Demand side response method based on spatial shifting of communication base station load

Abstract

The application discloses a demand side response method and system based on space transfer of communication base station load and a computer program product, and belongs to the technical field of intelligent power distribution networks. The method comprises the following steps: equivalent transferable loads between communication nodes to active power output capacity, constructing a maximum schedulable capacity calculation model of each communication node of a power distribution network to obtain the maximum schedulable capacity of each communication node; combining the electricity price and the carbon emission coefficient of each communication node, fitting an optimal cost curve of a high-voltage power distribution network considering carbon emission to obtain the minimum value of the operation cost and carbon emission of the high-voltage power distribution network; and constructing a high-voltage power distribution network dispatching optimization model, distributing according to a given dispatching instruction to obtain the unit group distribution output of each communication node of the high-voltage power distribution network at the minimum operation cost and carbon emission. The application realizes the collaborative optimization of the communication network performance considering carbon emission and the economic operation of the power distribution network.

Description

Technical Field

[0001] This application relates to the field of smart distribution networks, and in particular to a demand-side response method based on spatial load transfer from communication base stations. Background Technology

[0002] With the grid connection of high proportions of distributed renewable energy (such as photovoltaics, energy storage, and controllable loads) and the development of microgrids, load spatial transfer between communication nodes has become an important means of optimizing grid operation. Traditional dispatching methods usually treat load spatial transfer as a simple power adjustment, treating it only as a power consumption unit, and fail to fully explore its equivalent relationship with the active power output of nodes.

[0003] In existing technologies, power market dispatch methods based on nodal marginal pricing (LMP) reflect supply and demand by calculating nodal prices and allocating output in conjunction with line transmission constraints. Demand-side response (DR) technology incentivizes load migration based on time-of-use / regional pricing differences according to electricity prices or distribution network security needs. However, current data center power consumption continues to grow, but existing technologies still mainly focus on optimizing communication network performance, failing to achieve efficient load management and economic dispatch from a power system perspective. Furthermore, in terms of environmental considerations, the economics of load spatial transfer and carbon emission coefficients are usually modeled independently, failing to effectively integrate dynamic pricing curves and carbon emission constraints, resulting in a lack of coordinated optimization and making it difficult for dispatch strategies to balance economic and low-carbon goals.

[0004] For example, economic modeling of node active power output is often based on the unit generation cost curve, using a quadratic function to describe marginal cost, and achieving economic dispatch by minimizing total cost. This approach struggles to adapt to real-time price fluctuations caused by load spatial shifts, resulting in insufficient optimization flexibility. Furthermore, although some studies have attempted to combine economic dispatch with carbon emission constraints, a unified linkage mechanism between the price curve and the carbon emission curve is still lacking, limiting the comprehensive optimization capability of dispatch strategies. Summary of the Invention

[0005] This application aims to provide a demand-side response method that considers the performance of communication networks and the economic operation of power systems while taking into account carbon emissions, so as to improve the economy and low-carbon nature of power grid operation.

[0006] To achieve the above objectives, the technical solution of this application is as follows:

[0007] A demand-side response method based on the spatial transfer of load from communication base stations includes: equating the transferable load between communication nodes to the active power output capacity of each communication node; setting the minimum load power consumption of each communication node in the high-voltage distribution network as the objective function; constructing a calculation model for the maximum dispatchable capacity of each communication node in the distribution network; and obtaining the maximum dispatchable capacity of each communication node.

[0008] Based on the maximum dispatchable capacity of each communication node, combined with the electricity price and carbon emission coefficient of each communication node, the optimal cost curve of the high-voltage distribution network considering carbon emissions is fitted, and the minimum operating cost and carbon emissions of the high-voltage distribution network are obtained.

[0009] Based on the optimal cost curve of the high-voltage distribution network considering carbon emissions and the maximum dispatchable capacity of each communication node, a high-voltage distribution network dispatch optimization model is constructed. According to the given dispatch instructions, the power output of each communication node unit of the high-voltage distribution network is allocated to meet the requirements of minimizing the operating cost and carbon emissions of the high-voltage distribution network, thereby realizing demand response.

[0010] Optionally, the minimum load power consumption of each communication node in the high-voltage distribution network is set as the objective function, and a calculation model for the maximum dispatchable capacity of each communication node in the distribution network is constructed. The objective function is expressed as follows:

[0011] Obj1.1:minLD i,min =pbbu i +paau i

[0012] Among them, LD i,min pbbu represents the minimum load power consumption under the i-th communication node; i Paau represents the total power consumption generated by all indoor baseband processing units in all macro base station units cascaded under the i-th communication node. i This represents the total power consumption generated by all active antenna units in all macro base station units cascaded under the i-th communication node;

[0013] The total power consumption generated by all indoor baseband processing units in all macro base station units cascaded under the i-th communication node is expressed as follows:

[0014] sub_Obj1.1.1:pbbu i =pst i +pdy i +pmi i

[0015] Among them, pst i pdy represents the total static power consumption of all indoor baseband processing units in all macro base station units under the i-th communication node; i pmi represents the total dynamic power consumption of all indoor baseband processing units in each macro base station unit under the i-th communication node. i This represents the total transmission power consumption generated when the distribution of task packets among all macro base station units changes.

[0016] Optionally, the constraints of the calculation model for the maximum schedulable capacity of each communication node in the distribution network include: constraints on the distribution of task packets, constraints on the transfer of task packets between macro base station units, constraints on the calculation time delay of each communication node in processing task packets, and constraints on the transmission time delay of each communication node in transmitting task packets through the optical network.

[0017] The constraints on task package distribution are as follows:

[0018]

[0019] The distribution of task packets is represented by a 0-1 variable; that is, the m-th task packet is represented as 1 if it is processed at the i-th communication node, and 0 otherwise. i,m This represents the m-th task packet under the i-th communication node; N represents the total number of communication nodes in the high-voltage distribution network; NTASK represents the total number of task packets across all communication nodes;

[0020] The task packet transfer constraints between macro base station units are expressed as follows:

[0021]

[0022] The computation time delay constraints for each communication node to process the task packet are expressed as follows:

[0023]

[0024] Among them, l m This represents the size of the m-th task packet under the i-th communication node; cap i This represents the maximum processing capacity of the i-th communication node for the task packet;

[0025] The transmission time delay constraint for transmitting task packets through the optical network at each communication node is expressed as follows:

[0026]

[0027] Where RA represents the transmission rate of the task packet; τ m Indicates the given acceptable transmission time for each task packet; b i,m This represents the distribution of the m-th task packet under the i-th communication node before migration.

[0028] Optionally, obtaining the maximum schedulable capacity of each communication node includes: obtaining the total load power consumption of each communication node when it is not transmitting task packets through real-time information, and subtracting the total load power consumption of each communication node when it is not transmitting task packets from the minimum load power consumption of each communication node when transmitting task packets to obtain the maximum schedulable capacity of each communication node.

[0029] Optionally, based on the maximum dispatchable capacity of each communication node, and combined with the electricity price and carbon emission coefficient of each communication node, the optimal cost curve of the high-voltage distribution network considering carbon emissions is fitted to obtain the minimum operating cost and carbon emissions of the high-voltage distribution network, including:

[0030] Step S21: Receive the maximum schedulable capacity of each communication node, sort the maximum schedulable capacity of each communication node in ascending order, and divide it into segments according to equal intervals.

[0031] Step S22: Input the m-th task packet under the i-th communication node as the independent variable, and set the schedulable capacity of each communication node to be equal to the minimum schedulable capacity of a single communication node.

[0032] Step S23: Solve the optimal cost curve fitting model for each communication node considering carbon emissions, and obtain the minimum operating cost and carbon emissions of each communication node after optimization, as well as the power-optimal cost point at each power point after segmentation.

[0033] Step S24: Determine whether the schedulable capacity of each communication node is equal to the maximum schedulable capacity of each communication node. If not, proceed to step S25; if yes, proceed to step S26.

[0034] Step S25: Set the schedulable capacity of each communication node to equal the schedulable capacity of each communication node plus the capacity at equal intervals, and return to step S23;

[0035] Step S26: Using the schedulable capacity of each communication node as the horizontal axis and the minimum operating cost and carbon emissions of each communication node as the vertical axis, arrange all the scattered points to obtain a power-optimal cost dot plot of capacity points with equal spacing.

[0036] Step S27: Fit the curve using a scatter plot curve fitting algorithm to obtain the optimal cost curve for each communication node considering carbon emissions;

[0037] Step S28: Based on the maximum schedulable capacity of each communication node after sorting, sequentially accumulate the capacity of each equally spaced node and the corresponding total local cost, fit the optimal cost curve of the high-voltage distribution network considering carbon emissions, and obtain the minimum operating cost and minimum carbon emissions of the high-voltage distribution network.

[0038] Alternatively, a model for fitting the optimal cost curve of each communication node under carbon emission conditions can be considered, with the objective function being the minimum operating cost and carbon emission of each communication node, as shown below:

[0039] Obj2.1:minCost i =pbbu i C i +λ i E i

[0040] Among them, minCost i C represents the optimal cost of the i-th communication node; i λ represents the electricity price of the i-th communication node; i E represents the carbon emission factor of the i-th communication node; i This represents the total power consumption generated by all indoor baseband processing units in all macro base station units cascaded under the i-th communication node, expressed as pbbu. i Carbon emissions at that time.

[0041] Optionally, the constraints of the optimal cost curve fitting model for each communication node under carbon emissions include:

[0042] Carbon emission constraints for each communication node, power consumption constraints of the equivalent macro base station under each communication node, and task package constraints of the macro base station under each communication node.

[0043] The carbon emission constraints for each communication node are as follows:

[0044] Const2.1: E i =e i ×pbbu i

[0045] Among them, e i This represents the carbon emission intensity of the i-th communication node, in yuan / ton of carbon dioxide;

[0046] The power consumption constraints of the equivalent macro base station under each communication node are expressed as follows:

[0047]

[0048] The constraints of the macro base station task packets under each communication node are as follows:

[0049] Const2.3:db i,min ≤db i,m ≤db i,max

[0050] Among them, db i,min This represents the minimum number of macro base station task packets under the i-th communication node; db i,max This represents the maximum number of macro base station task packets under the i-th communication node.

[0051] Optionally, the high-voltage distribution network dispatch optimization model takes the minimum operating cost and carbon emissions of the high-voltage distribution network as the objective function, which is expressed as follows:

[0052]

[0053] Wherein, min Cost represents the optimal cost of the high-voltage distribution network, that is, the minimum operating cost and carbon emissions of the high-voltage distribution network;

[0054] The constraints of the high-voltage distribution network dispatch optimization model include: carbon emission constraints of each communication node, power consumption constraints of the equivalent macro base station under each communication node, task package constraints of the macro base station under each communication node, and constraints of the given dispatch instructions.

[0055] A demand-side response system based on spatial load transfer of communication base stations is provided for executing the demand-side response method based on spatial load transfer of communication base stations as described above. The demand-side response system based on spatial load transfer of communication base stations includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the demand-side response method based on spatial load transfer of communication base stations as described above.

[0056] A computer program product includes a computer program that, when executed by a processor, implements the demand-side response method based on the spatial transfer of communication base station load as described above.

[0057] This application establishes an equivalent mapping relationship between the transferable computational load of communication nodes and their active power output capacity. By combining dynamic pricing curves and carbon emission curves, it achieves coordinated optimization of communication network performance and distribution network economic operation while considering carbon emissions. Specifically, by treating the load spatial transfer capability of each node as equivalent to its active power output capacity, and combining the electricity price and carbon emission coefficient of each node, it fits the optimal cost curve for high-voltage distribution networks considering carbon emissions. Under the premise of satisfying given dispatch instructions, it allocates the active power output of each node with the optimization objectives of minimizing total operating costs and total carbon emissions, thereby achieving the goal of minimizing distribution network costs through computational load spatial transfer. This not only overcomes the limitations of traditional demand response methods that separate economic and environmental considerations, but also significantly improves the flexibility and economy of distribution network dispatch in scenarios involving renewable energy integration, providing an effective technical solution for building a low-carbon economic distribution network.

[0058] To make the above-mentioned features and advantages of the application more apparent and understandable, specific embodiments are provided below, and detailed descriptions are given in conjunction with the accompanying drawings. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the structure of a macro base station unit in a specific embodiment of this application.

[0060] Figure 2 This is a topology diagram of the macro base station cluster in a certain equivalent node module of the high-voltage distribution network in a specific embodiment of this application, corresponding to the distribution network topology.

[0061] Figure 3A flowchart of the demand-side response method based on spatial load transfer of communication base stations provided in this application.

[0062] Figure 4 This is a flowchart of step S2.

[0063] Figure 5 Figure (a) shows the power-optimal cost plot obtained by arranging all the scattered points to obtain equally spaced capacity points.

[0064] Figure 5 Figure (b) in the figure is a schematic diagram of the optimal cost curve of each communication node considering carbon emissions, obtained by fitting the curve using a scatter plot fitting algorithm. Detailed Implementation

[0065] To make the objectives and technical solutions of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the described embodiments of this application without creative effort are within the scope of protection of this application.

[0066] This application proposes a demand-side response method based on the spatial transfer of load from communication base stations. The communication base station cluster is connected to the high-voltage distribution network, and the demand-side response is achieved through the scheduling of the high-voltage distribution network.

[0067] In one specific embodiment of this application, the communication base station is a macro base station.

[0068] At the high-voltage distribution network level, each high-voltage distribution network communication node is composed of the equivalent output of photovoltaic or wind turbine distributed generation units and macro base station clusters (MBSUs). A macro base station cluster includes multiple macro base station units (MBSUs).

[0069] A macro base station unit includes: a high-power node (MBS) of an active antenna unit (AAU), a centralized building baseband unit (C-BBU), an optical network, and multiple radio node modules. Each radio node module includes a remote radio head (RRH) and a user equipment (UE). Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a schematic diagram of the structure of a macro base station unit in a specific embodiment. The macro base station unit 11 includes 6 radio node modules, specifically radio node module 111 to radio node module 116.

[0070] Among them, the remote radio head is a low-power distributed radio node that connects to the user equipment wirelessly to amplify, convert uplink / downlink, and filter noise in incoming signals; the high-power node provides large-scale coverage for data services and control signals; the indoor baseband processing unit is a unit composed of high-performance programmable processors for signal processing; the optical network includes a fronthaul network and an X2 interface, which are responsible for interconnecting communication nodes.

[0071] High-voltage distribution networks may include multiple communication nodes; please refer to [link / reference]. Figure 2 , Figure 2 This is a topology diagram of a macro base station cluster in a certain equivalent node module of a high-voltage distribution network, corresponding to the distribution network in a specific embodiment. It includes macro base station cluster 1 and equivalent node module 2 of the high-voltage distribution network. For example... Figure 2 As shown, the macro base station cluster 1 includes three macro base station units 11 and one cloud computing center 12. The equivalent node module 2 of the high-voltage distribution network includes five communication nodes, specifically communication nodes 21 to 25. Communication nodes 22 and 25 are connected to power generation units 221 and 251 respectively, communication node 21 is connected to the cloud computing center 12, and communication nodes 23 and 24 are connected to the macro base station units 11 respectively. Communication node 21 represents a communication node in the high-voltage distribution network. Including the total output of the macro base station cluster 1, it can be considered as an aggregate formed by connecting a series of macro base station units 11 and the total output of the cloud computing center 12. Communication nodes 22 to 25 represent communication nodes of the lower voltage level network connected to communication node 21. Each communication node can contain multiple macro base station units 11 according to the output requirements of the communication nodes. Figure 2 It can be seen that the physical characteristics of a macro base station cluster are equivalent to the physical characteristics of the equivalent node module of a high-voltage distribution network.

[0072] In one specific embodiment of this application, the macro base station unit is a 5G macro base station unit.

[0073] In a specific embodiment of this application, please refer to Figure 3 , Figure 3 The flowchart of the demand-side response method based on the spatial transfer of communication base station load provided in this application includes steps S1 to S3.

[0074] Step S1: Equivalent the transferable load between communication nodes to the active power output capacity of each communication node, set the minimum load power consumption of each communication node in the high-voltage distribution network as the objective function, construct a calculation model for the maximum dispatchable capacity of each communication node in the distribution network, and obtain the maximum dispatchable capacity of each communication node.

[0075] Step S2: Based on the maximum dispatchable capacity of each communication node, and combined with the electricity price and carbon emission coefficient of each communication node, fit the optimal cost curve of the high-voltage distribution network considering carbon emissions, and obtain the minimum operating cost and carbon emissions of the high-voltage distribution network.

[0076] Step S3: Based on the optimal cost curve of the high-voltage distribution network considering carbon emissions and the maximum dispatchable capacity of each communication node, construct a high-voltage distribution network dispatch optimization model. Allocate power according to the given dispatch instructions to obtain the power output of each communication node unit of the high-voltage distribution network when the demand for minimizing the operating cost and carbon emissions of the high-voltage distribution network is met, thereby realizing demand response.

[0077] The demand-side response method based on load spatial transfer from communication base stations provided in this application establishes an equivalent mapping relationship between the transferable computational load of communication nodes and their active power output capacity. By combining dynamic pricing curves and carbon emission curves, it achieves coordinated optimization of communication network performance and distribution network economic operation while considering carbon emissions. Specifically, by treating the load spatial transfer capacity of each node as equivalent to its active power output capacity, and combining the electricity price and carbon emission coefficient of each node, it fits the optimal cost curve for high-voltage distribution networks considering carbon emissions. Under the premise of satisfying given dispatch instructions, it allocates the active power output of each node with the optimization objectives of minimizing total operating costs and total carbon emissions, thereby achieving the goal of minimizing distribution network costs through load spatial transfer calculation. This not only overcomes the limitation of separating economic and environmental considerations in traditional demand response methods but also significantly improves the flexibility and economy of distribution network dispatch in renewable energy integration scenarios, providing an effective technical solution for building a low-carbon economic distribution network.

[0078] In step S1, please refer to Figure 3 In step S1, the transferable load between communication nodes is equivalent to the active power output capacity of each communication node. The minimum load power consumption of each communication node in the high-voltage distribution network is set as the objective function. A calculation model for the maximum dispatchable capacity of each communication node in the distribution network is constructed to obtain the maximum dispatchable capacity of each communication node.

[0079] As an example, the transfer of task packets between macro base station units connected to communication nodes in a high-voltage distribution network will reflect changes in the power of the communication nodes, i.e., factors affecting the load level of the communication nodes. These include changes in the load level of communication nodes connected to lower-level macro base station units, as well as changes in the load level of the corresponding aggregated communication nodes in the high-voltage distribution network. Therefore, considering the capacity aggregation of communication nodes in the high-voltage distribution network, i.e., considering the upper and lower limits of the output of the communication nodes, i.e., considering the maximum and minimum load levels of the communication nodes, is crucial. Based on the results of communication node capacity aggregation, the equivalent maximum schedulable capacity of each communication node is obtained.

[0080] As an example, the equivalent maximum schedulable capacity of each communication node is obtained by comparing the total load power consumption of each macro base station unit under each communication node when not transmitting task packets with the minimum load power consumption of each macro base station unit under each communication node when transmitting task packets. Here, load power consumption represents the transferable load between communication nodes.

[0081] As an example, let's set the m-th task packet db under the i-th communication node at time T. i,m The distribution of these variables is the independent variable in the calculation model of the maximum dispatchable capacity of each communication node in the distribution network.

[0082] As an example, the objective function is set as the minimum load power consumption of each communication node in a high-voltage distribution network, as follows:

[0083] Obj1.1:minLD i,min =pbbu i +paau i (1.1)

[0084] Among them, LD i,min pbbu represents the minimum load power consumption under the i-th communication node; i Paau represents the total power consumption generated by all indoor baseband processing units in all macro base station units cascaded under the i-th communication node. i The total power consumption generated by all active antenna units in all macro base station units cascaded under the i-th communication node can be calculated as a fixed value by combining the communication volume of macro base station units in each time period.

[0085] As an example, during the optimization process, changes in the independent variables cause task packet migration between macro base station units, resulting in a change in the distribution of task packets among them. This task packet migration process consumes a certain amount of power, specifically the transmission power required to transfer a task packet from one indoor baseband processing unit to another via the optical network. The total power consumption (pbbu) generated by all indoor baseband processing units in all macro base station units under each communication node is also considered. i It is expressed as follows:

[0086] sub_Obj1.1.1:pbbu i =pst i +pdy i +pmi i (1.2)

[0087] Among them, pst i This represents the total static power consumption of all indoor baseband processing units in all macro base station units under the i-th communication node. It is the power consumption required to support the computing environment, i.e., the power consumption generated when no task processing or task packet transmission is performed; pdyi PMI represents the total dynamic power consumption of all indoor baseband processing units in each macro base station unit under the i-th communication node, that is, the power consumed by the macro base station unit when processing different task packets; i This represents the total transmission power consumption generated when the distribution of task packets among all macro base station units changes.

[0088] Specifically, the indoor baseband processing unit is in a long-term non-sleep state, and the total static power consumption (pst) of all indoor baseband processing units in all macro base station units under each communication node is... i It is expressed as follows:

[0089] sub_Obj1.1.2:pst i =η i ×θ i (1.3)

[0090] Where, η i θ represents the number of indoor baseband processing units under the i-th communication node; i This represents the unit rated static power consumption of the indoor baseband processing unit under the i-th communication node.

[0091] Specifically, the total dynamic power consumption pdy of all indoor baseband processing units in each macro base station unit under the i-th communication node. i It is expressed as follows:

[0092]

[0093] Among them, pbu i This represents the power consumption of the indoor baseband processing unit at the i-th communication node when it provides maximum processing power; uf i This represents the resource utilization rate of the indoor baseband processing unit under the i-th communication node; cap i This represents the maximum processing capacity of the i-th communication node for task packets; l m NTASK represents the size of the m-th task packet under the i-th communication node; NTASK represents the total number of task packets across all communication nodes.

[0094] Specifically, the total transmission power consumption (pmi) generated when the distribution of task packets changes among all macro base station units. i It is expressed as follows:

[0095]

[0096] Where, β opt b represents the transmission power consumption coefficient caused by packet migration; i,m This represents the distribution of the m-th task packet under the i-th communication node before migration.

[0097] Specifically, the objective function of the maximum dispatchable capacity calculation model for each communication node in the distribution network is sub_Obj1.1:minLD i,min It consists of formulas sub_Obj1.1.1 to sub_Obj1.1.4.

[0098] As an example, the constraints of the maximum schedulable capacity calculation model for each communication node in the distribution network include: task packet distribution constraints, task packet transfer constraints between macro base station units, computation time delay constraints for the i-th communication node to process task packets, and transmission time delay constraints for the i-th communication node to transmit task packets through the optical network.

[0099] As an example, the constraints on task package distribution are represented as follows:

[0100]

[0101] The distribution of task packages is represented by a 0-1 variable, that is, the m-th task package is represented as 1 if it is processed under the i-th communication node, and 0 otherwise; N represents the total number of communication nodes in the high-voltage distribution network.

[0102] As an example, the task packet transfer constraint between macro base station units, that is, the constraint that a task packet is defined as being transferred to only one macro base station unit, is expressed as follows:

[0103]

[0104] As an example, the computation time delay constraint for the i-th communication node to process the task packet is expressed as follows:

[0105]

[0106] As an example, the transmission time delay constraint for transmitting task packets via optical network under the i-th communication node is expressed as follows:

[0107]

[0108] Where RA represents the transmission rate of the task packet; τ m This indicates the given acceptable transmission time for each task packet.

[0109] As an example, after obtaining the minimum load power consumption of each communication node in the high-voltage distribution network based on the objective function and constraints, the total load power consumption of each communication node when not transmitting task packets is obtained through real-time information. The maximum schedulable capacity of each communication node is obtained by subtracting the two, as shown below:

[0110]

[0111] in, This represents the maximum schedulable capacity of each communication node, i.e., the maximum schedulable power of each communication node; LD i This represents the total load power consumption generated when no task packets are transmitted by each communication node. Scheduled capacity represents the active power output capacity of each communication node. The maximum scheduled capacity of each communication node is obtained by equating the transferable load between communication nodes to the active power output capacity of each communication node.

[0112] In step S2, please refer to Figure 3 In step S2, based on the maximum dispatchable capacity of each communication node and combined with the electricity price and carbon emission coefficient of each communication node, the optimal cost curve of the high-voltage distribution network considering carbon emissions is fitted, and the minimum operating cost and carbon emissions of the high-voltage distribution network are obtained.

[0113] As an example, considering the fitting of the optimal cost curve of the high-voltage distribution network under carbon emissions, firstly, the maximum dispatchable capacity of each communication node and the cost quotation of each communication node obtained by optimization in step S1 are fitted with the optimal cost curve. Then, considering the carbon emission price and corresponding quotation of each region, the carbon emission and optimal cost curve of the high-voltage distribution network are fitted by multi-segment linear simulation.

[0114] Specifically, the objective function is set as minimizing the operating cost and carbon emissions of each communication node. A curve fitting model for the optimal cost of each communication node considering carbon emissions is constructed, and the objective function is expressed as follows:

[0115] Obj2.1:minCost i =pbbu i C i +λ i E i (2.1)

[0116] Among them, minCost i C represents the optimal cost of the i-th communication node, i.e., the minimum operating cost and carbon emissions of the i-th communication node; i λ represents the electricity price of the i-th communication node, in yuan / megawatt. i E represents the carbon emission factor of the i-th communication node, in yuan / ton of carbon dioxide; i This represents the total power consumption generated by all indoor baseband processing units in all macro base station units cascaded under the i-th communication node, expressed as pbbu. i Carbon emissions per hour, in tons of carbon dioxide.

[0117] As an example, the constraints of the optimal cost curve fitting model for each communication node under carbon emission include: carbon emission constraints of the i-th communication node, power consumption constraints of the equivalent macro base station under the i-th communication node, and macro base station task package constraints under the i-th communication node.

[0118] As an example, the carbon emission constraint for the i-th communication node is represented as follows:

[0119] Const2.1: E i =e i ×pbbu i (2.2)

[0120] Among them, e i This represents the carbon emission intensity of the i-th communication node, in yuan / ton of carbon dioxide.

[0121] As an example, the power consumption constraint of the equivalent macro base station under the i-th communication node is expressed as follows:

[0122]

[0123] As an example, the constraints of the macro base station task packet under the i-th communication node are represented as follows:

[0124] Const2.3:db i,min ≤db i,m ≤db i,max (2.4)

[0125] Among them, db i,min This represents the minimum number of macro base station task packets under the i-th communication node; db i,max This represents the maximum number of macro base station task packets under the i-th communication node.

[0126] For example, please refer to Figure 4 , Figure 4 The flowchart for step S2 is as follows. Based on the maximum dispatchable capacity of each communication node, and combined with the electricity price and carbon emission coefficient of each communication node, the optimal cost curve of the high-voltage distribution network considering carbon emissions is fitted to obtain the minimum operating cost and carbon emissions of the high-voltage distribution network. Specifically, this includes steps S21 to S28.

[0127] Step S21: Receive the maximum schedulable capacity of each communication node, sort the maximum schedulable capacity of each communication node in ascending order, and divide it into segments according to the equal interval capacity ΔS.

[0128] Step S22: Input the independent variable db i,m This ensures that the schedulable capacity S of each communication node is equal to the minimum schedulable capacity S of a single communication node. min .

[0129] Step S23: Solve the optimal cost curve fitting model for each communication node considering carbon emissions, and obtain the optimized minimum operating cost and carbon emissions (minCost) for each communication node. i The power-optimal cost point at each power segment after segmentation.

[0130] Step S24: Determine whether the schedulable capacity S of each communication node is equal to the maximum schedulable capacity of each communication node. If not, proceed to step S25; if yes, proceed to step S26.

[0131] Step S25: Set the schedulable capacity of each communication node to S = S + ΔS, and return to step S23.

[0132] Step S26: Using the schedulable capacity S of each communication node as the x-axis, find the minimum operating cost and carbon emissions (minCost) of each communication node. i Using the vertical axis as the ordinate, arrange all the scattered points to obtain a power-optimal cost plot with equally spaced capacity points.

[0133] Step S27: Fit the curve using a scatter plot curve fitting algorithm to obtain the optimal cost curve for each communication node considering carbon emissions.

[0134] Step S28: Based on the maximum schedulable capacity of each communication node after sorting, sequentially accumulate the capacity ΔS of each equally spaced node and the corresponding total local cost, fit the optimal cost curve of the high-voltage distribution network considering carbon emissions, and obtain the minimum operating cost and minimum carbon emissions of the high-voltage distribution network.

[0135] Specifically, the minimum operating costs and carbon emissions of high-voltage distribution networks are expressed as follows:

[0136]

[0137] Wherein, min Cost represents the optimal cost of the high-voltage distribution network, that is, the minimum operating cost and carbon emissions of the high-voltage distribution network.

[0138] For example, please refer to Figure 5 , Figure 5 Figure (a) shows a power-optimal cost plot where all scattered points are arranged to obtain equally spaced capacity points. Figure 5 Figure (b) shows the optimal cost curves for each communication node considering carbon emissions, obtained by fitting the curves using a scatter plot algorithm; point B represents the maximum schedulable capacity of each communication node.

[0139] In step S3, please refer to Figure 3 In step S3, based on the optimal cost curve of the high-voltage distribution network considering carbon emissions and the maximum dispatchable capacity of each communication node, a high-voltage distribution network scheduling optimization model is constructed. According to the given scheduling instructions, the power output of each communication node unit of the high-voltage distribution network is allocated to meet the requirements of minimizing the operating cost and carbon emissions of the high-voltage distribution network, thereby realizing demand response.

[0140] As an example, we set the minimum operating cost and carbon emissions of the high-voltage distribution network as the objective function and construct a high-voltage distribution network dispatch optimization model. The objective function is expressed as follows:

[0141]

[0142] As an example, the constraints of the high-voltage distribution network dispatch optimization model include: carbon emission constraints of the i-th communication node, power consumption constraints of the equivalent macro base station under the i-th communication node, task package constraints of the macro base station under the i-th communication node, and constraints of the given dispatch instructions.

[0143] As an example, the carbon emission constraint for the i-th communication node is represented as follows:

[0144] Const3.1: E i =e i ×pbbu i (3.2)

[0145] As an example, the power consumption constraint of the equivalent macro base station under the i-th communication node is expressed as follows:

[0146]

[0147] As an example, the constraints of the macro base station task packet under the i-th communication node are represented as follows:

[0148] Const3.3:db i,min ≤db i,m ≤db i,max (3.4)

[0149] As an example, a given scheduling instruction constraint is represented as follows:

[0150]

[0151] Where A represents the total task transmission volume required by a given scheduling instruction.

[0152] As an example, by solving the high-voltage distribution network scheduling optimization model, we can obtain the m-th task packet db at time T under the i-th communication node, which satisfies the requirements of minimizing the operating cost and carbon emissions of the high-voltage distribution network. i,m The distribution of the task package db is further optimized based on the results. i,m The distribution of power output at each communication node of the high-voltage distribution network is obtained through formula Const3.2, thereby optimizing the performance of the communication network and the economic operation of the high-voltage distribution network power system under the consideration of carbon emissions, and realizing demand-side response.

[0153] The demand-side response method based on load spatial transfer from communication base stations provided in this application establishes an equivalent mapping relationship between the transferable computational load of communication nodes and their active power output capacity. By combining dynamic pricing curves and carbon emission curves, it achieves coordinated optimization of communication network performance and distribution network economic operation while considering carbon emissions. Specifically, by treating the load spatial transfer capacity of each node as equivalent to its active power output capacity, and combining the electricity price and carbon emission coefficient of each node, it fits the optimal cost curve for high-voltage distribution networks considering carbon emissions. Under the premise of satisfying given dispatch instructions, it allocates the active power output of each node with the optimization objectives of minimizing total operating costs and total carbon emissions, thereby achieving the goal of minimizing distribution network costs through load spatial transfer calculation. This not only overcomes the limitation of separating economic and environmental considerations in traditional demand response methods but also significantly improves the flexibility and economy of distribution network dispatch in renewable energy integration scenarios, providing an effective technical solution for building a low-carbon economic distribution network.

[0154] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed 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 performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0155] In another embodiment, this application also provides a demand-side response system based on spatial load transfer of communication base stations. The high-voltage distribution network system includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement any of the high-voltage distribution network methods described above.

[0156] In another embodiment, this application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is able to perform the various steps of the high-voltage power distribution network method provided in the above embodiments.

[0157] The computer-executable instructions used to implement the methods of this application may be written in any combination of one or more programming languages. These computer-executable instructions may be provided to the processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the computer-executable instructions cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer-executable instructions may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a standalone software package, or entirely on a remote machine or electronic device.

[0158] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices may also be used to provide interaction with the user; feedback provided to the user may be any form of sensory feedback (e.g., visual feedback or haptic feedback); and input from the user may be received in any form, including: sound input, voice input, or haptic input.

[0159] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as data electronic devices), or computing systems that include middleware components (e.g., application electronic devices), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication (e.g., a communication network) of any form or medium. Examples of communication networks include Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

[0160] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0161] Although this application has been disclosed above with reference to embodiments, it is not intended to limit this application. Anyone skilled in the art may make some modifications and refinements without departing from the spirit and scope of this application. Therefore, the scope of protection of this application shall be determined by the appended claims.

Claims

1. A demand-side response method based on spatial load transfer of communication base stations, characterized in that, include, The transferable load between communication nodes is equivalent to the active power output capacity of each communication node. The minimum load power consumption of each communication node in the high-voltage distribution network is set as the objective function. A calculation model for the maximum dispatchable capacity of each communication node in the distribution network is constructed to obtain the maximum dispatchable capacity of each communication node. Based on the maximum dispatchable capacity of each communication node, combined with the electricity price and carbon emission coefficient of each communication node, the optimal cost curve of the high-voltage distribution network considering carbon emissions is fitted, and the minimum operating cost and carbon emissions of the high-voltage distribution network are obtained. Based on the optimal cost curve of the high-voltage distribution network considering carbon emissions and the maximum dispatchable capacity of each communication node, a high-voltage distribution network dispatch optimization model is constructed. According to the given dispatch instructions, the power output of each communication node unit of the high-voltage distribution network is allocated to meet the requirements of minimizing the operating cost and carbon emissions of the high-voltage distribution network, thereby realizing demand response. The objective function is set as the minimum load power consumption of each communication node in the high-voltage distribution network. A calculation model for the maximum dispatchable capacity of each communication node in the distribution network is constructed, and the objective function is expressed as follows: in, LD i,min This represents the minimum load power consumption under the i-th communication node; pbbu i This represents the total power consumption generated by all indoor baseband processing units in all macro base station units cascaded under the i-th communication node; paau i This represents the total power consumption generated by all active antenna units in all macro base station units cascaded under the i-th communication node; The total power consumption generated by all indoor baseband processing units in all macro base station units cascaded under the i-th communication node is expressed as follows: in, This represents the total static power consumption of all indoor baseband processing units in all macro base station units under the i-th communication node; This represents the total dynamic power consumption of all indoor baseband processing units in each macro base station unit under the i-th communication node; This represents the total transmission power consumption generated when the distribution of task packets among all macro base station units changes.

2. The demand-side response method based on spatial load transfer of communication base stations as described in claim 1, characterized in that, The constraints of the calculation model for the maximum schedulable capacity of each communication node in the distribution network include: task packet distribution constraints, task packet transfer constraints between macro base station units, calculation time delay constraints for each communication node to process task packets, and transmission time delay constraints for each communication node to transmit task packets through the optical network. The constraints on task package distribution are as follows: The distribution of task packets is represented by a 0-1 variable, that is, if the m-th task packet is processed under the i-th communication node, it is represented as 1, otherwise it is represented as 0; This represents the m-th task packet under the i-th communication node; N represents the total number of communication nodes in the high-voltage distribution network; NTASK represents the total number of task packets across all communication nodes; The task packet transfer constraints between macro base station units are expressed as follows: The computation time delay constraints for each communication node to process the task packet are expressed as follows: in, This represents the size of the m-th task packet under the i-th communication node; This represents the maximum processing capacity of the i-th communication node for the task packet; The transmission time delay constraint for transmitting task packets through the optical network at each communication node is expressed as follows: in, Indicates the transmission rate of the task packet; Indicates the given acceptable transmission time for each task packet; This represents the distribution of the m-th task packet under the i-th communication node before migration.

3. The demand-side response method based on spatial load transfer of communication base stations as described in claim 2, characterized in that, The maximum schedulable capacity of each communication node is obtained by: acquiring the total load power consumption of each communication node when it is not transmitting task packets through real-time information, and subtracting the minimum load power consumption of each communication node when it is not transmitting task packets from the minimum load power consumption of each communication node when transmitting task packets to obtain the maximum schedulable capacity of each communication node.

4. The demand-side response method based on spatial load transfer of communication base stations as described in claim 2, characterized in that, Based on the maximum dispatchable capacity of each communication node, and combined with the electricity price and carbon emission coefficient of each communication node, the optimal cost curve of the high-voltage distribution network considering carbon emissions is fitted, yielding the minimum operating cost and carbon emissions of the high-voltage distribution network, including: Step S21: Receive the maximum schedulable capacity of each communication node, sort the maximum schedulable capacity of each communication node in ascending order, and divide it into segments according to equal intervals. Step S22: Input the m-th task packet under the i-th communication node as the independent variable, and set the schedulable capacity of each communication node to be equal to the minimum schedulable capacity of a single communication node. Step S23: Solve the optimal cost curve fitting model for each communication node considering carbon emissions, and obtain the minimum operating cost and carbon emissions of each communication node after optimization, as well as the power-optimal cost point at each power point after segmentation. Step S24: Determine whether the schedulable capacity of each communication node is equal to the maximum schedulable capacity of each communication node. If not, proceed to step S25; if yes, proceed to step S26. Step S25: Set the schedulable capacity of each communication node to equal the schedulable capacity of each communication node plus the capacity at equal intervals, and return to step S23; Step S26: Using the schedulable capacity of each communication node as the horizontal axis and the minimum operating cost and carbon emissions of each communication node as the vertical axis, arrange all the scattered points to obtain a power-optimal cost dot plot of capacity points with equal spacing. Step S27: Fit the curve using a scatter plot curve fitting algorithm to obtain the optimal cost curve for each communication node considering carbon emissions; Step S28: Based on the maximum schedulable capacity of each communication node after sorting, sequentially accumulate the capacity of each equally spaced node and the corresponding total local cost, fit the optimal cost curve of the high-voltage distribution network considering carbon emissions, and obtain the minimum operating cost and minimum carbon emissions of the high-voltage distribution network.

5. The demand-side response method based on spatial load transfer of communication base stations as described in claim 4, characterized in that, The optimal cost curve fitting model for each communication node under carbon emission considerations takes the minimum operating cost and carbon emission of each communication node as the objective function, and is expressed as follows: in, This represents the optimal cost of the i-th communication node; This represents the electricity price of the i-th communication node; Represents the carbon emission factor of the i-th communication node; The total power consumption generated by all indoor baseband processing units in all macro base station units cascaded under the i-th communication node is . Carbon emissions at that time.

6. The demand-side response method based on spatial load transfer of communication base stations as described in claim 5, characterized in that, The constraints of the optimal cost curve fitting model for each communication node under carbon emission considerations include: Carbon emission constraints for each communication node, power consumption constraints of the equivalent macro base station under each communication node, and task package constraints of the macro base station under each communication node. The carbon emission constraints for each communication node are as follows: in, This represents the carbon emission intensity of the i-th communication node, in yuan / ton of carbon dioxide; The power consumption constraints of the equivalent macro base station under each communication node are expressed as follows: The constraints of the macro base station task packets under each communication node are as follows: in, This represents the minimum number of macro base station task packets under the i-th communication node; This represents the maximum number of macro base station task packets under the i-th communication node.

7. The demand-side response method based on spatial load transfer of communication base stations as described in claim 3, characterized in that, The high-voltage distribution network dispatch optimization model takes the minimum operating cost and carbon emissions of the high-voltage distribution network as its objective function, which is expressed as follows: in, This represents the optimal cost of a high-voltage distribution network, i.e., the minimum operating cost and carbon emissions of a high-voltage distribution network. The constraints of the high-voltage distribution network dispatch optimization model include: carbon emission constraints of each communication node, power consumption constraints of the equivalent macro base station under each communication node, task package constraints of the macro base station under each communication node, and constraints of the given dispatch instructions.

8. A demand-side response system based on spatial load transfer from a communication base station, characterized in that, For executing the demand-side response method based on spatial load transfer of communication base stations as described in any one of claims 1 to 7, the demand-side response system based on spatial load transfer of communication base stations includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the demand-side response method based on spatial load transfer of communication base stations as described in any one of claims 1 to 7.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the demand-side response method based on the spatial transfer of communication base station load as described in any one of claims 1 to 7.

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