Base station load demand response real-time control method considering multi-agent utility

Abstract

The invention relates to the technical field of base station load demand response, solves the technical problem that cooperation of a mobile operator and a mobile user and comprehensive utility of the mobile user and the mobile operator are not considered in the prior art, and particularly relates to a base station load demand response real-time control method considering multi-agent utility. The method comprises the following steps: initializing a power grid demand response unit subsidy parameter, and enabling the number of iterations k to be equal to 0; solving the mobile user optimization model to obtain the total utility # imgabs0 # of the mobile user m in the time period t and a mobile user optimization strategy; and solving the mobile operator optimization model to obtain the total utility # imgabs1 # of the mobile operator in the time period t and a mobile operator optimization strategy. According to the invention, when power supply and demand are insufficient or renewable energy is difficult to consume, the load of the base station in the peak period of power utilization can be reduced by guiding the user to adjust the communication demand, the pressure of the power grid can be relieved, and the stability and safety of the power system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of base station load demand response, and in particular to a base station load demand response real-time control method considering multi-agent utilities. Background Art

[0002] Communication base stations can participate in demand response and adjust their own power consumption patterns through intelligent dispatching technology according to the demand and price changes in the power market, so as to achieve effective utilization and conservation of power resources. This not only helps mobile operators reduce electricity costs, but also provides a stable power supply for the power grid, improving the stability and reliability of the power grid.

[0003] The main way for base stations to participate in demand response is to use mobile operators as load aggregators. Load aggregators adjust base station loads by shutting down active antenna units at base stations, setting the temperature of refrigeration equipment, discharging base station energy storage devices, and migrating communication services. Among them, communication service migration is the mainstream way for base stations to participate in demand response. During the migration process, the electricity prices of different base stations and the basic communication needs of mobile users will be considered to determine which base station the mobile user load should be transferred from and to.

[0004] When base stations participate in demand response by migrating communication services, the impact on mobile users' communication needs needs to be considered. However, the existing technology does not consider the coordination between mobile operators and mobile users to adjust electricity demand by reducing user communication needs, nor does it consider the comprehensive benefits of mobile users and mobile operators. As a result, when electricity supply and demand are tight or renewable energy is difficult to absorb, the base station load continues to increase and load adjustment cannot be achieved. Therefore, it is impossible to flexibly adjust electricity demand to participate in demand response, which in turn reduces the stability and security of the power system. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a real-time control method for base station load demand response taking into account the utility of multiple subjects, which solves the technical problem that the prior art does not take into account the collaboration between mobile operators and mobile users, and the comprehensive utility of mobile users and mobile operators.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a base station load demand response real-time control method considering multi-agent utilities, the method comprising the following steps:

[0007] S1. Initialize the grid demand response unit subsidy parameters and set the number of iterations k = 0;

[0008] S2. Solve the mobile user optimization model to obtain the total utility of mobile user m in time period t And mobile user optimization strategy, mobile user optimization strategy includes information transmission rate ratio α m,t , information transmission rate

[0009] S3. Solve the mobile operator optimization model to obtain the total utility of the mobile operator in time period t and the mobile operator optimization strategy, which includes the variable x m,n,t , Energy consumption of a single base station participating in demand response

[0010] S4. Based on total utility and total utility Calculate the combined utility of mobile users and mobile operators

[0011] S5, determine whether k-1 is greater than or equal to 1;

[0012] If yes, then go to step S6 and compare the comprehensive utility of k-1 times and If not, proceed to step S7;

[0013] S6. Judgment Is it greater than

[0014] If yes, then record the mobile user optimization strategy and mobile operator optimization strategy of the kth iteration; if no, proceed to step S7;

[0015] S7. Let k = k + 1 to determine the unit subsidy for grid demand response Whether the maximum value has been reached;

[0016] If yes, then end; if no, proceed to step S8;

[0017] S8. Adjust the grid demand response unit subsidy and return to step S2.

[0018] Further, in step S1, the grid demand response unit subsidy parameter includes the grid demand response unit subsidy Unit subsidy minimum Unit subsidy ceiling

[0019] Furthermore, in step S2, the specific process includes the following steps:

[0020] S21, constructing a mobile user optimization model, the mobile user optimization model including a mobile user function, a mobile user information transmission utility model and a mobile user demand response utility model;

[0021] S22. Taking the mobile user utility function model as the target, the mobile user information transmission utility model and the mobile user demand response utility model as the constraints, solve the mobile user optimization strategy and the total utility of mobile user m in time period t.

[0022] Furthermore, in step S21, the mobile user utility function satisfies the following relationship:

[0023]

[0024] Where: is the utility function of the mobile operator; is a variable, representing the total utility of mobile user m in time period t; is a variable, representing the information transmission rate of user m in time period t;

[0025] The utility model of mobile user information transmission is:

[0026]

[0027] Where: is the utility of mobile users at different information transmission rates; α m,t is a variable, which represents the information transmission rate ratio of user m in time period t;

[0028] The utility model of mobile user demand response is:

[0029]

[0030] Where: is the utility of mobile users participating in demand response; β is the unit subsidy ratio given by mobile operators to mobile users.

[0031] Furthermore, in step S3, the specific process includes the following steps:

[0032] S31, constructing a mobile operator optimization model, the mobile operator optimization model including a mobile operation utility function, a base station energy cost model, a base station participation demand response benefit model, a base station energy consumption model, and a communication condition constraint model;

[0033] S32. Taking the utility function of mobile operators as the target, the base station energy cost model, the base station participation demand response benefit model, the base station energy consumption model, and the communication condition constraint model as constraints, solve the optimization strategy of mobile operators and the total utility of mobile operators in time period t.

[0034] Furthermore, in step S31, the mobile operation utility function is:

[0035]

[0036] Where: is the utility function of the mobile operator; is a variable, representing the total utility of the mobile operator in period t; is a variable, which represents the energy consumption of the nth base station participating in the demand response in time period t; is a constant, which represents the energy consumption of the nth base station that does not participate in demand response in time period t, corresponding to the baseline load;

[0037] The energy cost model of the base station is:

[0038]

[0039] Where: is the energy cost of all base stations incurred by the mobile operator in time period t; is a constant, representing the electricity price of base station n in time period t; N is the set of mobile base stations; Δt is a constant, representing the time interval;

[0040] The profit model of base station participation in demand response is:

[0041]

[0042] Where: is the revenue obtained by the mobile operator from participating in demand response during time period t; is a variable, which indicates the unit subsidy provided by the power grid company for participating in demand response; β is the unit subsidy ratio given by the mobile operator to mobile users;

[0043] The base station energy consumption model is:

[0044]

[0045] Where: is a constant, representing the static power consumption of the nth base station; is a variable, representing the dynamic power consumption of the nth base station in time period t; γ represents the energy efficiency coefficient of the base station;

[0046] The communication condition constraint model is:

[0047]

[0048] Where: x m,n,t It is a 0-1 variable, indicating whether the mth mobile user accesses the nth base station in time period t. If yes, it is 1, otherwise it is 0.

[0049] Furthermore, in step S4, the specific process includes the following steps:

[0050] S41. Obtain the minimum transmission rate requirement of mobile users from the power grid and establish the information transmission rate constant matrix E for mobile users, namely:

[0051]

[0052] Where: is a constant, indicating the minimum transmission rate requirement of user m in time period t; T DRST is a constant, indicating the time period when demand response starts; T DREND is a constant, indicating the end time of demand response; M is the user set;

[0053] S42, using the information transmission rate constant matrix E, and using the entropy weight method to obtain the weight coefficient matrix of different users, that is:

[0054] G=[g1,...,g m ,...,g M ]

[0055] Where: g m is a constant, representing the weight of the mth mobile user; G is a constant matrix, representing the weight coefficient matrix of the mobile user at the mobile operator, which is a 1×M matrix, and the matrix elements satisfy the following constraints∑ m∈M g m =1;

[0056] S43, constructing a modified weight coefficient matrix including mobile operators based on the weight coefficient matrix G, which is defined as follows:

[0057] GS=[σG,1-σ]

[0058] Where: GS is a constant matrix, representing the modified 1×(M+1) weight coefficient matrix; σ is a constant set by the mobile operator, and 1-σ is the weight of the mobile operator;

[0059] S44. Construct a comprehensive utility matrix including mobile users and mobile operators, namely:

[0060]

[0061] Where: C is a constant matrix, representing a 1×(M+1) comprehensive utility matrix; is a variable, representing the total utility of mobile user m in time period t; is a variable, representing the total utility of the mobile operator in period t;

[0062] S45. Calculate the comprehensive utility of k iterations based on the comprehensive utility matrix C and the weight coefficient matrix GS Right now:

[0063]

[0064] Where: GS' is the transposed matrix of the matrix coefficient GS; .* represents the dot product symbol.

[0065] By means of the above technical solution, the present invention provides a base station load demand response real-time control method considering multi-agent utilities, which has at least the following beneficial effects:

[0066] 1. The present invention supports the base station to adjust the electricity demand by changing the user's communication demand, and then participates in the demand response. On the one hand, it can release the load regulation potential of the base station by reducing and changing the communication demand of mobile users. On the other hand, the comprehensive utility of mobile users and mobile operators is fully considered in the load control process.

[0067] 2. When electricity supply and demand are tight or renewable energy is difficult to absorb, the present invention can guide users to adjust their communication needs and reduce the load of base stations during peak hours, which helps to relieve grid pressure and improve the stability and security of the power system. In addition, by flexibly adjusting electricity demand to participate in demand response, the flexibility and resilience of the power system can be enhanced, and the ability of the power grid to respond to emergencies can be improved.

[0068] 3. Through intelligent scheduling and traffic management, the present invention guides users to adjust their communication needs when electricity supply and demand are tight or renewable energy is difficult to absorb, thereby reducing the load of base stations during peak periods of electricity consumption, reducing dependence on traditional energy, and promoting the absorption and utilization of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0070] Figure 1 The present invention is a flow chart of a base station load demand response real-time control method. DETAILED DESCRIPTION

[0071] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0072] In this embodiment, demand response refers to dealing with short-term electricity supply and demand tensions, difficulties in absorbing renewable energy power, etc., through measures mainly based on economic incentives, guiding electricity users to voluntarily adjust their electricity consumption behavior according to the needs of power system operation, achieve peak shaving and valley filling, improve the flexibility of the power system, ensure the safe and stable operation of the power system, and promote the absorption of renewable energy power.

[0073] Demand-side resources refer to adjustable loads, distributed power sources, new energy storage, and other power resources that are widely distributed on the user side and can be aggregated and optimized to participate in the operation and regulation of the power system.

[0074] In the prior art, when base stations participate in demand response by migrating communication services, the impact on mobile users' communication needs needs to be considered. However, the prior art does not consider the coordination between mobile operators and mobile users to adjust electricity demand by reducing user communication needs, nor does it consider the comprehensive benefits of mobile users and mobile operators. As a result, when electricity supply and demand are tight or renewable energy is difficult to absorb, the base station load continues to increase and load adjustment cannot be achieved. Therefore, it is impossible to flexibly adjust electricity demand to participate in demand response, which in turn reduces the stability and security of the power system.

[0075] Please refer to Figure 1 This embodiment proposes a real-time control method for base station load demand response that takes into account the utility of multiple subjects. When power supply and demand are tight or renewable energy is difficult to absorb, it can guide users to adjust communication needs and reduce the load of base stations during peak power consumption periods, which helps to relieve grid pressure and improve the stability and security of the power system. And by flexibly adjusting power demand to participate in demand response, it can enhance the flexibility and resilience of the power system and improve the ability of the power grid to respond to emergencies. The method includes the following steps:

[0076] S1. Initialize the grid demand response unit subsidy parameters, set the number of iterations k = 0, the grid demand response unit subsidy parameters include the grid demand response unit subsidy Unit subsidy minimum Unit subsidy ceiling in is a constant, which represents the lower limit of the unit subsidy provided by the power grid for mobile operators to participate in demand response in time period t; is a constant, which represents the upper limit of the unit subsidy provided by the power grid for mobile operators to participate in demand response; is a variable, which represents the unit subsidy provided by the power grid to mobile operators participating in demand response in period t.

[0077] In this embodiment, the acquired demand response control parameters, mobile operator base station and user information are also included. The demand response control parameters include T DRST , T DREND ,t, Where T DRST is a constant, indicating the time period when demand response starts. DREND is a constant, indicating the end time of demand response. t is a variable, indicating the tth time period, let t = T DRST Δq DRis a constant, which indicates the interval coefficient of the unit subsidy in the iterative calculation process. The mobile operator base station and user information includes N, M, n, and m. N is the mobile base station set, and N is the number of base stations in the set N. M is the user set. n is a variable, which indicates the nth base station, n∈N. m is a variable, which indicates the mth mobile user, m∈M, and M is the number of users in the user set M.

[0078] S2. Solve the mobile user optimization model to obtain the total utility of mobile user m in time period t And mobile user optimization strategy, mobile user optimization strategy includes information transmission rate ratio α m,t , information transmission rate In step S2, the specific process includes the following steps:

[0079] S21, constructing a mobile user optimization model, the mobile user optimization model including a mobile user function, a mobile user information transmission utility model and a mobile user demand response utility model;

[0080] The mobile user utility function satisfies the following relationship:

[0081]

[0082] Where: is the utility function of the mobile operator; is a variable, representing the total utility of mobile user m in time period t; is a variable, representing the information transmission rate of user m in time period t.

[0083]

[0084] Where: The utility of mobile users at different information transmission rates; is the utility of mobile users participating in demand response; ρ is a constant, indicating the proportion of different utility functions in the total utility.

[0085] The utility model of mobile user information transmission is:

[0086] The utility of mobile users at different information transmission rates is defined as follows:

[0087]

[0088] Where: α m,t is a variable, which represents the information transmission rate ratio of user m in time period t and is defined as follows:

[0089]

[0090] Where: is a constant, which represents the minimum transmission rate requirement of user m in time period t; is a constant, indicating the maximum transmission rate requirement of user m in time period t; is a variable, representing the information transmission rate of user m in time period t.

[0091] The utility model of mobile user demand response is:

[0092] The utility of mobile users participating in demand response is defined as follows:

[0093]

[0094] Where: β is the unit subsidy ratio given by mobile operators to mobile users.

[0095] S22. Taking the mobile user utility function model as the target, the mobile user information transmission utility model and the mobile user demand response utility model as the constraints, solve the mobile user optimization strategy and the total utility of mobile user m in time period t. In this embodiment, mathematical programming and optimization algorithms (such as linear programming, dynamic programming, genetic algorithm, etc.) can be selected to solve the mobile user optimization strategy, that is, to maximize the total utility of the mobile user under the premise of satisfying the constraints.

[0096] Therefore, by optimizing the communication behavior and demand response behavior of mobile users, the overall efficiency of the communication system and the power system can be improved. At the same time, the energy consumption and operating costs of base stations can be reduced, and communication fees can be saved for users, achieving a win-win situation. This will improve the service quality and satisfaction of users, and enhance users' trust and support for communication services and power grid demand response plans.

[0097] This embodiment uses intelligent scheduling and traffic management to guide users to adjust their communication needs when electricity supply and demand are tight or renewable energy is difficult to consume, thereby reducing the load of base stations during peak power consumption periods, reducing dependence on traditional energy, and promoting the consumption and utilization of renewable energy.

[0098] S3. Solve the mobile operator optimization model to obtain the total utility of the mobile operator in time period t and the mobile operator optimization strategy, which includes the variable x m,n,t , Energy consumption of a single base station participating in demand response In step S3, the specific process includes the following steps:

[0099] S31, constructing a mobile operator optimization model, the mobile operator optimization model including a mobile operation utility function, a base station energy cost model, a base station participation demand response benefit model, a base station energy consumption model, and a communication condition constraint model;

[0100] The mobile operation utility function is:

[0101]

[0102] Where: is the utility function of the mobile operator; is a variable, representing the total utility of the mobile operator in period t; is a variable, which represents the energy consumption of the nth base station participating in the demand response in time period t; is a constant, which represents the energy consumption of the nth base station that does not participate in demand response in time period t, corresponding to the baseline load.

[0103]

[0104] Where: is the energy cost of all base stations incurred by the mobile operator in time period t; is the revenue obtained by the mobile operator from participating in demand response in period t.

[0105] The energy cost model of the base station is:

[0106]

[0107] Where: is a constant, representing the electricity price of base station n in time period t; N is the set of mobile base stations; Δt is a constant, representing the time interval.

[0108] The profit model of base station participation in demand response is:

[0109]

[0110] Where: is a variable, indicating the unit subsidy provided by the grid company for participation in demand response;

[0111] The base station energy consumption model is:

[0112] In the process of participating in demand response, communication operators can shut down some base station equipment with low load rate while ensuring basic service quality, thereby reducing regional load. The energy consumption model of a single base station is as follows:

[0113]

[0114] Where: is a constant, representing the static power consumption of the nth base station; is a variable, representing the dynamic power consumption of the nth base station in time period t; γ represents the energy efficiency coefficient of the base station.

[0115] The communication condition constraint model is:

[0116] In order to obtain services, mobile users need to establish a connection with a base station during a specific period of time. There can be only one connection, and the connection constraints are as follows:

[0117]

[0118] Where: x m,n,t is a 0-1 variable, indicating whether the mth mobile user accesses the nth base station in time period t. If it does, it is 1, otherwise it is 0. m,n,t The value is constrained by Shannon's formula.

[0119] Among them, Shannon's formula for mobile user m's signal-to-interference-to-noise ratio, bandwidth, and transmission rate constraints are as follows:

[0120]

[0121] Where: B m,n,t is a variable, which represents the bandwidth that can be allocated to the mth mobile user accessing the nth base station in time period t, and the constraints are as follows:

[0122]

[0123] In the formula, y m,n,t is a 0-1 variable, indicating whether the mth mobile user participates in demand response when accessing the nth base station in time period t. If so, it is 1, otherwise it is 0; Indicates that when user m does not participate in demand response, the bandwidth allocated to the nth base station is accessed in time period t; It indicates that when participating in demand response, user m accesses the bandwidth allocated by the nth base station in time period t.

[0124] In the formula, E m,n,t is a variable, which represents the information transmission rate after the mth mobile user accesses the nth base station in time period t, and the constraints are as follows:

[0125]

[0126] In the formula, δ m,n,t is a variable, which represents the signal to interference and noise ratio after the mth mobile user accesses the nth base station in time period t, that is:

[0127]

[0128] Where: θ is a constant, representing the average power of the channel white noise; h m,n is a constant, which represents the channel gain when mobile user m transmits a signal to base station n; is a variable, representing the base station dynamic power consumption of the nth base station in time period t; represents the dynamic power consumption of the base station of the lth mobile user in time period t; h l,nIt represents the channel gain when mobile user l transmits signal to base station n.

[0129] By transforming the formula, we can get the following constraints:

[0130]

[0131] -Ox m,n,t ≤y m,n,t ≤Ox m,n,t

[0132] -O(1-x m,n,t )+y m,n,t ≤z m,n,t ≤O(1-x m,n,t )+y m,n,t

[0133] -Ox m,n,t ≤z m,n,t ≤Ox m,n,t

[0134]

[0135] -Oy m,n,t ≤k m,n,t ≤Oy m,n,t

[0136]

[0137] -Ox m,n,t ≤v m,n,t ≤Ox m,n,t

[0138]

[0139] Where: z m,n,t , k m,n,t 、v m,n,t They are all auxiliary variables introduced, which are N×M matrices, and O is a maximum value.

[0140] S32. Taking the utility function of mobile operators as the target, the base station energy cost model, the base station participation demand response benefit model, the base station energy consumption model, and the communication condition constraint model as constraints, solve the optimization strategy of mobile operators and the total utility of mobile operators in time period t. In this embodiment, mathematical programming and optimization algorithms (such as linear programming, dynamic programming, genetic algorithm, etc.) can be selected to solve the mobile user optimization strategy, that is, to maximize the total utility of the mobile user under the premise of satisfying the constraints.

[0141] Therefore, through the implementation of optimization strategies, mobile operators can manage base station resources more efficiently, reduce operating costs, and improve overall operating efficiency. At the same time, they can improve the quality of communication services and user satisfaction, enhance the market competitiveness of mobile operators, and thus promote the efficient use of base station energy and the access to renewable energy, contributing to the green development of the communications industry.

[0142] S4. Based on total utility and total utility Calculate the combined utility of mobile users and mobile operators In step S4, the specific process includes the following steps:

[0143] S41, obtaining the minimum transmission rate requirement of mobile users from the power grid, and establishing an information transmission rate constant matrix E for mobile users; specifically, the minimum transmission rate requirement of mobile users satisfies a weight function, namely:

[0144]

[0145] Where: is the weight function. The weight function can output the weight matrix. The calculation is mainly based on a comprehensive evaluation algorithm, which is obtained by estimation:

[0146]

[0147] Where: is a constant, indicating the minimum transmission rate requirement of user m in time period t; T DRST is a constant, indicating the time period when demand response starts; T DREND is a constant, indicating the end time period of demand response; M is the user set.

[0148] S42, using the information transmission rate constant matrix E, and using the entropy weight method to obtain the weight coefficient matrix of different users, that is:

[0149] G=[g1,...,g m ,...,g M ]

[0150] Where: g m is a constant, representing the weight of the mth mobile user; G is a constant matrix, representing the weight coefficient matrix of the mobile user at the mobile operator, which is a 1×M matrix, and the matrix elements satisfy the following constraints∑ m∈M g m =1;

[0151] S43, constructing a modified weight coefficient matrix including mobile operators based on the weight coefficient matrix G, which is defined as follows:

[0152] GS=[σG,1-σ]

[0153] Where: GS is a constant matrix, representing the modified 1×(M+1) weight coefficient matrix; σ is a constant set by the mobile operator, and 1-σ is the weight of the mobile operator;

[0154] S44. Construct a comprehensive utility matrix including mobile users and mobile operators, namely:

[0155]

[0156] Where: C is a constant matrix, representing a 1×(M+1) comprehensive utility matrix; is a variable, representing the total utility of mobile user m in time period t; is a variable, representing the total utility of the mobile operator in period t;

[0157] S45. Calculate the comprehensive utility of k iterations based on the comprehensive utility matrix C and the weight coefficient matrix GS Right now:

[0158]

[0159] Where: GS' is the transposed matrix of the matrix coefficient GS; .* represents the dot product symbol.

[0160] S5: Determine whether k-1 is greater than or equal to 1. If yes, proceed to step S6 and compare the comprehensive utility of k-1 times. and Otherwise, go to step S6:

[0161] S6. Judgment Is it greater than If yes, record the mobile user optimization strategy and mobile operator optimization strategy of the kth iteration, otherwise go to step S7;

[0162] S7. Let k = k + 1 to determine the unit subsidy for grid demand response Whether the maximum value has been reached;

[0163] like End if Then proceed to step S8;

[0164] S8. Adjustment of grid demand response unit subsidies make And return to step S2, Δq DR It is a constant, which represents the interval coefficient of unit subsidy during the iterative calculation process.

[0165] This embodiment supports the base station to adjust the power demand by changing the user's communication demand, and then participates in the demand response, while fully considering the comprehensive utility of mobile users and mobile operators. On the one hand, it can release the load adjustment potential of the base station by reducing and changing the mobile user's communication demand, and on the other hand, it fully considers the comprehensive utility of mobile users and mobile operators in the load control process. When the power supply and demand are tight or the renewable energy is difficult to absorb, it can guide users to adjust their communication demand and reduce the load of the base station during the peak period of power consumption, which helps to relieve the pressure on the power grid and improve the stability and security of the power system.

[0166] This embodiment can ensure that the service quality of users is not significantly reduced while adjusting the power demand, thereby improving user satisfaction and loyalty, while reducing the operating cost and energy consumption cost of the base station, and improving the economic benefits and competitiveness of operators.

[0167] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, so the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0168] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the above embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0169] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A real-time control method for base station load demand response considering multi-agent utility, characterized in that: The method comprises the following steps: S1. Initialize the grid demand response unit subsidy parameters and set the number of iterations k = 0; S2. Solve the mobile user optimization model to obtain the total utility of mobile user m in time period t And mobile user optimization strategy, mobile user optimization strategy includes information transmission rate ratio α m,t , information transmission rate S3. Solve the mobile operator optimization model to obtain the total utility of the mobile operator in time period t and the mobile operator optimization strategy, which includes the variable x m,n,t , Energy consumption of a single base station participating in demand response S4. Based on total utility and total utility Calculate the combined utility of mobile users and mobile operators S5, determine whether k-1 is greater than or equal to 1; If yes, then go to step S6 and compare the comprehensive utility of k-1 times and If not, proceed to step S7; S6. Judgment Is it greater than If yes, then record the mobile user optimization strategy and mobile operator optimization strategy of the kth iteration; if no, proceed to step S7; S7. Let k = k + 1 to determine the unit subsidy for grid demand response Whether the maximum value has been reached; If yes, then end; if no, proceed to step S8; S8. Adjust the grid demand response unit subsidy and return to step S2.

2. The base station load demand response real-time control method according to claim 1, characterized in that: In step S1, the grid demand response unit subsidy parameter includes the grid demand response unit subsidy Unit subsidy minimum Unit subsidy ceiling 3. The base station load demand response real-time control method according to claim 1, characterized in that: In step S2, the specific process includes the following steps: S21, constructing a mobile user optimization model, the mobile user optimization model including a mobile user function, a mobile user information transmission utility model and a mobile user demand response utility model; S22. Taking the mobile user utility function model as the target, the mobile user information transmission utility model and the mobile user demand response utility model as the constraints, solve the mobile user optimization strategy and the total utility of mobile user m in time period t.

4. The base station load demand response real-time control method according to claim 3, characterized in that: In step S21, the mobile user utility function satisfies the following relationship: Where: is the utility function of the mobile operator; is a variable, representing the total utility of mobile user m in time period t; is a variable, representing the information transmission rate of user m in time period t; The utility model of mobile user information transmission is: Where: is the utility of mobile users at different information transmission rates; α m,t is a variable, which represents the information transmission rate ratio of user m in time period t; The utility model of mobile user demand response is: Where: is the utility of mobile users participating in demand response; β is the unit subsidy ratio given by mobile operators to mobile users.

5. The base station load demand response real-time control method according to claim 1, characterized in that: In step S3, the specific process includes the following steps: S31, constructing a mobile operator optimization model, the mobile operator optimization model including a mobile operation utility function, a base station energy cost model, a base station participation demand response benefit model, a base station energy consumption model, and a communication condition constraint model; S32. Taking the utility function of mobile operators as the target, the base station energy cost model, the base station participation demand response benefit model, the base station energy consumption model, and the communication condition constraint model as constraints, solve the optimization strategy of mobile operators and the total utility of mobile operators in time period t.

6. The base station load demand response real-time control method according to claim 5, characterized in that: In step S31, the mobile operation utility function is: Where: is the utility function of the mobile operator; is a variable, representing the total utility of the mobile operator in period t; is a variable, which represents the energy consumption of the nth base station participating in the demand response in time period t; is a constant, which represents the energy consumption of the nth base station that does not participate in demand response in time period t, corresponding to the baseline load; The energy cost model of the base station is: Where: is the energy cost of all base stations incurred by the mobile operator in time period t; is a constant, representing the electricity price of base station n in time period t; N is the set of mobile base stations; Δt is a constant, representing the time interval; The profit model of base station participation in demand response is: Where: is the revenue obtained by the mobile operator from participating in demand response during time period t; is a variable, which indicates the unit subsidy provided by the power grid company for participating in demand response; β is the unit subsidy ratio given by the mobile operator to mobile users; The base station energy consumption model is: Where: is a constant, representing the static power consumption of the nth base station; is a variable, representing the dynamic power consumption of the nth base station in time period t; γ represents the energy efficiency coefficient of the base station; The communication condition constraint model is: Where: x m,n,t It is a 0-1 variable, indicating whether the mth mobile user accesses the nth base station in time period t. If yes, it is 1, otherwise it is 0.

7. The base station load demand response real-time control method according to claim 1, characterized in that: In step S4, the specific process includes the following steps: S41. Obtain the minimum transmission rate requirement of mobile users from the power grid and establish the information transmission rate constant matrix E for mobile users, namely: Where: is a constant, indicating the minimum transmission rate requirement of user m in time period t; T DRST is a constant, indicating the time period when demand response starts; T DREND is a constant, indicating the end time of demand response; M is the user set; S42, using the information transmission rate constant matrix E, and using the entropy weight method to obtain the weight coefficient matrix of different users, that is: G=[g1,...,g m ,...,g M ] Where: g m is a constant, representing the weight of the mth mobile user; G is a constant matrix, representing the weight coefficient matrix of the mobile user at the mobile operator, which is a 1×M matrix, and the matrix elements satisfy the following constraints∑ m∈M g m =1; S43, constructing a modified weight coefficient matrix including mobile operators based on the weight coefficient matrix G, which is defined as follows: GS=[σG,1-σ] Where: GS is a constant matrix, representing the modified 1×(M+1) weight coefficient matrix; σ is a constant set by the mobile operator, and 1-σ is the weight of the mobile operator; S44. Construct a comprehensive utility matrix including mobile users and mobile operators, namely: Where: C is a constant matrix, representing a 1×(M+1) comprehensive utility matrix; is a variable, representing the total utility of mobile user m in time period t; is a variable, representing the total utility of the mobile operator in period t; S45. Calculate the comprehensive utility of k iterations based on the comprehensive utility matrix C and the weight coefficient matrix GS Right now: Where: GS' is the transposed matrix of the matrix coefficient GS; .* represents the dot product symbol.

8. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the base station load demand response real-time control method as described in any one of claims 1 to 7.

9. A computer device, characterized in that: It comprises a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the base station load demand response real-time control method as described in any one of claims 1 to 7 is implemented.

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