Automatic generation control method and device based on base station backup battery virtual power plant

Abstract

The present application relates to a kind of based on base station backup battery virtual power plant automatic generation control method and equipment, comprising the following steps: S1, the real-time state vector of each 5G base station is obtained;S2, the control signal inside the unified virtual power plant of base station backup battery is obtained using the frequency modulation signal transformation method based on virtual clearing, specific process is as follows: obtain AGC frequency modulation signal, calculate VPP total response power;The virtual bid vector of each 5G base station is constructed, and is aggregated into ladder type virtual bid curve;The intersection of virtual bid curve and total response power is solved, and the control signal inside the unified B3VPP is obtained;S3, the actual response power of each 5G base station backup battery is transformed into the control signal inside the unified B3VPP by response algorithm, adjusts the charge-discharge state of each backup battery, and coordinates and controls automatic generation.Compared with prior art, the present application realizes the accurate response of B3VPP to AGC frequency modulation signal with distributed control mechanism, and guarantees the backup power demand of communication equipment in each base station.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic power generation control, and in particular relates to an automatic power generation control method and device based on a base station backup battery virtual power plant. Background Art

[0002] In new power systems, as renewable energy generation systems gradually replace conventional power sources, the total system inertia will continue to decrease, impacting the system's frequency regulation capabilities. At the same time, the volatility of renewable energy output increases the grid's demand for rapid frequency regulation resources. These factors pose serious challenges to traditional automatic generation control (AGC), which primarily relies on conventional generators. Battery energy storage, capable of responding quickly and accurately to regulation commands, has become a competitive resource for rapid frequency regulation. However, deploying energy storage specifically for frequency regulation requires significant investment, making tapping existing energy storage resources a more economical approach. 5G base station backup batteries are an important example of user-side energy storage. Effectively activating these long-idle energy storage resources could provide an effective solution to both the grid's insufficient rapid frequency regulation resources and the high cost of base station electricity.

[0003] Base station backup batteries belong to distributed energy storage. The literature Exploring the Cellular Base Station Dispatch Potential Towards Power System Frequency Regulation (YONG Pei, ZHANGNing, LIU Yuxiao, et al., IEEE Transactions on Power Systems, 2022, Vol. 37(1): 820-823) and Robust Control Scheme for Distributed Battery Energy Storage Systems in Load Frequency Control (OSHNOEI A, KHERADMANDI M, MUYEEN S M., IEEE Transactions on Power Systems, 2020, 35(6): 4781-4791) respectively proposed control schemes for distributed energy storage to participate in AGC based on the consistency principle and model predictive control method; the literature Coordinated Control Scheme for Provisionof Frequency Regulation Service by Virtual Power Plants (OSHNOEI A, KHERADMANDIM, BLAABJERG F, et al., Applied Energy, 2022, 325:119734) aggregates distributed energy storage and heat pumps into a virtual power plant (VPP) to participate in AGC, and adjusts power within the VPP based on response speed and available capacity. However, this approach has two shortcomings: first, it fails to analyze the economic viability of 5G base stations participating in AGC; second, it fails to consider the special battery backup requirements of 5G base stations.

[0004] In response to the aforementioned particularities of 5G base stations, researchers have proposed various coordinated control methods for backup batteries in recent years, applying them to scenarios such as demand response, distribution network congestion management, and economic dispatch. However, the time scale of these applications is typically 1 hour, and they are not sensitive to the real-time nature, lightweight nature, and communication latency of the control. At the same time, applications such as peak shaving and congestion management are not routinely implemented, and therefore little attention is paid to the sustainability of battery SOC, which is crucial for 5G base stations to participate in AGC. Therefore, it is necessary to design an automatic power generation control method that aggregates large-scale 5G base station backup batteries to reduce the grid's dispatching costs for distributed energy storage, enable the base station backup battery virtual power plant to accurately respond to the automatic power generation control frequency modulation signal, and at the same time, ensure the backup power needs of the communication equipment in each base station. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and to provide an automatic power generation control method and equipment based on a base station backup battery virtual power plant, so as to realize the precise response of the base station backup battery virtual power plant to the automatic power generation control frequency modulation signal through a distributed control mechanism, and at the same time, fully guarantee the backup power demand of the communication equipment in each base station.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides an automatic power generation control method based on a base station backup battery virtual power plant, which is applicable to an automatic power generation control system in which 5G base station backup batteries participate in coordination. The system includes a power grid dispatching center and a base station backup battery virtual power plant. The base station backup battery virtual power plant includes multiple 5G base stations, each of which includes communication equipment and corresponding backup batteries. The battery capacity of each backup battery is constrained by a backup power priority constraint condition to ensure the backup power demand of the corresponding communication equipment.

[0008] The method for automatic power generation control by the above system includes the following steps:

[0009] S1. Obtain the real-time state vector Ω of each 5G base station i,t , the real-time state vector Ω i,t Including backup battery state of charge indicator S i,t , Current charge and discharge status of the backup battery Z i,t , Maximum charging power of backup battery and maximum discharge power of backup battery Among them, Z i,t The values ​​are -1, 1 and 0, which represent charging, discharging and idle respectively. The maximum charging power of the backup battery The value is the rated charging power of the backup battery and the maximum discharge power of the backup battery The value is the smaller one between the rated discharge power of the backup battery and the power of the communication equipment;

[0010] S2. Use the frequency modulation signal conversion method based on virtual clearing to obtain the unified control signal inside the base station backup battery virtual power plant The specific process is:

[0011] S201, obtain the automatic power generation control frequency modulation signal R sent by the power grid dispatching center t , calculate the total response power P of the virtual power plant VPP,t ;

[0012] S202, according to the real-time state vector Ω of each 5G base station i,t , a virtual bidding strategy is used to construct the virtual bidding vector (p i,t ,q i,t ), where p i,t is the virtual bidding price, which is used to indicate the priority of backup battery charging and discharging. i,t is the bid quantity, which is used to express the charge and discharge power of the backup battery;

[0013] S203, based on the delay aggregation strategy, the virtual bidding vector (p i,t ,q i,t ) aggregated into a step-shaped virtual bidding curve;

[0014] S204, obtain virtual bidding curve and total response power P VPP,t The intersection of the base station backup battery virtual power plant obtains the unified control signal inside

[0015] S3, through the response algorithm to the base station backup battery virtual power plant unified control signal Converted into the actual response power P of each 5G base station backup battery i,t , based on which the charge and discharge status of each 5G base station backup battery is adjusted, and automatic power generation is coordinated and controlled.

[0016] Furthermore, the expression of the backup power priority constraint condition is specifically as follows:

[0017]

[0018] Among them, E i,t is the battery capacity of the backup battery in the i-th 5G base station at time t, E max,i is the upper limit of battery power, s is the upper limit coefficient of battery power, E N,i is the rated capacity of the backup battery, E min,i,t is the minimum backup capacity of the backup battery at time t, is the average communication load of the Γ period predicted a day ago, Γt is the time period to which time t belongs, T b The minimum standby time.

[0019] Furthermore, the backup battery state of charge indicator S i,t The specific expression is as follows:

[0020]

[0021] Among them, E i,t is the battery capacity of the backup battery in the i-th 5G base station at time t, E max,i is the maximum value of the battery power, E min,i,t is the minimum backup power of the backup battery at time t.

[0022] Furthermore, in step S201, the total response power P of the virtual power plant VPP,t The calculation formula is as follows:

[0023]

[0024] in, is the base power of the virtual power plant, is the frequency regulation capacity of the virtual power plant, which is calculated using the following formula:

[0025]

[0026] in, and are the rated charge and discharge power of the backup battery respectively, is the average communication load in period Γ predicted on the day before.

[0027] Furthermore, the base power of the virtual power plant Regularly adjust using the following formula:

[0028]

[0029] Among them, γ base is the base point power adjustment coefficient, For backup battery virtual power plant in Γ t Average backup battery state of charge index value for the time period, S ideal is the ideal backup battery state of charge index value of the backup battery virtual power plant.

[0030] Furthermore, in step S202, a price offset is introduced into the virtual bidding strategy. Specifically, if the battery is in a non-charging state in the previous cycle, its virtual bidding price is offset by 1 to reduce the charging priority.

[0031] Furthermore, the virtual bidding strategy is as follows:

[0032] When the backup battery is in the charge and discharge state Z of the previous control cycle i,t-1 =-1, if the total response power P VPP,t <0, then p i,t =-S i,t , If the total response power P VPP,t >0, then p i,t =S i,t ,

[0033] When the backup battery is in the charge and discharge state Z of the previous control cycle i,t-1 =0, if the total response power P VPP,t <0, then p i,t =-(S i,t +1), If the total response power P VPP,t >0, then p i,t =S i,t ,

[0034] When the backup battery is in the charge and discharge state Z of the previous control cycle i,t-1 =1, if the total response power P VPP,t <0, then p i,t =-(S i,t +1), If the total response power P VPP,t >0, then p i,t =S i,t +1,

[0035] Furthermore, the delayed aggregation strategy is specifically as follows:

[0036] The virtual bidding vector (p i,t ,q i,t ) is aggregated once per minute and completed in parallel with step S204 and step S3, and both step S204 and step S3 are performed in a cycle of 4 seconds.

[0037] Furthermore, in step S3, the response algorithm is specifically as follows:

[0038] If the base station backup battery virtual power plant has a unified control signal And the virtual bid price The backup battery is charged; if the base station backup battery virtual power plant internal unified control signal And the virtual bid price The standby battery discharges; the rest of the cases stop charging and discharging; the power of the standby battery charging and discharging is taken as the bid amount q i,t .

[0039] The application further provides an electronic device, including a memory, a processor, and a program stored in the memory, and the processor implements the above method when executing the program.

[0040] Compared with the prior art, the application has the following beneficial effects:

[0041] 1. The application provides an automatic power generation control method based on a base station standby battery virtual power plant, and an automatic power generation control system with coordinated participation of 5G base station standby batteries is constructed, the system including a power grid dispatching center and a base station standby battery virtual power plant, the base station standby battery virtual power plant including a plurality of 5G base stations, each 5G base station including a communication device and a corresponding standby battery, and the battery capacity of each standby battery is constrained by a standby power priority constraint condition, so that the standby power demand of the corresponding communication device can be guaranteed. The automatic power generation control method through the above system includes the following steps: firstly, the real-time state vector of each 5G base station is obtained; then, a frequency modulation signal transformation method based on virtual clearing is used to obtain a unified control signal inside the base station standby battery virtual power plant, and the specific process is as follows: firstly, the automatic power generation control frequency modulation signal issued by the power grid dispatching center is obtained, and the total response power of the virtual power plant is calculated; secondly, according to the real-time state vector of each 5G base station, a virtual bidding strategy is used to construct a virtual bidding vector of each 5G base station; thirdly, based on a delay aggregation strategy, the virtual bidding vectors of the 5G base stations are aggregated into a step-type virtual bidding curve; subsequently, the intersection of the virtual bidding curve and the total response power is calculated to obtain the unified control signal inside the base station standby battery virtual power plant; after the above steps are completed, the unified control signal inside the base station standby battery virtual power plant is transformed into the actual response power of each 5G base station standby battery through a response algorithm, and the charging and discharging state of each 5G base station standby battery is adjusted accordingly; the above method can realize accurate response of the base station standby battery virtual power plant to the automatic power generation control frequency modulation signal as a whole in a distributed control mechanism, and at the same time, the standby power demand of each base station communication device is fully guaranteed.

[0042] 2. In the frequency modulation signal conversion method based on virtual clearing proposed in the present invention, the base point power of the virtual power plant is adjusted regularly, so that the charge state index values ​​of the backup batteries of each base station can be made consistent and restored to near the ideal value; a price offset is introduced in the virtual bidding strategy. If the battery is in a non-charging state in the previous cycle, its virtual bidding price is offset by 1 to reduce the charging priority. The above setting can minimize the frequency of backup battery state conversion, which is conducive to giving full play to its performance advantage in responding to fast frequency modulation signals; in addition, a delayed aggregation strategy is designed to reduce the impact of uplink communication delay within the virtual power plant, and can significantly reduce the communication and calculation amount of the aggregation link, so that the process of the virtual power plant responding to automatic control signals is closer to that of conventional units.

[0043] 3. On the one hand, the response algorithm of the present invention can ensure that the backup battery will not be in the charging and discharging states at the same time in the virtual power plant, thereby avoiding unnecessary energy loss; on the other hand, the power of charging and discharging the backup battery is taken as the bid amount. According to the virtual bidding strategy, the bid amount depends on the backup battery state of charge index. Therefore, the present invention allocates frequency modulation power according to the battery state of charge index, so that each base station can obtain charging and discharging opportunities fairly. During the process of alternating positive and negative changes in the automatic power generation control signal, the backup battery state of charge index values ​​of each base station can be made to converge. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The structure diagram of the automatic power generation control system coordinated by the backup battery of the base station;

[0045] Figure 2 This is a schematic diagram of the adjustable capacity of the backup battery.

[0046] Among them, (2a) is the base station communication load prediction curve, (2b) is the diagram of the relationship between the adjustable capacity and the backup capacity of the backup battery, is the average communication load of the Γ period predicted a day ago, S is the state of charge indicator of the backup battery;

[0047] Figure 3 It is a step-type virtual bidding curve;

[0048] Among them, (3a) corresponds to VPP charging, (3b) corresponds to VPP discharging, P VPP,t is the total response power, is the base power of the virtual power plant, is the frequency regulation capacity of the virtual power plant, R t is the AGC frequency modulation signal;

[0049] Figure 4 This is a schematic diagram of the B3VPP delayed aggregation strategy.

[0050] Among them, (4a) corresponds to the clearing-execution phase (with a period of 4 seconds), and (4b) corresponds to the aggregation phase (with a period of 1 minute);

[0051] Figure 5 These are the typical load curves of four base stations.

[0052] Among them, (5a), (5b), (5c), and (5d) are typical load curves of a base station respectively;

[0053] Figure 6 The calculation results of the frequency regulation capacity of B3VPP in each period;

[0054] Figure 7 is a schematic diagram of communication delay distribution,

[0055] Among them, (7a) is the downlink communication delay between the power grid and the aggregator, (7b) is the downlink communication delay between the aggregator and the base station, and (7c) is the uplink communication delay between the base station and the aggregator.

[0056] Figure 8 is the daily charge and discharge state switching frequency distribution of the base station backup battery,

[0057] Among them, (8a) is the number of charge and discharge state switching times in scenario 1, and (8b) is the number of charge and discharge state switching times in scenario 2;

[0058] Figure 9 The percentage of base stations whose battery status changes in each AGC cycle,

[0059] Among them, (9a) corresponds to scenario 1, and (9b) corresponds to scenario 2;

[0060] Figure 10 Schematic diagram of tracking delay of AGC signal;

[0061] Figure 11 This is a schematic diagram of the effect of VC-RST.

[0062] Among them, (11a) is the response RegD signal, and (11b) is the response RegA signal;

[0063] Figure 12 This is a schematic diagram of the base point power adjustment effect (response RegD).

[0064] Among them, (12a) is the change of VPP average SOC, (12b) is the change of VPPS value;

[0065] Figure 13 This is a schematic diagram of the SOC change considering the base station power outage.

[0066] Among them, curves of different colors correspond to different backup batteries in the power outage base station. DETAILED DESCRIPTION

[0067] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0068] Example 1

[0069] Currently, China Tower Corporation (CTCC) builds and operates most of my country's 5G macro base stations. The communication equipment within the base stations (including baseband units and active antenna units) belongs to the respective telecom operators, while the remaining equipment, including backup batteries, belongs to the CTCC. Therefore, the CTCC has the authority to monitor base station power and regulate backup batteries, enabling it to build a virtual power plant (VPP) of base station backup batteries (B3VPP) as an operator and participate in automatic generation control (AGC) ancillary services.

[0070] This embodiment provides an automatic power generation control method based on a base station backup battery virtual power plant, which is applicable to an automatic power generation control system in which base station backup batteries participate in coordination, such as Figure 1 As shown, the system includes a power grid dispatch center and B3VPP. B3VPP includes multiple 5G base stations, each of which includes communication equipment and corresponding backup batteries. This embodiment uses "time division multiplexing" to improve the utilization rate of the base station backup batteries. That is, when the base station is normally powered by the mains through a switching power supply, the backup battery participates in the AGC auxiliary service. However, if the base station loses mains power, the battery immediately switches to backup power mode, ensuring continuous and uninterrupted operation of the communication equipment.

[0071] The power grid dispatcher generally sends out AGC frequency modulation signals with a period of 4 seconds, which is recorded as R t ∈[-1,1]. B3VPP needs to coordinate a large number of base stations to accurately respond to this signal, which can be described by the mathematical formula:

[0072]

[0073] Among them, f VPP () represents the decomposition algorithm of B3VPP, which aims to t Forming a unified control signal inside VPP Function g i () represents the response algorithm of each base station, which is responsible for Converted into the actual response power P of the base station backup battery i,t ; The subscript i represents different base stations. B3VPP relies on broadcasting a unified signal to each base station Distributed control is implemented without sending individual adjustment commands (which corresponds to centralized control), thus adapting to the scale characteristics of 5G base stations.

[0074] The above decomposition and response algorithm must meet the following constraints:

[0075] 1) The VPP as a whole should accurately respond to the FM signal, that is, it should meet the following requirements:

[0076]

[0077] In the above formula, M is the total number of base stations in the VPP, P VPP,t is the total response power (aggregate power), is the base power of the virtual power plant, is the frequency regulation capacity of the virtual power plant, Then is the frequency modulation power that the VPP needs to respond to, Γ t is the time period to which time t belongs, and the power is positive when the battery is discharging.

[0078] 2) Each base station should maintain the operational feasible domain of its backup battery. The charge and discharge power of the backup battery should meet the following requirements:

[0079]

[0080] In the above formula, and are the battery charging and discharging power, and are the rated charge and discharge power, P com,i,t is the base station communication equipment power, is the maximum charging power of the backup battery, is the maximum discharge power of the backup battery. Equation (3c) is used to ensure that the base station's switching power supply does not reverse power, and Equation (3d) is the mutually exclusive constraint for the battery's charge and discharge states.

[0081] The relationship between the power and capacity of the backup battery is:

[0082]

[0083] In the above formula, E i,t is the battery capacity of the backup battery in the i-th 5G base station at time t, Δt is the time interval, η cha and η dch are the battery charge and discharge efficiency, respectively.

[0084] Unlike conventional generators, backup batteries are energy-limited resources. When continuously participating in frequency regulation, their remaining capacity (SOC) should always meet the following requirements:

[0085]

[0086] In the above formula, E max,i is the upper limit of battery power, s is the upper limit coefficient of battery power, E N,i is the rated capacity of the backup battery, is the average communication load of the Γ period predicted by the day before, such as Figure 2 As shown in (2a), T b is the minimum standby time, E min,i,t is the minimum backup capacity of the backup battery at time t, such as Figure 2 As shown in (2b).

[0087] Since the grid frequency modulation signal R t ∈[-1,1], so the power grid usually hopes that the AGC resources can provide positive and negative symmetric regulation capabilities. The present invention estimates the frequency regulation capacity of B3VPP in each time period according to the following formula:

[0088]

[0089] The present invention achieves the above requirements and performs AGC control by the following steps:

[0090] S1. Obtain the real-time state vector Ω of each 5G base station i,t , real-time state vector Ω i,t It consists of the following four tuples:

[0091]

[0092] Among them, S i,t is the state of charge indicator of the backup battery relative to the adjustable capacity and is defined as follows:

[0093]

[0094] Z i,t The current charge and discharge status of the backup battery. The values ​​are -1, 1, and 0, indicating charging, discharging, and idle, respectively.

[0095] To ensure that the variables in constraints (2)-(5) are consistent with the actual values, the real-time state vector of the backup battery is periodically updated through step S1.

[0096] S2, using the virtual clearing-based frequency modulation signal conversion method (VC-RST) to obtain the unified control signal inside the base station backup battery virtual power plant The specific process is:

[0097] S201 and B3VPP obtain the AGC signal R sent by the power grid dispatching center in real time t , calculate the total response power P of the virtual power plant according to formula (2) VPP,t .

[0098] Because the AGC signal mean is not 0 and the battery itself charging and discharging loss and other factors, the standby battery needs to be charged according to a certain strategy to restore its S value to the ideal value, therefore, the embodiment proposes a VPP base point power adjustment strategy, which regularly adjusts the S value of B3VPP in the time period Γ .

[0099] Firstly, the S value of B3VPP at time t is defined as:

[0100]

[0101] Then the average S value of B3VPP in the time period Γ t is defined as:

[0102]

[0103] In the above formula, |Γ t | is the number of times of calculating formula (9) in a time period.

[0104] Finally, the base point power of VPP is calculated as:

[0105]

[0106] In the above formula, γ base is the base point power adjustment coefficient, S ideal is the ideal standby battery state of charge index value of the virtual power plant, which is taken as 0.5 in the embodiment.

[0107] In the embodiment, the base point power is adjusted once every hour according to formula (11), and the base point power is added to the real-time frequency modulation power according to formula (2), and the total power obtained is finally redistributed to each base station by the VC-RST method.

[0108] S202, according to the real-time state vector Ω i,t of each 5G base station, a virtual bidding strategy is used to construct a virtual bidding vector (p i,t , q i,t ) of each 5G base station, wherein p i,t is the virtual bid price, which is used to represent the priority of the standby battery charging and discharging, and q i,t is the bid quantity, which is used to represent the charging and discharging power of the standby battery.

[0109] Energy storage power control allows for a dynamic response time of 2s and an adjustment time of 3s, which is close to the AGC signal cycle. Therefore, minimizing the frequency of battery state transitions is beneficial to fully leveraging its performance advantage in responding to fast frequency modulation signals. To achieve the above objectives, the present invention introduces a price offset into the virtual bidding strategy. If the battery is in a non-charging state in the previous control cycle, its virtual bidding price is offset by 1 to reduce the charging priority. The virtual bidding strategy is specifically shown in Table 1:

[0110] Table 1 Virtual bidding strategy

[0111]

[0112] S203, the virtual bidding vector (p i,t ,q i,v ) are aggregated into a step-type virtual bidding curve.

[0113] VPP is based on the total response power P VPP,t The symbol of each base station is p i,t The order from high to low is aggregated into Figure 3 The step-type virtual bidding curve is shown, where (3a) corresponds to VPP charging and (3b) corresponds to VPP discharging. Each line segment in the bidding curve corresponds to a virtual bidding vector (p i,t ,q i,t ), whose color indicates the charge and discharge status of the base station battery in the previous control cycle.

[0114] S204, obtain virtual bidding curve and total response power P VPP,t The intersection of the base station backup battery virtual power plant is marked as the unified control signal

[0115] S3, through the response algorithm to the base station backup battery virtual power plant unified control signal Converted into the actual response power P of each 5G base station backup battery i,t , and accordingly adjust the charge and discharge status of each 5G base station backup battery, and coordinate and control automatic power generation. The response algorithm is as follows:

[0116] If the base station backup battery virtual power plant has a unified control signal And virtual bidding price The backup battery is charged; if the base station backup battery virtual power plant internal unified control signal And virtual bidding price The backup battery discharges; the power of charging and discharging the backup battery is taken as the bid quantity q i,tIn all other cases, charging and discharging are stopped. According to the aforementioned response algorithm, batteries within the VPP will not be simultaneously charging and discharging, thus avoiding unnecessary energy loss. Furthermore, VC-RST allocates frequency modulation power according to the battery's S value, ensuring that each base station has equal access to charging and discharging opportunities. As the AGC signal alternates between positive and negative, this coordination strategy leads to convergence of the S values ​​across all base stations.

[0117] As can be seen from the above, the VC-RST method includes three links, namely aggregation (including step S1, step S201, step S202 and step S203), virtual clearing (step S204) and execution (step S3). Compared with conventional units, B3VPP is more susceptible to communication delays when participating in AGC. On the one hand, this is because B3VPP needs to aggregate large-scale base stations, and on the other hand, it is because it is usually impossible to use a dedicated power network in VPP. Taking into account the impact of communication delay, the present invention considers two aggregation strategies: 1) Synchronous aggregation: VPP performs aggregation in each AGC cycle, and completes it in series with the virtual clearing and execution links; 2) Lazy aggregation: such as Figure 4 As shown in (4b), VPP is aggregated only once per minute and is completed in parallel with the virtual clearing and execution phases, as shown in Figure 4 As shown in (4a), the virtual clearing and execution stages still have a cycle of 4 seconds.

[0118] Depend on Figure 4 As can be seen, the delayed aggregation strategy can reduce the impact of uplink communication delays within the VPP and significantly reduce the communication and computational complexity of the aggregation process, making the VPP's response to AGC signals more similar to that of conventional units. A drawback of delayed aggregation is that each base station may not be able to update the battery's S value in a timely manner. However, even if the battery is charged and discharged at a 1C rate, its SOC changes by less than 2% within 1 minute, which has a minimal impact on control accuracy.

[0119] The following example takes the case where the AGC signal is negative to illustrate how the VC-RST algorithm satisfies equations (1) to (5). In this case, B3VPP needs to be charged to achieve downward regulation, corresponding to the second column of Table 1 and Figure 3 Step S2 implements the function f in formula (1). VPP (), R t Converted into a unified control signal inside VPP Depend on Figure 3 As can be seen from (3a), batteries with lower S values ​​(relative SOC after deducting the reserve capacity) will be charged first, which will make all batteries more likely to remain in their original state (continue to charge or idle), maintaining their energy constraints, i.e., formula (5); It can be regarded as the equilibrium price, so its coordination result satisfies formula (2). Step S3 implements the function g in formula (1)i (),will transformed into the actual control power P i,t of the battery, the backup battery determines the bid amount according to formula (3), thereby ensuring its power constraint.

[0120] To prove the effectiveness of the above method, the embodiment establishes a benefit model of B3VPP participating in AGC, and further verifies it through a simulation example.

[0121] The benefit model of B3VPP participating in AGC includes the following two parts:

[0122] I. Service cost of B3VPP participating in AGC

[0123] The additional cost of the base station due to participation in AGC is called service cost, which is composed of three parts: electricity cost C energy,t , battery life loss cost C life-cost,t and additional investment cost C ex-cap .

[0124] (1) Electricity cost C energy

[0125]

[0126] In the above formula, p e,t is the electricity price. The total electricity cost in a day is mainly affected by the change of electricity price, and the charging and discharging loss will also potentially increase the electricity cost.

[0127] (2) Battery life loss cost C life-cost,t

[0128] The battery life loss is mainly related to the number of charging and discharging times and the depth of discharge (DOD). The embodiment calculates the equivalent full cycle number of the battery in this action according to the following formula:

[0129]

[0130] In the above formula, N 100％ is the cycle number of the battery at 100% DOD, d t is the DOD value of this charging and discharging, and a and b are fitting coefficients.

[0131] Then the life loss cost of the battery after this action is calculated by using the annuity method:

[0132]

[0133] In the above formula, C Ess is the purchase cost of the battery system, T Ess-life is the expected operation life of the battery, and r is the discount rate.

[0134] (3) Additional investment cost C ex-cap

[0135] Participating in AGC requires additional investment in a real-time power meter system, with the cost calculated at 200 yuan per base station. The additional investment cost per base station per day is calculated as follows:

[0136]

[0137] In the above formula, T met-life The expected operating life of the power meter system.

[0138] II.B3VPP's Benefits from Participating in AGC

[0139] (1) Frequency modulation benefits of the domestic “two detailed rules”

[0140] As the AGC ancillary services market matures, the compensation method for frequency regulation units has evolved from a single compensation based on frequency regulation power to market-based compensation based on frequency regulation performance. This embodiment uses the North China Power Grid (see the North China Energy Regulatory Bureau's Notice on Soliciting Opinions on the "Implementation Rules for the Management of Power Grid Connection Operation in the North China Region" and the "Implementation Rules for the Management of Power Ancillary Services in the North China Region" (Draft for Comments)) as an example to divide the unit frequency regulation compensation income into frequency regulation capacity compensation (BNC CAP) and frequency regulation power compensation:

[0141]

[0142] In the above formula, is the frequency regulation capacity compensation price, which is set to 10 yuan·(MW·h) in this embodiment. -1 , D is the actual daily adjustment depth, K p It is a comprehensive performance index, which is determined by the response time K1, the adjustment rate K2 and the adjustment accuracy K3. AGC is the frequency regulation power compensation price, which is set to 10 yuan·(MW·h) in this embodiment. -1 .

[0143] (2) Frequency regulation revenue in the PJM market in the United States

[0144] To enable conventional generators and energy storage to jointly participate in AGC services, the US PJM decomposes the frequency regulation signal into a conventional signal, RegA, and a high-frequency signal, RegD. Conventional generators respond to the conventional signal, reducing regulation losses; fast-acting resources like energy storage respond to the high-frequency signal, improving frequency regulation performance. Furthermore, because the high-frequency RegD component has a mean of zero over a certain period of time, it significantly reduces the required energy storage capacity.

[0145] Frequency regulation revenue in the PJM frequency regulation market is divided into capacity revenue and adjust mileage earnings

[0146]

[0147] In the above formula, p cap is the capacity compensation price ($ / (MW·h)), p mileage is the mileage compensation price, M ratio is the mileage ratio, S PJM It is the frequency modulation response performance index, according to the accuracy coefficient S p , correlation coefficient S corr and the delay coefficient S delay calculate.

[0148] The following is a specific example analysis:

[0149] Ⅰ. Example Setup

[0150] The AGC signal (including RegD and RegA) of the present invention adopts the actual data of the PJM market in the United States in October 2017, and the electricity price adopts the power purchase price of the Shanghai power grid in May 2024. The present invention aggregates 400 5G base stations to form a VPP. The four typical load curves of the base stations are as follows: Figure 5 (5a), (5b), (5c), and (5d) are shown in Table 2. The frequency regulation capacity of B3VPP in each period calculated by formula (6) is as follows: Figure 6 The present invention obtains the communication delay distribution according to the field measurement, as shown in Figure 7 As shown in Figure 3, (7a) is the downlink communication delay between the grid and the aggregator, (7b) is the downlink communication delay between the aggregator and the base station, and (7c) is the uplink communication delay between the base station and the aggregator. In addition to communication delay, the total AGC delay also includes the battery response delay. The regulation characteristics of the backup batteries of each base station are randomly generated according to Table 3.

[0151] Table 2 Main parameter settings of base station

[0152]

[0153] Table 3 Regulation characteristics of battery energy storage

[0154]

[0155]

[0156] II. AGC real-time response performance

[0157] To examine the real-time response capability of the AGC of B3VPP, this embodiment uses the fast frequency modulation signal RegD and the PJM response performance assessment method to design two comparison scenarios. Scenario 1: No price offset is introduced in Table 1, and a synchronous aggregation strategy is adopted; Scenario 2: Price offset is introduced, and a delayed aggregation strategy is adopted. The comparison results are shown in Table 4. As can be seen from Table 4, since Scenario 2 reduces the number of battery state switching and reduces communication delay, its accuracy coefficient S is 2. p and the delay coefficient S delay Improved accordingly.

[0158] Table 4 AGC response performance indicators

[0159]

[0160] The following are analyzed separately:

[0161] A. Number of battery status switching times.

[0162] Depend on Figure 8 From the comparison of (8b) and (8a), it can be seen that after the price offset is introduced in scenario 2, the average daily charge and discharge state switching of the battery is significantly reduced from 7148 times to 498 times; Figure 9 The comparison between (9b) and (9a) shows that in each AGC cycle, the proportion of base stations with battery charging and battery replacement status changes is significantly reduced. This improves the accuracy coefficient S of scenario 2. p .

[0163] B. Communication delay.

[0164] In each AGC cycle, the average delay from the grid dispatching issuing the command to the base station battery response signal is 6.9s and 4.4s in scenario 1 and scenario 2 respectively. Figure 10 It directly reflects the tracking of AGC instructions by B3VPP. As shown in Table 4, the delayed aggregation strategy improves the S delay Performance indicators.

[0165] The subsequent simulations in this embodiment are all based on Scenario 2, that is, introducing price offset and adopting a delayed aggregation strategy.

[0166] III. Backup battery state of charge maintenance effect

[0167] Next, we verify whether B3VPP can maintain the backup battery SOC within the limit and meet the backup power demand while continuously responding to the AGC signal. The above capabilities are jointly guaranteed by the VC-RST method and the VPP base point power adjustment strategy of the present invention.

[0168] A. VC-RST effect.

[0169] VC-RST is used to reasonably distribute the FM power within VPP. To verify its effect, let VPP respond to RegD and RegA signals respectively. Figure 11 visible:

[0170] 1) VC-RST converts the AGC signal into a unified control signal within the VPP Since the virtual bid price is offset by 1, It may be greater than 1. From the actual results, All less than 2. Figure 3 This indicates that the control signal does not cause the S value of the backup battery in VPP to exceed the limit, which is also from Figure 11 The distribution of S values ​​was verified.

[0171] 2) When responding to the RegD signal, Basically around 1.5, this is because the RegD signal changes frequently and its mean is approximately 0, which keeps the S value of each backup battery around 0.5. However, the RegA signal does not have positive and negative symmetry, so The S value of the backup battery also fluctuates within a wider range. However, judging from the distribution of the backup battery SOC and the backup capacity, the VC-RST method ensures the backup power requirements of all base stations, regardless of whether it responds to RegD or RegA.

[0172] 3) The VC-RST method allocates power based on the backup battery's S value, ensuring that each base station has equal access to charging and discharging opportunities. During the alternating positive and negative transitions of the AGC signal, the S value of each base station's backup battery remains approximately the same.

[0173] B. VPP base point power adjustment effect.

[0174] Taking the response to the RegD signal as an example, the results with and without base point power adjustment are compared.

[0175] Depend on Figure 12 As shown in (12a) and (12b), if the base power adjustment strategy is not adopted, even if the mean value of the RegD signal is approximately 0, the average SOC and S value of the backup battery in the VPP will gradually decrease over time due to the influence of charge and discharge losses. However, when the base power adjustment strategy is adopted, the average SOC and S value of the VPP are well maintained.

[0176] C. The base station has a power outage.

[0177] According to statistics on power outages at base stations in a certain urban area, 88.4% of base stations experience no more than one power outage per month, and 90% of base stations experience an average power outage duration of less than 3 hours. Using the above data to set the power outage probability and duration of the base station, and re-simulating the response RegD as an example, the results show that the response performance index SPJM is reduced to 0.963 (a decrease of 2%). Figure 13 As can be seen, the backup battery of the power outage base station is switched to the backup power state, and its SOC continues to decrease. However, when the power is restored, its SOC can quickly recover and become consistent with that of other base stations.

[0178] IV. Economic Benefits

[0179] The following compares the benefits of B3VPP responding to RegD and RegA signals. The former calculates compensation based on PJM's evaluation indicators, while the latter uses my country's "Two Detailed Regulations." Furthermore, the benefits of B3VPP participating in peak-valley price arbitrage and quasi-linear demand response are compared. The results, converted to $, are shown in Table 5. These two applications were chosen because they can be implemented regularly, allowing for direct comparison. However, peak shaving and valley filling are implemented only a limited number of times per year, so they are not included in this comparison.

[0180] Table 5 Benefits of B3VPP when participating in different applications ($)

[0181]

[0182] According to Table 5, the following conclusions can be drawn:

[0183] 1) Regarding service costs: B3VPPs participating in AGC will incur high service costs, primarily due to electricity costs and battery cycle life loss caused by electricity price fluctuations. Peak-valley arbitrage and quasi-linear response can exploit electricity price arbitrage, resulting in negative electricity costs. Furthermore, these two non-real-time applications do not require the installation of a real-time power meter system, resulting in zero additional investment costs.

[0184] 2) Regarding economic compensation: PJM's compensation for energy storage-based rapid frequency regulation resources is higher, $300.7 more than under the "Two Regulations." Table 5 shows that this is primarily due to the fact that the frequency regulation capacity compensation under the "Two Regulations" is far lower than the performance compensation, while the PJM market prioritizes the capacity value of frequency regulation resources. Furthermore, as shown in Equation (17), PJM considers the response performance (SPJM) when calculating both capacity and performance compensation. Due to the superior performance advantages of energy storage response (RegD), higher compensation is achieved.

[0185] 3) Regarding VPP net returns: Given the current peak-valley price spread and battery cycle count, peak-valley arbitrage using backup batteries is marginally profitable. In contrast, B3VPP can significantly increase returns by participating in quasi-linear responses or responding to RegD signals. For example, the latter can increase VPP net returns by seven times that of peak-valley arbitrage scenarios.

[0186] In summary, the 5G base station backup battery capacity is small, and the position is dispersed. The B3VPP can aggregate these resources, make them have the external characteristics of the conventional frequency modulation unit, and report the frequency modulation capacity and track the AGC signal as a whole. This significantly reduces the dispatching cost of the power grid to the distributed energy storage, and also enables the distributed energy storage to participate in the AGC auxiliary service. The application adopts the frequency modulation signal transformation (VC-RST) method based on virtual clearing and a distributed control mechanism, and combines the delay aggregation strategy and the base point power adjustment strategy, which can realize the real-time decomposition and accurate response of the frequency modulation power, guarantee the backup power demand of each base station, and meet the application demand of the large-scale response of the 5G base station. The example analysis shows that the above method can accurately respond to the AGC signal and obtain considerable frequency modulation income under the premise of guaranteeing the backup power of each base station.

[0187] Embodiment 2

[0188] The embodiment provides an electronic device, including a memory and a processor, wherein the processor is used for executing a program stored in the memory, and the program includes a plurality of instructions, and all or part of steps of the method in the embodiment 1 can be executed. The memory includes a computer readable storage medium, and specifically can be a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk and various storage medium capable of storing program codes.

[0189] The above description of the embodiments is for the purpose of facilitating the understanding and use of the application by the ordinary skilled person in the art. The person skilled in the art can obviously easily make various modifications to the embodiments, and apply the general principles described herein to other embodiments without the need for creative labor. Therefore, the application is not limited to the above embodiments, and the improvements and modifications made by the person skilled in the art according to the disclosure of the application without departing from the scope of the application should be within the protection scope of the application.

Claims

1. An automatic power generation control method based on a base station backup battery virtual power plant, characterized in that: An automatic power generation control system applicable to 5G base station backup batteries participating in coordination, the system includes a power grid dispatching center and a base station backup battery virtual power plant, the base station backup battery virtual power plant includes multiple 5G base stations, each of which includes communication equipment and corresponding backup batteries. The battery capacity of each backup battery is constrained by the backup power priority constraint condition to ensure the backup power demand of the corresponding communication equipment; The method for automatic power generation control by the above system includes the following steps: S1. Obtain the real-time state vector Ω of each 5G base station i,t , the real-time state vector Ω i,t Including backup battery state of charge indicator S i,t , Current charge and discharge status of the backup battery Z i,t , Maximum charging power of backup battery and maximum discharge power of backup battery Among them, Z i,t The values ​​are -1, 1 and 0, which represent charging, discharging and idle respectively. The maximum charging power of the backup battery The value is the rated charging power of the backup battery and the maximum discharge power of the backup battery The value is the smaller one between the rated discharge power of the backup battery and the power of the communication equipment; S2. Use the frequency modulation signal conversion method based on virtual clearing to obtain the unified control signal inside the base station backup battery virtual power plant The specific process is: S201, obtain the automatic power generation control frequency modulation signal R sent by the power grid dispatching center t , calculate the total response power P of the virtual power plant VPP,t ; S202, according to the real-time state vector Ω of each 5G base station i,t , a virtual bidding strategy is used to construct the virtual bidding vector (p i,t ,q i,t ), where p i,t is the virtual bidding price, which is used to indicate the priority of backup battery charging and discharging. i,t is the bid quantity, which is used to express the charge and discharge power of the backup battery; S203, based on the delay aggregation strategy, the virtual bidding vector (p i,t ,q i,t ) aggregated into a step-shaped virtual bidding curve; S204, obtain virtual bidding curve and total response power P VPP,t The intersection of the base station backup battery virtual power plant obtains the unified control signal inside S3, through the response algorithm to the base station backup battery virtual power plant unified control signal Converted into the actual response power P of each 5G base station backup battery i,t , based on which the charge and discharge status of each 5G base station backup battery is adjusted, and automatic power generation is coordinated and controlled.

2. The automatic power generation control method based on a base station backup battery virtual power plant according to claim 1, characterized in that: The expression of the backup power priority constraint condition is as follows: Among them, E i,t is the battery capacity of the backup battery in the i-th 5G base station at time t, E max,i is the upper limit of battery power, s is the upper limit coefficient of battery power, E N,i is the rated capacity of the backup battery, E min,i,t is the minimum backup capacity of the backup battery at time t, is the average communication load of the Γ period predicted a day ago, Γ t is the time period to which time t belongs, T b The minimum standby time.

3. The automatic power generation control method based on a base station backup battery virtual power plant according to claim 1, characterized in that: The backup battery state of charge indicator S i,t The specific expression is as follows: Among them, E i,t is the battery capacity of the backup battery in the i-th 5G base station at time t, E max,i is the maximum value of the battery power, E min,i,t is the minimum backup power of the backup battery at time t.

4. The automatic power generation control method based on a base station backup battery virtual power plant according to claim 1, characterized in that: In step S201, the total response power P of the virtual power plant VPP，t The calculation formula is as follows: in, is the base power of the virtual power plant, is the frequency regulation capacity of the virtual power plant, which is calculated using the following formula: in, and are the rated charge and discharge power of the backup battery respectively, is the average communication load in period Γ predicted on the day before.

5. The automatic power generation control method based on a base station backup battery virtual power plant according to claim 4, characterized in that: Base power of virtual power plants Regularly adjust using the following formula: Among them, γ base is the base point power adjustment coefficient, For backup battery virtual power plant in Γ t Average backup battery state of charge index value for the time period, S ideal is the ideal backup battery state of charge index value of the backup battery virtual power plant.

6. The automatic power generation control method based on a base station backup battery virtual power plant according to claim 1, characterized in that: In step S202, a price offset is introduced into the virtual bidding strategy. Specifically, if the battery is in a non-charging state in the previous cycle, its virtual bidding price is offset by 1 to reduce the charging priority.

7. The automatic power generation control method based on a base station backup battery virtual power plant according to claim 6, characterized in that: The virtual bidding strategy is as follows: When the backup battery is in the charge and discharge state Z of the previous control cycle i,t-1 =-1, if the total response power P VPP,t <0, then p i,t =-S i,t , If the total response power P VPP,t >0, then p i,t =S i,t , When the backup battery is in the charge and discharge state Z of the previous control cycle i,t-1 =0, if the total response power P VPP,t <0, then p i,t =-(S i,t +1), If the total response power P VPP,t >0, then p i,t =S i,t , When the backup battery is in the charge and discharge state Z of the previous control cycle i,t-1 =1, if the total response power P VPP,t <0, then p i,t =-(S i,t +1), If the total response power P VPP,t >0, then p i,t =S i,t +1, 8. The automatic power generation control method based on a base station backup battery virtual power plant according to claim 1, characterized in that: The delayed aggregation strategy is specifically as follows: The virtual bidding vector (p i,t ,q i,t ) is aggregated once per minute and completed in parallel with step S204 and step S3, and both step S204 and step S3 are performed in a cycle of 4 seconds.

9. The automatic power generation control method based on a base station backup battery virtual power plant according to claim 1, characterized in that: In step S3, the response algorithm is as follows: If the base station backup battery virtual power plant has a unified control signal And virtual bidding price The backup battery is charged; if the base station backup battery virtual power plant internal unified control signal And the virtual bid price The backup battery discharges; in other cases, charging and discharging are stopped; the power of charging and discharging of the backup battery is taken as the bid quantity q i,t .

10. An electronic device comprising a memory, a processor, and a program stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 9 is implemented.

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