objective, establishing a 5G base station demand response model and solving it under preset constraint conditions to obtain the 5G base station working strategy, the generating unit working strategy, and the new energy consumption strategy.

[0009] Among them, the preset constraint conditions include the spatio-temporal migration constraints of the communication tasks of the 5G base stations. The spatio-temporal migration constraints of the communication tasks include: the amount of communication tasks migrated backward by the 5G base station at any moment does not exceed the amount of non-real-time communication tasks in its original communication tasks; the non-real-time communication tasks in the same 5G base station can only be migrated backward; the amount of communication tasks migrated by the 5G base station at any moment does not exceed its original communication task amount; the task amount of the 5G base station at any moment is the sum of its original communication task amount and the task increment caused by task migration; and the total amount of communication tasks within the 5G base station resource aggregation point remains unchanged before and after task migration.

[0010] Further, the expression of the spatio-temporal migration constraints of the communication tasks includes:

[0011]

[0012]

[0013]

[0014]

[0015]

[0016] Among them, T represents the scheduling period, t and t' represent the scheduling moments; J represents the set of 5G base stations within the 5G base station resource aggregation point, j and j' represent 5G base stations; L(j,t) represents the original communication task volume of 5G base station j at moment t, L'(j,t) represents the communication task volume of 5G base station j after task migration at moment t, and η delay represents the proportion of non-real-time communication tasks; M time (j,t,t') represents the non-real-time communication task volume migrated by 5G base station j from moment t to moment t', and M time (j,t,t') represents the non-real-time communication task volume migrated by 5G base station j from moment t' to moment t; M space (t,j,j') represents the communication task volume migrated by 5G base station j to 5G base station j' at moment t, and M space (t,j',j) represents the communication task volume migrated by 5G base station j' to 5G base station j at moment t.

[0017] Furthermore, the preset constraint conditions also include: the energy storage battery capacity constraint of the 5G base station; the energy storage battery capacity constraint includes: the minimum available capacity of the energy storage battery of the 5G base station at any moment is the product of the energy consumption of the 5G base station at that moment and the emergency duration.

[0018] Furthermore, the energy storage battery capacity constraint also includes: the capacity of the energy storage battery at any moment does not exceed the maximum capacity and is not lower than the minimum available capacity; the capacity of the energy storage battery is the same at the start and end moments of each scheduling period; the capacity of the energy storage battery at any moment is the sum of the capacity at the previous moment and the capacity increment caused by charging and discharging.

[0019] Furthermore, the expression of the energy storage battery capacity constraint is:

[0020]

[0021]

[0022]

[0023]

[0024] Among them, V min (j,t) represents the minimum available capacity of the energy storage battery in 5G base station j at moment t, and T res represents the emergency duration of the 5G base station energy storage battery, and P B(j, t) represents the energy consumption of 5G base station j at time t; V(j, t) and V(j, t - 1) represent the capacities of the energy storage battery in 5G base station j at time t and time t - 1 respectively, and V(j, 0) and V(j, T - 1) represent the capacities of the energy storage battery in 5G base station j at the start time and end time of the scheduling period respectively; V max represents the maximum capacity of the energy storage battery; I ch (j, t - 1) represents the charging working state of the energy storage battery in 5G base station j at time t - 1, 0 means no charging, and 1 means charging; P ch (j, t - 1) represents the charging power of the energy storage battery in 5G base station j at time t - 1; I dis (j, t - 1) represents the discharging working state of the energy storage battery in 5G base station j at time t - 1, 0 means no discharging, and 1 means discharging; P dis (j, t - 1) represents the discharging power of the energy storage battery in 5G base station j at time t - 1; ΔT is the optimization time interval.

[0025] Furthermore, the preset constraint conditions also include: the charge and discharge constraints of the energy storage battery of the 5G base station; the charge and discharge constraints of the energy storage battery include: the energy storage battery cannot charge and discharge simultaneously; the upper and lower bounds constraints of the charge and discharge power.

[0026] Furthermore, the preset constraint conditions also include: the minimum operating energy consumption constraint of the 5G base station, and the expression is:

[0027]

[0028] wherein, represents the minimum operating energy consumption of 5G base station j at time t; α and β are the base station energy consumption coefficients.

[0029] Furthermore, the new energy includes wind power, and the preset constraint conditions also include: the upper and lower bounds constraints of wind power output, and the expression is:

[0030]

[0031]

[0032] wherein, P forecast (t) represents the predicted wind power output value at time t; G[] represents the normal distribution; P load (t') represents the system load demand at time t'; Rand(0, T - 1) represents randomly selecting a scheduling time within a scheduling period, E(P load ) represents the daily system load demand mean value; η wind represents the wind power penetration ratio coefficient of the system; η adjust represents the wind power uncertainty adjustment coefficient; P W(t) represents the wind power consumption of the system at time t.

[0033] Further, the preset constraint conditions further include: unit output constraint, minimum start-stop time constraint of the unit, ramp constraint during unit operation, start-stop ramp constraint of the unit, and power grid power balance constraint.

[0034] According to another aspect of the present invention, the present invention provides a 5G base station demand response system considering new energy consumption and unit commitment. The 5G base station network is divided into multiple 5G base station resource aggregation points, and the 5G base station resource aggregation points are connected to the power system nodes; communication tasks can be migrated among the 5G base stations within the same 5G base station resource aggregation point, and the non-real-time communication tasks in the 5G base stations can be migrated backward within the same scheduling period.

[0035] The 5G base station demand response system considering new energy consumption and unit commitment includes: a computer-readable storage medium and a processor. The computer-readable storage medium is used to store a computer program, and the processor is used to read the computer program in the computer-readable storage medium and execute the 5G base station demand response method provided by the present invention considering new energy consumption and unit commitment.

[0036] Generally speaking, through the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0037] (1) The 5G base station demand response method considering new energy consumption and unit commitment provided by the present invention proposes a new 5G base station operation control mode, that is, dividing the 5G base station network into 5G base station resource aggregation points, and accessing the power system nodes by the 5G base station resource aggregation points. This operation control mode fully considers the operation characteristics of 5G base stations, that is, the characteristics of a large number of base stations and small coverage range and low energy consumption of a single base station. Compared with the existing method in which a single base station directly interacts with the power grid, this operation control mode can effectively reduce the interaction burden and scheduling difficulty of the power system, and is conducive to promoting the collaborative work of the 5G base station network and the power grid; on the basis of the proposed 5G base station operation control mode, the present invention further analyzes and makes full use of the migratable characteristics of communication tasks in 5G base stations, allows non-real-time communication tasks in the same base station to migrate backward in time, and allows communication tasks between different base stations in the same 5G base station resource aggregation point to migrate in space, and accordingly formulates communication task spatio-temporal migration constraints. Thus, during the process of 5G base stations participating in demand response, the characteristics of communication tasks in 5G base stations can be fully considered, the demand response space and potential of 5G base stations can be fully explored, and the effect of demand response scheduling can be further improved; in addition, the 5G base station demand response model established by the present invention considers not only the operation cost of 5G base stations, but also the operation cost of generating units and the new energy abandonment cost. Thus, the impact of 5G base stations participating in demand response on the power grid side can be fully considered, and finally the overall optimal scheduling of the 5G base station communication network and the power grid can be realized. Generally speaking, the present invention can fully consider the operation characteristics of 5G base stations and the spatio-temporal migration characteristics of communication tasks during the process of 5G base stations participating in demand response, combine with the operation of the power grid side power system, and realize the overall optimal scheduling of the 5G base station communication network and the power grid, so as to increase the new energy consumption and obtain higher comprehensive benefits.

[0038] (2) The 5G base station demand response method considering new energy consumption and unit commitment provided by the present invention further considers the correlation between the energy storage and energy consumption of energy storage batteries in 5G base stations in the constraint conditions of the 5G demand response model, that is, the minimum available capacity of the energy storage battery of the 5G base station at any moment is the product of the energy consumption of the 5G base station at that moment and the emergency duration. This constraint more realistically reflects the actual operation characteristics of 5G base stations and has more practical significance and usability.

[0039] (3) In the preferred embodiment of the 5G base station demand response method considering new energy consumption and unit commitment provided by the present invention, when specifying wind energy among renewable energies, the output of wind power at each moment is characterized by a normal distribution, and the mean and variance of the normal distribution are calculated according to the load demand at a randomly selected moment and the daily load demand mean. When calculating the mean and variance of the normal distribution, a wind power penetration ratio coefficient and a wind power uncertainty adjustment coefficient are also introduced, which improves the flexibility of wind power prediction and adjustment, and can accurately predict the output of wind power in different wind power environments. Description of the Drawings

[0040] Figure 1 Schematic diagram of the 5G base station operation control mode provided by the present invention;

[0041] Figure 2 Schematic diagram of the 5G base station demand response method considering new energy consumption and unit commitment provided by the embodiment of the present invention;

[0042] Figure 3 Schematic diagram of the system structure obtained by connecting 5G base stations and wind farms to the IEEE 9-node test system as an example in the embodiment of the present invention;

[0043] Figure 4 System load data diagram provided by the embodiment of the present invention;

[0044] Figure 5 Schematic diagram of the system output under different conditions provided by the embodiment of the present invention; among them, (a) is the case where the 5G base station does not participate in demand response (i.e., Case1), (b) is the case where the 5G base station participates in demand response but does not consider the spatio-temporal migration of communication tasks (i.e., Case2), and (c) is the case where the 5G base station participates in demand response and considers the spatio-temporal migration of communication tasks (Case3);

[0045] Figure 6 For Figure 5 The obtained wind power output curve graph for different cases;

[0046] Figure 7 For Figure 5 The obtained schematic diagram of the base station working conditions for different cases; among them, (a) is the energy consumption situation of the 5G base station, and (b) is the energy storage working situation of the 5G base station. Detailed Embodiment

[0047] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0048] In the present invention, terms such as "first", "second", etc. (if any) in the present invention and the accompanying drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0049] In order to realize the overall optimal scheduling of the 5G base station communication network and the power grid and improve the comprehensive benefit, the present invention provides a 5G base station demand response method and system considering new energy consumption and unit commitment. The overall idea is as follows: Based on the operating characteristics of 5G base stations, an operating control mode based on 5G base station resource aggregation points is proposed to facilitate the scheduling of 5G base stations by the power grid and the interaction between the two; on the basis of the provided operating control mode, fully consider the spatio-temporal migration characteristics of tasks in 5G base stations, design a spatio-temporal migration mechanism for communication tasks, and formulate corresponding constraints to fully explore the demand response space and potential of 5G base stations; when establishing an optimization model, comprehensively consider the operating cost of generating units, the cost of abandoning new energy, and the operating cost of 5G base stations, thereby realizing the overall optimal scheduling of the 5G base station communication network and the power grid and improving the comprehensive benefit.

[0050] In order to facilitate the participation of 5G base stations in demand response, the present invention proposes a new operating control mode based on the operating characteristics of 5G base stations. Specifically, since the coverage range of 5G base stations is small (usually within 1000 m), and the energy consumption of a single base station is low (the no-load energy consumption is about 2.2 - 2.3 kW, and the full-load energy consumption is about 3.7 - 3.9 kW), and the number of base stations in the 5G base station network is quite large. If a single base station directly interacts with the power grid, the burden on the power system is extremely huge and it is difficult to carry out scheduling. Based on this, in the operating control mode proposed by the present invention, the 5G base station network is divided into multiple 5G base station resource aggregation points, and the 5G base station resource aggregation points are connected to the power system nodes, such as Figure 1As shown in the figure, multiple 5G base stations within the same 5G base station resource aggregation point will be uniformly scheduled by the power system. In practical applications, a central controller can be equipped for each 5G base station resource aggregation point, and a local energy controller can be equipped for each base station within the 5G base station resource aggregation point. During the demand response process, the local energy controller is used to collect, organize, analyze, and process information such as the energy consumption, energy storage battery power, and energy storage battery behavior of the base station at the current moment while meeting the communication service requirements of users within its coverage area. After the processing is completed, the relevant information obtained will be transmitted to the central controller of the 5G base station resource aggregation point to which it belongs. The central controller is used to overall plan the working conditions of all 5G base stations in the 5G base station resource aggregation point to prepare for subsequent interaction with the power grid. The power system interacts with each 5G base station resource aggregation point through the central controller. The power system will inform the central controller of the grid load at that moment. Finally, the behavior decisions of each base station will be sent to the local energy controller through the corresponding central controller, and each base station will control its own behavior according to the corresponding behavior decisions and participate in the operation of the power grid to achieve the purpose of demand response for 5G base stations in Daocheng.

[0051] It is easy to understand that, for the convenience of resource aggregation and unified scheduling, in practical applications, multiple 5G base stations that are physically close (for example, belonging to the same area) will be divided into the same 5G base station resource aggregation point.

[0052] In the following embodiments, 5G base stations all interact with the power system through the above operation control mode. The following are the embodiments.

[0053] Embodiment 1:

[0054] A 5G base station demand response method considering new energy consumption and unit commitment. The 5G base station network is divided into multiple 5G base station resource aggregation points, and the 5G base station resource aggregation points are connected to the power system nodes; communication tasks can be migrated among 5G base stations within the same 5G base station resource aggregation point, and non-real-time communication tasks in the 5G base station can be migrated backward within the same scheduling period.

[0055] As Figure 2 shown in the figure, this embodiment includes: taking the minimum of the operating cost of the generator set, the cost of abandoning new energy, and the operating cost of the 5G base station as the goal, establishing a 5G base station demand response model and solving it under preset constraints to obtain the 5G base station working strategy, the generator set working strategy, and the new energy consumption strategy.

[0056] The 5G base station demand response model established in this embodiment aims to minimize the operating costs of generator sets, the cost of abandoned new energy, and the operating costs of 5G base stations. It can be combined with the grid-side demand to better help the grid and 5G base stations reduce operating costs, while increasing the consumption of wind power and obtaining higher comprehensive benefits.

[0057] To ensure the effect of optimal scheduling, this embodiment proposes corresponding constraints in the constraint conditions of the 5G demand response model based on the operating characteristics of 5G base stations, generator sets, and new energy systems, as follows.

[0058] Different from general energy storage batteries, the primary goal of the 5G base station energy storage battery is to serve as the backup power supply of the base station. When an accident or fault occurs in the power grid, causing the 5G base station to be unable to obtain power from the mains, the energy storage battery must ensure that the 5G base station can still operate to meet the communication needs within its coverage area. This limits the minimum available capacity of the 5G base station energy storage battery. To make full use of the 5G base station energy storage battery resources, it is necessary to clarify its schedulable capacity in advance. The 5G base station energy storage battery capacity can be divided into two parts: the minimum available capacity and the schedulable capacity. Among them, the minimum available capacity is used to ensure that the base station still has sufficient power supply during the emergency duration, while the schedulable capacity is the redundant capacity of the energy storage battery after removing the minimum available capacity. According to the energy consumption of the 5G base station and the emergency duration, the minimum available capacity of the base station backup energy storage battery can be obtained through the following formula, clarifying the correlation between energy storage and energy consumption:

[0059]

[0060] Where t represents the scheduling moment; represents the minimum available capacity of the energy storage battery at moment t; P b (t) represents the base station power load at moment t; represents the emergency duration of the energy storage battery. At present, most of the emergency durations of 5G base stations are 4 hours, and the backup power capacity of the configured energy storage battery needs to meet this power consumption demand to ensure the power supply reliability of the 5G base station.

[0061] Since it is difficult to accurately obtain the real-time energy consumption of the base station during a power grid fault, and the energy consumption of the 5G base station fluctuates little during the emergency duration, it is assumed that when determining the minimum available capacity of the energy storage battery at each moment of the 5G base station, it is based on the current energy consumption of the base station. Based on the above assumption, the constraint of the minimum available capacity of the 5G base station energy storage battery at each moment is shown in the following formula:

[0062]

[0063] Among them, T represents the scheduling period. Optionally, in this embodiment, the scheduling period is one day, and each day is divided into 24 scheduling moments; J represents the set of 5G base stations in the 5G base station resource aggregation point, j represents a 5G base station, and V min (j, t) represents the minimum available capacity of the energy storage battery in 5G base station j at moment t, and T res represents the emergency duration of the 5G base station energy storage battery, and P B (j, t) represents the energy consumption of 5G base station j at moment t.

[0064] For the energy storage battery of the base station, the battery capacity cannot exceed the maximum capacity of the energy storage battery after each charge, and the battery capacity cannot be lower than the minimum available capacity after each discharge. The corresponding expressions are as follows:

[0065]

[0066] Among them, V(j, t) represents the capacity of the energy storage battery in 5G base station j at moment t, and V max represents the maximum capacity of the energy storage battery.

[0067] For the consideration of actual schedulability, in order to ensure that the constructed 5G base station demand response model can be solved normally, this embodiment also puts forward the following constraints on the capacity of the energy storage battery: the capacity of the energy storage battery is the same at the start and end moments of each scheduling period; the corresponding expression is as follows:

[0068]

[0069] Among them, V(j, 0) and V(j, T - 1) are the capacities of the energy storage battery in 5G base station j at the start and end moments of the scheduling period respectively. In this implementation, the start moment of the scheduling period is the 0th moment, and the end moment is the 23rd moment. Therefore, the above formula can also be expressed as: Through the constraint shown in formula (3), even for different days, it has practical scheduling significance.

[0070] The capacity of the energy storage battery at each moment should match its capacity and behavior at the previous moment, that is, the sum of the capacity at the previous moment and the capacity increment caused by charging and discharging. The relevant expressions are as follows:

[0071]

[0072] Among them, V(j, t - 1) respectively represents the capacity of the energy storage battery in 5G base station j at moment t - 1, and I ch (j, t - 1) represents the charging working state of the energy storage battery in 5G base station j at moment t - 1, 0 represents not charging, and 1 represents charging; P ch(j, t - 1) represents the charging power of the energy storage battery in 5G base station j at time t - 1; I dis (j, t - 1) represents the discharging working state of the energy storage battery in 5G base station j at time t - 1, where 0 indicates no discharging and 1 indicates discharging; P dis (j, t - 1) represents the discharging power of the energy storage battery in 5G base station j at time t - 1; ΔT is the optimization time interval, which is set to 1h in this example.

[0073] The energy storage battery cannot charge and discharge simultaneously, and the corresponding expression is:

[0074]

[0075] Among them, I ch (j, t) and I dis (j, t) respectively represent the charging and discharging working states of base station j at time t.

[0076] For the energy storage battery, there are limitations on its charging and discharging power, and the corresponding expression is:

[0077]

[0078]

[0079] Among them, P ch (j, t) and P dis (j, t) respectively represent the charging and discharging powers of base station j at time t; represents the maximum charging power of the energy storage battery; represents the maximum discharging power of the energy storage battery.

[0080] For the overall 5G base station, the energy consumption of the 5G base station is approximately linearly related to the communication task volume:

[0081] P b = α b + β b L b

[0082] Among them, P b is the base station energy consumption; L b is the communication task volume; α b and β b are the base station energy consumption coefficients.

[0083] The primary task of the 5G base station is to meet the 5G network communication needs of users within its coverage area. This means that the energy consumption of the 5G base station has a minimum energy consumption limit at different times. According to the communication task volume at time t, the minimum energy consumption of 5G base station j at this time t can be obtained as shown in the following formula:

[0084]

[0085] Among them, represents the minimum operating energy consumption of 5G base station j at time t; α and β are base station energy consumption coefficients.

[0086] The communication task volumes of 5G base stations at different positions and different times are different, and the energy consumption and energy storage operations of 5G base stations are closely related to the communication task volume. Therefore, the communication tasks of 5G base stations are reasonably migrated to adjust the energy consumption and energy storage operations of 5G base stations, so as to fully explore and utilize their demand response space.

[0087] Among the neighboring 5G base stations within the same 5G base station resource aggregation point, communication tasks can be migrated, that is, the base station can migrate its own communication task space to neighboring base stations or receive the communication tasks migrated by neighboring base stations according to the operation of the overall system to adjust the communication load rate of the base station. At the same time, among the communication tasks of 5G base stations, some are tasks with low requirements for real-time performance, such as resource downloading, file uploading, etc. For such tasks, the time migration of the communication tasks of the 5G base station itself can be carried out according to the actual situation, that is, the base station can postpone such non-real-time tasks in time according to user needs and the operation of the overall system. For the spatio-temporal migration of 5G base station communication tasks, the following constraints need to be met:

[0088] The time-migrated communication task volume of any base station at any time cannot exceed the non-real-time task volume in its original communication task. The relevant expression is:

[0089]

[0090] Among them, j' represents the 5G base station, M time (j, t, t') represents the non-real-time communication task volume migrated by 5G base station j from time t to time t', L(j, t) represents the original communication task volume of 5G base station j at time t, and η delay represents the proportion of non-real-time communication tasks.

[0091] The time migration of communication tasks can only be migrated backward from the current time and cannot be migrated forward. The relevant expressions are as follows:

[0092]

[0093] The spatio-temporal migration communication task volume of any base station at any time cannot exceed its original communication task volume. The relevant expressions are as follows:

[0094]

[0095] Among them, M space(t, j, j') represents the communication task volume migrated from 5G base station j to 5G base station j' at time t.

[0096] After the spatio-temporal migration of communication tasks, the update method of the communication task volume in the base station is as follows: The task volume of the 5G base station at any time is the sum of its original communication task volume and the task increment caused by task migration (including time migration and space migration). The corresponding expression is as follows:

[0097]

[0098] Among them, M space (t, j', j) represents the communication task volume migrated from 5G base station j' to 5G base station j at time t, and L'(j, t) represents the communication task volume of 5G base station j after task migration at time t.

[0099] After spatio-temporal transfer, the total communication task volume remains unchanged. The relevant expression is as follows:

[0100]

[0101] According to the new communication task volume obtained after spatio-temporal migration, the minimum energy consumption of 5G base station j operating at time t is updated using equation (8)

[0102] Based on the above spatio-temporal migration mechanism of communication tasks, this embodiment can fully consider the characteristics of communication tasks in 5G base stations during the process of 5G base stations participating in demand response, fully explore the demand response space and potential of 5G base stations, and further improve the effect of demand response scheduling.

[0103] Regarding the characteristics of power system generator sets, this embodiment correspondingly sets the following constraints in the constraint conditions of the 5G demand response model:

[0104] 1) Generator set output constraint: The output of each generator set i in the power system has a feasible interval limit in each time period; In this embodiment, only the active power output part is considered. The relevant expression is as follows;

[0105]

[0106] Among them, I represents the set of generator sets, and i represents the generator set; P G (i, t) represents the actual active power output of generator set i at time t; represents the minimum value of the active power output of generator set i at time t, represents the maximum value of the active power output of generator set i at time t; I(i, t) represents the working state of generator set i at time t, 0 indicates that the generator set is in the off state, and 1 indicates that the generator set is in the working state.

[0107] 2) Minimum start - stop time constraint of the unit: Once the unit is started, it must remain in the starting state for a certain period of time. Once the unit is shut down, it must remain in the shutdown state for a certain period of time. The relevant expressions are as follows:

[0108]

[0109]

[0110] Among them, I(i,t), I(i,t'), and I(i,t - 1) represent the operating states of generator set i at time t, time t', and time t - 1 respectively. 0 represents that the generator set is in the shutdown state, and 1 represents that the generator set is in the operating state; T on (i) represents the minimum startup time of generator set i; T off (i) represents the minimum shutdown time of generator set i.

[0111] 3) Ramp - rate constraint during unit operation: During the operation of the generator set, there are constraint limitations on the change in output power between consecutive time instants. The relevant expressions are as follows:

[0112]

[0113] Among them, η down (i) represents the maximum downward ramp - rate of the active power output of generator set i; η up (i) represents the maximum upward ramp - rate of the active power output of generator set i; P G (i,t - 1) represents the actual active power output of generator set i at time t - 1.

[0114] 4) Ramp - rate constraint during unit start - up and shutdown: When the generator set starts up and shuts down, there are output power limitations. The relevant expressions are as follows:

[0115]

[0116] Among them, represents the maximum change value of the output power when generator set i shuts down; represents the maximum change value of the output power when generator set i starts up; I(i,t) and I(i,t - 1) represent the operating states of generator set i at time t and time t - 1 respectively. 0 represents that the generator set is in the shutdown state, and 1 represents that the generator set is in the operating state.

[0117] Optionally, in this embodiment, the new - energy system only includes a wind farm; according to the characteristics of the wind farm, the following constraints are correspondingly set in the constraint conditions of the 5G demand - response model in this embodiment:

[0118] Assume that the output of wind power in the power system at each moment conforms to the normal distribution G(μ,σ) (one of the probability functions of various wind power distributions), where μ represents the predicted value and σ represents the variance. The specific construction is as follows:

[0119]

[0120] Among them, P forecast (t) represents the predicted output value of wind power at time t; G[] represents the normal distribution; P load (t') represents the system load demand at time t', Rand(0,T-1) represents randomly selecting a scheduling time within a scheduling cycle, E(P load ) represents the daily average system load demand; η wind represents the wind power penetration ratio coefficient in the system; η adjust represents the wind power uncertainty adjustment coefficient. The wind power uncertainty adjustment coefficient η adjust reflects the fluctuation degree of wind power output. When the wind power fluctuation degree is large, its value can be set relatively large; when the wind power fluctuation degree is small, its value can be set relatively small. In practical applications, η wind and η adjust can be set according to the actual wind power environment. In this embodiment, the wind power penetration ratio coefficient and the wind power uncertainty adjustment coefficient are introduced, which improves the flexibility of wind power prediction and adjustment, and can accurately predict the wind power output under different wind power environments.

[0121] The wind power consumption in the system must be within the range of the actual wind power generation value. The corresponding expression is as follows:

[0122]

[0123] Among them, P W (t) represents the wind power consumption of the system at time t.

[0124] It should be noted that in practical applications, if the new energy system also includes other forms of new energy, the corresponding output prediction should be carried out, and the consumption of this new energy should be constrained based on the output prediction results.

[0125] Using the symbols U G , U W and U B to represent the operating cost of the generator set, the cost of wind curtailment, and the operating cost of the 5G base station respectively, then in this embodiment, the objective function of the 5G base station demand response model can be expressed as:

[0126] min(U G +U W +U B )

[0127] The operating cost U of the generator setG The calculation formula is as follows:

[0128]

[0129] f[P G (i, t)] = a(i) + b(i)P G (i, t) + c(i)P G (i, t) 2 (22)

[0130] Among them, f[P G (i, t)] represents the power generation cost of generator set i at time t; SU(i) represents the start-up cost of generator set i; SD(i) represents the shutdown cost of generator set i; a(i), b(i), c(i) represent the cost coefficients of generator set i;

[0131] The curtailment cost U W The calculation formula is as follows:

[0132]

[0133] Among them, η punish represents the curtailment cost coefficient.

[0134] The operating cost U of the 5G base station B The calculation formula is as follows:

[0135]

[0136] Among them, C buy (j, t) represents the electricity purchase price of users from the power grid in the area where base station j is located at time t; C sell (j, t) represents the electricity selling price of users to the power grid in the area where base station j is located at time t.

[0137] In addition, the power grid power balance constraint also needs to be considered for the system operation, and the relevant expressions are as follows:

[0138]

[0139] Based on the above objective function and constraint conditions, the 5G base station demand response model established in this embodiment can be expressed as follows:

[0140] min(U G + U W + U B ) (26)

[0141] s.t. (1)-(25)

[0142] By solving the above 5G base station demand response model, the working strategies of 5G base stations, the working strategies of generator sets, and the new energy consumption strategies can be obtained; the working strategies of 5G base stations specifically include the energy consumption P B (j, t), charging power P ch (j, t), and discharging power P dis (j, t) of each 5G base station at each moment; the working strategies of generator sets specifically include the actual active power output P G (i, t) of each generator set at each moment; the wind power consumption strategy specifically includes the wind power consumption P W (t) at each moment.

[0143] It should also be noted that in practical applications, if the new energy system also includes other forms of new energy, such as a photovoltaic system, the goal of the established 5G demand response model also includes minimizing the abandonment cost of this new energy.

[0144] Generally speaking, this embodiment analyzes the operation characteristics of 5G base stations, designs the operation control mode of 5G base stations, which is conducive to the collaborative work of 5G network - power grid; analyzes the correlation between the working energy consumption of 5G base stations and their energy storage configurations, obtains the correlation mathematical model of the operation of 5G base station energy storage and energy consumption, and establishes an energy storage model considering the energy consumption of 5G base stations; considers the transferable characteristics of 5G base station communication tasks in time and space, and establishes a 5G base station demand response model considering the spatio - temporal migration of 5G base station communication load rate; considers the working operation characteristics of generator sets in the power system, establishes a power system unit commitment model; analyzes the working conditions of wind power in the power system, establishes a wind power operation model, and considers the wind power consumption situation; optimizes with the minimum of the operation cost of 5G base stations, the operation cost of generator sets, and the wind abandonment cost as the objective function, and establishes a 5G base station demand response model considering wind power consumption and unit commitment; uses the 5G base station demand response model considering wind power consumption and unit commitment to obtain the working strategies of 5G base stations, the working strategies of generator sets in the power system, and the wind power consumption strategies. The embodiment can effectively reduce the operation cost of 5G base stations, and at the same time can effectively reduce the operation cost of generator sets, and increase the wind power consumption, and finally can realize the overall optimal scheduling of the 5G base station communication network and the power grid, and improve the comprehensive benefits.

[0145] Embodiment 2:

[0146] A 5G base station demand response system considering new energy consumption and unit commitment is provided. The 5G base station network is divided into multiple 5G base station resource aggregation points, and the 5G base station resource aggregation points are connected to the power system nodes; communication tasks can be migrated between 5G base stations within the same 5G base station resource aggregation point, and non - real - time communication tasks in 5G base stations can be migrated backward within the same scheduling period;

[0147] This embodiment includes: a computer-readable storage medium and a processor. The computer-readable storage medium is used to store a computer program, and the processor is used to read the computer program in the computer-readable storage medium and execute the 5G base station demand response method considering new energy consumption and unit commitment provided in Embodiment 1 above.

[0148] The technical solution of the present invention and the beneficial effects that can be achieved are further explained below in combination with specific application examples.

[0149] Taking the IEEE 9-node test system as an example, 5G base stations and wind farms are connected to it, and the resulting system structure is as Figure 3 shown. 5G base station resource aggregation points are connected to nodes 4-9, and a wind farm is connected to node 8. To simplify the complexity of solving the constructed model, it is assumed that all 5G base stations within each 5G base station resource aggregation point have the same operating conditions, that is, all base stations within the aggregation point have the same communication load rate, operating energy consumption, and charge and discharge power of the energy storage battery at each moment; at the same time, to not overly reduce the generality of the system, it is assumed that each 5G base station resource aggregation point connected to the system consists of 4 small resource aggregation points. The base stations within the small resource aggregation points meet the above assumptions, while the operating conditions of the 5G base stations between the small resource aggregation points are different. If there are 50 5G base stations in a small resource aggregation point, then a total of 200 base stations are connected to the system within one resource aggregation point, and the total number of 5G base stations connected to the system is 50×4×6 = 1200.

[0150] Taking the typical daily load of a certain area as the system load P load (t), as Figure 4 shown. The wind power penetration ratio coefficient η wind is taken as 0.6, the wind power uncertainty adjustment coefficient η adjust is taken as 0.08, and the curtailment cost coefficient η punish is taken as 60 $.

[0151] The technical parameters of the generator sets connected to the system are shown in Table 1.

[0152] Table 1 Technical parameters of generator sets

[0153]

[0154] The technical parameters of the 5G base stations connected to the system are shown in Table 2. The energy storage battery of the base station is two groups of 400AH lead-acid batteries, and the maximum energy consumption of the base station is the energy consumption when it works at full communication load rate.

[0155] Table 2 Technical parameters of 5G base stations

[0156]

[0157] It is assumed that the users within the coverage of 5G base stations conform to the Poisson distribution. The communication load rate L(j,t) within the coverage of the base station at each moment is generated according to the rules of a public dataset of base station traffic in a certain community: from 2:00 to 7:00 every day is the low-traffic period, and the communication load rate within the coverage of the base station during this period follows a truncated normal distribution with a mean of 30 and a standard deviation of 7.5 within the interval [0, 60]; from 0:00 to 1:00 and from 8:00 to 23:00 every day are the high-traffic periods, and the communication load rate within the coverage of the base station during these periods follows a truncated normal distribution with a mean of 80 and a standard deviation of 5 within the interval [60, 100].

[0158] The 5G base station demand response model considering wind power consumption and unit commitment constructed in the present invention is a convex optimization problem, and with the help of existing solving software, the problem can be solved quickly and efficiently. In this example, the solver Gurobi is selected to program in Pycharm Community 2021.3.2 - 1 to solve the established model. At the same time, a 5G base station non-participation demand response model and a 5G base station participation demand response model without considering the spatio-temporal migration of communication tasks are constructed and solved for effect comparison to verify the effectiveness of the method of the present invention.

[0159] In the 5G base station non-participation demand response model, the energy storage battery of the base station does not perform charge and discharge behaviors, and the power consumption of the base station is completely provided by the power system. At the same time, the 5G base station does not perform spatio-temporal migration of communication tasks and operates with the lowest energy consumption to meet the communication task requirements, that is:

[0160]

[0161] The power grid power balance condition also needs to be adjusted accordingly:

[0162]

[0163] The system output obtained by solving is as Figure 5 shown, Figure 5 in which (a), (b), and (c) respectively represent three situations: 5G base station non-participation demand response (Case 1), 5G base station participation demand response without considering the spatio-temporal migration of communication tasks (Case 2), and 5G base station participation demand response with considering the spatio-temporal migration of communication tasks (Case 3). From Figure 5It can be seen that in Case 1, the generator set 3 operates throughout the day. The generator set 1 generates a large amount of power from 16:00 to 21:00 to meet the electricity demand, while the generator set 2 only operates from 7:00 to 15:00 and from 22:00 to 23:00. In Case 2 and Case 3, regardless of whether the spatio-temporal migration of communication tasks is considered, due to the flexible response resources provided by the 5G base stations, the wind power output increases while the generator set output decreases. The generator set 3 only operates from 5:00 to 22:00, the generator set 1 only operates from 8:00 to 21:00, and the generator set 2 does not operate. Good results have been achieved for the unit commitment scheduling adjustment of the power system.

[0164] The wind power output curves under different scenarios are as Figure 6 shown. The specific wind power accommodation situation is shown in Table 3. It can be seen that compared with Case 1, due to the flexible response resources provided by the 5G base stations in Case 2, the wind power accommodation ratio has increased by more than 5%, reaching a high accommodation ratio of 99.51%. In Case 3, after considering the spatio-temporal migration of the communication tasks designed in the present invention, on the basis of the original high accommodation, the wind power accommodation ratio still has a small increase of 0.06%. This shows that considering the spatio-temporal migration of communication tasks has a positive impact on the wind power accommodation of the power system, and can still assist the power system in improving the wind power accommodation in the case of a high proportion of wind power accommodation.

[0165] Table 3 Wind power accommodation in different scenarios

[0166]

[0167] The operating conditions of the 5G base stations under different scenarios are as Figure 7 shown. Figure 7 (a) and (b) in it respectively represent the energy consumption situation of the 5G base station and the energy storage operation situation of the 5G base station. Combining the above-mentioned generator set output situation and wind power accommodation situation, the system operation cost is shown in Table 4. In Case 1, the base station operates with the lowest energy consumption, and the energy storage is in a non-operating state, resulting in a very high base station operation cost, and there is a lack of flexible response resources regulation in the power system, and both the wind power abandonment cost and the generator set operation cost remain at a high level. In Case 2, when the 5G base station is added to the demand response, although the energy consumption of the base station increases, due to the output regulation effect of the energy storage battery, the comprehensive cost is reduced. Comparing Case 3 and Case 2, it can be seen that through the spatio-temporal migration of the base station communication tasks in the present invention, the energy consumption of the base station and the energy storage operation situation are further adjusted, and the system operation cost is further reduced.

[0168] Table 4 System operation cost in different scenarios

[0169]

[0170] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included within the protection scope of the present invention.

Claims

1. A 5G base station demand response method considering new energy consumption and unit commitment, characterized in that The 5G base station network is divided into multiple 5G base station resource aggregation points, and the 5G base station resource aggregation points are connected to the power system nodes; the 5G base stations within the same 5G base station resource aggregation point can migrate communication tasks, and the non-real-time communication tasks in the 5G base stations can be migrated backward within the same scheduling period; The method includes: aiming at minimizing the operating cost of the generator set, the new energy abandonment cost, and the operating cost of the 5G base station, establishing a 5G base station demand response model and solving it under preset constraint conditions to obtain the 5G base station working strategy, the generator set working strategy, and the new energy consumption strategy; Among them, the preset constraint conditions include the spatio-temporal migration constraint of the communication tasks of the 5G base station; the spatio-temporal migration constraint of the communication tasks includes: the amount of communication tasks migrated backward by the 5G base station at any moment does not exceed the amount of non-real-time communication tasks in its original communication tasks, the non-real-time communication tasks in the same 5G base station can only be migrated backward, the amount of communication tasks migrated by the 5G base station at any moment does not exceed its original communication task amount, the task amount of the 5G base station at any moment is the sum of its original communication task amount and the task increment caused by task migration, and the total amount of communication tasks within the 5G base station resource aggregation point remains unchanged before and after task migration; The expression of the spatio-temporal migration constraint of the communication tasks includes: Among them, T represents the scheduling period, t and t' represent the scheduling moments; J represents the set of 5G base stations within the 5G base station resource aggregation point, and j and j' represent 5G base stations; L(j,t) represents the original communication task volume of 5G base station j at moment t, L'(j,t) represents the communication task volume of 5G base station j after task migration at moment t, and η delay represents the proportion of non-real-time communication tasks; M time (j,t,t') represents the non-real-time communication task volume migrated by 5G base station j from moment t to moment t', and M time (j,t,t') represents the non-real-time communication task volume migrated by 5G base station j from moment t' to moment t; M space (t,j,j') represents the communication task volume migrated by 5G base station j to 5G base station j' at moment t, and M space (t,j',j) represents the communication task volume migrated by 5G base station j' to 5G base station j at moment t.

2. The 5G base station demand response method considering new energy consumption and unit commitment according to claim 1, characterized in that The preset constraint conditions also include: the energy storage battery capacity constraint of the 5G base station; the energy storage battery capacity constraint includes: the minimum available capacity of the energy storage battery of the 5G base station at any moment is the product of the energy consumption of the 5G base station at that moment and the emergency duration.

3. The demand response method for 5G base stations considering new energy consumption and unit commitment according to claim 2, characterized in that The energy storage battery capacity constraint also includes: the capacity of the energy storage battery at any moment does not exceed the maximum capacity and is not lower than the minimum available capacity; the capacity of the energy storage battery is the same at the beginning and end of each scheduling period; the capacity of the energy storage battery at any moment is the sum of the capacity at the previous moment and the capacity increment caused by charge and discharge.

4. The demand response method for 5G base stations considering new energy accommodation and unit commitment according to claim 3, characterized in that The expression of the energy storage battery capacity constraint is: Among them, V min (j, t) represents the minimum available capacity of the energy storage battery in 5G base station j at time t, and T res represents the emergency duration of the energy storage battery of the 5G base station. P B (j, t) represents the energy consumption of 5G base station j at time t; V(j, t) and V(j, t - 1) respectively represent the capacities of the energy storage battery in 5G base station j at time t and time t - 1, and V(j, 0) and V(j, T - 1) respectively represent the capacities of the energy storage battery in 5G base station j at the start time and end time of the scheduling period; I ch (j, t - 1) represents the charging working state of the energy storage battery in 5G base station j at time t - 1, 0 indicates no charging, and 1 indicates charging; P ch (j, t - 1) represents the charging power of the energy storage battery in 5G base station j at time t - 1; I dis (j, t - 1) represents the discharging working state of the energy storage battery in 5G base station j at time t - 1, 0 indicates no discharging, and 1 indicates discharging; P dis (j, t - 1) represents the discharging power of the energy storage battery in 5G base station j at time t - 1; ΔT is the optimization time interval.

5. The demand response method for 5G base stations considering new energy accommodation and unit commitment according to claim 4, characterized in that The preset constraint conditions also include: the charge and discharge constraint of the energy storage battery of the 5G base station; the charge and discharge constraint of the energy storage battery includes: the energy storage battery cannot charge and discharge simultaneously; the upper and lower bounds constraints of the charge and discharge power.

6. The demand response method for 5G base stations considering new energy consumption and unit commitment as claimed in claim 5, wherein The preset constraint conditions also include: the minimum operating energy consumption constraint of the 5G base station, and the expression is: Among them, represents the minimum operating energy consumption of 5G base station j at time t; α and β are base station energy consumption coefficients.

7. The demand response method for 5G base stations considering new energy consumption and unit commitment according to any one of claims 1 to 6, characterized in that, The new energy includes wind power, and the preset constraint conditions also include: the upper and lower bounds constraint of the wind power output, and the expression is: Among them, P forecast (t) represents the predicted wind power output value at time t; G[] represents the normal distribution; P load (t') represents the system load demand at time t'; Rand(0, T - 1) represents randomly selecting a scheduling time within a scheduling period, E(P load ) represents the daily average system load demand; η wind represents the wind power penetration ratio coefficient of the system; η adjust represents the wind power uncertainty adjustment coefficient; P W (t) represents the wind power consumption of the system at time t.

8. The demand response method for 5G base stations considering new energy consumption and unit commitment according to claim 7, characterized in that The preset constraint conditions also include: the unit output constraint, the minimum start-stop time constraint of the unit, the ramp-up constraint during unit operation, the start-stop ramp-up constraint of the unit, and the power balance constraint of the power grid.

9. A 5G base station demand response system considering new energy consumption and unit commitment, characterized in that, The 5G base station network is divided into multiple 5G base station resource aggregation points, and the 5G base station resource aggregation points are connected to the power system nodes; the 5G base stations within the same 5G base station resource aggregation point can migrate communication tasks, and the non-real-time communication tasks in the 5G base stations can be migrated backward within the same scheduling period; The 5G base station demand response system considering new energy consumption and unit commitment includes: a computer-readable storage medium and a processor. The computer-readable storage medium is used to store a computer program, and the processor is used to read the computer program in the computer-readable storage medium and execute the 5G base station demand response method considering new energy consumption and unit commitment according to any one of claims 1 to 8.

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