A Real-Time Control Method for Base Station Load Demand Response Considering Multi-Agent Utility

Abstract

This invention relates to the field of base station load demand response technology, solving the technical problem that existing technologies do not consider the collaboration between mobile operators and mobile users, as well as the comprehensive utility of mobile users and mobile operators. In particular, it relates to a real-time control method for base station load demand response that considers the utility of multiple stakeholders. This method includes: initializing the grid demand response unit subsidy parameter and setting the iteration number k = 0; solving the mobile user optimization model to obtain the total utility of mobile user m in time period t and the mobile user optimization strategy; and solving the mobile operator optimization model to obtain the total utility of the mobile operator in time period t and the mobile operator optimization strategy. This invention can help alleviate grid pressure and improve the stability and security of the power system by guiding users to adjust their communication needs during periods of power supply and demand tension or difficulty in renewable energy absorption.

Description

Technical Field

[0001] This invention relates to the field of base station load demand response technology, and in particular to a real-time control method for base station load demand response that considers the utility of multiple entities. Background Technology

[0002] Communication base stations can participate in demand response, adjusting their power consumption patterns through intelligent dispatching technology based on electricity market demand and price changes, thereby achieving efficient utilization and conservation of electricity resources. This not only helps mobile operators reduce electricity costs but also provides a stable power supply to the grid, improving its stability and reliability.

[0003] The main way base stations participate in demand response is by having mobile operators act as load aggregators. These load aggregators adjust base station load through measures such as shutting down active antenna units, setting cooling equipment temperatures, discharging base station energy storage devices, and migrating communication services. Among these, migrating communication services is the mainstream method 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 are considered to determine which base station the mobile user load should be transferred from to which base station.

[0004] When base stations participate in demand response through the migration of communication services, the impact on mobile users' communication needs must be considered. However, existing technologies do not consider the collaboration between mobile operators and mobile users to adjust electricity demand by reducing user communication needs, nor do they consider the combined utility of mobile users and mobile operators. This leads to an inability to adjust loads during periods of power supply and demand tension or difficulties in renewable energy integration, resulting in a continuous increase in base station load and hindering the flexible adjustment of electricity demand to participate in demand response. Consequently, this reduces the stability and security of the power system. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a real-time control method for base station load demand response that considers the utility of multiple stakeholders, solving the technical problem that existing technologies do not consider the collaboration between mobile operators and mobile users, as well as the comprehensive utility of mobile users and mobile operators.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a real-time control method for base station load demand response considering the utilities of multiple entities, the method comprising the following steps:

[0007] S1. Initialize the unit subsidy parameters for grid demand response, and set the iteration number 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 strategies, including the 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 mobile operator optimization strategies, which include variable x m,n,t Energy consumption of a single base station participating in demand response

[0010] S4, Based on Total Utility Total Utility Calculate the combined utility including mobile users and mobile operators.

[0011] S5. Determine whether k-1 is greater than or equal to 1;

[0012] If so, proceed to step S6 and compare the combined utility of k-1 iterations. and The size; 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 for the kth iteration; otherwise, proceed to step S7.

[0015] S7. Let k = k + 1, determine the unit subsidy for grid demand response. Has the maximum value been reached?

[0016] If yes, then end; otherwise, 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 cap

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

[0020] S21. Construct a mobile user optimization model, which includes a mobile user function, a mobile user information transmission utility model, and a mobile user demand response utility model.

[0021] S22. Using the mobile user utility function model as the objective and the mobile user information transmission utility model and mobile user demand response utility model as constraints, solve for 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] In the formula: For mobile operators' utility functions; Let be a variable representing the total utility of mobile user m during time period t; Let be a variable representing the information transmission rate of user m during time period t;

[0025] The utility model for mobile user information transmission is as follows:

[0026]

[0027] In the formula: The utility of mobile users at different information transmission rates; α m,t Let be a variable, representing the proportion of information transmission rate of user m in time period t;

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

[0029]

[0030] In the formula: β represents the utility of mobile users participating in demand response; β represents the unit subsidy ratio provided by mobile operators to mobile users.

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

[0032] S31. Construct a mobile operator optimization model, which includes a mobile operation utility function, a base station energy cost model, a base station participation demand response revenue model, a base station energy consumption model, and a communication condition constraint model.

[0033] S32. Using the mobile operator's utility function as the objective and the base station energy cost model, base station participation in demand response revenue model, base station energy consumption model, and communication condition constraint model as constraints, solve for the mobile operator's optimization strategy and the mobile operator's total utility in time period t.

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

[0035]

[0036] In the formula: For mobile operators' utility functions; Let be a variable representing the total utility of the mobile operator in time period t; Let be a variable representing the energy consumption of the nth base station in time period t for participating in demand response; is a constant, representing the energy consumption of the nth base station that does not participate in demand response during time period t, corresponding to the baseline load;

[0037] The energy cost model for base stations is as follows:

[0038]

[0039] In the formula: The energy cost incurred by mobile operators for all base stations during time period t; Δt 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 base station participation demand response benefit model is as follows:

[0041]

[0042] In the formula: The revenue that mobile operators gain from participating in demand response during time period t; β is a variable representing the unit subsidy provided by the power grid company for participating in demand response; β is the unit subsidy ratio provided by the mobile operator to mobile users.

[0043] The base station energy consumption model is as follows:

[0044]

[0045] In the formula: is a constant, representing the static power consumption of the nth base station; Let be 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 as follows:

[0047]

[0048] In the formula: x m,n,t It is a 0-1 variable, indicating whether the m-th mobile user accesses the n-th base station during time period t. It is 1 if the user accesses the base station, and 0 otherwise.

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

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

[0051]

[0052] In the formula: Let T be a constant, representing the minimum transmission rate requirement of user m during time period t; DRST T is a constant representing the time period during which the demand response begins; DREND The constant represents the end point of the demand response; M is the user set;

[0053] S42. Using the information transmission rate constant matrix E, the weight coefficient matrix for different users is obtained using the entropy weighting method, i.e.:

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

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

[0056] S43. Construct a modified weighting coefficient matrix, including mobile operators, based on the weighting coefficient matrix G, as defined below:

[0057] GS = [σG, 1-σ]

[0058] In the formula: GS is a constant matrix, representing the corrected 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 that includes mobile users and mobile operators, namely:

[0060]

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

[0062] S45. Calculate the overall utility for k iterations based on the overall utility matrix C and the weighting coefficient matrix GS. Right now:

[0063]

[0064] In the formula: GS' is the transpose of the matrix coefficients GS; .* represents the dot product symbol.

[0065] By employing the above technical solution, the present invention provides a real-time control method for base station load demand response that considers the utilities of multiple stakeholders, which has at least the following beneficial effects:

[0066] 1. This invention supports base station power demand adjustment by changing user communication needs, thereby participating in demand response. On the one hand, it can release the potential of base station load adjustment by reducing changes in mobile user communication needs. On the other hand, the load control process fully considers the comprehensive benefits of mobile users and mobile operators.

[0067] 2. This invention can help alleviate grid pressure and improve the stability and security of the power system by guiding users to adjust their communication needs and reducing the load on base stations during peak electricity consumption periods when power supply and demand are tight or renewable energy consumption is difficult. Furthermore, by flexibly adjusting electricity demand to participate in demand response, it can enhance the flexibility and resilience of the power system and improve the grid's ability to cope with emergencies.

[0068] 3. This invention, through intelligent scheduling and traffic management, guides users to adjust their communication needs when there is a shortage of power supply and demand or difficulties in the absorption of renewable energy, thereby reducing the load on base stations during peak power consumption periods, reducing dependence on traditional energy sources, and promoting the absorption and utilization of renewable energy. Attached Figure Description

[0069] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0070] Figure 1 This is a flowchart of the real-time control method for base station load demand response according to the present invention. Detailed Implementation

[0071] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0072] In this embodiment, demand response refers to measures primarily based on economic incentives to guide electricity users to voluntarily adjust their electricity consumption behavior according to the needs of the power system operation in response to short-term power supply and demand tensions and difficulties in the consumption of renewable energy. This aims to 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 consumption of renewable energy.

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

[0074] In existing technologies, when base stations participate in demand response through the migration of communication services, the impact on mobile users' communication needs needs to be considered. However, existing technologies do not consider the collaboration between mobile operators and mobile users to adjust electricity demand by reducing user communication needs, nor do they consider the combined utility of mobile users and mobile operators. This leads to a situation where, during periods of tight power supply and demand or difficulties in renewable energy integration, the continuous increase in base station load prevents load adjustment, thus hindering the flexible adjustment of electricity demand to participate in demand response, thereby reducing 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 considers the utilities of multiple stakeholders. When power supply and demand are strained or renewable energy absorption is difficult, this method can guide users to adjust their communication demands, reducing the load on base stations during peak power consumption periods. This helps alleviate grid pressure and improve the stability and security of the power system. Furthermore, by flexibly adjusting power demand to participate in demand response, it can enhance the flexibility and resilience of the power system and improve the grid's ability to cope with emergencies. The method includes the following steps:

[0076] S1. Initialize the grid demand response unit subsidy parameter, setting the iteration number k = 0. The grid demand response unit subsidy parameter includes the grid demand response unit subsidy. Unit subsidy minimum Unit subsidy cap in is a constant, representing the lower limit of the unit subsidy provided by the power grid for mobile operators' participation in demand response during time period t; is a constant, representing the upper limit of the unit subsidy provided by the power grid for mobile operators' participation in demand response; Let be a variable, representing the unit subsidy provided by the power grid to mobile operators participating in demand response during time period t. Let

[0077] In this embodiment, it also includes acquired demand response control parameters, mobile operator base station information, and user information. The demand response control parameters include T... DRST T DREND ,t, Where T DRST T is a constant representing the time period during which the demand response begins. DREND Let be a constant, representing the time period at which the demand response ends. Let t be a variable, representing the t-th time period, let t = T. DRST Δq DRis a constant representing the interval coefficient of the unit subsidy during the iterative calculation. Mobile operator base station and user information includes N, M, n, and m. Where N is the set of mobile base stations, and N is the number of base stations in set N. M is the set of users. n is a variable representing the nth base station, n∈N. m is a variable representing the mth mobile user, m∈M, and M is the number of users in 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 strategies, including the information transmission rate ratio α. m,t Information transmission rate In step S2, the specific process includes the following steps:

[0079] S21. Construct a mobile user optimization model, which includes 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] In the formula: For mobile operators' utility functions; Let be a variable representing the total utility of mobile user m during time period t; Let be a variable representing the information transmission rate of user m during time period t.

[0083]

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

[0085] The utility model for mobile user information transmission is as follows:

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

[0087]

[0088] In the formula: α m,t Let be a variable representing the proportion of information transmission rate of user m in time period t, defined as follows:

[0089]

[0090] In the formula: is a constant, representing the minimum transmission rate requirement of user m during time period t; is a constant, representing the maximum transmission rate requirement of user m during time period t; Let be a variable representing the information transmission rate of user m during time period t.

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

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

[0093]

[0094] In the formula: β is the unit subsidy ratio given by the mobile operator to the mobile user.

[0095] S22. Using the mobile user utility function model as the objective and the mobile user information transmission utility model and mobile user demand response utility model as constraints, solve for 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 algorithms, etc.) can be used to solve the mobile user optimization strategy, that is, to maximize the total utility of mobile users under the premise of satisfying the constraints.

[0096] Therefore, by optimizing mobile users' communication and demand response behaviors, the overall efficiency of communication and power systems can be improved. This simultaneously reduces base station energy consumption and operating costs, saving users communication expenses and achieving a win-win situation. Ultimately, this improves user service quality and satisfaction, enhancing user 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 there is a shortage of power supply and demand or difficulties in the absorption of renewable energy, thereby reducing the load on base stations during peak power consumption periods, reducing dependence on traditional energy sources, and promoting the absorption 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 mobile operator optimization strategies, which include 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. Construct a mobile operator optimization model, which includes a mobile operation utility function, a base station energy cost model, a base station participation demand response revenue model, a base station energy consumption model, and a communication condition constraint model.

[0100] The mobile operation utility function is:

[0101]

[0102] In the formula: For mobile operators' utility functions; Let be a variable representing the total utility of the mobile operator in time period t; Let be a variable representing the energy consumption of the nth base station in time period t for participating in demand response; is a constant, representing the energy consumption of the nth base station that does not participate in demand response during time period t, corresponding to the baseline load.

[0103]

[0104] In the formula: The energy cost incurred by mobile operators for all base stations during time period t; This refers to the revenue that mobile operators gain from participating in demand response during time period t.

[0105] The energy cost model for base stations is as follows:

[0106]

[0107] In the formula: Δt 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 base station participation demand response benefit model is as follows:

[0109]

[0110] In the formula: Let be a variable, representing the unit subsidy provided by the power grid company for participating in demand response;

[0111] The base station energy consumption model is as follows:

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

[0113]

[0114] In the formula: is a constant, representing the static power consumption of the nth base station; Let be 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 as follows:

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

[0117]

[0118] In the formula: x m,n,t The variable is 0-1, representing whether the m-th mobile user accesses the n-th base station during time period t; 1 indicates access, 0 indicates otherwise. m,n,t The value is constrained by Shannon's formula.

[0119] The Shannon formula has the following constraints regarding the signal-to-interference-plus-noise ratio (SINR), bandwidth, and transmission rate for mobile user m:

[0120]

[0121] In the formula: B m,n,t Let be a variable representing the bandwidth that the m-th mobile user can be allocated to when accessing the n-th base station in time period t, with the following constraints:

[0122]

[0123] In the formula, y m,n,t It is a 0-1 variable, indicating whether the m-th mobile user participates in demand response when accessing the n-th base station in time period t. If it participates, it is 1; otherwise, it is 0. This indicates the bandwidth allocated to user m by the nth base station during time period t when not participating in demand response; This represents the bandwidth allocated to user m when accessing the nth base station during time period t, when participating in demand response.

[0124] In the formula, E m,n,t Let be a variable representing the information transmission rate of the m-th mobile user after accessing the n-th base station in time period t, with the following constraints:

[0125]

[0126] In the formula, δ m,n,t Let be a variable representing the signal-to-interference-plus-noise ratio (SIR) of the m-th mobile user after accessing the n-th base station in time period t, i.e.:

[0127]

[0128] In the formula: θ is a constant, representing the average power of the channel white noise; h m,n is a constant representing the channel gain when mobile user m transmits signals to base station n; Let be a variable representing the dynamic power consumption of the nth base station during time period t; h represents the dynamic power consumption of the base station for the l-th mobile user during time period t; l,nThis represents the channel gain when mobile user l transmits signals to base station n.

[0129] Transforming the expression, we obtain 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] In the formula: z m,n,t k m,n,t v m,n,t All of these are introduced auxiliary variables, forming an N×M matrix, with O being a local maximum.

[0140] S32. Using the mobile operator's utility function as the objective and the base station energy cost model, base station participation in demand response revenue model, base station energy consumption model, and communication condition constraint model as constraints, solve for the mobile operator's optimization strategy and the mobile operator's total utility in time period t. In this embodiment, mathematical programming and optimization algorithms (such as linear programming, dynamic programming, genetic algorithms, etc.) can be used to solve the mobile user optimization strategy, that is, to maximize the total utility of mobile users under the premise of satisfying the constraints.

[0141] Therefore, by implementing optimization strategies, mobile operators can manage base station resources more efficiently, reduce operating costs, and improve overall operational efficiency. This also enhances communication service quality and user satisfaction, strengthens the market competitiveness of mobile operators, promotes the efficient use of base station energy and the integration of renewable energy, and contributes to the green development of the communications industry.

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

[0143] S41. Obtain the minimum transmission rate requirement for mobile users from the power grid, and establish an information transmission rate constant matrix E for mobile users; specifically, the minimum transmission rate requirement for mobile users satisfies a weighting function, namely:

[0144]

[0145] In the formula: This refers to the weighting function. The weighting function outputs the weight matrix, which is mainly calculated using a comprehensive evaluation algorithm, obtained through estimation.

[0146]

[0147] In the formula: Let T be a constant, representing the minimum transmission rate requirement of user m during time period t; DRST T is a constant representing the time period during which the demand response begins; DREND is a constant, representing the end point of the demand response; M is the user set.

[0148] S42. Using the information transmission rate constant matrix E, the weight coefficient matrix for different users is obtained using the entropy weighting method, i.e.:

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

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

[0151] S43. Construct a modified weighting coefficient matrix, including mobile operators, based on the weighting coefficient matrix G, as defined below:

[0152] GS = [σG, 1-σ]

[0153] In the formula: GS is a constant matrix, representing the corrected 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 that includes mobile users and mobile operators, namely:

[0155]

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

[0157] S45. Calculate the overall utility for k iterations based on the overall utility matrix C and the weighting coefficient matrix GS. Right now:

[0158]

[0159] In the formula: GS' is the transpose of the matrix coefficients GS; .* represents the dot product symbol.

[0160] S5. Determine if k-1 is greater than or equal to 1. If yes, proceed to step S6 and compare the overall utility of k-1 times. and If the size is not specified, proceed to step S6:

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

[0162] S7. Let k = k + 1, determine the unit subsidy for grid demand response. Has the maximum value been reached?

[0163] like Then it ends, if Then proceed to step S8;

[0164] S8. Adjusting subsidies for grid demand response units make And return to step S2, Δq DR is a constant, representing the interval coefficient of the unit subsidy during the iterative calculation process.

[0165] This embodiment supports base station regulation of power demand by altering user communication needs, thereby participating in demand response. It also fully considers the combined utility of mobile users and mobile operators. On one hand, it can release the potential for base station load regulation by reducing changes in mobile user communication needs; on the other hand, the load control process fully considers the combined utility of mobile users and mobile operators. When power supply and demand are tight or renewable energy absorption is difficult, it can guide users to adjust their communication needs, reducing the load on base stations during peak power consumption periods, thus helping to alleviate grid pressure and improve the stability and security of the power system.

[0166] This embodiment ensures that while regulating power demand, it does not significantly reduce the quality of service for users, thereby improving user satisfaction and loyalty. Simultaneously, it reduces the operating and energy costs of base stations, enhancing the operator's economic efficiency and competitiveness.

[0167] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this 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 code.

[0168] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Since the above embodiments are substantially similar to the method embodiments, their descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0169] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A real-time control method for base station load demand response considering the utilities of multiple stakeholders, characterized in that, The method includes the following steps: S1. Initialize the unit subsidy parameters for grid demand response, and set the iteration number 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 strategies, including the 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 mobile operator optimization strategies, which include variable x m,n,t Energy consumption of a single base station participating in demand response S4, Based on Total Utility Total Utility Calculate the combined utility including mobile users and mobile operators. S5. Determine whether k-1 is greater than or equal to 1; If so, proceed to step S6 and compare the combined utility of k-1 iterations. and The size; 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 for the kth iteration; otherwise, proceed to step S7. S7. Let k = k + 1, determine the unit subsidy for grid demand response. Has the maximum value been reached? If yes, then end; otherwise, proceed to step S8. S8. Adjust the grid demand response unit subsidy and return to step S2.

2. The real-time control method for base station load demand response 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 cap 3. The real-time control method for base station load demand response according to claim 1, characterized in that, In step S2, the specific process includes the following steps: S21. Construct a mobile user optimization model, which includes a mobile user function, a mobile user information transmission utility model, and a mobile user demand response utility model. S22. Using the mobile user utility function model as the objective and the mobile user information transmission utility model and mobile user demand response utility model as constraints, solve for the mobile user optimization strategy and the total utility of mobile user m in time period t.

4. The real-time control method for base station load demand response according to claim 3, characterized in that, In step S21, the mobile user utility function satisfies the following relationship: In the formula: For mobile operators' utility functions; Let be a variable representing the total utility of mobile user m during time period t; Let be a variable representing the information transmission rate of user m during time period t; The utility model for mobile user information transmission is as follows: In the formula: The utility of mobile users at different information transmission rates; α m,t Let be a variable, representing the proportion of information transmission rate of user m in time period t; The mobile user demand response utility model is as follows: In the formula: β represents the utility of mobile users participating in demand response; β represents the unit subsidy ratio provided by mobile operators to mobile users.

5. The real-time control method for base station load demand response according to claim 1, characterized in that, In step S3, the specific process includes the following steps: S31. Construct a mobile operator optimization model, which includes a mobile operation utility function, a base station energy cost model, a base station participation demand response revenue model, a base station energy consumption model, and a communication condition constraint model. S32. Using the mobile operator's utility function as the objective and the base station energy cost model, base station participation in demand response revenue model, base station energy consumption model, and communication condition constraint model as constraints, solve for the mobile operator's optimization strategy and the mobile operator's total utility in time period t.

6. The real-time control method for base station load demand response according to claim 5, characterized in that, In step S31, the mobile operation utility function is: In the formula: For mobile operators' utility functions; Let be a variable representing the total utility of the mobile operator in time period t; Let be a variable representing the energy consumption of the nth base station in time period t for participating in demand response; is a constant, representing the energy consumption of the nth base station that does not participate in demand response during time period t, corresponding to the baseline load; The energy cost model for base stations is as follows: In the formula: The energy cost incurred by mobile operators for all base stations during time period t; Δt 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 base station participation demand response benefit model is as follows: In the formula: The revenue that mobile operators gain from participating in demand response during time period t; β is a variable representing the unit subsidy provided by the power grid company for participating in demand response; β is the unit subsidy ratio provided by the mobile operator to mobile users. The base station energy consumption model is as follows: In the formula: is a constant, representing the static power consumption of the nth base station; Let be 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 as follows: In the formula: x m,n,t It is a 0-1 variable, indicating whether the m-th mobile user accesses the n-th base station during time period t. It is 1 if the user accesses the base station, and 0 otherwise.

7. The real-time control method for base station load demand response according to claim 1, characterized in that, In step S4, the specific process includes the following steps: S41. Obtain the minimum transmission rate requirements for mobile users from the power grid, and establish the information transmission rate constant matrix E for mobile users, that is: In the formula: Let T be a constant, representing the minimum transmission rate requirement of user m during time period t; DRST T is a constant representing the time period during which the demand response begins; DREND The constant represents the end point of the demand response; M is the user set; S42. Using the information transmission rate constant matrix E, the weight coefficient matrix for different users is obtained using the entropy weighting method, i.e.: G=[g1,...,g m ,...,g M ] Where: g m Let be a constant, representing the weight of the m-th mobile user; G is a constant matrix, representing the weight coefficient matrix of mobile users at the mobile operator, which is a 1×M matrix, and the matrix elements satisfy the following constraint ∑ m∈M g m =1; S43. Construct a modified weighting coefficient matrix, including mobile operators, based on the weighting coefficient matrix G, as defined below: GS = [σG, 1-σ] In the formula: GS is a constant matrix, representing the corrected 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 that includes mobile users and mobile operators, namely: In the formula: C is a constant matrix, representing a 1×(M+1) comprehensive utility matrix; Let be a variable representing the total utility of mobile user m during time period t; Let be a variable representing the total utility of the mobile operator in time period t; S45. Calculate the overall utility for k iterations based on the overall utility matrix C and the weighting coefficient matrix GS. Right now: In the formula: GS' is the transpose of the matrix coefficients 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, implement the real-time control method for base station load demand response as described in any one of claims 1 to 7.

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

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