A building energy consumption regulation optimization method, system, terminal and medium
By acquiring fluctuation data of building electricity load and generator sets, the upper and lower limits of electricity load and the scheduling optimization range are determined, and a control optimization model is established. This solves the problem of ignoring fluctuations in building energy consumption control, and achieves balanced energy consumption distribution and improved power system stability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- YANTAI POWER SUPPLY COMPANY OF STATE GRID SHANDONG ELECTRIC POWER
- Filing Date
- 2022-12-23
- Publication Date
- 2026-06-26
AI Technical Summary
Existing technologies neglect fluctuations on the power supply and consumption sides in building energy consumption control, leading to frequent and large-scale compensation adjustments. This restricts the transformation of traditional energy sources to replace renewable energy sources and fails to achieve a balanced distribution of energy consumption across buildings.
By acquiring fluctuation data of electricity load from generator sets and buildings, the upper and lower limits of electricity load are determined, a scheduling optimization interval is established, and an energy consumption control strategy is generated using a control optimization model. Taking into account the randomness of new energy sources on the generation side and the changes in demand on the electricity consumption side, a balanced distribution of energy consumption among buildings is achieved.
This achieves a balanced distribution of energy consumption among buildings under demand intensity with relatively small errors, reduces the frequency and magnitude of compensation and regulation on the power generation side, enhances the stability of the power system, and promotes the substitution of new energy power generation.
Smart Images
Figure CN116131253B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of energy consumption control technology, and more specifically, to a method, system, terminal, and medium for optimizing building energy consumption control. Background Technology
[0002] In a power system, power generation, transmission, and distribution are generally planned based on the power load demand of various buildings within the power supply coverage area. It is required that the power supply and power consumption in a certain area reach a balance between supply and demand. For areas that do not meet the balance between supply and demand, power dispatch is required to achieve an overall balance between supply and demand. Power dispatch is mainly achieved through power generation output control and / or power load control.
[0003] Currently, power generation control relies on the assumption that electricity load data is stable, and vice versa. Therefore, existing technologies neglect fluctuations on at least one side (power supply or demand) when regulating building electricity consumption. In practice, power generation control typically requires compensating for and regulating renewable energy generation using the adjustability margin of traditional generator sets. If fluctuations on the demand side are not ignored, frequent and significant compensation and regulation operations are necessary. This imposes a lower limit on the installed capacity of traditional generator sets in the power system, thus restricting the transformation of renewable energy generation into alternatives to traditional energy generation. Furthermore, when using load control, the varying load demands of different buildings make it impossible to accurately achieve a balanced distribution of energy consumption across buildings without considering fluctuations on the power generation side.
[0004] Therefore, how to research and design a building energy consumption control and optimization method, system, terminal and medium that can overcome the above defects is an urgent problem that we need to solve. Summary of the Invention
[0005] To address the shortcomings of existing technologies, the present invention aims to provide a method, system, terminal, and medium for optimizing building energy consumption control. When using electricity load control, it considers both the fluctuation trends caused by the randomness and uncertainty of new energy sources connected to the power system on the power generation side and the demand change trends of various buildings on the electricity consumption side. It can achieve a balanced distribution of energy consumption among various buildings while meeting the demand intensity of each building with small errors.
[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution:
[0007] Firstly, a method for optimizing building energy consumption control is provided, including the following steps:
[0008] Acquire power output fluctuation data of generator sets in the target area within a fixed period and power load fluctuation data of each target building in the target area within the corresponding fixed period;
[0009] Based on the load fluctuations before and after the target time, determine the upper limit and lower limit of the electricity load of the target building at the corresponding target time.
[0010] Based on the fluctuation trend of power output fluctuation data and the corresponding upper or lower limit of power load, the scheduling optimization interval of the target building in the corresponding time is determined;
[0011] The regulation optimization factor is determined based on the fluctuation rate of the electricity load fluctuation data, and the regulation optimization value of the target building is determined by the product of the regulation optimization deviation and the regulation optimization factor. The regulation optimization model is established with the minimum sum of the regulation optimization values of all target buildings as the optimization objective.
[0012] The scheduling optimization intervals of each target building and the corresponding output fluctuation data are input into the regulation optimization model to solve for the energy consumption regulation strategy of each target building.
[0013] Furthermore, the process for determining the upper limit and lower limit of the electricity load is as follows:
[0014]
[0015] in, This represents the upper limit of the electrical load corresponding to the target building at time t; This represents the electricity load fluctuation data corresponding to the target building at time k; ΔT1 represents the lower limit of the electrical load corresponding to the target building at time t; ΔT2 represents the front width; ΔT2 represents the rear width; and T represents the fluctuation width.
[0016] Furthermore, the process for determining the upper limit and lower limit of the electricity load is as follows:
[0017]
[0018] Where P0 represents the cumulative fluctuation threshold; This represents the electricity load fluctuation data corresponding to the target building at time k; This represents the fluctuation data of the target building's electricity load at time t; This represents the upper limit of the electrical load corresponding to the target building at time t; ΔT represents the lower limit of the electrical load of the target building at time t; d Indicates the front width and / or back width.
[0019] Furthermore, the process of determining the scheduling optimization interval is as follows:
[0020] If the power output fluctuation data shows an increasing fluctuation trend at the target time, then the scheduling optimization range is determined by the power load fluctuation data at the target time and the upper limit of the power load.
[0021] If the power output fluctuation data shows a decreasing trend at the target time, the scheduling optimization interval is determined by the power load fluctuation data at the target time and the lower limit of the power load.
[0022] Furthermore, the specific expression of the regulation optimization model is as follows:
[0023]
[0024]
[0025] in, This represents the target value for the regulation and optimization of the i-th target building at time t; n represents the number of target buildings. This represents the electricity load fluctuation data corresponding to the i-th target building at time t; This represents the rate of change of the i-th target building at time t; Let A represent the control optimization factor for the i-th target building at time t; let A represent the scheduling optimization interval. This represents the power output fluctuation data of the generator set at time t.
[0026] Furthermore, the regulation optimization factor and the volatility change rate are positively correlated.
[0027] Furthermore, the regulation optimization factor and the volatility change rate exhibit an increasing step function distribution.
[0028] Secondly, a building energy consumption control and optimization system is provided, including:
[0029] The data acquisition module is used to acquire the power output fluctuation data of generator sets in the target area within a fixed period and the power load fluctuation data of each target building in the target area within the corresponding fixed period.
[0030] The limit analysis module is used to determine the upper limit and lower limit of the electricity load of the target building at the corresponding target time based on the load fluctuations before and after the target time.
[0031] The interval determination module is used to determine the scheduling optimization interval of the target building at the corresponding time based on the fluctuation trend of the power output fluctuation data and the corresponding upper limit or lower limit of the power load.
[0032] The model building module is used to determine the control optimization factor based on the fluctuation rate of the electricity load fluctuation data, and to determine the control optimization value of the target building by the product of the control optimization deviation and the control optimization factor. The control optimization model is established with the minimum sum of the control optimization values of all target buildings as the optimization objective.
[0033] The strategy optimization module is used to input the scheduling optimization interval of each target building and the corresponding output fluctuation data into the regulation optimization model to solve for the energy consumption regulation strategy of each target building.
[0034] Thirdly, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a building energy consumption control and optimization method as described in any one of the first aspects.
[0035] Fourthly, a computer-readable medium is provided having a computer program stored thereon, the computer program being executed by a processor to implement a building energy consumption control and optimization method as described in any one of the first aspects.
[0036] Compared with the prior art, the present invention has the following beneficial effects:
[0037] 1. The building energy consumption regulation and optimization method provided by the present invention takes into account both the fluctuation trend caused by the randomness and uncertainty of the access of new energy sources to the power system on the power generation side and the demand change trend of each building on the power consumption side when using power load regulation. It can achieve a balanced distribution of energy consumption of each building while meeting the demand intensity of each building with small error.
[0038] 2. When the energy consumption control strategy generated by the regulation and optimization model of this invention realizes the energy consumption control of buildings, it can effectively reduce the frequency and magnitude of the compensation and regulation of the power generation output on the power generation side, enhance the stability of the power system operation, and facilitate the transformation of the power system to replace traditional energy generation with new energy generation.
[0039] 3. When determining the upper limit and lower limit of electricity load, this invention can analyze based on a fixed fluctuation width or a fixed fluctuation accumulation threshold, and can flexibly select according to different situations, making it highly adaptable. Attached Figure Description
[0040] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:
[0041] Figure 1 This is a flowchart from an embodiment of the present invention;
[0042] Figure 2 This is a system block diagram in an embodiment of the present invention. Detailed Implementation
[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.
[0044] Example 1: A method for optimizing building energy consumption control, such as Figure 1 As shown, it includes the following steps:
[0045] Step S1: Obtain the power output fluctuation data of generator sets in the target area within a fixed period and the power load fluctuation data of each target building in the target area within the corresponding fixed period;
[0046] Step S2: Determine the upper limit and lower limit of the electricity load of the target building at the corresponding target time based on the load fluctuations before and after the target time.
[0047] Step S3: Determine the scheduling optimization interval of the target building in the corresponding time period based on the fluctuation trend of the power output fluctuation data and the corresponding upper limit or lower limit of the power load;
[0048] Step S4: Determine the control optimization factor based on the fluctuation rate of the electricity load fluctuation data, and determine the control optimization value of the target building by the product of the control optimization deviation and the control optimization factor. Establish a control optimization model with the minimum sum of the control optimization values of all target buildings as the optimization objective.
[0049] Step S5: Input the scheduling optimization interval of each target building and the corresponding output fluctuation data into the regulation optimization model to solve for the energy consumption regulation strategy of each target building.
[0050] As an optional implementation method, the process for determining the upper limit and lower limit of electricity load is as follows:
[0051]
[0052] in, This represents the upper limit of the electrical load corresponding to the target building at time t; This represents the electricity load fluctuation data corresponding to the target building at time k; ΔT1 represents the lower limit of the electrical load corresponding to the target building at time t; ΔT2 represents the front width; ΔT2 represents the rear width; and T represents the fluctuation width.
[0053] As another optional implementation method, the process for determining the upper limit and lower limit of electricity load is as follows:
[0054]
[0055] Where P0 represents the cumulative fluctuation threshold; This represents the electricity load fluctuation data corresponding to the target building at time k; This represents the fluctuation data of the target building's electricity load at time t; This represents the upper limit of the electrical load corresponding to the target building at time t; ΔT represents the lower limit of the electrical load of the target building at time t; d Indicates the front width and / or back width.
[0056] The process of determining the scheduling optimization interval is as follows: if the power output fluctuation data is in an increasing trend at the target time, the scheduling optimization interval is determined by the power load fluctuation data at the target time and the upper limit of the power load; if the power output fluctuation data is in a decreasing trend at the target time, the scheduling optimization interval is determined by the power load fluctuation data at the target time and the lower limit of the power load.
[0057] It should be noted that if there is no obvious fluctuation trend at the target time, then no intervention is required. Furthermore, the scheduling optimization range can be determined by combining the lower limit of electricity load during peak periods and the upper limit of electricity load during trough periods; there are no restrictions on this.
[0058] The specific expression for the regulation and optimization model is as follows:
[0059]
[0060]
[0061] in, This represents the target value for the regulation and optimization of the i-th target building at time t; n represents the number of target buildings. This represents the electricity load fluctuation data corresponding to the i-th target building at time t; This represents the rate of change of the i-th target building at time t; Let A represent the control optimization factor for the i-th target building at time t; let A represent the scheduling optimization interval. This represents the power output fluctuation data of the generator set at time t.
[0062] As an optional implementation method, the regulation optimization factor and the volatility change rate are positively correlated by a functional distribution, such as a linear function with a slope greater than 0.
[0063] As an optional implementation method, the regulation optimization factor and the volatility change rate exhibit an increasing step function distribution.
[0064] Example 2: A building energy consumption control and optimization system, which implements a building energy consumption control and optimization method described in Example 1, such as... Figure 2 As shown, it includes a data acquisition module, a limit analysis module, an interval determination module, a model building module, and a strategy optimization module.
[0065] The system comprises the following modules: a data acquisition module for acquiring power output fluctuation data of generator units in the target area within a fixed period and power load fluctuation data of each target building in the target area within the corresponding fixed period; a limit analysis module for determining the upper and lower limits of power load for each target building at the corresponding target time based on the load fluctuations before and after the target time; an interval determination module for determining the scheduling optimization interval for each target building at the corresponding time based on the fluctuation trend of the power output fluctuation data and the corresponding upper or lower limit of power load; a model construction module for determining the regulation optimization factor based on the fluctuation rate of the power load fluctuation data, determining the regulation optimization value of the target building by multiplying the regulation optimization deviation by the regulation optimization factor, and establishing a regulation optimization model with the minimum sum of the regulation optimization values of all target buildings as the optimization objective; and a strategy optimization module for inputting the scheduling optimization interval of each target building and the power output fluctuation data at the corresponding time into the regulation optimization model to solve for the energy consumption regulation strategy of each target building.
[0066] Working principle: When using electricity load regulation, this invention considers both the fluctuation trend caused by the randomness and uncertainty of the access of new energy sources to the power system on the generation side and the demand change trend of various buildings on the electricity consumption side. It can achieve a balanced distribution of energy consumption among various buildings while meeting the demand intensity of each building with small errors. This invention can also provide reference data for the independent operation of the power system in different regions, which is conducive to the development of a high proportion of distributed new energy sources.
[0067] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.
[0068] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0069] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0070] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0071] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for optimizing building energy consumption control, characterized in that, Includes the following steps: Acquire power output fluctuation data of generator sets in the target area within a fixed period and power load fluctuation data of each target building in the target area within the corresponding fixed period; Based on the load fluctuations before and after the target time, determine the upper limit and lower limit of the electricity load of the target building at the corresponding target time. Based on the fluctuation trend of power output fluctuation data and the corresponding upper or lower limit of power load, the scheduling optimization interval of the target building in the corresponding time is determined; The regulation optimization factor is determined based on the fluctuation rate of the electricity load fluctuation data, and the regulation optimization value of the target building is determined by the product of the regulation optimization deviation and the regulation optimization factor. The regulation optimization model is established with the minimum sum of the regulation optimization values of all target buildings as the optimization objective. The scheduling optimization interval of each target building and the corresponding output fluctuation data are input into the regulation optimization model to solve for the energy consumption regulation strategy of each target building. The specific expression of the regulation and optimization model is as follows: ； in, This represents the target value for regulation and optimization of the i-th target building at time t; Indicates the number of target buildings; This represents the electricity load fluctuation data corresponding to the i-th target building at time t; This represents the rate of change of the i-th target building at time t; This represents the control and optimization factor for the i-th target building at time t; Indicates the scheduling optimization interval; This represents the power output fluctuation data of the generator set at time t.
2. The building energy consumption control and optimization method according to claim 1, characterized in that, The process for determining the upper and lower limits of the electricity load is as follows: ； in, This represents the upper limit of the electrical load corresponding to the target building at time t; This represents the electricity load fluctuation data corresponding to the target building at time k; This represents the lower limit of the electrical load corresponding to the target building at time t; Indicates the width of the front side; Indicates the width of the rear side; Indicates the width of fluctuation.
3. The building energy consumption control and optimization method according to claim 1, characterized in that, The process for determining the upper and lower limits of the electricity load is as follows: ； in, Indicates the cumulative fluctuation threshold; This represents the electricity load fluctuation data corresponding to the target building at time k; This represents the fluctuation data of the target building's electricity load at time t; This represents the upper limit of the electrical load corresponding to the target building at time t; This represents the lower limit of the electrical load corresponding to the target building at time t; Indicates the front width and / or back width.
4. The building energy consumption control and optimization method according to claim 1, characterized in that, The process of determining the scheduling optimization interval is as follows: If the power output fluctuation data shows an increasing fluctuation trend at the target time, then the scheduling optimization range is determined by the power load fluctuation data at the target time and the upper limit of the power load. If the power output fluctuation data shows a decreasing trend at the target time, the scheduling optimization interval is determined by the power load fluctuation data at the target time and the lower limit of the power load.
5. The building energy consumption control and optimization method according to claim 1, characterized in that, The regulation and optimization factors are positively correlated with the volatility rate.
6. The building energy consumption control and optimization method according to claim 1, characterized in that, The regulation optimization factor and the volatility change rate exhibit an increasing step function distribution.
7. A building energy consumption control and optimization system, characterized in that, This system is used to implement a building energy consumption control and optimization method according to any one of claims 1-6, comprising: The data acquisition module is used to acquire the power output fluctuation data of generator sets in the target area within a fixed period and the power load fluctuation data of each target building in the target area within the corresponding fixed period. The limit analysis module is used to determine the upper limit and lower limit of the electricity load of the target building at the corresponding target time based on the load fluctuations before and after the target time. The interval determination module is used to determine the scheduling optimization interval of the target building at the corresponding time based on the fluctuation trend of the power output fluctuation data and the corresponding upper limit or lower limit of the power load. The model building module is used to determine the control optimization factor based on the fluctuation rate of the electricity load fluctuation data, and to determine the control optimization value of the target building by the product of the control optimization deviation and the control optimization factor. The control optimization model is established with the minimum sum of the control optimization values of all target buildings as the optimization objective. The strategy optimization module is used to input the scheduling optimization interval of each target building and the corresponding output fluctuation data into the regulation optimization model to solve for the energy consumption regulation strategy of each target building.
8. A computer terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a building energy consumption control and optimization method as described in any one of claims 1-6.
9. A computer-readable medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, can implement a building energy consumption control and optimization method as described in any one of claims 1-6.