Automatic demand response dispatch method and system considering optimal generation-side efficiency, medium and processor

Abstract

The present invention relates to the technical field of demand response of power systems. Disclosed are an automatic demand response dispatch method and system considering optimal generation-side efficiency, a medium and a processor. The method comprises: collecting state data of source and load sides; establishing a demand response model, and predicting load and electricity price variations; on the basis of a prediction result, formulating a generation-side strategy; using an optimization algorithm to determine a demand response strategy achieving an optimal generation-side efficiency, determining an optimal initiation timing thereof, and implementing the demand response strategy; and adjusting algorithm parameters and optimizing the control strategy. The present invention automatically adjusts power generation plans and output on the basis of real-time conditions, thus avoiding human intervention and errors; the present invention enables faster response speed of power systems, allowing adaptation to load variations and unexpected events within a short time, thereby improving the regulation efficiency and accuracy; the present invention reduces the operating cost and carbon emissions of power systems, thus satisfying the current societal requirements for green and environmentally friendly practices; thus, compared with traditional peak shaving and valley filling means, the present invention offers multiple advantages such as high efficiency, flexibility, economy, precision and environmental friendliness.

Description

An automatic demand response command method, system, medium, and processor that considers optimal generation-side efficiency. Technical Field

[0001] This invention relates to the field of power system demand response technology, and in particular to an automatic demand response command method, system, medium, and processor that takes into account the optimal efficiency of the generation side. Background Technology

[0002] The large-scale integration of new energy generating units into the power system presents significant challenges to the system's safe and reliable operation. To effectively address the impact of this integration, appropriate technologies and strategies need to be developed and applied to improve the flexibility and reliability of the power system. Demand response, as an emerging technology in the power system, enables flexible control and adjustment of end-user electricity consumption behavior to respond to grid demands and improve system efficiency, reliability, and economy.

[0003] On the generation side, the automatic demand response command technology for determining the optimal generation efficiency is an important research area. Its main purpose is to quickly and accurately determine the optimal output and generation plan on the generation side through automation, based on current market conditions and user response, in order to achieve optimal power system efficiency and economy.

[0004] To achieve automated demand response dispatching that optimizes generation efficiency, several underlying technologies are required. First, user load needs to be predicted and analyzed to determine future load demand. Second, real-time data collection and processing of grid status and user response data are necessary, along with effective communication with the user side. Simultaneously, intelligent scheduling and control algorithms need to be designed to adjust generation plans and output based on real-time conditions to achieve optimal efficiency. Finally, to ensure system safety and stability, various technical means are required, such as intelligent protection devices and fault prediction technologies.

[0005] Traditional peak shaving and valley filling methods adjust the power system according to changes in load demand by regulating generator output. However, this inevitably leads to frequent start-ups and shutdowns of power generation equipment, resulting in serious consequences such as increased equipment wear and operating costs. Therefore, it is essential to rationally allocate demand-side resources and formulate reasonable demand response plans to maintain the stability, reliability, and economy of the power grid. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides an automatic demand response command method, system, medium, and processor that takes into account the optimal efficiency of the power generation side, thereby improving the efficiency and accuracy of regulation and reducing the operating cost of the power system.

[0007] The specific technical solution is as follows:

[0008] An automatic demand response command method considering optimal generation-side efficiency includes collecting state data from both the source and load sides; establishing a demand response model to predict load and electricity price changes; formulating generation-side strategies based on the prediction results; identifying the optimal generation-side demand response strategy and determining its optimal start time through an optimization algorithm, and implementing the demand response strategy; and adjusting algorithm parameters to optimize the control strategy.

[0009] Preferably, the establishment of the corresponding model to predict load and electricity price changes specifically involves:

[0010] Based on time series models and utilizing historical and real-time data, a demand response model is established. The expression for predicting load and electricity price changes is: min C 总 =C 负荷 +C DR +C 不确定性 +C 正则化

[0011] In the above formula, C 总 C represents the total cost of the system throughout the entire process. 负荷 To match the generation cost on the generation side with load demand; C DR To incentivize costs during the system's demand response process; C 不确定性 The cost of absorbing uncertainties in the system; C 正则化 This is the cost of regularization.

[0012] Preferably, the step of formulating a power generation-side strategy based on the prediction results specifically involves:

[0013] Based on load and electricity price forecasts and generator status information on the source side, the dispatch center formulates a power generation strategy and adjusts unit output and market power supply using the following expression:

[0014] In the above formula, P 可控机组出力 and P 机组最大出力 These represent the output and maximum output of the generating unit, respectively; P 供电中断缺口 and P 市场售电量 These represent the demand on the load side and the total electricity sales on the market side, respectively; P 供电中断缺口 and P 预设可靠性指标 These are the power outage threshold and the preset reliability limit specified by the system, respectively; P 特点用户负荷 and P 用户需求限制 These are the load requirements of specific users and the general demand limits for users, respectively; T 机组启停时间 and T 机组最小启停时间 These refer to the start-up and shutdown times and the minimum start-up and shutdown time of the generator set.

[0015] Preferably, the adjustment algorithm parameter optimization control strategy includes feedback correction to optimize the optimal power generation side scheduling scheme.

[0016] An automatic demand response command system that considers optimal power generation efficiency, applied to the method described, includes a data acquisition module, a data prediction module, an efficiency optimization module, a demand response module, and a feedback evaluation module.

[0017] The data acquisition module is used to collect and analyze data from both the supply and demand sides, as well as market electricity prices.

[0018] The data prediction module is used to predict electricity demand and changes in electricity prices.

[0019] The efficiency optimization module is used to evaluate power generation efficiency and formulate power generation strategies.

[0020] The demand response module is used to determine the start time and implement the demand response.

[0021] The feedback evaluation module is used for evaluating demand response and monitoring and adjusting the plan.

[0022] Preferably, the data on both the supply and demand sides include the power generation on the power supply side, the demand on the load side, and the parameters for accessing new energy sources.

[0023] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method.

[0024] A processor for running a program, wherein the program executes the method during runtime.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] This invention automatically adjusts the power generation plan and output based on real-time conditions, avoiding manual intervention and errors. It provides a faster response to the power system and can adapt to load changes and emergencies in a short time, thereby improving the efficiency and accuracy of regulation, reducing the operating costs and carbon emissions of the power system, and meeting the current social requirements for green and environmentally friendly practices. Compared with traditional peak shaving and valley filling methods, it has multiple advantages such as high efficiency, flexibility, economy, accuracy, and environmental protection. Attached Figure Description

[0027] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0028] Figure 1 is a flowchart of the method of the present invention;

[0029] Figure 2 is a system schematic diagram of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention 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 the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0032] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0033] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0034] Please refer to Figure 1. This invention provides an automatic demand response command method that considers optimal generation-side efficiency, comprising:

[0035] S1. Collect status data from both the power supply and load sides. Specifically, by collecting data from the power supply side and the load side, the status data includes the power generation on the power supply side, the demand on the load side, relevant parameters such as the access of new energy sources, the generating units on the power supply side, and relevant information such as the load and market electricity price on the market side, and transmit the data to the central control system for subsequent processing.

[0036] S2. Establish a demand response model to predict load and electricity price changes; specifically, establish a demand response model based on time series analysis that can be trained and optimized using historical and real-time data to predict the trend of grid load and market electricity price changes in the future.

[0037] S3. Formulate generation-side strategies based on forecast results; specifically, formulate generation-side strategies based on forecast results and generator status information, automatically issue commands to adjust the output level of generator units to meet grid load demand, and sell electricity when market electricity prices are highest.

[0038] S4. By optimizing the algorithm, determine the demand response strategy with the best efficiency on the power generation side and determine its optimal start time, and implement the demand response strategy.

[0039] S5. Adjust algorithm parameters and optimize control strategies; specifically, monitor and evaluate the system in real time, promptly identify problems, and take measures such as adjusting algorithm parameters and optimizing control strategies.

[0040] In a preferred embodiment, step S2 is as follows:

[0041] Based on time series models and utilizing historical and real-time data, a demand response model is established. The expression for predicting load and electricity price changes is: min C 总 =C 负荷 +C DR +C 不确定性 +C 正则化

[0042] In the above formula, C 总 C represents the total cost of the system throughout the entire process. 负荷 To match the generation cost on the generation side with load demand; C DR To incentivize costs during the system's demand response process; C 不确定性 The cost of absorbing uncertainties in the system; C 正则化 This is the cost of regularization.

[0043] In a preferred embodiment, step S3 is as follows:

[0044] Based on load and electricity price forecasts and generator status information on the source side, the dispatch center formulates a power generation strategy and adjusts unit output and market power supply using the following expression:

[0045] In the above formula, P 可控机组出力 and P 机组最大出力 These represent the output and maximum output of the generating unit, respectively; P 供电中断缺口 and P 市场售电量 These represent the demand on the load side and the total electricity sales on the market side, respectively; P 供电中断缺口 and P 预设可靠性指标 These are the power outage threshold and the preset reliability limit specified by the system, respectively; P 特点用户负荷 and P 用户需求限制 These are the load requirements of specific users and the general demand limits for users, respectively; T机组启停时间 and T 机组最小启停时间 These refer to the start-up and shutdown times and the minimum start-up and shutdown time of the generator set.

[0046] In a preferred embodiment, the optimization control strategy by adjusting algorithm parameters includes feedback correction to optimize the optimal power generation-side scheduling scheme.

[0047] As shown in Figure 2, an automatic demand response command system considering optimal power generation efficiency, applied to the method described, includes a data acquisition module, a data prediction module, an efficiency optimization module, a demand response module, and a feedback evaluation module.

[0048] The data acquisition module is used to collect and analyze data from both the supply and demand sides, as well as market electricity prices. It can be understood that the power system uses this module to collect and process data from both the supply and demand sides, including parameters such as power generation on the supply side, demand on the load side, and renewable energy access.

[0049] The data prediction module is used to predict electricity demand and electricity price changes; it can be understood as using the analyzed and processed data to predict various types of load conditions within the system.

[0050] The efficiency optimization module is used to evaluate power generation efficiency and formulate power generation strategies. It can be understood that, based on data analysis and prediction results, the optimal power generation plan on the power generation side is calculated through optimization algorithms to ensure that power generation efficiency is maximized while meeting users' electricity demand.

[0051] The demand response module is used to determine the start time and implement demand response. It can be understood that the demand response module, based on the optimal power generation plan of the power generation side, and the demand response scheduling module, supply the remaining power to users for demand response scheduling. Through communication and control with users, the automatic adjustment and management of user load is realized.

[0052] The feedback evaluation module is used for demand response assessment and monitoring and adjustment schemes. It can be understood that the feedback evaluation module continuously monitors the operating status of the power system and the user's response, promptly feeds back data to the central control system, and dynamically adjusts the power generation plan on the generation side according to the actual situation to maintain supply and demand balance and stable operation of the power system.

[0053] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method.

[0054] A processor for running a program, wherein the program executes the method during runtime.

[0055] In summary, the working principle of this embodiment is as follows: Data on system load, generator status, and market electricity prices are collected and transmitted to the central control system; a demand response model based on time series analysis is established, capable of being trained and optimized using historical and real-time data to predict the future trends of grid load and market electricity prices; based on the prediction results and generator status information, generation-side strategies are formulated, automatically issuing commands to adjust the output level of generators to meet grid load demand and selling electricity when market prices are highest; optimization algorithms are designed to determine the optimal demand response optimization strategy for generation-side efficiency; the system is monitored and evaluated in real time, problems are promptly identified, and adjustments are made to algorithm parameters and control strategies.

[0056] This invention comprehensively utilizes technologies such as data acquisition, machine learning, and intelligent control to achieve intelligent management and optimization of the power system, thereby ensuring the economical and stable operation of the power system.

[0057] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0058] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0059] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0060] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. An automatic demand response command method that considers optimal generation-side efficiency, characterized in that, include: Collect status data from both sides of the source load; Establish a demand response model to predict load and electricity price changes; Develop power generation strategies based on forecast results; By optimizing the algorithm, the optimal demand response strategy for the power generation side is determined and its optimal start time is identified, and the demand response strategy is implemented. Adjust algorithm parameters to optimize control strategy.

2. The automatic demand response command method considering optimal generation-side efficiency according to claim 1, characterized in that, The establishment of the corresponding model to predict load and electricity price changes specifically involves: Based on time series models and utilizing historical and real-time data, a demand response model is established. The expression for predicting load and electricity price changes is as follows: my C 总 =C 负荷 +C DR +C 不确定性 +C 正则化 In the above formula, C 总 C represents the total cost of the system throughout the entire process. 负荷 To match the generation cost on the generation side with load demand; C DR To incentivize costs during the system's demand response process; C 不确定性 The cost of absorbing uncertainties in the system; C 正则化 This is the cost of regularization.

3. The automatic demand response command method considering optimal generation-side efficiency according to claim 1, characterized in that, The specific steps for formulating power generation-side strategies based on prediction results are as follows: Based on load and electricity price forecasts and generator status information on the source side, the dispatch center formulates a power generation strategy and adjusts unit output and market power supply using the following expression: In the above formula, P 可控机组出力 and P 机组最大出力 These represent the output and maximum output of the generating unit, respectively; P 供电中断缺口 and P 市场售电量 These represent the demand on the load side and the total electricity sales on the market side, respectively; P 供电中断缺口 and P 预设可靠性指标 These are the power outage threshold and the preset reliability limit specified by the system, respectively; P 特点用户负荷 and P 用户需求限制 These are the load requirements of specific users and the general demand limits for users, respectively; T 机组启停时间 and T 机组最小启停时间 These refer to the start-up and shutdown times and the minimum start-up and shutdown time of the generator set.

4. The automatic demand response command method considering optimal generation-side efficiency according to claim 1, characterized in that, The adjusted algorithm parameters optimization control strategy includes feedback correction and optimization of the optimal generation-side scheduling scheme.

5. An automatic demand response command system that considers optimal power generation efficiency, characterized in that, The method applied to any one of claims 1 to 4 includes a data acquisition module, a data prediction module, a performance optimization module, a demand response module, and a feedback evaluation module. The data acquisition module is used to collect and analyze data from both the supply and demand sides, as well as market electricity prices. The data prediction module is used to predict electricity demand and changes in electricity prices. The efficiency optimization module is used to evaluate power generation efficiency and formulate power generation strategies. The demand response module is used to determine the start time and implement the demand response. The feedback evaluation module is used for evaluating demand response and monitoring and adjusting the plan.

6. The automatic demand response command system considering optimal generation-side efficiency according to claim 5, characterized in that, The data on both the supply and demand sides include the power generation on the power supply side, the demand on the load side, and the parameters for accessing new energy sources.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 4.

8. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 4 when it runs.

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