Virtual power plant scheduling method and system containing distributed 5G base station
By building a refined model of 5G base stations and introducing communication load rate, optimizing energy management strategies, the safety, stability and energy consumption problems of power side regulation in the existing technology are solved, and more efficient energy management and grid reliability are achieved.
Patent Information
- Application Number
- CN202411450758.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-03-04
AI Technical Summary
The existing methods that rely on power-side adjustment have problems such as insufficient safety, poor stability, insufficient coverage and poor signal, and it is difficult to achieve the goal of maximizing and reducing energy consumption.
By introducing communication load rate, establish the relationship between energy flow and information flow, build a refined model of 5G base stations, including the base station power load demand model, self-provided energy storage scheduling capacity evaluation model, and self-provided energy storage participating grid scheduling interaction model, and optimize energy management strategies through the dynamic regulation architecture of virtual power plants.
The 5G base station energy storage system is realized to better respond to grid scheduling needs, provide frequency regulation and peak-to-valley load balance, enhance grid flexibility and reliability, improve access to renewable energy, and reduce operating costs.
Smart Images
Figure CN119561149A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of dynamic control of virtual power plants, and specifically to a virtual power plant scheduling method and system including distributed 5G base stations. Background Art
[0002] As a new operation mode, virtual power plant (VPP) can effectively aggregate distributed power sources, flexible loads and other resources in the power grid, participate in the unified dispatch of the power grid as a whole, make full use of the coordination and complementarity of distributed resources, realize the reasonable optimization and utilization of resources, and thus improve the operation efficiency and service level of the power grid. The traditional safety and stability guarantee method that only relies on the regulation capability of the power supply side can no longer meet the needs of the new power system. In contrast, demand-side resources are more diversified and highly flexible. Therefore, it is urgent and necessary for power grid companies to promote the transformation of the power system from "source follows load" to "source and load interaction", give full play to the role of demand-side resources in the new power system, and ensure the safe and stable operation of the demand-side power grid.
[0003] With the high-quality development of new infrastructure, the fifth generation of mobile communications (5G) has become a key driver for the digital transformation and upgrading of the real economy. By the end of June 2023, China's 5G base stations will have reached 2.937 million, covering all prefecture-level cities and county towns, and the breadth and depth of coverage will continue to expand in the future. Due to the large number and wide distribution of base station UPS and TCS, the power grid cannot accurately and effectively regulate them; on the other hand, the current energy-saving means used by 5G base stations are mainly software energy saving, and the power saving ratio is generally around 19%, which has not yet achieved the goal of maximizing energy consumption reduction. In addition, while saving energy, part of the 5G network performance has been sacrificed, resulting in weak coverage in some areas and unstable signals. Therefore, energy saving and carbon reduction for 5G base stations is also an urgent problem that the power grid needs to solve.
[0004] In view of the above problems, the present invention provides a virtual power plant scheduling method and system including distributed 5G base stations. Firstly, the relationship between energy flow and information flow is established by introducing the communication load rate, so as to study the characteristic model of 5G base stations; secondly, a refined model of 5G base stations is established by constructing a base station power load demand model, a base station self-provided energy storage dispatchable capacity evaluation model, and a base station self-provided energy storage participating in power grid scheduling interaction model; finally, the inclusion of 5G base station energy storage in VPP and the impact on the comprehensive operating costs of base stations and VPP after the energy-saving function of base station equipment is comprehensively considered, and a VPP scheduling optimization model is established to study the problem of the impact of DOD of base station energy storage batteries on the economic efficiency of base station operation. Summary of the invention
[0005] In view of the above-mentioned problems, the present invention is proposed.
[0006] Therefore, the technical problem solved by the present invention is: the existing method relying on power supply side regulation has the problems of insufficient safety, poor stability, insufficient coverage and poor signal, and how to achieve the goal of maximizing energy consumption reduction.
[0007] To solve the above technical problems, the present invention provides the following technical solutions: a virtual power plant scheduling method containing distributed 5G base stations, including introducing a communication load rate to describe a 5G base station characteristic model; establishing a 5G base station refined model based on a base station power load demand model, a base station self-provided energy storage dispatchable capacity evaluation model, and a base station self-provided energy storage participating in a power grid scheduling interaction model; and optimizing and adjusting the energy management strategy of the base station through VPP scheduling.
[0008] As a preferred solution of the virtual power plant scheduling method containing distributed 5G base stations described in the present invention, the introduction of the communication load rate includes introducing the communication load rate to quantify the heavy degree of business undertaken by the base station, and the communication load rate is expressed as:
[0009]
[0010] in, is the communication load rate of base station m during period t, R m,t is the bandwidth occupied by the user data transmission demand matched by base station m during period t, is the maximum available bandwidth of base station m.
[0011] As a preferred solution of the virtual power plant scheduling method containing distributed 5G base stations described in the present invention, the base station power load demand model includes dividing the base station power consumption into static power consumption that is independent of the communication load and dynamic power consumption that is sensitive to the communication load, which is expressed by a linear model as follows:
[0012]
[0013] in, is the power consumption of base station m during period t, is the static power consumption of base station m during period t, ξ m is the energy efficiency scaling factor of base station m, is the dynamic power consumption of base station m during period t. The RF output power generated by the communication load is proportional to the communication load rate and is expressed as:
[0014]
[0015] in, is the communication load rate of base station m during period t, is the maximum dynamic power consumption of base station m, that is, the maximum RF output power corresponding to the full communication load.
[0016] As a preferred scheme of the virtual power plant dispatching method containing distributed 5G base stations described in the present invention, the dispatchable capacity evaluation model of the base station's self-provided energy storage includes dividing the base station's self-provided energy storage into a backup capacity for ensuring high-reliability power supply to the base station and a dispatchable capacity for participating in demand response, linking the base station's self-provided energy storage capacity and charge state with the communication load rate to determine the dynamically changing dispatchable capacity.
[0017] As a preferred scheme of the virtual power plant dispatching method containing distributed 5G base stations described in the present invention, the base station self-provided energy storage participating in the grid dispatching interaction model includes calculating the maximum chargeable and dischargeable power of the base station self-provided energy storage pre-dispatching interaction considering the communication load state, and constructing a minimization model with the goal of minimizing current.
[0018] As a preferred scheme of the virtual power plant scheduling method containing distributed 5G base stations described in the present invention, wherein: the energy management strategy of optimizing and adjusting the base station through VPP scheduling includes taking the comprehensive minimum operating cost of VPP as the goal, considering the benefits of new energy surplus power access to the grid, the benefits of peak shaving and valley filling of base station energy storage batteries, the fuel cost of diesel generator sets, the management cost of each power generation and energy storage unit and the power purchase cost to construct an objective function.
[0019] As an optimal solution of the virtual power plant scheduling method containing distributed 5G base stations described in the present invention, the energy management strategy of optimizing and adjusting the base station through VPP scheduling includes setting constraints, and the total interactive power shall not exceed the local transformer and line limits, power balance constraints, output constraints, and base station energy storage battery charging and discharging power constraints.
[0020] Another object of the present invention is to provide a virtual power plant dispatching system including distributed 5G base stations, which can more accurately predict and monitor the power consumption of 5G base stations through the base station power load demand model, thereby achieving more refined energy management, reducing energy consumption, and reducing operating costs, solving the problem that the current method relying on power supply side regulation does not achieve the goal of maximizing energy consumption reduction.
[0021] As a preferred solution of the virtual power plant dispatching system containing distributed 5G base stations described in the present invention, it includes: a characteristic description module, a model refinement module, and a strategy optimization module; the characteristic description module is used to introduce the communication load rate to describe the 5G base station characteristic model; the model refinement module is used to establish a 5G base station refined model based on the base station power load demand model, the base station self-provided energy storage dispatchable capacity evaluation model and the base station self-provided energy storage participating in the grid dispatch interaction model; the strategy optimization module is used to optimize and adjust the energy management strategy of the base station through VPP scheduling.
[0022] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a virtual power plant scheduling method containing distributed 5G base stations.
[0023] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a virtual power plant scheduling method containing distributed 5G base stations.
[0024] Beneficial effects of the present invention: The virtual power plant dispatching method containing distributed 5G base stations provided by the present invention participates in the grid dispatching interactive model through the base station's self-provided energy storage, so that the energy storage system of the 5G base station can better respond to the dispatching needs of the grid, provide necessary frequency regulation and peak-valley load balancing, enhance the flexibility of the grid, and improve the reliability and stability of the grid. Through the dynamic control architecture of the virtual power plant, distributed resources such as 5G base stations can be effectively integrated, and the grid's access capacity to renewable energy can be improved. The present invention achieves better results in terms of stability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0026] Figure 1 An overall flow chart of a virtual power plant scheduling method including distributed 5G base stations provided for the first embodiment of the present invention.
[0027] Figure 2 Another flow chart of a virtual power plant scheduling method including distributed 5G base stations provided for the first embodiment of the present invention. DETAILED DESCRIPTION
[0028] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.
[0029] Example 1, reference Figure 1-Figure 2 , as an embodiment of the present invention, provides a virtual power plant scheduling method including distributed 5G base stations, comprising:
[0030] S1: Introduce the communication load rate to describe the 5G base station characteristic model.
[0031] Furthermore, the primary function of the self-storage of 5G base stations is to provide backup capacity for base stations to prevent the base stations from losing power and affecting communication services when the distribution network fails. The self-storage of base stations can be regarded as consisting of two parts: one part is the backup capacity used to ensure high-reliability power supply of base stations. The other part is the dispatchable capacity that can participate in demand response. Therefore, the biggest difference between formulating the call strategy of the self-storage of base stations and conventional ESS is that the key problem of evaluating the dispatchable capacity should be solved first, which is directly affected by the communication load state of the base station. It can be seen that when the base station is in the peak load state, the power consumption of the base station is large, so the backup power requirement is high and the dispatchable capacity is small. On the contrary, the backup power requirement is low and the dispatchable capacity is small. Based on this, starting from the corresponding indicators, the self-storage capacity and state of charge (SOC) of the base station are linked to the communication load rate, and then the dynamically changing dispatchable capacity is determined.
[0032] In order to make the base station's self-storage participate in the grid dispatch interaction without affecting its backup function as much as possible, the maximum and minimum SOC values set by the base station m's self-storage under the real-time communication load state are considered. Taking into account the dynamic changes over time, it should not be lower than the SOC lower limit of the self-storage itself, otherwise the battery safety cannot be guaranteed by excessive deep discharge. Therefore, a comparison function is introduced for the two sets of constraints, and the backup capacity of the base station m's self-storage under the communication load state in period t is further examined.
[0033] 5G base stations are the intersection of power systems and communication systems. Due to the strong coupling relationship between the energy flow and information flow inside the base station, 5G base stations cannot be treated as general loads or energy storage batteries in isolation. The impact of information flow factors such as communication service intensity, bandwidth, and communication rate on the power consumption and dispatchable capacity of the base station must be considered. To this end, the communication load rate indicator is introduced to establish the connection between energy flow and information flow. The introduction of the communication load rate includes the introduction of the communication load rate to quantify the heavy degree of business undertaken by the base station. The communication load rate is expressed as:
[0034]
[0035] in, is the communication load rate of base station m during period t, R m,t is the bandwidth occupied by the user data transmission demand matched by base station m during period t, is the maximum available bandwidth of base station m.
[0036] It should be noted that during data transmission, the bandwidth allocated by the base station to the connected user cannot exceed the total bandwidth that the base station can provide, that is, the constraint is satisfied:
[0037]
[0038] From this we can see that
[0039] S2: Establish a refined model of 5G base stations based on the base station power load demand model, the dispatchable capacity assessment model of the base station’s self-provided energy storage, and the interactive model of the base station’s self-provided energy storage participating in the grid dispatch.
[0040] Furthermore, the power load of 5G base stations is more sensitive to changes in communication load. Through experiments and summarization, it is found that the power consumption of base stations can be divided into static power consumption that is independent of communication load and dynamic power consumption that is sensitive to communication load, which can be expressed by a linear model as follows:
[0041]
[0042] in, is the power consumption of base station m during period t, is the static power consumption of base station m in period t, which is the fixed power consumption measured when the RF output is zero without communication load, mainly including the power consumption generated by power supply, signal processing and cooling. m is the energy efficiency scaling factor of base station m, is the dynamic power consumption of base station m during period t. The RF output power generated by the communication load is proportional to the communication load rate and is expressed as:
[0043]
[0044] in, is the communication load rate of base station m during period t, is the maximum dynamic power consumption of base station m, that is, the maximum RF output power corresponding to the full communication load.
[0045] It should be noted that the primary function of the self-storage of 5G base stations is to provide backup capacity for base stations to prevent the base stations from losing power and affecting communication services when the distribution network fails. The self-storage of base stations can be regarded as consisting of two parts: one part is the backup capacity used to ensure high-reliability power supply of base stations. The other part is the dispatchable capacity that can participate in demand response. Therefore, the biggest difference between formulating the call strategy of the self-storage of base stations and conventional ESS is that the key problem of evaluating the dispatchable capacity should be solved first, which is directly affected by the communication load state of the base station. It can be seen that when the base station is in the peak load state, the power consumption of the base station is large, so the backup power requirement is high and the dispatchable capacity is small. On the contrary, the backup power requirement is low and the dispatchable capacity is small. Based on this, starting from the corresponding indicators, the self-storage capacity and state of charge (SOC) of the base station are linked to the communication load rate, and then the dynamically changing dispatchable capacity is determined.
[0046] In order to make the base station's self-provided energy storage participate in the grid dispatch interaction without affecting its backup function as much as possible, the communication load state is considered to set The solution is as follows:
[0047]
[0048] in, They are the maximum and minimum SOC values set for the self-provided energy storage of base station m under the real-time communication load state. is the maximum SOC value of the self-storage energy of base station m. χ is the energy-information coupling coefficient, is the communication load rate of base station m during period t. The reason for this solution is that charging the base station's self-provided energy storage as much as possible is beneficial to power backup and dispatch, so its SOC upper limit is not corrected. When calling for discharge, the communication load state should be strictly considered. Therefore, the present invention sets the energy-information coupling coefficient χ, combined with the communication load rate, to obtain It plays a connecting and conversion role between the information domain variable (communication load rate) and the energy domain variable (SOC). On the one hand, although the interaction between the base station's self-provided energy storage and the power grid can enable communication operators to reduce costs and increase efficiency, frequent charging and discharging will affect the battery life and performance, thereby increasing the workload of maintenance and replacement. On the other hand, the self-provided energy storage of many dispersed and small-capacity base stations will add a burden to the regulation of the power grid. Therefore, the assignment of χ reflects the willingness of communication operators and the power grid to interact and cooperate. The smaller the value is within a reasonable range, the stronger the willingness of both parties to cooperate.
[0049] Considering It changes dynamically with the time period, but it should not be lower than the SOC lower limit of the self-storage itself, otherwise the battery safety cannot be guaranteed by excessive discharge. Therefore, a comparison function is introduced for the two sets of constraints, and the upper and lower limits of the SOC actually adopted by the base station self-storage under the communication load state are:
[0050]
[0051] in, They are the upper and lower limits of SOC used when the self-storage energy of base station m is actually called upon during period t. It is the minimum SOC value of the self-storage energy of base station m.
[0052] Furthermore, the reserve capacity of the self-storage of base station m under the communication load state during period t is:
[0053]
[0054] Among them, E m,UPS,t It is the spare capacity of the self-storage of base station m under the communication load state during period t. It is the lower limit of the dischargeable capacity used when calling the self-storage of base station m during period t. is the rated capacity of the self-provided energy storage of base station m.
[0055]
[0056] They are respectively the rechargeable capacity, dischargeable capacity and SOC value of the self-provided energy storage of base station m in period t. It is the upper limit of the charging depth allowed by the self-provided energy storage of base station m.
[0057] It should also be noted that the interactive model of base station self-storage participating in grid dispatch includes calculating the maximum charge and discharge power of the base station self-storage pre-dispatching interaction considering the communication load state. Since high current charging and discharging will damage battery performance and shorten operating life, a minimization model is constructed with the goal of minimizing current, which is expressed as:
[0058]
[0059] in, are the maximum charging and discharging power limits of the self-provided energy storage of base station m respectively. are the maximum charging and discharging power limits used when calling the self-storage energy storage of base station m during period t, are the chargeable capacity and dischargeable capacity of the self-storage energy of base station m in period t, respectively. Δt is the duration corresponding to period t.
[0060] S3: Optimize and adjust the energy management strategy of the base station through VPP scheduling.
[0061] Furthermore, the energy management strategy of the base station is optimized and adjusted through VPP scheduling, including taking the VPP comprehensive minimum operating cost as the goal, considering the benefits of renewable energy surplus power access, peak load shifting benefits of base station energy storage batteries, fuel costs of diesel generator sets, management costs of various power generation and energy storage units, and power purchase costs to construct an objective function, which is expressed as:
[0062]
[0063] Among them, Z t Y is the comprehensive operating cost of VPP during period t. t S is the total cost of VPP operation during period t. t Y is the total revenue of VPP in period t. t , S t The expressions are:
[0064] Y t =M t +D t,1 +R t
[0065] S t =D t,2 +D t,3
[0066] Among them, M t is the management cost of each power generation and energy storage unit during period t. t,1 R is the VPP electricity purchase cost during period t. t D is the power generation cost of the diesel generator set during period t. t,2 D is the income from the surplus electricity grid connection during period t. t,3 M is the revenue of the base station energy storage battery participating in peak load shifting during period t. t , D t,1 , R t , D t,2 , D t,3 The expressions are:
[0067]
[0068] Among them, g PW , g PV , g O , g B They are the management cost coefficients of wind power, photovoltaic power, diesel generator sets and energy storage batteries respectively. They are the output of wind power, photovoltaic power, diesel generator set, and the discharge power and charging power of base station energy storage battery during period t. is the power purchase price from the power grid during period t. is the power shortage during period t. O It is the fuel cost per kilowatt-hour output of the diesel generator set. is the wind and solar power generation surplus during period t. S The price for surplus electricity to be connected to the grid. They are the charging power of the grid and wind and solar power to the battery, are the peak and valley electricity prices respectively. h is the dispatching time, i.e. h = 1h.
[0069] It should be noted that the power balance constraint, the energy of the base station energy storage battery, as well as the charging and discharging power constraint, the diesel generator set processing constraint, and the grid line power constraint are comprehensively considered to meet the objective function.
[0070] Set constraints: the total interactive power must not exceed the local transformer and line limits, power balance constraints, output constraints, and base station energy storage battery charging and discharging power constraints.
[0071] VPP needs to interact with the power grid in two ways: selling and purchasing electricity. The total interactive power must not exceed the local transformer and line limits. The constraints are:
[0072]
[0073] in, is the total interactive power between VPP and the grid at time t. It is the maximum power limit of the local transformer and line.
[0074] The power balance constraint is:
[0075]
[0076] in, are the cell and base station load powers during period t respectively. is the power purchased by VPP from the grid during period t.
[0077] The output constraints are:
[0078]
[0079] in, are the maximum output power of the i-th wind, photovoltaic, and diesel generator sets in period t. PW 、N PV 、N O They are the number of wind, photovoltaic and diesel generator sets in the system respectively.
[0080] The charging and discharging power constraints of the base station energy storage battery are:
[0081]
[0082] in, is the energy stored in the battery at time t. are the maximum charging and discharging powers of the energy storage battery respectively. C , η D are the charging and discharging efficiency of the energy storage battery respectively. They are the maximum depth of charge and discharge of the energy storage battery respectively.
[0083] Reference Figure 2 , it can be seen that the present invention introduces the communication load rate to establish the relationship between energy flow and information flow, so as to study the 5G base station characteristic model. Secondly, a 5G base station refined model is established by constructing a base station power load demand model, a base station self-provided energy storage dispatchable capacity evaluation model, and a base station self-provided energy storage participating in the grid dispatch interaction model. Finally, the objective function is constructed with the VPP comprehensive minimum operating cost as the goal, taking into account constraints such as power balance constraints, output constraints, and base station energy storage battery charging and discharging power constraints, and establishing a grid power interaction model and a VPP dispatch optimization model, and scheduling is performed accordingly.
[0084] Example 2, an embodiment of the present invention, provides a virtual power plant scheduling method containing distributed 5G base stations. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
[0085] First, in order to verify the effectiveness of the 5G base station self-contained energy storage scheduling strategy based on communication load rate proposed in this invention, an actual test was carried out in a large urban area. The experimental scenario selected 8 5G base stations distributed in the area, all of which were equipped with a self-contained lithium battery energy storage system with a rated capacity of 200kWh. The maximum available bandwidth of the base station was 100MHz, the experimental period was 24 hours, and the time interval was 4 hours.
[0086] During the experiment, the communication load rate of each base station is first monitored in real time, and the number of bandwidths occupied by users in different time periods is recorded. Then, according to the monitored communication load rate, the upper and lower limits of the state of charge (SOC) of the base station's self-provided energy storage are dynamically adjusted. Specifically, during periods of low communication load rates, the energy storage system is allowed to discharge appropriately to participate in grid dispatching and increase dispatchable capacity; during periods of high communication load rates, the SOC lower limit of the energy storage system is increased to ensure that the base station has sufficient spare capacity to cope with emergencies.
[0087] In order to compare the advantages of the method of the present invention, the above 8 base stations are divided into two groups: a control group and an experimental group. The control group adopts the traditional energy storage scheduling strategy, does not consider the impact of the communication load rate on the energy storage system, and the SOC upper and lower limits are fixed at 90% and 20%; the experimental group adopts the scheduling strategy proposed by the present invention, and dynamically adjusts the SOC upper and lower limits according to the real-time communication load rate. The energy-information coupling coefficient χ is set to 0.7, the maximum SOC of the energy storage battery is 90%, and the minimum is 20%.
[0088] During the experiment, the communication load rate, base station power consumption, energy storage system SOC, dispatchable capacity and power interaction with the power grid of each base station in different time periods were recorded. All data were collected from actual base station equipment and statistically processed to ensure the authenticity and reliability of the data.
[0089] Table 1 Experimental data comparison table
[0090]
[0091]
[0092] From the above experimental data, it can be seen that the experimental group dynamically adjusted the upper and lower limits of the SOC of the energy storage system according to the communication load rate in different time periods, thereby optimizing the dispatchable capacity and power interaction with the grid. During periods with low communication load rates (such as 0:00-4:00 and 20:00-24:00), the energy storage SOC of the experimental group remained at a high level, and the dispatchable capacity was also large, reaching 70kWh and 50kWh respectively. This enables the base station to output excess power to the grid without affecting the backup capacity (the power interaction with the grid is 30kW and 20kW respectively), achieving the effect of peak shaving and valley filling.
[0093] In contrast, the control group had fixed upper and lower limits of energy storage SOC throughout the experiment, and failed to fully utilize the energy storage scheduling opportunities during periods of low communication load, resulting in the dispatchable capacity always being 0 and the power interaction with the grid also being 0. This shows that traditional energy storage scheduling strategies have obvious deficiencies in improving energy utilization efficiency and participating in grid interaction.
[0094] During periods with high communication load (such as 8:00-12:00 and 16:00-20:00), the experimental group appropriately increased the SOC lower limit of the energy storage system (such as from 20% to 80%) to ensure that the base station has sufficient backup capacity to cope with possible grid failures under high load conditions. At the same time, the experimental group did not output power to the grid during these periods to avoid affecting the normal operation of the base station.
[0095] Through the above data analysis, it can be seen that the energy storage scheduling strategy based on communication load rate proposed in the present invention can effectively improve the dispatchable capacity of the energy storage system, improve the interaction efficiency with the power grid, and ensure the high reliability of power supply of the base station. It fully considers the communication service characteristics of the base station, organically combines the energy flow and information flow, and overcomes the shortcomings of the prior art that the base station energy storage system is simply regarded as a general energy storage device.
[0096] In addition, the experimental group achieved efficient energy utilization and reduced operating costs throughout the scheduling process, reflecting the innovation and practical value of the present invention. In contrast, the control group failed to fully utilize the potential of the energy storage system, and the energy management strategy was relatively rigid and could not adapt to the dynamic changes of communication loads.
[0097] Example 3, an embodiment of the present invention, provides a virtual power plant scheduling system containing distributed 5G base stations, including a feature description module, a model refinement module, and a strategy optimization module.
[0098] Among them, the characteristic description module is used to introduce the communication load rate to describe the 5G base station characteristic model. The model refinement module is used to establish a 5G base station refinement model based on the base station power load demand model, the base station self-storage dispatchable capacity evaluation model, and the base station self-storage participating in the grid dispatch interaction model. The strategy optimization module is used to optimize and adjust the energy management strategy of the base station through VPP scheduling.
[0099] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.
[0100] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.
[0101] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.
[0102] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.
[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. A virtual power plant scheduling method containing distributed 5G base stations, characterized in that: include: Introduce the communication load rate to describe the 5G base station characteristic model; A refined model of 5G base stations is established based on the base station power load demand model, the dispatchable capacity evaluation model of base station self-provided energy storage, and the interactive model of base station self-provided energy storage participating in power grid dispatch; The energy management strategy of the base station is optimized and adjusted through VPP scheduling.
2. The virtual power plant scheduling method containing distributed 5G base stations according to claim 1, characterized in that: The introduction of the communication load rate includes introducing the communication load rate to quantify the heavy degree of the business undertaken by the base station, and the communication load rate is expressed as: in, is the communication load rate of base station m during period t, R m,t is the bandwidth occupied by the user data transmission demand matched by base station m during period t, is the maximum available bandwidth of base station m.
3. The virtual power plant scheduling method containing distributed 5G base stations according to claim 2 is characterized in that: The base station power load demand model includes dividing the base station power consumption into static power consumption that is independent of the communication load and dynamic power consumption that is sensitive to the communication load, which is expressed by a linear model as follows: in, is the power consumption of base station m during period t, is the static power consumption of base station m during period t, ξ m is the energy efficiency scaling factor of base station m, is the dynamic power consumption of base station m during period t. The RF output power generated by the communication load is proportional to the communication load rate and is expressed as: in, is the communication load rate of base station m during period t, is the maximum dynamic power consumption of base station m, that is, the maximum RF output power corresponding to the full communication load.
4. The virtual power plant scheduling method containing distributed 5G base stations according to claim 3 is characterized in that: The dispatchable capacity evaluation model of the base station self-storage includes dividing the base station self-storage into a spare capacity for ensuring high-reliability power supply of the base station and a dispatchable capacity for participating in demand response, linking the base station self-storage capacity and charge state with the communication load rate, and determining the dynamically changing dispatchable capacity.
5. The virtual power plant scheduling method containing distributed 5G base stations according to claim 4 is characterized in that: The base station self-provided energy storage participating in the grid dispatch interaction model includes calculating the maximum chargeable and dischargeable power of the base station self-provided energy storage pre-dispatching interaction considering the communication load state, and constructing a minimization model with the goal of minimizing current.
6. The virtual power plant scheduling method including distributed 5G base stations according to claim 5, characterized in that: The energy management strategy of optimizing and adjusting the base station through VPP scheduling includes taking the comprehensive minimum operating cost of VPP as the goal, considering the benefits of grid-connected surplus power from new energy, peak-shaving and valley-filling benefits of base station energy storage batteries, fuel costs of diesel generator sets, management costs of various power generation and energy storage units, and power purchase costs to construct an objective function.
7. The virtual power plant scheduling method containing distributed 5G base stations according to claim 6, characterized in that: The energy management strategy for optimizing and adjusting the base station through VPP scheduling includes setting constraints, the total interactive power must not exceed the local transformer and line limits, power balance constraints, output constraints, and base station energy storage battery charging and discharging power constraints.
8. A system using a virtual power plant dispatching method including distributed 5G base stations as claimed in any one of claims 1 to 7, characterized in that: Including feature description module, model refinement module, and strategy optimization module; The characteristic description module is used to introduce the communication load rate to describe the 5G base station characteristic model; The model refinement module is used to establish a 5G base station refinement model based on the base station power load demand model, the base station self-storage dispatchable capacity assessment model, and the base station self-storage participating in the grid dispatch interaction model; The strategy optimization module is used to optimize and adjust the energy management strategy of the base station through VPP scheduling.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, it implements the steps of the virtual power plant scheduling method containing distributed 5G base stations described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the virtual power plant scheduling method containing distributed 5G base stations described in any one of claims 1 to 7 are implemented.