Communication base station and distribution network collaborative planning method, device, equipment and medium

Abstract

The present invention discloses a method for collaborative planning of communication base stations and distribution networks, comprising the steps of: obtaining the element conditions of communication base station layout and the element conditions of distribution network configuration; obtaining the objective function and constraint conditions of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration, and the objective function and constraint conditions of the deterministic multi-objective optimization model converted from the objective function and constraint conditions of the multi-objective interval optimization model; obtaining the collaborative planning result of the communication base station and distribution network based on the solution of the objective function and constraint conditions of the deterministic multi-objective optimization model. The present application constructs a multi-objective collaborative planning model of the distribution network and the communication base station, and converts it into a deterministic multi-objective optimization problem solution to achieve synergistic efficiency between the communication base station and the distribution network, and obtain comprehensive economic and environmental benefits.

Description

Technical Field

[0001] The present invention relates to the field of electricity management technology, and in particular to a method, device, electronic device and storage medium for collaborative planning of 5G communication base stations and distribution networks. Background Art

[0002] With the rapid rise of 5G digitalization and its applications, the scale of 5G communication base stations, as core infrastructure connecting mobile users with wireless access networks, has seen explosive growth. The electricity consumption of 5G communication base stations is becoming a significant new load on the power system. To address the power consumption issues of 5G communication base stations, two main approaches are currently available. First, addressing the communication equipment level involves developing more energy-efficient hardware and constructing a more efficient cellular network structure. By overcoming key technologies that restrict the energy efficiency of base station communication equipment, overall power consumption can be reduced, thereby improving the grid-friendly integration and low-carbon operation capabilities of base stations. Second, from a system planning perspective, collaborative interaction between the distribution network and 5G communication base stations can be established. By comprehensively optimizing their integration strategies, the flexible response capabilities of the power grid's source, network, and load components can be exploited, thereby jointly promoting coordinated and optimized operation of the entire system. However, addressing the grid integration and low-carbon operation issues of 5G communication base stations through equipment upgrades and energy efficiency improvements is extremely difficult and has significant limitations in engineering applications. In terms of the coordinated optimization between distribution networks and 5G communication base stations, existing technologies only analyze the autonomous energy consumption control and responsiveness of 5G communication base stations to distribution networks at the operational level, and do not analyze the flexible potential of 5G communication base stations to participate in grid interaction from the perspective of medium- and long-term investment planning. There are still few studies considering the coordinated planning of distribution networks and 5G communication base stations. The exploration of coordinated planning issues that comprehensively consider the operational characteristics and internal mechanisms of power grids and communication networks is still insufficient. The proposed model is difficult to effectively adapt to the inherent requirements for the development of distribution systems containing 5G communication base stations under the background of dual carbon emissions, and ignores the impact of various uncertain factors on system decision-making, which may lead to the system's planning scheme not being truly globally optimal.

[0003] Therefore, a collaborative planning method for communication base stations and distribution networks is needed, which can effectively adapt to the multi-objective interval optimization planning method of the two in an uncertain environment to achieve the simultaneous optimization of economic and environmental benefits. Summary of the Invention

[0004] To this end, the present invention provides a method, device, electronic device and storage medium for collaborative planning of communication base stations and distribution networks, in an effort to solve or at least alleviate at least one of the above problems.

[0005] According to one aspect of the present invention, a method for collaborative planning of communication base stations and distribution networks is provided. The method constructs an interval uncertainty multi-objective optimization model to jointly optimize the installation of communication base stations and the site selection and sizing of renewable energy sources, and controls the operation of communication base stations and distribution networks in each time period to obtain comprehensive economic and environmental benefits. The method comprises the following steps:

[0006] Obtaining element conditions for communication base station layout and element conditions for distribution network configuration, wherein the element conditions for communication base station layout include energy consumption characteristics, communication capacity characteristics, coverage characteristics, and configurable parameters of communication base station bandwidth resources, and the element conditions for distribution network configuration include distribution network transmission parameters and renewable energy distribution parameters;

[0007] Obtaining objective functions and constraints of a multi-objective interval optimization model for communication base station installation and renewable energy site selection and sizing based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration;

[0008] Obtaining, based on the objective function and constraints of the multi-objective interval optimization model for communication base station installation and renewable energy site selection and sizing, the objective function and constraints of a deterministic multi-objective optimization model converted from the objective function and constraints of the multi-objective interval optimization model;

[0009] The collaborative planning result of the communication base station and the distribution network is obtained based on the solution results of the objective function and the constraint conditions of the deterministic multi-objective optimization model.

[0010] Optionally, the step of obtaining element conditions for the layout of communication base stations includes:

[0011] Calculating the total power consumption of a single communication base station based on the energy consumption of fixed loss components of the single communication base station and the energy consumption of the service load of the communication base station, and obtaining the communication base station energy consumption characteristics of the communication base station;

[0012] Obtain the communication capacity characteristics of a single communication base station based on the user received signal noise ratio parameter, communication information transmission rate parameter, communication base station total bandwidth resource and cellular link spectrum efficiency parameter;

[0013] According to the signal coverage area of ​​a single communication base station and the Voronoi diagram algorithm, the candidate point variables of the communication base station are obtained;

[0014] Based on the load status information of the adjacent communication base stations obtained by each communication base station, the configurable parameters of the communication base station bandwidth resources are obtained to control the sleep or start of the corresponding communication base station equipment.

[0015] Optionally, the step of obtaining the objective function and constraint conditions of the multi-objective interval optimization model for the communication base station installation and renewable energy site selection and sizing based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration includes:

[0016] Obtaining an objective function of total investment and operating costs for the element conditions of the communication base station layout and the element conditions of the distribution network configuration;

[0017] According to the objective function of the total investment and operating costs of the elements and conditions of the communication base station layout and the elements and conditions of the distribution network configuration, the equipment installation quantity constraint conditions of the communication base station layout and the constraint conditions of the communication base station operation and the distribution network configuration are obtained.

[0018] Optionally, the step of obtaining the objective function and constraint conditions of a deterministic multi-objective optimization model converted from the objective function and constraint conditions of the multi-objective interval optimization model for communication base station installation and renewable energy site selection and sizing includes:

[0019] The objective function of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy is converted into the objective function of a deterministic multi-objective optimization model by using an interval order relation method;

[0020] The constraints of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy are converted into constraints of a deterministic multi-objective optimization model through the interval possibility method.

[0021] Optionally, the step of obtaining the objective function and constraint conditions of the deterministic multi-objective optimization model converted from the objective function and constraint conditions of the multi-objective interval optimization model further includes:

[0022] Obtaining input line electrical parameters of the distribution network and renewable energy site selection and sizing parameter information;

[0023] According to the input line electrical parameters of the distribution network and the renewable energy site selection and sizing parameter information, the relevant parameters of NSGA-II are set, and the initial population is generated through a random function;

[0024] Calculating the interval boundaries of the objective function and constraint conditions of the multi-objective interval optimization model by using interval analysis method based on the individuals of the initial population;

[0025] According to the interval boundaries of the objective function of the deterministic multi-objective optimization model, the midpoint and radius of the objective function of the multi-objective interval optimization model are calculated by the interval order relation, and the possibility of the constraint conditions of the multi-objective interval optimization model is obtained by the interval possibility degree;

[0026] The objective function and constraints of the deterministic multi-objective optimization model obtained by transforming the objective function and constraints of the multi-objective interval optimization model are obtained through the midpoint and radius of the objective function of the multi-objective interval optimization model and the possibility of the constraints of the multi-objective interval optimization model.

[0027] Optionally, the step of obtaining the solution results of the objective function and constraint conditions of the deterministic multi-objective optimization model includes:

[0028] A fast non-dominated sorting method is used to stratify the population of the deterministic multi-objective optimization model;

[0029] According to the population stratification of the deterministic multi-objective optimization model, obtaining a ranking result of the population stratification of the deterministic multi-objective optimization model;

[0030] Determining the number of non-dominated layers of the population stratification of the deterministic multi-objective optimization model according to the sorting result of the population stratification of the deterministic multi-objective optimization model;

[0031] Obtaining the fitness of individuals in the non-dominated layers of the population stratification of the deterministic multi-objective optimization model according to the non-dominated layers of the population stratification of the deterministic multi-objective optimization model;

[0032] Obtaining a maximum evolutionary generation of the deterministic multi-objective optimization model according to the fitness of individuals in the non-dominated layers of the population stratification of the deterministic multi-objective optimization model;

[0033] The optimal solution set of the deterministic multi-objective optimization model is obtained according to the comparison result of the maximum evolutionary generation of the deterministic multi-objective optimization model and the set specified value.

[0034] Optionally, the step of stratifying the population of the deterministic multi-objective optimization model using a fast non-dominated sorting method includes:

[0035] Obtaining all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model;

[0036] Obtaining, according to all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model, a first non-dominated layer of all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model;

[0037] Obtaining, based on the first non-dominated layer, non-inferior solution individuals of the deterministic multi-objective optimization model outside the first non-dominated layer;

[0038] Obtaining, based on the non-inferior solution individuals of the deterministic multi-objective optimization model other than the first non-dominated layer, the second non-dominated layer including the non-inferior solution individuals of the deterministic multi-objective optimization model other than the first non-dominated layer;

[0039] Repeat the above steps until the hierarchical sorting of all population individuals in the deterministic multi-objective optimization model is completed and the number of non-dominated layers is determined.

[0040] According to another aspect of the present invention, a collaborative planning device for communication base stations and distribution networks is provided. The device constructs an interval uncertainty multi-objective optimization model to jointly optimize the installation of communication base stations and the site selection and sizing of renewable energy sources, controls the operation of communication base stations and distribution networks in each time period, and obtains comprehensive economic and environmental benefits. The device includes:

[0041] an element acquisition module, configured to acquire element conditions for the layout of communication base stations and element conditions for the configuration of distribution networks, wherein the element conditions for the layout of communication base stations include energy consumption characteristics, communication capacity characteristics, coverage characteristics, and configurable parameters of communication base station bandwidth resources, and the element conditions for the configuration of distribution networks include power transmission parameters of distribution networks and renewable energy distribution parameters;

[0042] a parameter determination module for obtaining, based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration, the objective function and constraints of the multi-objective interval optimization model for the communication base station installation and renewable energy site selection and sizing; and obtaining, based on the objective function and constraints of the multi-objective interval optimization model for the communication base station installation and renewable energy site selection and sizing, the objective function and constraints of a deterministic multi-objective optimization model converted from the objective function and constraints of the multi-objective interval optimization model;

[0043] The planning completion module is used to obtain the collaborative planning results of the communication base station and the distribution network based on the solution results of the objective function and constraint conditions of the deterministic multi-objective optimization model.

[0044] According to another aspect of the present invention, a computing device is provided, comprising: one or more processors; and a memory; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, and the one or more programs include instructions for executing any of the methods described above in the collaborative planning method of communication base stations and distribution networks.

[0045] According to another aspect of the present invention, a computer-readable storage medium storing one or more programs is provided, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute any one of the methods for collaborative planning of communication base stations and distribution networks as described above.

[0046] According to the scheme based on the collaborative planning of communication base stations and distribution networks of the present invention, by obtaining the element conditions of the layout of communication base stations and the element conditions of the distribution network configuration; according to the element conditions of the layout of communication base stations and the element conditions of the distribution network configuration, the objective function and constraint conditions of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy are obtained; according to the objective function and constraint conditions of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy, the objective function and constraint conditions of the deterministic multi-objective optimization model converted from the objective function and constraint conditions of the multi-objective interval optimization model are obtained; according to the solution results of the objective function and constraint conditions of the deterministic multi-objective optimization model, the collaborative planning results of the communication base station and the distribution network are obtained. This application constructs a multi-objective collaborative planning model of the distribution network and the communication base station by comprehensively considering the constraint conditions of the communication base station, and converts it into a deterministic multi-objective optimization problem solution through interval order relations and possibility, so as to achieve synergistic efficiency between the communication base station and the distribution network and obtain comprehensive economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] To achieve the above and related purposes, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles disclosed herein may be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The above and other objects, features, and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout this disclosure, the same reference numerals generally refer to the same parts or elements.

[0048] Figure 1 A schematic diagram illustrating an architecture for collaborative planning of a communication base station and a distribution network according to an embodiment of the present invention is shown; and

[0049] Figure 2 A schematic diagram of an application scenario according to an embodiment of the present invention is shown; and

[0050] Figure 3 A schematic diagram showing the structure of a computing device 100 according to one embodiment of the present invention; and

[0051] Figure 4 A flowchart of a method 200 for collaborative planning of a communication base station and a distribution network according to an embodiment of the present invention is shown; and

[0052] Figure 5 A schematic structural diagram of a communication base station and distribution network collaborative planning device 300 according to another embodiment of the present invention is shown. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0054] like Figure 1 As shown, Figure 1 This is a schematic diagram of the architecture for collaborative planning of communication base stations and distribution networks. In this invention, a 5G communication base station generally consists of an active antenna unit (AAU), a baseband processing unit (BBU), network transmission equipment, a power supply, and energy storage batteries. As the core equipment of the next-generation wireless access network, a 5G communication base station enables wireless signal transmission between the wired communication network and wireless terminals. The construction and operation of large-scale 5G communication base stations will provide a solid guarantee for the communication needs of mobile users. The number and power consumption of 5G communication base stations will increase significantly. The coordination between communication base stations and the distribution network is achieved by sensing the communication load carried by neighboring base stations. This allows for preliminary adjustments to the communication load connected to the base stations, altering the base station-user connection relationship and shifting load to base stations handling less communication load. A cellular breathing mechanism is used to dynamically adjust the coverage of each base station, thereby putting some base stations into hibernation and reducing the overall power consumption of the base station cluster. Furthermore, through the rational allocation of transmission bandwidth by the base station controller, the energy consumption characteristics of the base station cluster can be further flexibly adjusted while ensuring the quality of service for mobile users. This means that by changing the operating characteristics of the communication domain, the power consumption of each node in the active distribution network and the line flow distribution can be influenced. This helps ease the expansion and transformation of distribution network lines and promotes the integration of renewable energy.

[0055] Figure 2 This is a schematic diagram of an application scenario of an embodiment of the present invention. The communication base station and distribution network collaborative planning method provided in this application can be applied to Figure 2 In the application environment shown, the communication base station and distribution network collaborative planning method is applied to a communication base station and distribution network collaborative planning device. The communication base station and distribution network collaborative planning device is configured in the server 010, or partially configured in the terminal 020 and partially configured in the server 010. The communication base station and distribution network collaborative planning method is completed by the interaction between the terminal 020 and the server 010.

[0056] The terminal 020 and the server 010 can communicate via a network.

[0057] Among them, the terminal 020 can be but is not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server 010 of the present application can be implemented as an independent server or a server cluster composed of multiple servers.

[0058] Figure 3 is a block diagram of an example computing device 100. In a basic configuration 102, computing device 100 typically includes a system memory 106 and one or more processors 104. A memory bus 108 may be used for communication between processor 104 and system memory 106.

[0059] Depending on the desired configuration, the processor 104 can be any type of processor, including, but not limited to, a microprocessor (μP), a microcontroller (μC), a digital signal processing unit (DSP), or any combination thereof. The processor 104 can include one or more levels of cache, such as a level 1 cache 110 and a level 2 cache 112, a processor core 114, and registers 116. An example processor core 114 can include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. An example memory controller 118 can be used with the processor 104, or in some implementations, the memory controller 118 can be an internal part of the processor 104.

[0060] Depending on the desired configuration, the system memory 106 can be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.) or any combination thereof. The system memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some embodiments, the application 122 can be arranged to operate using the program data 124 on the operating system. In some embodiments, the computing device 100 is configured to execute a communication base station and distribution network collaborative planning method 200. The method 200 jointly optimizes the installation of communication base stations and the site selection and sizing of renewable energy by constructing an interval uncertainty multi-objective optimization model, controls the operation of communication base stations and distribution networks in each time period, and obtains comprehensive economic and environmental benefits. The program data 124 includes instructions for executing the method 200.

[0061] The computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via the bus / interface controller 130. Example output devices 142 include a graphics processing unit 148 and an audio processing unit 150. These can be configured to facilitate communication with various external devices such as a display or speakers via one or more A / V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which can be configured to facilitate communication with external devices such as input devices (e.g., a keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., a printer, scanner, etc.) via one or more I / O ports 158. Example communication devices 146 may include a network controller 160, which can be arranged to facilitate communication with one or more other computing devices 162 via a network communication link via one or more communication ports 164.

[0062] A network communication link can be an example of a communication medium. The communication medium can generally be embodied as computer-readable instructions, data structures, program modules in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium. A "modulated data signal" can be a signal in which one or more of its data sets or its changes can be performed in a manner that encodes information in the signal. As non-limiting examples, the communication medium can include wired media such as a wired network or a dedicated line network, and various wireless media such as sound, radio frequency (RF), microwave, infrared (IR) or other wireless media. The term computer-readable medium used herein can include both storage media and communication media. In some embodiments, one or more programs are stored in the computer-readable medium, and the one or more programs include instructions for executing certain methods, such as according to an embodiment of the present invention, the computing device 100 executes the communication base station and distribution network collaborative planning method 200 through the instructions.

[0063] The computing device 100 may be implemented as part of a small portable (or mobile) electronic device, such as a cellular phone, a personal digital assistant (PDA), a personal media player device, a wireless network browsing device, a personal head-mounted device, an application-specific device, or a hybrid device that may include any of the above functions. The computing device 100 may also be implemented as a personal computer including desktop and notebook computer configurations.

[0064] Figure 4 FIG. 1 shows a flow chart of a method 200 for collaborative planning of a communication base station and a distribution network according to an embodiment of the present invention. Figure 4As shown, the method 200 constructs an interval uncertainty multi-objective optimization model to jointly optimize the installation of communication base stations and the site selection and sizing of renewable energy, and controls the operation of communication base stations and distribution networks in each time period to obtain comprehensive economic and environmental benefits.

[0065] Specifically, constructing an interval uncertainty multi-objective optimization model requires comprehensive consideration of system economic and environmental benefit objectives. The economic objective takes into account the costs of both the planning and operation stages. During the investment planning stage, with the goal of minimizing system investment costs, the expansion of power grid lines, the location and number of 5G communication base station installations, and the site selection and capacity of renewable energy are jointly optimized. During the operation and scheduling stage, with the goal of minimizing the annual system operating costs, the operation control strategies of 5G communication base stations and distribution networks are optimized for each time period. Environmental objectives take into account carbon emission mechanisms and enhance the system's low-carbon operation capabilities. For situations where the distribution functions of electricity users, communication loads, and renewable energy output are unknown, an interval method is used to handle uncertainty, and uncertainty modeling and analysis is achieved with the help of variable boundary information, which has good practical significance.

[0066] The communication base station and distribution network collaborative planning method 200 implemented in the present application begins with step S210, obtaining the element conditions for the layout of the communication base station and the element conditions for the configuration of the distribution network. The element conditions for the layout of the communication base station include the energy consumption characteristics, communication capacity characteristics, coverage characteristics and configurable parameters of the communication base station bandwidth resources. The element conditions for the configuration of the distribution network include the transmission parameters of the distribution network and the distribution parameters of renewable energy.

[0067] Specifically, the elements and conditions for the layout of communication base stations mainly include the analysis of the energy consumption characteristics, communication capacity characteristics, coverage characteristics, and the cellular breathing mechanism that the communication base stations need to adopt when providing information services to mobile users.

[0068] Specifically, in one embodiment of the present application, the step of obtaining the element conditions of the communication base station layout includes:

[0069] Calculating the total power consumption of a single communication base station based on the energy consumption of fixed loss components of the single communication base station and the energy consumption of the service load of the communication base station, and obtaining the communication base station energy consumption characteristics of the communication base station;

[0070] Obtain the communication capacity characteristics of a single communication base station based on the user received signal noise ratio parameter, communication information transmission rate parameter, communication base station total bandwidth resource and cellular link spectrum efficiency parameter;

[0071] According to the signal coverage area of ​​a single communication base station and the Voronoi diagram algorithm, the candidate point variables of the communication base station are obtained;

[0072] Based on the load status information of the adjacent communication base stations obtained by each communication base station, the configurable parameters of the communication base station bandwidth resources are obtained to control the sleep or start of the corresponding communication base station equipment.

[0073] Specifically, the energy consumption characteristics of a communication base station mainly include static power consumption and dynamic power consumption. Among them, static power consumption refers to the energy demand that is not related to the business load and output transmission power of the communication base station, and is mainly composed of the fixed losses of the power supply system, BBU baseband unit signal processing and cooling system; while dynamic power consumption refers to the energy demand related to the business load of the communication base station, which is a function of the output transmission power of the communication base station. Therefore, the total power consumption of a single communication base station is It can be expressed as:

[0074]

[0075] Where, Ω BS ,Ω T They are communication base station set and time period set respectively; The power consumption of the power supply system and cooling system, is the power consumption of the communication equipment of the i-th base station in time period t, expressed as:

[0076]

[0077] Where, is the no-load power consumption; Δp i P is the slope of the dynamic power consumption of the transceiver and the load; i sleep is the power consumption of the transceiver in sleep state; I i,t It is a 0-1 variable that indicates the operating state of the transceiver. When it is in working state, its value is 1, and when it is in sleeping state, its value is 0.

[0078] In formula (2) is the output transmission power of the transceiver of the communication base station, which is a function of the signaling power and the user data power. The specific calculation formula is as follows:

[0079]

[0080] Where p OH is the ratio of fixed signaling signal to transmit power; P i max is the maximum transmission power of the i-th base station transceiver; is the maximum bandwidth utilization of the transceiver; k is a weighting factor, which represents the level of base station signaling power under different operating conditions. Its value is related to the operating state of the transceiver and the data transmission bandwidth in the current period; is the amount of bandwidth used for data transmission.

[0081] Specifically, regarding the communication capacity characteristics of the communication base station, during the operation phase of the communication base station group and the distribution network, the operation characteristics of the communication layer and the power layer need to be considered respectively. In the communication network, the number of communication base stations is I, and there are M mobile users of the base stations. Then the connection relationship between the mobile users and the communication base stations is x i,m,t Expressed as:

[0082]

[0083] Where, Ω User is a collection of users; m is the user's serial index.

[0084] In a wireless access network, each mobile user needs to connect to and can only connect to one communication base station, which is expressed as:

[0085]

[0086] The signal-to-noise ratio (SINR) is the ratio of the received effective signal strength to the noise signal strength. The effective signal strength is the signal strength received by the mobile user from the access base station. The noise signal includes interference signals from other base stations and thermal noise interference. The SINR expression for user m after connecting to base station i is:

[0087]

[0088] Where g i,m is the channel gain between communication base station i and user m; σ 2 is the noise power in W.

[0089] g i,m It is related to the signal transmission path loss based on distance, and its expression is:

[0090]

[0091] Where A is the fixed loss value of channel gain; η is the path loss exponent; d0 is the reference distance; d i,m is the distance between the communication base station and the mobile user.

[0092] According to Shannon's theorem, the communication information transmission rate R i,m,t (unit: Mbps) and bandwidth B i,m,t And the expression of signal-to-noise ratio is:

[0093]

[0094] Where M i is the set of users connected to communication base station i.

[0095] To ensure the quality of communication services for mobile users, Ri,m,t Should meet the minimum information transmission rate requirement of user m at time t Right now:

[0096]

[0097] For a single user, it can only communicate and obtain bandwidth resources from the base station to which it is connected. The amount of bandwidth obtained should not exceed the total bandwidth of the connected communication base station. Right now:

[0098]

[0099] For a single base station, the sum of the bandwidth allocated to connected users cannot exceed the total bandwidth that the communication base station can provide, that is:

[0100]

[0101] The bandwidth occupied by each communication base station is:

[0102]

[0103] In addition, in order to ensure the service quality of all communication base stations, the cellular link spectrum efficiency SE also needs to be considered. C , the following conditions must be met:

[0104]

[0105] SE i,m,t ≥γ C ………………………………………………(14)

[0106] Where, γ C Indicates the minimum spectrum efficiency requirement of the cellular link.

[0107] Specifically, regarding the coverage characteristics of communication base stations, based on the energy consumption operation characteristics and communication transmission characteristics of communication base stations, the coordinated planning of communication base stations and distribution networks, including the coordinated joint planning of power grids and communication networks, should meet their respective constraints and operation requirements. For the planning of communication networks containing communication base stations, it is necessary to plan the location and number of base stations under the conditions of comprehensive consideration of signal quality, construction cost, coverage constraints and other network constraints, combined with the distribution network structure and renewable energy distribution. In the communication base station planning problem, the signal coverage constraint is met, and the coverage range of the communication base station represents the maximum distance that the signal transmitted by the communication base station can reach in the downlink. The coverage radius of the communication base station is r, and the corresponding average signal coverage area is Area A covered by mobile user signals total Included in the coverage of each communication base station, the number of communication base stations to be built is:

[0108]

[0109]

[0110]

[0111] The Voronoi diagram theory and the maximum hollow circle strategy are used to plan base stations. The Voronoi diagram is a nearest neighbor partition of space. Given some targets, the space is divided into several regions. Any point in all divided regions is closest to the target in the region.

[0112] The set of β candidate communication base station points on the plane is P = {p1, p2, ..., p β}, where p i Represents any point in the set, d(p,q) is the Euclidean distance between point p and point q, then:

[0113] V(p i )={m|d(m,p i )<d(m,p j ),p i ∈p,p j ∈p,p i ≠p j}………………(18)

[0114] Set V(p)={V(p1),V(p2),…,V(p β )} is the Voronoi diagram of the communication base station layout.

[0115] When the number of base station growth points calculated according to the maximum hollow circle strategy is greater than 1, attention should be paid to the distance d (h) between the two node solutions. i , h j ) and the base station coverage radius r, the threshold ε is given by OK. In d(h i ,h j )≤ε, the nodes corresponding to the hollow circles with smaller radius are deleted, so the Euclidean distance between two adjacent base stations i1 and i2 is Should meet the following requirements:

[0116]

[0117] Sort the maximum hollow circle radius corresponding to the retained nodes from large to small, and determine the node positions corresponding to the first β hollow circles according to the number of newly added communication base stations β, which is the position of the newly added base station, thereby determining the base station variables of each candidate point

[0118] Specifically, regarding the aspects of obtaining load status information of adjacent communication base stations based on each communication base station, obtaining configurable parameters of communication base station bandwidth resources, and controlling the sleep or start of corresponding communication base station equipment, a cellular breathing mechanism is adopted.

[0119] The cellular breathing mechanism enables flexible interaction between communication base stations and the power grid. When the communication base station group is flexibly scheduled, the base station unloading / reallocation scheme is executed in each base station in a fixed order within the cluster and simultaneously executed in all clusters in a synchronized manner. The surrounding environment is sensed by obtaining the load status information of adjacent base stations. The mechanism sets the communication base station load rate threshold to control the number of communication base stations that are shut down. In addition, each communication base station i has a state, which is represented by I i,t =[0,1] indicates on or off, that is, 0 indicates OFF and 1 indicates ON.

[0120] Only when the bandwidth utilization Li is lower than the threshold A T The communication base station can execute the data load distribution / unloading method, at this time I i,t =0, which is expressed as:

[0121]

[0122] Where A T The bandwidth utilization limit for load distribution performed by communication base stations.

[0123] The communication base station selects those communication base stations with high load values ​​but still have sufficient resources to accept new users for load transfer. After the execution is completed, the next communication base station is notified to start executing the allocation plan.

[0124] There are three states of the cellular breathing mechanism of communication base stations: the first is that the base station switches to the sleep state, and the number of base stations in this state is χ0; the second is the base station with unchanged coverage, and the number of base stations in this state is χ1; the third is the base station with expanded coverage after cellular breathing, and the number is χ2.

[0125] χ0+χ1+χ2=β…………………………………………(21)

[0126] Then, considering the connection relationship of mobile users and the bandwidth resource allocation of each communication base station, the real-time operating status of the communication base station group is changed through flexible adjustment to achieve interaction with the active distribution network.

[0127] Step S220: Obtain the objective function and constraint conditions of the multi-objective interval optimization model for the communication base station installation and renewable energy site selection and sizing based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration.

[0128] Specifically, the objective function of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy mainly considers the economic and environmental benefits of the installation of communication base stations, and achieves the goal of minimizing the investment and operating costs of communication base stations and the lowest environmental cost. The objective function can be used to determine the constraints of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy, such as the minimum number of communication base stations installed and the energy supply of the distribution network.

[0129] Specifically, in one embodiment of the present application, the step of obtaining the objective function and constraint conditions of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy sources based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration includes:

[0130] Obtaining an objective function of total investment and operating costs for the element conditions of the communication base station layout and the element conditions of the distribution network configuration;

[0131] According to the objective function of the total investment and operating costs of the elements and conditions of the communication base station layout and the elements and conditions of the distribution network configuration, the equipment installation quantity constraint conditions of the communication base station layout and the constraint conditions of the communication base station operation and the distribution network configuration are obtained.

[0132] Specifically, the objective function of the total investment and operating costs of the elements and conditions for the layout of the communication base stations and the elements and conditions for the configuration of the distribution network is to minimize the total investment and operating costs, that is:

[0133] min f1=C INV +C OPT …………………………………………(twenty two)

[0134] Where C INV is the annualized investment cost, C OPT The annual operating cost.

[0135] In actual application, the investment cost mainly includes the cost of distribution network line transformation, the cost of distributed power supply installation, and the cost of communication base station configuration. The annualized investment cost is:

[0136]

[0137] Where, β Line , β WG , β BS , β TR are the annualized factors for line investment, renewable energy investment, base station investment, and antenna array group investment, respectively; Ω F ,Ω M ,Ω BS ,Ω WTThey are distribution network line collection, line transformation model collection, deployed communication base station collection, and renewable energy unit installation collection; 、c WG 、c BS 、c TR They are the unit cost of the mth type of line, the unit capacity installation cost of renewable energy units, the installation cost of a single communication base station, and the installation cost of a single antenna array group; is the length of line ij; A 0-1 variable indicating whether model m is selected for line ij. Its value is 1 if model m is selected, and 0 otherwise. Install renewable energy capacity for node number d; A 0-1 variable indicating whether a communication base station is deployed at the candidate point d; The number of transmitting and receiving antenna array groups installed for the dth communication base station candidate site.

[0138] The operating costs include: the cost of purchasing electricity from the main grid, the cost of maintaining distributed power generation, the cost of maintaining and operating communication base stations, and the cost of line network losses, which are:

[0139]

[0140] Where τ is the number of days in a year; t and Ω T The time period index number and time period set; c WG-opt 、c BS-opt are the time-of-use electricity price purchased from the main grid during period t, the annual maintenance cost of the wind turbine, and the maintenance cost of the base station communication equipment; Δt is the duration of a single period, which is 1 hour; R ij is the impedance value of line ij; P t Grid The electricity purchased from the main grid; I ij,t is the current value from node i to node j during time period t.

[0141] The objective function of the total investment and operating cost of the elements and conditions for the layout of the communication base stations and the configuration of the distribution network also includes minimizing the impact on the environment, namely:

[0142]

[0143] Where ω represents the carbon emissions per unit of non-renewable energy generation; f is the non-renewable energy consumption coefficient per unit of power generation in the external power grid.

[0144] Specifically, the constraints on the operation of the communication base station and the configuration of the distribution network mainly include:

[0145] During the planning phase, the main constraints are the number of equipment installed in the communication base stations and distribution networks. A maximum of one model can be selected for line reconstruction, which is:

[0146]

[0147] Candidate base station points in communication network planning should meet the following requirements:

[0148]

[0149] The number of transmit and receive antenna array groups installed in each communication base station must be within a certain limit, and the expression is:

[0150]

[0151] Where, α max The maximum number of transmit and receive antenna array groups that can be installed at a single base station.

[0152] The installed renewable energy capacity in the active distribution network system should be less than the maximum allowed installed capacity, expressed as:

[0153]

[0154] Where, The maximum capacity of distributed generation allowed to be installed at node i in the system.

[0155] During the operation phase of the distribution network, the constraints that need to be met for the operation of the distribution network and communication base stations include:

[0156] The number of transmitting and receiving antenna array groups enabled for each base station cannot exceed the number of installed antenna array groups of the base station, and the expression is:

[0157]

[0158] Where, The number of antenna array groups installed at base station d.

[0159] To ensure the safe operation of the active distribution network including communication base stations, the voltage of each node should be maintained within a certain range and should meet the following requirements:

[0160]

[0161] Where U i,t is the voltage value of node i, are the minimum and maximum voltages allowed at node i.

[0162] The power flow constraints that should be satisfied between adjacent lines are:

[0163]

[0164] Where, P ij,t , Q ij,t are the active power and reactive power transmitted on line ij respectively; are the resistance and reactance before line transformation, R ij,m 、X ij,m are the resistance and reactance of the line after transformation.

[0165] According to the law of conservation of energy, each node in the system should satisfy the power balance, which is:

[0166]

[0167] Where, P t grid Purchase power for the main grid; Active power output and reactive power output of renewable energy units; are the active power and reactive power of other loads connected to node i except the communication base station at time t; Provide reactive power for communication base stations.

[0168] The transmission power of each line in the system needs to meet the capacity upper limit constraint, which is:

[0169]

[0170]

[0171] Where, are the maximum capacity limits of the lines respectively.

[0172] The power input from the upper power grid to the active distribution network should meet the power limit, which is expressed as:

[0173] P t G min ≤P t grid ≤P t G max ………………………………………………(36)

[0174] Where, P t G min 、P t G max are the minimum and maximum values ​​of the interaction power between the upper power grids.

[0175] When the installed renewable energy generator sets are actually operating, their active output should not exceed the predicted output value. Assuming that the power factor of the renewable energy generator sets is constant during operation, the relevant constraints are:

[0176]

[0177]

[0178] Where, is the predicted value of the output of renewable energy units; is the power factor angle.

[0179] Step S230, based on the objective function and constraints of the multi-objective interval optimization model for communication base station installation and renewable energy site selection and sizing, obtain the objective function and constraints of the deterministic multi-objective optimization model converted from the objective function and constraints of the multi-objective interval optimization model.

[0180] The deterministic multi-objective optimization model for transforming the objective function and constraint conditions of the multi-objective interval optimization model includes objective function transformation and constraint condition transformation.

[0181] For the objective function conversion, the algorithm is based on:

[0182] The objective function y of the multi-objective interval optimization model i (X,Z), can be measured by interval It represents the objective function value interval corresponding to the effect of the uncertain variable Z on the optimization variable X. y i (X), They represent the lower and upper limits of the objective function value fluctuation, respectively, which can be obtained through interval analysis and can be obtained by the following formula:

[0183]

[0184] For objective functions with interval parameters Through the interval order relation method, it is equivalently transformed as follows:

[0185]

[0186] in, and are the midpoint and radius of the interval, respectively reflecting the expected benefits of the planning scheme and its sensitivity to the impact of uncertainty factors. In order to meet the different tendencies of decision makers between investment risk and return, the and The linear weighted summation method is used for processing, and the standardized objective function expression is further obtained as follows:

[0187]

[0188] For constraint conversion, the algorithm is based on:

[0189] Constraints g with interval parameters i(X,Z), can be measured by interval Represents the set of values ​​generated by the action of the uncertain variable Z on the optimization variable X:

[0190]

[0191] According to the interval possibility method, the above uncertain constraints are transformed into the following deterministic constraints:

[0192]

[0193] Where, is the numerical interval of the i-th uncertain constraint; ψ(·) is the interval possibility; δ i is the possibility limit of the i-th constraint condition.

[0194] Specifically, in one embodiment of the present application, the step of obtaining the objective function and constraints of the deterministic multi-objective optimization model converted from the objective function and constraints of the multi-objective interval optimization model for communication base station installation and renewable energy site selection and sizing includes:

[0195] The objective function of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy is converted into the objective function of a deterministic multi-objective optimization model by using an interval order relation method;

[0196] The constraints of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy are converted into constraints of a deterministic multi-objective optimization model through the interval possibility method.

[0197] Specifically, in one embodiment of the present application, the step of obtaining the objective function and constraint conditions of the deterministic multi-objective optimization model converted from the objective function and constraint conditions of the multi-objective interval optimization model further includes:

[0198] Obtaining input line electrical parameters of the distribution network and renewable energy site selection and sizing parameter information;

[0199] According to the input line electrical parameters of the distribution network and the renewable energy site selection and sizing parameter information, the relevant parameters of NSGA-II are set, and the initial population is generated through a random function;

[0200] Calculating the interval boundaries of the objective function and constraint conditions of the multi-objective interval optimization model by using interval analysis method based on the individuals of the initial population;

[0201] According to the interval boundaries of the objective function of the deterministic multi-objective optimization model, the midpoint and radius of the objective function of the multi-objective interval optimization model are calculated by the interval order relation, and the possibility of the constraint conditions of the multi-objective interval optimization model is obtained by the interval possibility degree;

[0202] The objective function and constraints of the deterministic multi-objective optimization model obtained by transforming the objective function and constraints of the multi-objective interval optimization model are obtained through the midpoint and radius of the objective function of the multi-objective interval optimization model and the possibility of the constraints of the multi-objective interval optimization model.

[0203] Specifically, in practical applications, NSGA-II is a non-dominated sorting genetic algorithm, which has better performance in terms of computational efficiency and accuracy.

[0204] Step S240: Obtain the collaborative planning result of the communication base station and the distribution network based on the solution results of the objective function and the constraint conditions of the deterministic multi-objective optimization model.

[0205] Specifically, in one embodiment of the present application, the step of obtaining the solution results of the objective function and constraint conditions of the deterministic multi-objective optimization model includes:

[0206] A fast non-dominated sorting method is used to stratify the population of the deterministic multi-objective optimization model;

[0207] According to the population stratification of the deterministic multi-objective optimization model, obtaining a ranking result of the population stratification of the deterministic multi-objective optimization model;

[0208] Determining the number of non-dominated layers of the population stratification of the deterministic multi-objective optimization model according to the sorting result of the population stratification of the deterministic multi-objective optimization model;

[0209] Obtaining the fitness of individuals in the non-dominated layers of the population stratification of the deterministic multi-objective optimization model according to the non-dominated layers of the population stratification of the deterministic multi-objective optimization model;

[0210] Obtaining a maximum evolutionary generation of the deterministic multi-objective optimization model according to the fitness of individuals in the non-dominated layers of the population stratification of the deterministic multi-objective optimization model;

[0211] The optimal solution set of the deterministic multi-objective optimization model is obtained according to the comparison result of the maximum evolutionary generation of the deterministic multi-objective optimization model and the set specified value.

[0212] Specifically, in one embodiment of the present application, the step of stratifying the population of the deterministic multi-objective optimization model using the fast non-dominated sorting method includes:

[0213] Obtaining all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model;

[0214] Obtaining, according to all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model, a first non-dominated layer of all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model;

[0215] Obtaining, based on the first non-dominated layer, non-inferior solution individuals of the deterministic multi-objective optimization model outside the first non-dominated layer;

[0216] Obtaining, based on the non-inferior solution individuals of the deterministic multi-objective optimization model other than the first non-dominated layer, the second non-dominated layer including the non-inferior solution individuals of the deterministic multi-objective optimization model other than the first non-dominated layer;

[0217] Repeat the above steps until the hierarchical sorting of all population individuals in the deterministic multi-objective optimization model is completed and the number of non-dominated layers is determined.

[0218] Specifically, in NSGA-II, individual fitness includes the number of non-dominated layers and the crowding degree of individuals in each layer. The population is stratified using the fast non-dominated sorting method, that is, all non-inferior solution individuals in the current population are selected as the first non-dominated layer; new non-inferior solutions are found among individuals outside the dominated layer as the second non-dominated layer; this process is repeated until all individuals in the population are stratified and sorted, and the number of non-dominated layers is determined. On this basis, the crowding degree of individuals in each layer is calculated:

[0219] n d =n d +f m (i+1)-f m (i-1)………………………………(44)

[0220] Where: f m (i) is the value of the objective function m corresponding to individual i in the population; n d The individual distance.

[0221] Specifically, when determining whether convergence conditions have been met, this application uses the maximum number of evolutionary generations as the convergence condition. If the number of evolutionary generations reaches the above-specified value, the Pareto optimal solution set is output; otherwise, selection, crossover, and mutation operations are performed on the parent population to form individuals in the child population; then, based on the elite retention strategy, the next generation population is formed according to the individual fitness.

[0222] According to the scheme of the method for collaborative planning of communication base stations and distribution networks of the present invention, by obtaining the element conditions of the layout of communication base stations and the element conditions of the configuration of distribution networks; according to the element conditions of the layout of communication base stations and the element conditions of the configuration of distribution networks, the objective function and constraint conditions of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy are obtained; according to the objective function and constraint conditions of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy, the objective function and constraint conditions of the deterministic multi-objective optimization model converted from the objective function and constraint conditions of the multi-objective interval optimization model are obtained; according to the solution results of the objective function and constraint conditions of the deterministic multi-objective optimization model, the collaborative planning results of the communication base stations and distribution networks are obtained. This application constructs a multi-objective collaborative planning model of distribution networks and communication base stations by comprehensively considering the constraints of communication base stations, and converts it into a deterministic multi-objective optimization problem through interval order relations and possibility, so as to achieve synergistic efficiency between communication base stations and distribution networks and obtain comprehensive economic and environmental benefits.

[0223] It should be understood that although Figure 4 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 4 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0224] In one embodiment, Figure 5 As shown, a power system confidence capacity determination device 300 based on the needs of an Internet data center is provided. The device 300 jointly optimizes the installation of communication base stations and the site selection and capacity determination of renewable energy by constructing an interval uncertainty multi-objective optimization model, controls the operation of communication base stations and distribution networks in each time period, and obtains comprehensive economic and environmental benefits. The device 300 includes: an element acquisition module, a parameter determination module, and a planning completion module.

[0225] an element acquisition module, configured to acquire element conditions for the layout of communication base stations and element conditions for the configuration of distribution networks, wherein the element conditions for the layout of communication base stations include energy consumption characteristics, communication capacity characteristics, coverage characteristics, and configurable parameters of communication base station bandwidth resources, and the element conditions for the configuration of distribution networks include power transmission parameters of distribution networks and renewable energy distribution parameters;

[0226] a parameter determination module for obtaining, based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration, the objective function and constraints of the multi-objective interval optimization model for the communication base station installation and renewable energy site selection and sizing; and obtaining, based on the objective function and constraints of the multi-objective interval optimization model for the communication base station installation and renewable energy site selection and sizing, the objective function and constraints of a deterministic multi-objective optimization model converted from the objective function and constraints of the multi-objective interval optimization model;

[0227] The planning completion module is used to obtain the collaborative planning results of the communication base station and the distribution network based on the solution results of the objective function and constraint conditions of the deterministic multi-objective optimization model.

[0228] Specifically, in another embodiment of the present application, the factor acquisition module is used to calculate the total power consumption of a single communication base station based on the energy consumption of the fixed loss components of the single communication base station and the energy consumption of the communication base station business load, and obtain the communication base station energy consumption characteristics of the communication base station; obtain the communication capacity characteristics of a single communication base station based on the user received signal signal-to-noise ratio parameters, communication information transmission rate parameters, communication base station total bandwidth resources and cellular link spectrum efficiency parameters; obtain the communication base station candidate point variables based on the signal coverage area of ​​a single communication base station and the Voronoi graph theory algorithm; obtain the communication base station bandwidth resource configurable parameters based on the load status information of the adjacent communication base stations obtained by each communication base station, and control the sleep or power-on of the corresponding communication base station equipment.

[0229] Specifically, in another embodiment of the present application, the parameter determination module is used to obtain the objective function of the total investment and operating costs of the element conditions of the communication base station layout and the element conditions of the distribution network configuration; based on the objective function of the total investment and operating costs of the element conditions of the communication base station layout and the element conditions of the distribution network configuration, obtain the equipment installation quantity constraint conditions of the communication base station layout, and the constraint conditions of the communication base station operation and distribution network configuration.

[0230] Specifically, in another embodiment of the present application, the parameter determination module is used to convert the objective function of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy into the objective function of the deterministic multi-objective optimization model through the interval order relationship method; and convert the constraints of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy into the constraints of the deterministic multi-objective optimization model through the interval possibility method.

[0231] Specifically, in another embodiment of the present application, the parameter determination module is used to obtain the input line electrical parameters of the distribution network and the renewable energy site selection and sizing parameter information; according to the input line electrical parameters of the distribution network and the renewable energy site selection and sizing parameter information, the relevant parameters of NSGA-II are set, and the initial population is generated through a random function; according to the individuals of the initial population, the interval boundaries of the objective function and constraints of the multi-objective interval optimization model are calculated by the interval analysis method; according to the interval boundaries of the objective function of the deterministic multi-objective optimization model, the midpoint and radius of the objective function of the multi-objective interval optimization model are calculated by the interval order relationship, and the possibility of the constraints of the multi-objective interval optimization model is obtained through the interval possibility; through the midpoint and radius of the objective function of the multi-objective interval optimization model and the possibility of the constraints of the multi-objective interval optimization model, the objective function and constraints of the deterministic multi-objective optimization model converted from the objective function and constraints of the multi-objective interval optimization model are obtained.

[0232] Specifically, in another embodiment of the present application, the planning completion module is used to stratify the population of the deterministic multi-objective optimization model using a fast non-dominated sorting method; based on the population stratification of the deterministic multi-objective optimization model, obtain the sorting result of the population stratification of the deterministic multi-objective optimization model; based on the sorting result of the population stratification of the deterministic multi-objective optimization model, determine the number of non-dominated layers of the population stratification of the deterministic multi-objective optimization model; based on the number of non-dominated layers of the population stratification of the deterministic multi-objective optimization model, obtain the fitness of individuals in the non-dominated layers of the population stratification of the deterministic multi-objective optimization model; based on the fitness of individuals in the non-dominated layers of the population stratification of the deterministic multi-objective optimization model, obtain the maximum evolutionary generation of the deterministic multi-objective optimization model; based on the comparison result of the maximum evolutionary generation of the deterministic multi-objective optimization model with a set specified value, obtain the optimal solution set of the deterministic multi-objective optimization model.

[0233] Specifically, in another embodiment of the present application, the planning completion module is used to obtain all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model; based on all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model, obtain the first non-dominated layer containing all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model; based on the first non-dominated layer, obtain the non-inferior solution individuals of the deterministic multi-objective optimization model outside the first non-dominated layer; based on the non-inferior solution individuals of the deterministic multi-objective optimization model outside the first non-dominated layer, obtain the second non-dominated layer containing the non-inferior solution individuals of the deterministic multi-objective optimization model outside the first non-dominated layer; repeat the above steps until the hierarchical sorting of all population individuals of the deterministic multi-objective optimization model is completed and the number of non-dominated layers is determined.

[0234] According to the case of the communication base station and distribution network collaborative planning device of the present invention, the element conditions of the communication base station layout and the element conditions of the distribution network configuration are obtained through the element acquisition module; the parameter determination module obtains the objective function and constraint conditions of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration; the objective function and constraint conditions of the deterministic multi-objective optimization model converted from the objective function and constraint conditions of the multi-objective interval optimization model are obtained based on the objective function and constraint conditions of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy; the planning completion module obtains the collaborative planning result of the communication base station and the distribution network based on the solution result of the objective function and constraint conditions of the deterministic multi-objective optimization model. This application constructs a multi-objective collaborative planning model of the distribution network and the communication base station by comprehensively considering the constraint conditions of the communication base station, and converts it into a deterministic multi-objective optimization problem solution through interval order relationship and possibility, so as to achieve synergistic efficiency between the communication base station and the distribution network and obtain comprehensive economic and environmental benefits.

[0235] It should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0236] Those skilled in the art will appreciate that the modules, units, or components of the devices in the examples disclosed herein may be arranged in the device described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or further divided into multiple submodules.

[0237] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0238] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0239] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions described. Thus, a processor having the necessary instructions for implementing the method or method element forms a device for implementing the method or method element. Furthermore, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.

[0240] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved and are not intended to imply that the objects so described must have a given order in time, space, ranking, or in any other manner.

[0241] Although the present invention has been described with respect to a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A method for collaborative planning of communication base stations and distribution networks. The method constructs an interval uncertainty multi-objective optimization model to jointly optimize the installation of communication base stations and the site selection and sizing of renewable energy sources, and controls the operation of communication base stations and distribution networks during each time period to obtain comprehensive economic and environmental benefits. The method comprises the following steps: Obtaining element conditions for communication base station layout and element conditions for distribution network configuration, wherein the element conditions for communication base station layout include energy consumption characteristics, communication capacity characteristics, coverage characteristics, and configurable parameters of communication base station bandwidth resources, and the element conditions for distribution network configuration include distribution network transmission parameters and renewable energy distribution parameters; Based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration, the objective function and constraint conditions of the multi-objective interval optimization model for the communication base station installation and renewable energy site selection and sizing are obtained, including the objective function of obtaining the total investment and operating cost of the element conditions of the communication base station layout and the element conditions of the distribution network configuration, and based on the objective function of the total investment and operating cost of the element conditions of the communication base station layout and the element conditions of the distribution network configuration, the constraint conditions for the number of equipment installed in the communication base station layout and the constraint conditions for the operation of the communication base station and the distribution network configuration are obtained; According to the objective function and constraints of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy, obtaining the objective function and constraints of a deterministic multi-objective optimization model from which the objective function and constraints of the multi-objective interval optimization model are converted, including converting the objective function of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy into the objective function of the deterministic multi-objective optimization model through an interval order relation method, and converting the constraints of the multi-objective interval optimization model for the installation of communication base stations and the site selection and sizing of renewable energy into the constraints of the deterministic multi-objective optimization model through an interval possibility method; The collaborative planning result of the communication base station and the distribution network is obtained based on the solution results of the objective function and the constraint conditions of the deterministic multi-objective optimization model.

2. The method according to claim 1, wherein The step of obtaining the essential conditions for the layout of the communication base stations comprises: Calculating the total power consumption of a single communication base station based on the energy consumption of fixed loss components of the single communication base station and the energy consumption of the service load of the communication base station, and obtaining the communication base station energy consumption characteristics of the communication base station; Obtain the communication capacity characteristics of a single communication base station based on the user received signal noise ratio parameter, communication information transmission rate parameter, communication base station total bandwidth resource and cellular link spectrum efficiency parameter; According to the signal coverage area of ​​a single communication base station and the Voronoi diagram algorithm, the candidate point variables of the communication base station are obtained; Based on the load status information of the adjacent communication base stations obtained by each communication base station, the configurable parameters of the communication base station bandwidth resources are obtained to control the sleep or start of the corresponding communication base station equipment.

3. The method according to claim 1, wherein The step of obtaining the objective function and constraint conditions of the multi-objective interval optimization model and converting the objective function and constraint conditions of the deterministic multi-objective optimization model further includes: Obtaining input line electrical parameters of the distribution network and renewable energy site selection and sizing parameter information; According to the input line electrical parameters of the distribution network and the renewable energy site selection and sizing parameter information, the relevant parameters of NSGA-II are set, and the initial population is generated through a random function; Calculating the interval boundaries of the objective function and constraint conditions of the multi-objective interval optimization model by using interval analysis method based on the individuals of the initial population; According to the interval boundaries of the objective function of the deterministic multi-objective optimization model, the midpoint and radius of the objective function of the multi-objective interval optimization model are calculated by the interval order relation, and the possibility of the constraint conditions of the multi-objective interval optimization model is obtained by the interval possibility degree; The objective function and constraints of the deterministic multi-objective optimization model obtained by transforming the objective function and constraints of the multi-objective interval optimization model are obtained through the midpoint and radius of the objective function of the multi-objective interval optimization model and the possibility of the constraints of the multi-objective interval optimization model.

4. The method according to claim 3, wherein: The steps of obtaining the solution results of the objective function and constraint conditions of the deterministic multi-objective optimization model include: A fast non-dominated sorting method is used to stratify the population of the deterministic multi-objective optimization model; According to the population stratification of the deterministic multi-objective optimization model, obtaining a ranking result of the population stratification of the deterministic multi-objective optimization model; Determining the number of non-dominated layers of the population stratification of the deterministic multi-objective optimization model according to the sorting result of the population stratification of the deterministic multi-objective optimization model; Obtaining the fitness of individuals in the non-dominated layers of the population stratification of the deterministic multi-objective optimization model according to the non-dominated layers of the population stratification of the deterministic multi-objective optimization model; Obtaining a maximum evolutionary generation of the deterministic multi-objective optimization model according to the fitness of individuals in the non-dominated layers of the population stratification of the deterministic multi-objective optimization model; The optimal solution set of the deterministic multi-objective optimization model is obtained according to the comparison result of the maximum evolutionary generation of the deterministic multi-objective optimization model and the set specified value.

5. The method according to claim 4, wherein: The step of stratifying the population of the deterministic multi-objective optimization model using a fast non-dominated sorting method comprises: Obtaining all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model; Obtaining, according to all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model, a first non-dominated layer including all non-inferior solution individuals in the current population of the deterministic multi-objective optimization model; Obtaining, based on the first non-dominated layer, non-inferior solution individuals of the deterministic multi-objective optimization model outside the first non-dominated layer; Obtaining, based on the non-inferior solution individuals of the deterministic multi-objective optimization model other than the first non-dominated layer, the second non-dominated layer including the non-inferior solution individuals of the deterministic multi-objective optimization model other than the first non-dominated layer; Repeat the above steps until the hierarchical sorting of all population individuals in the deterministic multi-objective optimization model is completed and the number of non-dominated layers is determined.

6. A collaborative planning device based on communication base stations and distribution networks, characterized in that: The device constructs an interval uncertainty multi-objective optimization model to jointly optimize the installation of communication base stations and the site selection and capacity determination of renewable energy, and controls the operation of communication base stations and distribution networks in each time period to obtain comprehensive economic and environmental benefits. The device includes: an element acquisition module, configured to acquire element conditions for the layout of communication base stations and element conditions for the configuration of distribution networks, wherein the element conditions for the layout of communication base stations include energy consumption characteristics, communication capacity characteristics, coverage characteristics, and configurable parameters of communication base station bandwidth resources, and the element conditions for the configuration of distribution networks include power transmission parameters of distribution networks and renewable energy distribution parameters; a parameter determination module for obtaining the objective function and constraints of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy based on the element conditions of the communication base station layout and the element conditions of the distribution network configuration, including obtaining the objective function of the total investment and operating cost of the element conditions of the communication base station layout and the element conditions of the distribution network configuration, obtaining the equipment installation quantity constraint of the communication base station layout, and the constraint conditions of the operation of the communication base station and the distribution network configuration based on the objective function of the total investment and operating cost of the element conditions of the communication base station layout and the element conditions of the distribution network configuration; obtaining the objective function and constraints of the deterministic multi-objective optimization model from the objective function and constraints of the multi-objective interval optimization model based on the objective function and constraints of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy, including converting the objective function of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy into the objective function of the deterministic multi-objective optimization model through the interval order relation method, and converting the constraints of the multi-objective interval optimization model for the installation of the communication base station and the site selection and sizing of renewable energy into the constraints of the deterministic multi-objective optimization model through the interval possibility method; The planning completion module is used to obtain the collaborative planning results of the communication base station and the distribution network based on the solution results of the objective function and constraint conditions of the deterministic multi-objective optimization model.

7. An electronic device comprising: one or more processors; and memory; One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for executing any one of the methods according to claims 1-5.

8. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods according to claims 1-5.

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