Multi-microgrid and 5G base station collaborative optimization control method for improving power supply quality of photovoltaic power distribution network

Abstract

The invention discloses a multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic power distribution network, and relates to the technical field of electric power, and the method comprises the steps: analyzing the load characteristics of a 5G base station, and building a 5G base station cluster backup energy storage, variable frequency air conditioner and communication load transfer power scheduling model; aiming at a multi-microgrid scene under the same power distribution network, establishing a master-slave game model of the power distribution network and a multi-microgrid-5G base station cluster alliance; and solving a master-slave game model of the power distribution network and the multi-microgrid-5G base station cluster alliance, and carrying out collaborative optimization control according to a model solving result. According to the invention, the demand response potential of various loads of the 5G base station can be explored, and the power consumption cost is saved. And benefit balance between the power distribution network and the multi-microgrid 5G base station cluster system can be realized. Through power mutual aid among multiple micro-grids, the local consumption capability of new energy and the operation benefit of each micro-grid are improved.

Description

Technical Field

[0001] The present invention relates to the field of power technology, and particularly to a multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic-integrated distribution network. Background Art

[0002] With the rapid economic development, the energy demand is increasing continuously. The accompanying climate change is one of the global challenges faced by the current society. The carbon emissions generated by the power industry account for about 40% of the carbon emissions generated by fossil fuel combustion. In recent years, with the promotion of "New Infrastructure" in China, the scale of 5G base stations has been growing day by day. By the end of 2023, the total number of mobile communication base stations deployed nationwide reached 11.62 million, and the number of 5G base stations reached 29.1% of the total number of mobile base stations. Moreover, the coverage area of 5G base stations is smaller than that of 4G, further increasing the power consumption demand. At the same time, under the guidance of the "dual carbon" goal, the penetration rate of distributed new energy power generation in the distribution network is getting higher and higher. Microgrids are an effective way to achieve the consumption of distributed new energy. After microgrids are connected to the active distribution network, it is necessary to coordinate the power scheduling between the distribution network and the microgrids to promote the consumption of new energy.

[0003] How to use the equipment of 5G base stations to achieve demand response and deeply participate in power grid dispatching has become a key issue. With the rapid deployment of 5G base stations in China, 5G technology has not only promoted the innovation of the mobile communication industry but also brought huge energy consumption challenges. Due to the relatively small coverage area of 5G base stations, more base stations need to be deployed to ensure smooth signal transmission, which directly leads to a sharp increase in power consumption demand. At the same time, in the process of China's realization of the "dual carbon" goal, efforts are being made to promote the development of green energy, and distributed new energy power generation has gradually become an important part of the power system. As an effective means to support the consumption of distributed new energy, microgrids are increasingly connected to the active distribution network. However, the coordination and dispatching problem between microgrids and the distribution network has become the key to promoting the consumption of new energy. At this time, as a huge distributed power resource, 5G base stations can not only participate in the demand response of the power grid through intelligent control and optimized dispatching but also provide more accurate load forecasting and dispatching decisions for the power system through real-time data transmission and calculation, thereby reducing the energy pressure while improving the stability and reliability of the power system. By integrating the flexible dispatching ability of 5G base stations, the power grid fluctuations can be effectively reduced, and strong support can be provided for the more efficient utilization of green energy. Summary of the Invention

[0004] In view of the problems existing in the existing multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic-integrated distribution network, the present invention is proposed. Therefore, the problem to be solved by the present invention is how to provide a multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic-integrated distribution network.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a cooperative optimization control method for multi - microgrids and 5G base stations to improve the power supply quality of a photovoltaic - integrated distribution network, which includes analyzing the load characteristics of 5G base stations to establish a scheduling model for the backup energy storage, variable - frequency air conditioners, and communication load transfer power of a 5G base station cluster;

[0007] The 5G base station includes communication equipment and auxiliary equipment; the communication equipment includes an active antenna unit, a baseband unit, and a transmission device, which are used for the transmission, conversion, and reception of wireless signals; the auxiliary equipment includes a power supply device, a monitoring device, a lighting device, and an air - conditioning device;

[0008] For the multi - microgrid scenario under the same distribution network, a master - slave game model of the distribution network and the multi - microgrid - 5G base station cluster alliance is established; the master - slave game model of the distribution network and the multi - microgrid - 5G base station cluster alliance includes an upper - layer distribution network optimization model and a lower - layer multi - microgrid base station cluster alliance cooperative game model;

[0009] Solve the master - slave game model of the distribution network and the multi - microgrid - 5G base station cluster alliance, and perform cooperative optimization control according to the model solution results.

[0010] As a preferred embodiment of the cooperative optimization control method for multi - microgrids and 5G base stations to improve the power supply quality of a photovoltaic - integrated distribution network according to the present invention, wherein: the establishment of the scheduling model for the backup energy storage, variable - frequency air conditioners, and communication load transfer power of the 5G base station cluster includes the following:

[0011] The 5G base station backup energy storage is used as a backup power supply to ensure the uninterrupted power supply of the base station to maintain the communication requirements of the base station. An operation model of the backup energy storage battery is established, expressed as:

[0012]

[0013] In the formula, is the battery power of the energy storage of base station cluster i at time t, ρ is the charge - discharge efficiency of the energy storage battery; is the charging power of the energy storage battery of base station cluster i at time t, is the discharging power of the energy storage battery of base station cluster i at time t; is the maximum charge - discharge power of the energy storage battery of base station cluster i at time t; θ i,t is a Boolean variable representing the charge - discharge state; is the real - time power of the communication equipment of base station cluster i at time t; is the lower safety limit of the energy storage battery power of base station cluster i, is the upper safety limit of the energy storage battery power of base station cluster i; The reserved spare capacity of the energy storage battery for the communication equipment power supply of the base station cluster i at time t; The power outage standby time of the 5G base station; The state of charge of the energy storage battery of the base station cluster i at the initial time and the end time within the scheduling period, respectively;

[0014] The overall composed of the variable-frequency air conditioner and the base station computer room is equivalently regarded as a virtual air conditioner energy storage and incorporated into the optimization scheduling model. The simplified first-order equivalent thermal parameter model is used to represent the thermodynamic dynamic process of the air conditioner-computer room. The air conditioner in the computer room operates at the set temperature T set And the power without external control is the reference power of the air conditioner, which is expressed as:

[0015]

[0016] In the formula, P ACbase Represents the reference power of the base station air conditioner; T out Represents the indoor and outdoor temperatures of the computer room; R is the equivalent thermal resistance of the base station computer room; Q m Is the heating power of the equipment in the base station computer room; a and b are the relationship coefficients between the cooling power and the electric power of the air conditioner;

[0017] Using the current heat ratio of the computer room to represent the state of charge SOC of the air conditioner virtual energy storage, and the thermal power of the computer room equipment to represent the charging and discharging power of the air conditioner virtual energy storage, the operation constraints of the base station variable-frequency air conditioner virtual energy storage are established as:

[0018]

[0019] α = e -Δt / RC ,

[0020]

[0021] In the formula, Is the state of charge of the air conditioner energy storage of base station i at time t; T max Represents the maximum temperature set for the computer room, T min Represents the minimum temperature set for the computer room, Δt is the scheduling period, and C is the equivalent heat capacity of the base station computer room; Is the state of charge of the air conditioner energy storage of base station i at t + 1; Is the charging power of the air conditioner energy storage of base station i at time t, Represents the discharging power of the air conditioner energy storage of base station i at time t; Is the state of charge of the air conditioner energy storage at the initial time within the scheduling period, Is the state of charge of the air conditioner energy storage at the end time within the scheduling period, Represents the maximum value of the electric power of the base station variable-frequency air conditioner, Represents the reference power of the variable-frequency air conditioner of base station i at time t, Represents the minimum value of the electric power of the base station variable-frequency air conditioner;

[0022] The power consumption of the communication device is divided into static power consumption and dynamic power consumption, and the expression is:

[0023] P com = P static + γ com P dyn

[0024] In the formula, P com represents the power consumption of the base station communication device; P static , P dyn are the static power consumption and dynamic power consumption of the base station communication device respectively; γ com is the load rate of the base station communication device, indicating the proportion of resource blocks occupied by the active antenna unit AAU;

[0025] By transferring users located in the common coverage area, the communication load between different base stations is changed. Considering the communication power migration between base stations in units of base station clusters and not considering the connection relationship between a single base station and users, at time t, the communication load of adjacent 5G base station clusters is transferred, which is expressed as:

[0026]

[0027] In the formula, is the total communication load transfer power of base station cluster i at time t; is the dynamic power consumption of the communication device of base station cluster i at time t; is the power of the communication load transferred between base station cluster i and base station cluster j at time t; λ i represents the proportion of the transferable load of base station cluster i, and λ j represents the proportion of the transferable load of base station cluster j.

[0028] As a preferred scheme of the multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of the photovoltaic distribution network described in the present invention, wherein: the upper-layer distribution network optimization model includes the following:

[0029] The upper-layer distribution network optimization model is constructed with the minimum operating cost of the upper-layer distribution network operator DSO as the objective function and the purchase and sale electricity prices of the microgrid and 5G base station as the constraint conditions;

[0030] The objective function of the upper-layer distribution network optimization model is expressed as:

[0031] F DSO = min(C grid + C peak - C MGO - C BSO )

[0032]

[0033] Wherein, F DSO is the operating cost of the upper-layer distribution network operator DSO, and C grid is the power purchase cost of the distribution network from the external power grid; C peak is the penalty cost for the peak-valley difference of the net load; C MGO is the operating cost of the microgrid operator; C BGO is the operating cost of the communication network operator; is the selling electricity price of the superior power grid at time t, is the power purchase and sale power of the distribution network from the superior power grid at time t, is the peak-valley difference penalty index; is the net load power of the distribution network at time t, is the base load power at time t, is the output power of the wind turbine at time t, is the output power of the deployed distributed photovoltaic at time t; is the power purchase and sale power between the base station cluster i and the distribution network at time t, is the power purchase and sale power between the base station cluster i and the microgrid at time t, and n is the number of microgrids.

[0034] As a preferred scheme of the multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of the photovoltaic-integrated distribution network described in the present invention, wherein: the constraint conditions of the upper-layer distribution network optimization model include power balance constraint and distribution network power purchase and sale electricity price constraint, and the expression of the power balance constraint is:

[0035] P t L -P t grid = 0

[0036] The expression of the distribution network power purchase and sale electricity price constraint is:

[0037]

[0038] Wherein, is the lower limit of the transaction electricity price between the base station cluster i and the distribution network at time t, is the upper limit of the transaction electricity price between the base station cluster i and the distribution network at time t.

[0039] As a preferred scheme of the multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of the photovoltaic-integrated distribution network described in the present invention, wherein: the lower-layer multi-microgrid base station cluster alliance cooperation game model includes the following contents:

[0040] The lower-layer multi-microgrid base station cluster coalition cooperation game model aims to minimize the operation cost of the multi-microgrid base station cluster coalition, and uses the Shapley value method to solve the distribution problem of cooperation benefits. The objective function of the lower-layer multi-microgrid base station cluster coalition cooperation game model is expressed as:

[0041]

[0042] C MT = C MT,fuel + C MT,env

[0043]

[0044] In the formula, F MGO is the operation cost of the multi-microgrid base station cluster coalition, is the power interaction cost between the microgrid i and the upper-layer distribution network operator DSO, are respectively the operation and maintenance cost of the microturbine of the microgrid i and the loss cost of the energy storage battery of the base station cluster; C MT,fuel and C MT,env are respectively the fuel cost and environmental pollution control cost of the microturbine, a M , b M and c M are the power generation cost coefficients of the controllable power sources of the microgrid; p is the number of pollutant types, T is the pollution time, α cp is the pollutant emission, β cp is the pollutant emission cost, is the output of the microturbine of the microgrid i at time t, and δ is the battery loss coefficient of the base station energy storage.

[0045] As a preferred solution of the multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of the photovoltaic-integrated distribution network described in the present invention, wherein: the constraint conditions of the lower-layer multi-microgrid base station cluster coalition cooperation game model include power balance constraint, microgrid PCC interaction power constraint, gas turbine output upper and lower limit constraint, 5G base station cluster backup energy storage constraint, air conditioner energy storage constraint, and communication load transfer constraint;

[0046] The expression of the power balance constraint is:

[0047]

[0048] In the formula, respectively represent the photovoltaic and wind turbine powers of the microgrid i at time t; respectively represent the load of the microgrid i, the power of the microturbine, and the load power of the base station cluster i at time t; the base station load power consists of the base station communication load, the air conditioner reference power, and the power of other devices;

[0049] The expression for the interactive power constraint of the microgrid PCC is as follows:

[0050]

[0051] In the formula, is the maximum transmission power of the connection line between the microgrid and the distribution network, is the power transmitted between microgrids i at time t;

[0052] The expression for the upper and lower limit constraints of the gas turbine output is as follows:

[0053]

[0054] In the formula, is the lower power limit of the micro gas turbine of microgrid i, is the lower power limit of the micro gas turbine of microgrid i.

[0055] As a preferred solution of the multi - microgrid and 5G base station cooperative optimization control method for improving the power supply quality of the photovoltaic - containing distribution network described in the present invention, wherein: the use of the Shapley value method to solve the cooperative benefit includes that for a cooperative game participated by n agents, the calculation formula of the Shapley value of each agent is as follows:

[0056]

[0057] In the formula, v(i) is the Shapley value of the i - th agent; |s| is the number of sub - coalitions in the coalition s; ω(|s|) is the weight factor; n is the set of agents participating in the game; v(s) is the cooperative surplus of the sub - coalition s, and v(s / i) is the cooperative surplus of the sub - coalition excluding member i.

[0058] In a second aspect, the present invention provides a computer device, including a memory and a processor, where: when the processor executes the computer program, it implements the steps of the multi - microgrid and 5G base station cooperative optimization control method for improving the power supply quality of the photovoltaic - containing distribution network.

[0059] In a third aspect, the present invention provides a computer - readable storage medium, on which a computer program is stored, where: when the computer program is executed by the processor, it implements the steps of the multi - microgrid and 5G base station cooperative optimization control method for improving the power supply quality of the photovoltaic - containing distribution network.

[0060] The beneficial effects of the present invention are as follows: By establishing a demand response model for 5G base station clusters, the demand response potential of various loads such as energy storage, air conditioners, and communication equipment in 5G base stations can be explored, and the flexible resources of 5G base stations can be utilized to participate in scheduling to save electricity costs for 5G base station operators. Under the incentive of time-of-use electricity prices in the distribution network, the microgrid operator and the 5G base station operator formulate equipment output and energy storage charge-discharge scheduling plans, which can achieve peak shaving and valley filling in the distribution network. By using master-slave game optimization, the benefit balance of the distribution network and the 5G base station cluster system of multiple microgrids can be achieved. Through the power mutual assistance between multiple microgrids, the local consumption capacity of new energy and the operating efficiency of each microgrid are improved. By calculating the cost sharing problem of the microgrid-5G base station alliance using the Shapley value, the fairness of each subject participating in the cooperative game is ensured. Description of the Drawings

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0062] Figure 1 It is a flowchart of a cooperative optimization control method for multiple microgrids and 5G base stations to improve the power supply quality of a photovoltaic-integrated distribution network.

[0063] Figure 2 It is a game scheduling architecture diagram of an active distribution network.

[0064] Figure 3 It is a power data diagram of new energy and load in a microgrid.

[0065] Figure 4 It is a communication load rate data diagram of a 5G base station cluster.

[0066] Figure 5 It is a comparison diagram of the net load curves of the distribution network.

[0067] Figure 6 It is a power response result diagram of a 5G base station cluster.

[0068] Figure 7 It is a result diagram of the equipment output of each microgrid. Detailed Embodiments

[0069] To make the above objects, features, and advantages of the present invention more understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0070] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0071] Secondly, as used herein, an "embodiment" or "embodiments" refers to specific features, structures, or characteristics that may be included in at least one implementation of the present invention. The phrase "in an embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or selectively exclusive embodiment from other embodiments.

[0072] Embodiment 1

[0073] Referring to Figure 1 - Figure 2 , a collaborative optimization control method for multi - microgrids and 5G base stations to improve the power supply quality of photovoltaic - integrated distribution networks is provided for the first embodiment of the present invention, including:

[0074] S1: Analyze the load characteristics of 5G base stations to establish a scheduling model for the backup energy storage, variable - frequency air conditioners, and communication load transfer power of the 5G base station cluster;

[0075] Specifically, analyze the load characteristics of 5G base stations, explore the demand response potential of 5G base stations, and establish a scheduling model for the backup energy storage, variable - frequency air conditioners, and communication load transfer power of the 5G base station cluster;

[0076] 5G base stations mainly consist of two categories: communication equipment and auxiliary equipment. The communication equipment mainly includes active antenna units (AAUs), base - band units (BBUs), and transmission equipment (Pass and transmissions equipment of Network, PTN), which are mainly responsible for the transmission, conversion, and reception of wireless signals. The auxiliary equipment mainly includes power supply equipment, monitoring equipment, lighting equipment, air - conditioning equipment, etc. Among them: The power of the active antenna unit AAU device is linearly related to the communication load of the base station, and its power can be adjusted by transferring the communication load to adjacent base stations; The air - conditioning system that ensures the cooling of equipment in the computer room also has a certain degree of adjustment flexibility, and the power demand response can be achieved by controlling its power consumption within the controllable temperature range; The characteristics of the backup battery of the base station are similar to those of ordinary energy storage, which is used to ensure the normal operation of base - station equipment during power outages, and the charge - discharge of the backup energy storage can be adjusted within the operating range.

[0077] Due to the dense distribution and large quantity of 5G base stations, and the relatively small total power of a single 5G base station compared to the entire power distribution network, clustering and aggregating 5G base stations in different regions is beneficial for simplifying and optimizing the computational complexity of scheduling and improving the optimization efficiency. The power consumption of 5G communication equipment is the main part of the power consumption of the base station, and the power consumption of the AAU in the 5G base station can account for about 90% of the power consumption of the communication equipment at most, and it has a linear relationship with the communication load.

[0078] The power consumption of the communication equipment is divided into static power consumption and dynamic power consumption. Among them, the static power consumption is the fixed part of the power consumption of each device, and the dynamic power consumption is the part directly related to the communication load in the AAU.

[0079] P com =P static +γ com P dyn

[0080] In the formula, P com represents the power consumption of the base station communication equipment; P static , P dyn are the static power consumption and dynamic power consumption of the equipment respectively; γ com is the base station communication load rate, indicating the proportion of resource blocks occupied by the AAU.

[0081] To ensure the communication coverage rate, 5G base stations are densely deployed, and the coverage ranges between adjacent base stations often have partial overlaps. By transferring users located in the common coverage area, the communication load between different base stations is changed to achieve energy transfer in the spatial dimension.

[0082] Since the communication loads between adjacent base stations usually have similarities, the communication power migration between each other is considered in units of base station clusters, without considering the connection relationship between a single base station and users.

[0083] At time t, the communication load of adjacent 5G base station clusters is transferred, and the transfer power between base station clusters needs to meet certain constraint conditions: the power transferred into base station cluster i cannot exceed its remaining load rate, and the power transferred out cannot exceed its load rate; at the same time, the communication load transfer power between base station cluster i and base station cluster j needs to be less than the maximum transferable power of the two, which is expressed as:

[0084]

[0085] In the formula, is the total communication load transfer power of base station cluster i at time t; is the dynamic power consumption of the communication equipment of base station cluster i at time t; is the power of the communication load transferred between base station cluster i and base station cluster j at time t. Greater than 0 means transferring to this cluster, and less than 0 means transferring to other clusters; λ iRepresents the proportion of the transferable load of base station cluster i, λ j Represents the proportion of the transferable load of base station cluster j, which is related to the overlapping proportion of the coverage range.

[0086] The air-conditioning - building system has a certain heat storage and cold storage capacity, which can convert electrical energy into heat energy for storage within a certain period of time and adjust the temperature within an allowable range without affecting the normal operation of the equipment. Therefore, the overall composed of variable-frequency air conditioners and base station computer rooms can be equivalently regarded as a virtual air-conditioning energy storage and incorporated into the optimal scheduling model. A simplified first-order equivalent thermal parameter (ETP) model is used to represent the thermodynamic dynamic process of the air-conditioning - computer room, and the air conditioner in the computer room operates at the set temperature T set And the power without external control is the reference power of the air conditioner:

[0087]

[0088] In the formula, P ACbase Represents the reference power of the base station air conditioner; T out Represents the indoor and outdoor temperatures of the computer room; R is the equivalent thermal resistance of the base station computer room; Q m Is the heating power of the equipment in the base station computer room; a and b are the relationship coefficients between the refrigeration power and the electric power of the air conditioner.

[0089] Using the current heat ratio of the computer room to represent the state of charge SOC of the air-conditioning virtual energy storage, and the thermal power of the computer room equipment to represent the charging and discharging power of the air-conditioning virtual energy storage, the following operating constraints are established for the variable-frequency air-conditioning virtual energy storage in the base station:

[0090]

[0091] α = e -Δt / RC ,

[0092] In the formula, Is the state of charge of the air-conditioning energy storage of base station i at time t; T max Represents the maximum temperature set for the computer room, T min Represents the minimum temperature set for the computer room, Δt is the scheduling period, and C is the equivalent heat capacity of the base station computer room; Is the state of charge of the air-conditioning energy storage of base station i at time t + 1; Is the charging power of the air-conditioning energy storage of base station i at time t, Represents the discharging power of the air-conditioning energy storage of base station i at time t; Is the state of charge at the initial moment of the air-conditioning energy storage within the scheduling period, Is the state of charge at the end moment of the air-conditioning energy storage within the scheduling period, Represents the maximum value of the electric power of the variable-frequency air conditioner in the base station, Denote the reference power of the variable-frequency air conditioner at base station \(i\) at time \(t\). Denote the minimum value of the electric power of the variable-frequency air conditioner at the base station.

[0093] The main function of the backup energy storage of 5G base stations is to serve as a backup power supply to ensure the uninterrupted power supply of the base stations to maintain the communication requirements of the base stations. Therefore, the adjustable capacity of the 5G base station backup energy storage needs to reserve a part on the basis of the rated capacity to meet the backup power requirements of the DC equipment of the base stations. On this basis, an operation model of the backup energy storage battery is established:

[0094]

[0095] In the formula, \(\rho\) is the charge-discharge efficiency of the energy storage battery; The charging power and discharging power of the energy storage of base station \(i\) at time \(t\) respectively; \(\theta\) i,t Is a Boolean variable representing the charge-discharge state, ensuring that the energy storage cannot be charged and discharged simultaneously; Is the maximum charge-discharge power of the energy storage battery at the energy storage of base station cluster \(i\) at time \(t\); Are the upper and lower limits of the battery charge of the energy storage of base station cluster \(i\) for safety, Is the spare capacity reserved for the backup power supply of communication equipment by the energy storage of base station cluster \(i\) at time \(t\), taking And The maximum value of is taken as the minimum value of the operation of the backup energy storage power; Is the real-time power of the communication equipment of base station cluster \(i\); Is the power outage backup time of 5G base stations, usually taking 3h; Are the state of charge of the energy storage battery of base station cluster \(i\) at the initial moment and the end moment within the scheduling period respectively.

[0096] S2: For the multi-microgrid scenario under the same distribution network, establish a master-slave game model of the distribution network and the multi-microgrid-5G base station cluster alliance;

[0097] Specifically, for the multi-microgrid scenario under the same distribution network, a master-slave game model of the distribution network and the multi-microgrid-5G base station cluster alliance is established. The upper layer is led by the distribution network operator, formulating time-of-use electricity prices to implement peak shaving scheduling; the lower layer of each microgrid operator and 5G base station operator establish an alliance cooperation game, as followers to respond to the electricity price adjustment of equipment power consumption and power interaction plan, and allocate the benefits of the cooperation game through the Shapley method; the game scheduling architecture of the multi-microgrid power distribution system including 5G base station clusters is as Figure 2 Shown, and it is divided into two layers:

[0098] As the leader in the Stackelberg game, the upper-layer distribution system operator (DSO) aims to smooth the load curve and reduce operating costs. The DSO includes wind turbines (WT), photovoltaic (PV) systems, and conventional loads. Multiple micro-grids (MG) with distributed energy resources are connected to the grid as loads or power sources, and the 5G base stations within each micro-grid form a 5G base station cluster for unified regulation. To reduce the peak-to-valley difference of the net load in the distribution network, the DSO formulates time-of-use electricity prices for buying and selling electricity with each lower-layer MG to encourage its participation in demand response. In case of power shortages, electricity is purchased from the superior grid.

[0099] The upper-layer distribution network optimization model aims to minimize the operating cost of the DSO, with the electricity purchase and sale prices with the micro-grid and 5G base stations as decision variables:

[0100] F DSO =min(C grid +C peak -C MGO -C BSO )

[0101]

[0102] In the formula, F DSO is the operating cost of the upper-layer distribution system operator DSO, C grid is the electricity purchase cost of the distribution network from the external grid; C peak is the penalty cost for the peak-to-valley difference of the net load; C MGO is the operating cost of the micro-grid operator; C BGO is the operating cost of the communication network operator; is the electricity selling price of the grid at time t, is the electricity purchase and sale power of the distribution network from the superior grid at time t, is the peak-to-valley difference penalty index; is the net load power of the distribution network at time t, is the base load power at time t, is the output power of the wind turbine at time t, is the output power of the deployed distributed PV at time t; is the electricity purchase and sale power between base station cluster i and the distribution network at time t, is the electricity purchase and sale power between base station cluster i and the micro-grid at time t, n is the number of micro-grids, C MG is the electricity purchase and sale revenue between the distribution network and the micro-grid; is the electricity purchase and sale price between base station cluster i and the distribution network at time t, is the electricity purchase and sale price between base station cluster i and the micro-grid at time t.

[0103] The constraint conditions of the upper-layer distribution network optimization model include power balance constraint and distribution network power purchase and sale price constraint;

[0104] (1) The expression of the power balance constraint is:

[0105] P t L -P t grid = 0

[0106] (2) The expression of the distribution network power purchase and sale price constraint is:

[0107]

[0108] In the formula, is the lower limit of the transaction price between the distribution network and microgrid i at time t, is the upper limit of the transaction price between the distribution network and microgrid i at time t.

[0109] At the lower layer, each microgrid operator (MGO) and basestation operator (BSO) act as game followers, forming a basestation microgrid alliance, and participating in the demand response of the distribution network according to the power purchase and sale price of the upper-layer distribution network to maximize the operation benefit of the alliance.

[0110] The MGO aims to cooperate through mutual power interaction to reduce their respective operating costs and absorb new energy power; the BSO coordinates the output plans of each microgrid to regulate the equipment power of each 5G basestation cluster to reduce the power consumption cost of the basestations themselves.

[0111] For the lower-layer multi-microgrid basestation cluster alliance cooperation game model, the multi-microgrid basestation cluster alliance adjusts the output and power interaction plans of each entity within the alliance with the goal of maximizing the operation benefit of the multi-microgrid basestation cluster alliance according to the optimal time-of-use price strategy formulated by the DSO. Among them, the power interaction costs between the microgrid and the basestation cluster cancel each other out. Therefore, the objective function is expressed as the minimum operating cost of the multi-microgrid basestation cluster alliance:

[0112]

[0113] C MT = C MT,fuel + C MT,env

[0114]

[0115] In the formula, F MGO is the operating cost of the multi-microgrid basestation cluster alliance, is the power interaction cost between microgrid i and the upper-layer distribution network operator DSO. are respectively the operation and maintenance cost of the micro gas turbine in microgrid i and the battery loss cost of the base station cluster energy storage; C MT,fuel and C MT,env are respectively the fuel cost and environmental pollution control cost of the micro gas turbine, a M , b M and c M are the power generation cost coefficients of the controllable power sources in the microgrid; p is the number of pollutant types, T is the pollution time, α cp is the pollutant emission amount, β cp is the pollutant emission cost. is the output of the micro gas turbine in microgrid i at time t, and δ is the battery loss coefficient of the base station energy storage.

[0116] The constraint conditions of the lower-layer multi-microgrid base station cluster alliance cooperation game model include power balance constraint, microgrid PCC interaction power constraint, gas turbine output upper and lower limit constraint, backup energy storage constraint of the 5G base station cluster, air-conditioning energy storage constraint, and communication load transfer constraint;

[0117] (1) Power balance constraint

[0118]

[0119]

[0120] In the formula, respectively represent the photovoltaic and wind turbine power of microgrid i at time t; respectively represent the load of microgrid i, the power of the micro gas turbine, and the load power of base station cluster i at time t; the base station load power consists of the base station communication load, the air-conditioning reference power, and the power of other devices.

[0121] (2) Microgrid PCC interaction power constraint

[0122] The interaction power between the microgrid and the ADN and the mutual assistance power between microgrids are realized through the connection line between the microgrid and the distribution network, and the connection line power constraint needs to be satisfied.

[0123]

[0124] In the formula, is the maximum transmission power of the connection line between the microgrid and the distribution network, is the power transmitted between microgrids i at time t, a positive value indicates incoming, and a negative value indicates outgoing.

[0125] (3) Gas turbine output upper and lower limit constraint

[0126]

[0127] In the formula, is the lower power limit of the micro gas turbine of microgrid i, is the lower power limit of the micro gas turbine of microgrid i.

[0128] (4) 5G base station cluster communication load transfer constraint.

[0129] (5) 5G base station cluster backup energy storage constraint.

[0130] (6) 5G base station cluster air conditioning energy storage constraint.

[0131] Based on the Shapley value method for alliance revenue distribution, it is a necessary condition for the operation cost of each subject participating in the game to be less than the operation cost before cooperation after cooperation. The Shapley value is used to solve the distribution problem of cooperation revenue. For a cooperative game participated by n subjects, the Shapley value of each subject is as follows:

[0132]

[0133] In the formula, v(i) is the Shapley value of the i-th subject; |s| is the number of sub-alliances in alliance s; ω(|s|) is the weight factor; n is the set of subjects participating in the game; v(s) is the cooperation surplus of sub-alliance s, and v(s / i) is the cooperation surplus of sub-alliance excluding member i.

[0134] Embodiment 2

[0135] Refer to Figure 3 – Figure 7 , which is the second embodiment of the present invention. This embodiment provides a multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic distribution network. In order to verify the beneficial effects of the present invention, scientific demonstrations are carried out through economic benefit calculations and simulation experiments.

[0136] Assume that 3 microgrids and 3 5G base station clusters are connected in a commercial area, an industrial area, and a residential area in a certain area of the distribution network. The loads and distributed power generations of each microgrid are as Figure 3 , and the 5G base stations of each microgrid form a 5G base station cluster. The numbers of 5G base stations configured in each cluster are 40, 70, and 90 respectively. The predicted curve of the base station communication load rate is shown in Figure 4 . Among them, the static power consumption of a single base station communication device is 2.5 kW, the dynamic power consumption is 1.5 kW, the backup energy storage capacity of the base station is 19.2 kWh, the maximum charge and discharge power is 4.5 kW, and the maximum power of the base station air conditioner is 3 kW.

[0137] Three scenarios are set for comparison to verify the impact of the multi-load demand response of 5G base stations on the distribution network dispatching:

[0138] Scenario 1: BSO and MGO do not participate in demand response, and DSO supplies power at a fixed purchase and sale electricity price;

[0139] Scenario 2: DSO sets a fixed time-of-use electricity price, and MGO and BSO form a cooperative game, optimize their own electricity consumption according to the electricity price, and use the Shapley value to distribute the benefits;

[0140] Scenario 3: Adopt a master-slave game dispatching strategy with DSO as the leader and the MGO-BSO alliance as the follower. MGO and BSO conduct cooperative alliance revenue distribution, that is, the method described in this paper.

[0141] When the iteration reaches equilibrium, the purchase electricity prices of DSO and the microgrid are shown in Table 1. The selling electricity price is taken as 0.8 times the purchase electricity price. The time-of-use electricity price of the distribution network is divided into: Valley 1: 00-7:00, 23:00-1:00; Flat 7:00-10:00, 15:00-18:00, 21:00-23:00; Peak 10:00-15:00, 18:00-21:00. The selling electricity price of the superior power grid is taken as 0.65 yuan / kWh.

[0142] Table 1 Time-of-use purchase electricity price of the distribution network

[0143] Time T Microgrid 1 Microgrid 2 Microgrid 3 Valley 0.493 0.417 0.331 Flat 0.791 0.713 0.479 Peak 1.126 0.953 0.863

[0144] As shown in Table 2, in Scenario 3, the operating costs of DSO and MGO-BSO decreased by 1018.3 and 1229.9 respectively compared with Scenario 1. In Scenario 2, MGO-BSO reduced its own cost by using the time-of-use electricity price, but the cost of DSO increased. This is because the operating cost of DSO was not optimized, resulting in an unreasonable time-of-use electricity price setting. The net load curves of the distribution network in Scenario 3 and Scenario 1 are as Figure 5 shown. After the microgrid and the 5G base station cluster respond to the dynamic time-of-use electricity price of the distribution network, the peak power and valley power during a typical day decreased by 438.9 kW and increased by 300.7 kW respectively. The peak-valley difference of the net load of the distribution network decreased significantly, that is, the time-of-use electricity price mechanism can well improve the net load curve of the distribution network.

[0145] Table 2 Comparison of the operating costs of each subject in the master-slave game under different scenarios

[0146] DSO Cost / Yuan MGO - BSO Cost / Yuan Scenario 1 35125.5 14744 Scenario 2 35827.8 14275.4 Scenario 3 34107.2 13514.4

[0147] Based on the Shapley value allocation method, it can reflect the rationality and fairness of the allocation, and allocate the alliance benefits according to the contribution degree of each member in the alliance cooperation. As shown in Table 3, the total operating cost after the alliance of each MGO and BSO is 13,514.4 yuan, which is less than the sum of their independent operating costs of 14,744 yuan, and the costs shared by each entity after forming the alliance are less than their respective independent operating costs, indicating that the cooperation conditions can be met. Among them, BSO regulates the power dispatching of each 5G base station cluster to participate in the microgrid, with the greatest contribution and the most cost savings; at the same time, the power mutual assistance of MG3 to MG1 also saves a lot of costs for MGO1 and MGO3. Due to its good style complementarity, MGO2 has relatively less cost savings allocated.

[0148] Table 3 Comparison of the operating costs of each entity in the cooperative game under Scenario 3

[0149] Before Alliance / Yuan After Alliance / Yuan Cost Savings / Yuan MG1 3812.7 3648.6 164.1 MG2 2423.4 2347.5 75.9 MG3 625.6 382.1 243.5 BSO 7882.3 7136.2 746.1 Total 14744 13514.4 1229.9

[0150] This method is used to solve the load response power of each 5G base station cluster. Figure 6 It is the demand response power plan for 3 5G base station clusters. It can be seen that each 5G base station cluster uses the time-of-use electricity price of the distribution network for low charging and high discharging of energy storage or communication power transfer to save operating costs. The charging and discharging of the base station air conditioner and energy storage are basically concentrated at the peak and valley times. Although 15:00 - 18:00 and 9:00 are normal times, in order to meet the discharge demand at the peak time, a certain charging power is also increased. Clusters 1 and 2 are located in the commercial area and industrial area respectively, and the electricity price is relatively high. Therefore, at relevant times, the communication load of the communication equipment of the 5G base station cluster is transferred to a certain extent and transferred to Cluster 1 located in the residential area to save electricity costs.

[0151] The optimized dispatching output of each device of Microgrid 1, 2, and 3 is as Figure 7 shown. Among them, MG1 and MG3 have a large proportion of photovoltaic power, and it does not match the load power. When the photovoltaic output is large at noon and the electricity price is high, electricity is sold to the distribution network to obtain benefits; at the same time, the load power of MG1 is greater than that of the new energy generation equipment, while MG3 is the opposite, and the configured new energy output is greater than the load demand. Therefore, when the photovoltaic output is insufficient in the early morning and at night, MG3 will transfer its surplus power to MG1. Under the combined action of photovoltaic and wind power, the electricity consumption plan of MG2 is relatively balanced as a whole, and the demand response of the 5G base station cluster and the power mutual assistance between microgrids only play an auxiliary role. In addition, during the peak electricity consumption period, MG1 and MG2 start gas turbines to balance electricity consumption to avoid their high electricity prices, further reducing the electricity consumption cost of the microgrid.

[0152] This embodiment also provides a computer device, which is applicable to the scenario of the collaborative optimization control method of multi-microgrids and 5G base stations for improving the power supply quality of photovoltaic distribution networks, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement all or part of the steps of the method described in the embodiments of the present invention as proposed in the above embodiments.

[0153] This embodiment also provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, it executes the method in any optional implementation manner of the above embodiments. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0154] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0155] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic-integrated distribution network, characterized in that: Including, Analyze the load characteristics of 5G base stations to establish a scheduling model for the backup energy storage, variable-frequency air conditioners, and communication load transfer power of 5G base station clusters; The 5G base stations include communication devices and auxiliary devices; the communication devices include active antenna units, baseband units, and transmission devices for wireless signal transmission, conversion, and transceiver; the auxiliary devices include power supply devices, monitoring devices, lighting devices, and air conditioning devices; For the multi-microgrid scenario under the same distribution network, establish a master-slave game model for the distribution network and the multi-microgrid-5G base station cluster alliance; the master-slave game model for the distribution network and the multi-microgrid-5G base station cluster alliance includes an upper-layer distribution network optimization model and a lower-layer multi-microgrid base station cluster alliance cooperative game model; Solve the master-slave game model for the distribution network and the multi-microgrid-5G base station cluster alliance, and perform cooperative optimization control according to the model solution results.

2. The multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic-integrated distribution network according to claim 1, wherein: The establishment of the scheduling model for the backup energy storage, variable-frequency air conditioners, and communication load transfer power of 5G base station clusters includes the following: The 5G base station backup energy storage is used as a backup power supply to ensure the uninterrupted power supply of the base station to maintain the communication requirements of the base station. Establish an operation model for the backup energy storage battery, expressed as: In the formula, is the battery power of the base station cluster i at time t, and ρ is the charge-discharge efficiency of the energy storage battery; is the charging power of the energy storage battery of the base station cluster i at time t, is the discharging power of the energy storage battery of the base station cluster i at time t; is the maximum charge-discharge power of the energy storage battery of the base station cluster i at time t; θ i,t Boolean variable indicating the charge and discharge state; Real-time power of the communication device of base station cluster i at time t; Lower safety limit of the battery charge of base station cluster i, Upper safety limit of the battery charge of base station cluster i; Reserved spare capacity for power backup of the communication device by the energy storage battery of base station cluster i at time t; Power outage backup time of 5G base station; State of charge at the initial and end times within the scheduling period of the energy storage battery of base station cluster i, respectively; The overall combination of the variable-frequency air conditioner and the base station computer room is equivalently regarded as a virtual air conditioner energy storage and incorporated into the optimal scheduling model. A simplified first-order equivalent thermal parameter model is used to represent the thermodynamic dynamic process of the air conditioner-computer room. The power of the computer room air conditioner operating at the set temperature T set and without external control is taken as the reference power of the air conditioner, which is expressed as: where, PA Cbase represents the reference power of the base station air conditioner; T out represents the indoor and outdoor temperatures of the computer room; R is the equivalent thermal resistance of the base station computer room; Q m is the heating power of the equipment in the base station computer room; a and b are the relationship coefficients between the air-conditioning cooling power and the electric power; Use the current heat ratio of the computer room to represent the state of charge (SOC) of the air conditioner virtual energy storage, and the thermal power of the computer room equipment to represent the charging and discharging power of the air conditioner virtual energy storage. Establish the operation constraints for the variable-frequency air conditioner virtual energy storage of the base station as: Wherein, is the state of charge of the air - conditioner energy storage at base station i at time t; T max represents the maximum temperature set for the computer room, T min represents the minimum temperature set for the computer room, Δt is the scheduling period, and C is the equivalent heat capacity of the base - station computer room; is the state of charge of the air - conditioner energy storage at base station i at time t + 1; is the charging power of the air - conditioner energy storage at base station i at time t, represents the discharging power of the air - conditioner energy storage at base station i at time t; is the state of charge at the initial moment of the air - conditioner energy storage within the scheduling period, is the state of charge at the end moment of the air - conditioner energy storage within the scheduling period, represents the maximum value of the electric power of the base - station variable - frequency air - conditioner, represents the reference power of the variable - frequency air - conditioner at base station i at time t, represents the minimum value of the electric power of the base - station variable - frequency air - conditioner; Divide the power consumption of communication devices into static power consumption and dynamic power consumption, and the expression is: P com = P static + γ com P dyn Wherein, P com represents the power consumption of the base station communication device; P static , P dyn are respectively the static power consumption and the dynamic power consumption of the base station communication device; γ com is the load rate of the base station communication device, indicating the proportion of resource blocks occupied by the active antenna unit AAU; Change the communication load between different base stations by transferring users in the common coverage area. Consider the communication power migration between base stations in units of base station clusters, and do not consider the connection relationship between a single base station and users. At time t, transfer the communication load of adjacent 5G base station clusters, expressed as: Wherein, is the total communication load transfer power of base station cluster i at time t; is the dynamic power consumption of the communication equipment of base station cluster i at time t; is the power of the communication load transferred between base station cluster i and base station cluster j at time t; λ i represents the proportion of the transferable load of base station cluster i, λ j represents the proportion of the transferable load of base station cluster j.

3. The multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic distribution network according to claim 2, wherein: The upper-layer distribution network optimization model includes the following: The upper-layer distribution network optimization model is constructed with the minimum operating cost of the upper-layer distribution network operator DSO as the objective function and the purchase and sale electricity prices of microgrids and 5G base stations as the constraint conditions; The objective function of the upper-layer distribution network optimization model is expressed as: Where F DSO is the operating cost of the upper-level distribution network operator DSO, and C grid is the power purchase cost of the distribution network from the external power grid; C peak is the penalty cost for the peak-valley difference of the net load; C MGO is the operating cost of the microgrid operator; C BGO is the operating cost of the communication network operator; is the selling electricity price of the superior power grid at time t, is the power purchase and selling power of the distribution network from the superior power grid at time t, is the peak-valley difference penalty index; is the net load power of the distribution network at time t, is the base load power at time t, is the output power of the wind turbine at time t, is the output power of the deployed distributed PV at time t; is the power purchase and selling power between the base station cluster i and the distribution network at time t, is the power purchase and selling power between the base station cluster i and the microgrid at time t, and n is the number of microgrids.

4. The multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic-integrated distribution network according to claim 3, characterized in that: The constraint conditions of the upper-layer distribution network optimization model include power balance constraints and distribution network purchase and sale electricity price constraints. The expression of the power balance constraint is: The expression of the distribution network purchase and sale electricity price constraint is: wherein, is the lower limit of the trading electricity price between the base station cluster i and the distribution network at time t, is the upper limit of the trading electricity price between the base station cluster i and the distribution network at time t.

5. The multi - microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic - integrated distribution network according to claim 4, characterized in that: The lower-layer multi-microgrid base station cluster alliance cooperative game model includes the following: The lower-layer multi-microgrid base station cluster alliance cooperative game model takes the minimum operating cost of the multi-microgrid base station cluster alliance as the objective function, and uses the Shapley value method to solve the distribution problem of cooperative benefits. The objective function of the lower-layer multi-microgrid base station cluster alliance cooperative game model is expressed as: Where, F MGO is the operation cost of the multi - microgrid base station cluster alliance, is the power interaction cost between the microgrid i and the upper - layer distribution network operator DSO, are respectively the operation and maintenance cost of the micro - gas turbine of the microgrid i and the loss cost of the energy storage battery of the base station cluster; C MT,fuel and C MT,env are respectively the fuel cost and the environmental pollution control cost of the micro - gas turbine, a M , b M and c M are the power generation cost coefficients of the controllable power sources of the microgrid; p is the number of pollutant types, T is the pollution time, α cp is the pollutant emission amount, β cp is the pollutant emission cost, is the output of the micro - gas turbine of the microgrid i at time t, and δ is the battery loss coefficient of the base station energy storage.

6. The multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic distribution network according to claim 5, characterized in that: The constraint conditions of the lower-layer multi-microgrid base station cluster alliance cooperative game model include power balance constraints, microgrid PCC interaction power constraints, gas turbine output upper and lower limit constraints, backup energy storage constraints of 5G base station clusters, air conditioner energy storage constraints, and communication load transfer constraints; The expression of the power balance constraint is: In the formula, respectively represent the photovoltaic and wind turbine power of microgrid i at time t; respectively represent the load of microgrid i, the power of the micro gas turbine, and the load power of base station cluster i; the base station load power consists of the base station communication load, the air conditioner reference power, and the power of other equipment; The expression of the microgrid PCC interaction power constraint is: wherein, is the maximum transmission power of the connection line between the microgrid and the distribution network; is the power transmitted between microgrids i at time t; The expression of the gas turbine output upper and lower limit constraint is: wherein, is the lower power limit of the micro gas turbine of microgrid i, is the lower power limit of the micro gas turbine of microgrid i.

7. The multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic-integrated distribution network according to claim 6, characterized in that: The solution of the cooperative benefit by using the Shapley value method includes that for a cooperative game participated by n agents, the calculation formula of the Shapley value of each agent is as follows: In the formula, v(i) is the Shapley value of the i-th agent; |s| is the number of sub-coalitions in the coalition s; ω(|s|) is the weight factor; n is the set of agents participating in the game; v(s) is the cooperative surplus of the sub-coalition s, and v(s / i) is the cooperative surplus of the sub-coalition excluding the member i.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the multi-microgrid and 5G base station cooperative optimization control method for improving the power supply quality of the photovoltaic distribution network according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the multi-microgrid and 5G base station cooperative optimization control method for improving the power supply quality of the photovoltaic distribution network according to any one of claims 1 to 7.

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