Heat storage peak regulation power generation optimization method and system

Abstract

The invention discloses a heat storage and peak regulation power generation optimization method and system, and the method comprises the steps: obtaining historical power generation control data and historical heat storage and peak regulation power generation data of heat storage equipment, carrying out the analysis of the data, and determining characteristic parameters affecting the heat storage and peak regulation power generation; building a prediction model based on the characteristic parameters and a preset neural network model, and performing prediction according to the prediction model to obtain heat storage peak regulation power generation prediction data; acquiring power consumption data of the target area, analyzing the heat storage peak regulation power generation prediction data and the power consumption data, and determining a supply and demand difference time period and supply and demand difference characteristics thereof; analyzing and evaluating the power supply and demand difference of the heat storage equipment in the supply and demand difference time period based on the supply and demand difference characteristics to obtain a power supply and demand difference evaluation value; and according to the power supply and demand difference evaluation value, determining a heat storage peak regulation power generation optimization coefficient, and according to the heat storage peak regulation power generation optimization coefficient, optimizing the power generation control parameter. The operation efficiency and economic benefits of the heat storage equipment can be improved, and energy utilization is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal storage peak-shaving power generation, and in particular to a thermal storage peak-shaving power generation optimization method and system. Background Art

[0002] Thermal storage peak-shaving power generation usually refers to the use of thermal storage equipment (such as thermal storage wall-mounted boilers, water heaters, etc.) to release stored thermal energy during peak energy demand periods to meet energy demand. Its working principle is usually to use cheap electricity or other forms of energy to heat storage media (such as water, sand, etc.) when energy supply is in excess, and then release the stored thermal energy during peak energy demand periods to provide hot water, steam or directly use it for power generation.

[0003] However, the traditional thermal storage peak-shaving power generation method lacks a specific analysis of power generation and power consumption, which makes it impossible for thermal storage peak-shaving power generation to match power demand and difficult to flexibly respond to actual power demand. In addition, the operating parameters of the thermal storage equipment are not accurately controlled, which reduces the thermal energy conversion efficiency, resulting in reduced operating efficiency, energy waste and increased costs. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for optimizing thermal storage peak load regulation power generation, comprising:

[0005] Acquire historical power generation control data and historical thermal storage peak-shaving power generation data of the thermal storage device, analyze the historical power generation control data and historical thermal storage peak-shaving power generation data, and determine power generation control characteristic parameters that affect thermal storage peak-shaving power generation of the thermal storage device;

[0006] A thermal storage peak-shaving power generation prediction model for the thermal storage device is constructed based on the power generation control characteristic parameters and the preset neural network model, and prediction is performed according to the thermal storage peak-shaving power generation prediction model to obtain thermal storage peak-shaving power generation prediction data for the thermal storage device;

[0007] Obtain the electricity consumption data of the target area, and analyze the thermal storage peak-shaving power generation forecast data and electricity consumption data to determine the supply and demand difference time period and the corresponding supply and demand difference characteristics;

[0008] Analyze and evaluate the power supply and demand difference of the thermal storage equipment in the supply and demand difference time period based on the supply and demand difference characteristics, and obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period;

[0009] The thermal storage peak-shaving power generation optimization coefficient of the thermal storage equipment in each supply and demand difference time period is determined according to the power supply and demand difference evaluation value, and the power generation control parameters of the thermal storage equipment in each supply and demand difference time period are optimized according to the thermal storage peak-shaving power generation optimization coefficient.

[0010] Furthermore, the acquisition of historical power generation control data and historical thermal storage peak-shaving power generation data of the thermal storage device, analysis of the historical power generation control data and the historical thermal storage peak-shaving power generation data, and determination of power generation control characteristic parameters affecting thermal storage peak-shaving power generation of the thermal storage device include:

[0011] Acquire historical power generation control data of the thermal storage device after preprocessing, and divide the historical power generation control data into several groups of power generation control parameter data groups according to parameter types;

[0012] Determine the i-th power generation control parameter data group from a plurality of power generation control parameter data groups, and select historical thermal storage peak-shaving power generation data when other control parameters are the same;

[0013] Analyze the change in historical thermal storage peak-shaving power generation data corresponding to the i-th power generation control parameter data group, and determine the power generation control parameter corresponding to the i-th power generation control parameter data group whose change is greater than a preset change threshold as the power generation control characteristic parameter that affects the thermal storage peak-shaving power generation of the thermal storage equipment.

[0014] Furthermore, the heat storage peak-shaving power generation prediction model of the heat storage equipment is constructed based on the power generation control characteristic parameters and the preset neural network model, and prediction is performed according to the heat storage peak-shaving power generation prediction model to obtain the heat storage peak-shaving power generation prediction data of the heat storage equipment, including:

[0015] A data set is constructed based on the power generation control characteristic parameters and the corresponding historical thermal storage peak-shaving power generation data, and the data set is input into a preset neural network model to construct an initial model for thermal storage peak-shaving power generation prediction;

[0016] The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial model for thermal storage peak-shaving power generation prediction;

[0017] The initial model for predicting thermal storage peak-shaving power generation is trained and tested until the initial model for predicting thermal storage peak-shaving power generation meets a preset convergence condition, thereby obtaining a thermal storage peak-shaving power generation prediction model;

[0018] The power generation control parameters corresponding to the power generation control characteristic parameters in the thermal storage device are obtained, and the power generation control parameters are input into the thermal storage peak-shaving power generation prediction model. The thermal storage peak-shaving power generation prediction model performs prediction to obtain the thermal storage peak-shaving power generation prediction data of the thermal storage device.

[0019] Furthermore, the power consumption data of the target area is obtained, and the thermal storage peak-shaving power generation forecast data and power consumption data are analyzed to determine the supply and demand difference time period and the corresponding supply and demand difference characteristics, including:

[0020] Obtain the electricity consumption data of the target area, and compare the thermal storage peak-shaving power generation forecast data with the electricity consumption data to determine the time period when there is a difference between power generation and power consumption, and use this time period as the supply-demand difference time period;

[0021] Subtract the thermal storage peak-shaving power generation forecast data and the power consumption data corresponding to the supply-demand difference time period to obtain supply-demand difference data, and draw the supply-demand difference data into a supply-demand difference data curve graph;

[0022] Determine the first time period when electricity consumption is greater than power generation and the second time period when electricity consumption is less than power generation from the supply and demand difference data curve diagram, and determine the duration and number of the first time period and the second time period respectively;

[0023] The duration and number of the first-category time period and the second-category time period corresponding to each supply-demand difference time period are determined as the supply-demand difference characteristics of the supply-demand difference time period.

[0024] Furthermore, the power supply and demand difference of the thermal storage equipment in the supply and demand difference time period is analyzed and evaluated based on the supply and demand difference characteristics to obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period, including:

[0025] Obtain the duration and number of the first-type time period and the second-type time period corresponding to each supply-demand difference time period, and set the evaluation coefficients of the first-type time period and the second-type time period respectively;

[0026] A comprehensive calculation is performed based on the duration, number and evaluation coefficient corresponding to the first and second time periods to obtain an evaluation value of the power supply and demand difference of the thermal storage equipment in each supply and demand difference time period.

[0027] Furthermore, the calculation formula of the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period is:

[0028]

[0029] Among them, L is the evaluation value of each type of curve segment, α is the evaluation coefficient of the first type of time period, Pi is the duration corresponding to the i-th first type of time period, xi is the number corresponding to the i-th first type of time period, β is the evaluation coefficient of the second type of time period, Qi is the duration corresponding to the i-th second type of time period, and yi is the number corresponding to the i-th second type of time period.

[0030] Further, the step of determining the thermal storage peak-shaving power generation optimization coefficient of the thermal storage equipment in each supply and demand difference time period according to the power supply and demand difference evaluation value, and optimizing the power generation control parameters of the thermal storage equipment in each supply and demand difference time period according to the thermal storage peak-shaving power generation optimization coefficient, includes:

[0031] Preset the correspondence between the thermal storage peak-shaving power generation optimization coefficient and the power supply and demand difference evaluation value interval. For each power supply and demand difference evaluation value interval, the corresponding thermal storage peak-shaving power generation optimization coefficient is associated with the corresponding thermal storage peak-shaving power generation optimization coefficient.

[0032] Obtaining an electric power supply and demand difference assessment value, and based on a mapping relationship between an electric power supply and demand difference assessment value interval to which the electric power supply and demand difference assessment value belongs and a corresponding relationship between a thermal storage peak-shaving power generation optimization coefficient and an electric power supply and demand difference assessment value interval, selecting a thermal storage peak-shaving power generation optimization coefficient corresponding to the electric power supply and demand difference assessment value interval;

[0033] The power generation control characteristic parameters are optimized according to the thermal storage peak-shaving power generation optimization coefficient, and the thermal storage equipment works according to the optimized power generation control characteristic parameters to complete the optimization of the thermal storage peak-shaving power generation of the thermal storage equipment.

[0034] The present invention also provides a thermal storage peak-shaving power generation optimization system, comprising:

[0035] An acquisition module is used to acquire historical power generation control data and historical thermal storage peak-shaving power generation data of the thermal storage device, analyze the historical power generation control data and historical thermal storage peak-shaving power generation data, and determine power generation control characteristic parameters that affect thermal storage peak-shaving power generation of the thermal storage device;

[0036] A prediction module is used to construct a thermal storage peak-shaving power generation prediction model of the thermal storage device based on the power generation control characteristic parameters and the preset neural network model, and to perform predictions according to the thermal storage peak-shaving power generation prediction model to obtain thermal storage peak-shaving power generation prediction data of the thermal storage device;

[0037] A determination module is used to obtain the electricity consumption data of the target area, analyze the thermal storage peak-shaving power generation forecast data and electricity consumption data, and determine the supply and demand difference time period and the corresponding supply and demand difference characteristics;

[0038] An evaluation module, used to analyze and evaluate the power supply and demand difference of the thermal storage equipment in the supply and demand difference time period based on the supply and demand difference characteristics, and obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period;

[0039] The optimization module is used to determine the thermal storage peak-shaving power generation optimization coefficient of the thermal storage equipment in each supply and demand difference time period according to the power supply and demand difference evaluation value, and optimize the power generation control parameters of the thermal storage equipment in each supply and demand difference time period according to the thermal storage peak-shaving power generation optimization coefficient.

[0040] Compared with the prior art, the thermal storage peak load regulation power generation optimization method and system of the embodiment of the present invention has the following beneficial effects:

[0041] By constructing a neural network model, the present invention can more accurately predict the thermal storage peak-shaving power generation of the thermal storage equipment, which helps to make corresponding adjustments and optimizations in advance;

[0042] Based on historical data analysis and prediction models, the present invention can optimize the power generation control parameters of the thermal storage equipment to better adapt it to the actual supply and demand situation, improve power generation efficiency and reduce costs;

[0043] The present invention can realize real-time adjustment of power generation control parameters by analyzing and evaluating the power supply and demand difference of the thermal storage device in the supply and demand difference time period, so that the system can respond to the actual power demand more flexibly;

[0044] The present invention can improve the energy utilization efficiency of thermal storage equipment and reduce energy waste, thereby reducing energy costs, by accurately predicting and optimizing power generation control parameters;

[0045] The present invention can improve the stability and reliability of the system by evaluating and adjusting the difference between supply and demand, reduce the situation of excess or insufficient energy supply, and thus improve the stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 1 is a schematic diagram of the process structure of the thermal storage peak load generation optimization method in an embodiment of the present invention;

[0047] Figure 2 It is a schematic diagram of the composition of the thermal storage peak-shaving power generation optimization system in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0049] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0050] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0051] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technical personnel in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0052] like Figure 1 As shown, in an embodiment of the present application, a thermal storage peak-shaving power generation optimization method is provided, including: S100: acquiring historical power generation control data and historical thermal storage peak-shaving power generation data of a thermal storage device, analyzing the historical power generation control data and the historical thermal storage peak-shaving power generation data, and determining power generation control characteristic parameters that affect thermal storage peak-shaving power generation of the thermal storage device; S200: constructing a thermal storage peak-shaving power generation prediction model for the thermal storage device based on the power generation control characteristic parameters and a preset neural network model, and performing prediction according to the thermal storage peak-shaving power generation prediction model to obtain thermal storage peak-shaving power generation prediction data for the thermal storage device; S300: acquiring a target area The power consumption data of the domain is obtained, and the thermal storage peak-shaving power generation forecast data and power consumption data are analyzed to determine the supply and demand difference time period and the corresponding supply and demand difference characteristics; S400: based on the supply and demand difference characteristics, the power supply and demand difference of the thermal storage equipment in the supply and demand difference time period is analyzed and evaluated to obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period; S500: according to the power supply and demand difference evaluation value, the thermal storage peak-shaving power generation optimization coefficient of the thermal storage equipment in each supply and demand difference time period is determined, and the power generation control parameters of the thermal storage equipment in each supply and demand difference time period are optimized according to the thermal storage peak-shaving power generation optimization coefficient.

[0053] Furthermore, the present invention can more accurately predict the thermal storage peak-shaving power generation of the thermal storage equipment by constructing a neural network model, which is helpful to make corresponding adjustments and optimizations in advance; based on historical data analysis and prediction models, the present invention can optimize the power generation control parameters of the thermal storage equipment to make it better adapt to the actual supply and demand situation, improve power generation efficiency and reduce costs; by analyzing and evaluating the difference in power supply and demand of the thermal storage equipment in the supply and demand difference time period, the present invention can realize real-time adjustment of the power generation control parameters, so that the system can respond more flexibly to actual electricity demand; by accurately predicting and optimizing power generation control parameters, the present invention can improve the energy utilization efficiency of the thermal storage equipment, reduce energy waste, and thus reduce energy costs; by evaluating and adjusting the supply and demand difference, the present invention can improve the stability and reliability of the system, reduce the situation of excess or insufficient energy supply, and thus improve the stability of the system.

[0054] In an embodiment of the present application, a thermal storage peak-shaving power generation optimization method is provided, which obtains historical power generation control data and historical thermal storage peak-shaving power generation data of a thermal storage device, analyzes the historical power generation control data and the historical thermal storage peak-shaving power generation data, and determines power generation control characteristic parameters that affect thermal storage peak-shaving power generation of the thermal storage device, including: obtaining historical power generation control data of the thermal storage device after preprocessing, and dividing the historical power generation control data into several groups of power generation control parameter data groups according to parameter type; determining an i-th power generation control parameter data group from several power generation control parameter data groups, and screening historical thermal storage peak-shaving power generation data when other control parameters are the same; analyzing the change amount of the historical thermal storage peak-shaving power generation data corresponding to the i-th power generation control parameter data group, and determining the power generation control parameter corresponding to the i-th power generation control parameter data group whose change amount is greater than a preset change amount threshold as the power generation control characteristic parameter that affects thermal storage peak-shaving power generation of the thermal storage device.

[0055] Specifically, the historical power generation control data of the thermal storage equipment is collected, including various power generation control parameters, such as temperature, pressure, flow, etc.; the historical power generation control data is divided according to the parameter type to form several groups of power generation control parameter data groups, each group contains the same type of power generation control parameter data; the i-th power generation control parameter data group is determined from several power generation control parameter data groups, that is, a specific type of power generation control parameter data group is selected for subsequent analysis; for the i-th power generation control parameter data group, the historical thermal storage peak-shaving power generation data is screened out, and the data when other control parameters are the same is ensured; the historical thermal storage peak-shaving power generation data corresponding to the i-th power generation control parameter data group is analyzed, and the change amount of each parameter is calculated, and the power generation control parameter corresponding to the i-th power generation control parameter data group whose change amount is greater than the preset change amount threshold is determined as the power generation control characteristic parameter that affects the thermal storage peak-shaving power generation of the thermal storage equipment. This step determines the key control parameters that affect the thermal storage peak-shaving power generation of the thermal storage equipment through the analysis of historical data, providing an important basis for the subsequent optimization and prediction model establishment.

[0056] In an embodiment of the present application, a thermal storage peak-shaving power generation optimization method is provided, wherein a thermal storage peak-shaving power generation prediction model of a thermal storage device is constructed based on power generation control characteristic parameters and a preset neural network model, and prediction is performed according to the thermal storage peak-shaving power generation prediction model to obtain thermal storage peak-shaving power generation prediction data of the thermal storage device, including: constructing a data set based on the power generation control characteristic parameters and the corresponding historical thermal storage peak-shaving power generation data, and inputting the data set into the preset neural network model to construct an initial thermal storage peak-shaving power generation prediction model; dividing the data set into a training set and a test set according to a certain ratio, and inputting the training set and the test set into the initial thermal storage peak-shaving power generation prediction model; training and testing the initial thermal storage peak-shaving power generation prediction model until the initial thermal storage peak-shaving power generation prediction model meets the preset convergence conditions to obtain the thermal storage peak-shaving power generation prediction model; obtaining the power generation control parameters corresponding to the power generation control characteristic parameters in the thermal storage device, and inputting the power generation control parameters into the thermal storage peak-shaving power generation prediction model, and predicting by the thermal storage peak-shaving power generation prediction model to obtain the thermal storage peak-shaving power generation prediction data of the thermal storage device.

[0057] Specifically, a data set is constructed based on the power generation control characteristic parameters and the corresponding historical thermal storage peak-shaving power generation data to ensure that the data set contains key control parameters and corresponding power generation data; the data set is input into a preset neural network model to construct an initial model for thermal storage peak-shaving power generation prediction; the data set is divided into a training set and a test set according to a certain ratio to ensure that the model can also effectively predict on unseen data; the training set and the test set are input into the initial model for thermal storage peak-shaving power generation prediction, and the model is trained and tested until the preset convergence conditions are met to obtain a thermal storage peak-shaving power generation prediction model; the power generation control parameters corresponding to the power generation control characteristic parameters in the thermal storage device are obtained, and these parameters are input into the thermal storage peak-shaving power generation prediction model, and the model is used for prediction to obtain the thermal storage peak-shaving power generation prediction data of the thermal storage device. Through this step, a prediction model can be constructed using historical data, and future thermal storage peak-shaving power generation can be predicted, providing important reference information for energy scheduling and equipment operation.

[0058] In an embodiment of the present application, a method for optimizing thermal storage peak-shaving power generation is provided, wherein the power consumption data of a target area is obtained, and the predicted data for thermal storage peak-shaving power generation and the power consumption data are analyzed to determine a supply-demand difference time period and a corresponding supply-demand difference feature, including: obtaining the power consumption data of the target area, and comparing the predicted data for thermal storage peak-shaving power generation with the power consumption data to determine a time period in which there is a difference between power generation and power consumption, and using the time period as a supply-demand difference time period; subtracting the predicted data for thermal storage peak-shaving power generation and the power consumption data corresponding to the supply-demand difference time period to obtain supply-demand difference data, and plotting the supply-demand difference data into a supply-demand difference data curve chart; determining a first type of time period in which power consumption is greater than power generation and a second type of time period in which power consumption is less than power generation from the supply-demand difference data curve chart, and determining the duration and number of the first type of time period and the second type of time period respectively; determining the duration and number of the first type of time period and the second type of time period corresponding to each supply-demand difference time period as the supply-demand difference feature of the supply-demand difference time period.

[0059] Specifically, the electricity consumption data of the target area is obtained, including the time series data of historical electricity consumption; the thermal storage peak-shaving power generation forecast data and the electricity consumption data are compared to determine the time period when there is a difference between power generation and power consumption, and the time period is used as the supply and demand difference time period; the thermal storage peak-shaving power generation forecast data and the electricity consumption data corresponding to the supply and demand difference time period are subtracted to obtain the supply and demand difference data, and the supply and demand difference data are plotted as a supply and demand difference data curve; from the supply and demand difference data curve, the first time period when power consumption is greater than power generation and the second time period when power consumption is less than power generation are determined, and the duration and number of the first time period and the second time period are determined respectively, and the duration and number of the first time period and the second time period corresponding to each supply and demand difference time period are determined as the supply and demand difference characteristics of the supply and demand difference time period. Through this step, the difference between the thermal storage peak-shaving power generation forecast data and the actual power consumption data can be better understood, thereby providing important reference information for adjusting power generation plans and energy supply.

[0060] In an embodiment of the present application, a method for optimizing thermal storage peak-shaving power generation is provided, wherein the power supply and demand difference of the thermal storage equipment in the supply and demand difference time period is analyzed and evaluated based on the supply and demand difference characteristics to obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period, including: obtaining the duration and number of the first and second time periods corresponding to each supply and demand difference time period, and setting the evaluation coefficients of the first and second time periods respectively; performing a comprehensive calculation based on the duration, number and evaluation coefficient corresponding to the first and second time periods to obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period.

[0061] Specifically, the duration and number of the first and second time periods corresponding to each supply and demand difference time period are obtained; the evaluation coefficients of the first and second time periods are set respectively, and these coefficients can reflect the importance of different time periods to the difference in electricity supply and demand; based on the duration, number and evaluation coefficients corresponding to the first and second time periods, a comprehensive calculation is performed to obtain the evaluation value of the difference in electricity supply and demand of the thermal storage equipment in each supply and demand difference time period, and this evaluation value can reflect the response degree and impact of the thermal storage equipment to the difference in electricity supply and demand in different time periods. Through this step, the response of the thermal storage equipment to the difference in electricity supply and demand in different time periods can be evaluated more finely, providing an important reference basis for further optimization and adjustment.

[0062] In an embodiment of the present application, a method for optimizing thermal storage peak load generation is provided, wherein the calculation formula for the power supply and demand difference evaluation value of the thermal storage device in each supply and demand difference time period is:

[0063]

[0064] Among them, L is the evaluation value of each type of curve segment, α is the evaluation coefficient of the first type of time period, Pi is the duration corresponding to the i-th first type of time period, xi is the number corresponding to the i-th first type of time period, β is the evaluation coefficient of the second type of time period, Qi is the duration corresponding to the i-th second type of time period, and yi is the number corresponding to the i-th second type of time period.

[0065] In an embodiment of the present application, a method for optimizing thermal storage peak-shaving power generation is provided, wherein the thermal storage peak-shaving power generation optimization coefficient of the thermal storage device in each supply and demand difference time period is determined according to the power supply and demand difference evaluation value, and the power generation control parameters of the thermal storage device in each supply and demand difference time period are optimized according to the thermal storage peak-shaving power generation optimization coefficient, including: presetting a correspondence between the thermal storage peak-shaving power generation optimization coefficient and the power supply and demand difference evaluation value interval, wherein the correspondence between the thermal storage peak-shaving power generation optimization coefficient and the power supply and demand difference evaluation value interval is associated with a corresponding thermal storage peak-shaving power generation optimization coefficient for each power supply and demand difference evaluation value interval; obtaining the power supply and demand difference evaluation value, and selecting the thermal storage peak-shaving power generation optimization coefficient corresponding to the power supply and demand difference evaluation value interval based on a mapping relationship between the power supply and demand difference evaluation value interval to which the power supply and demand difference evaluation value belongs within the correspondence between the thermal storage peak-shaving power generation optimization coefficient and the power supply and demand difference evaluation value interval; optimizing the power generation control characteristic parameters according to the thermal storage peak-shaving power generation optimization coefficient, and having the thermal storage device operate according to the optimized power generation control characteristic parameters to complete the optimization of thermal storage peak-shaving power generation of the thermal storage device.

[0066] Specifically, for each power supply and demand difference assessment value interval, the corresponding thermal storage peak-shaving power generation optimization coefficient is pre-set; the current power supply and demand difference assessment value is obtained, and based on the interval to which the power supply and demand difference assessment value belongs, the thermal storage peak-shaving power generation optimization coefficient corresponding to the power supply and demand difference assessment value interval is selected within the pre-set thermal storage peak-shaving power generation optimization coefficient-power supply and demand difference assessment value interval correspondence relationship; according to the selected optimization coefficient, the power generation control characteristic parameters are optimized; the thermal storage equipment works according to the optimized power generation control characteristic parameters to complete the optimization of the thermal storage equipment thermal storage peak-shaving power generation. Through this step, the power generation control parameters of the thermal storage equipment can be adjusted according to the real-time power supply and demand difference situation to achieve more intelligent and efficient energy scheduling and utilization.

[0067] like Figure 2 As shown, in an embodiment of the present application, a thermal storage peak-shaving power generation optimization system is provided, including: an acquisition module, used to acquire historical power generation control data and historical thermal storage peak-shaving power generation data of a thermal storage device, analyze the historical power generation control data and the historical thermal storage peak-shaving power generation data, and determine the power generation control characteristic parameters that affect the thermal storage peak-shaving power generation of the thermal storage device; a prediction module, used to construct a thermal storage peak-shaving power generation prediction model for the thermal storage device based on the power generation control characteristic parameters and a preset neural network model, and perform predictions based on the thermal storage peak-shaving power generation prediction model to obtain the thermal storage peak-shaving power generation prediction data of the thermal storage device; a determination module, used to acquire a target The power consumption data of the region is collected, and the thermal storage peak-shaving power generation forecast data and power consumption data are analyzed to determine the supply and demand difference time period and the corresponding supply and demand difference characteristics; an evaluation module is used to analyze and evaluate the power supply and demand difference of the thermal storage equipment in the supply and demand difference time period based on the supply and demand difference characteristics, and obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period; an optimization module is used to determine the thermal storage peak-shaving power generation optimization coefficient of the thermal storage equipment in each supply and demand difference time period according to the power supply and demand difference evaluation value, and optimize the power generation control parameters of the thermal storage equipment in each supply and demand difference time period according to the thermal storage peak-shaving power generation optimization coefficient.

[0068] In summary, the embodiment of the present invention provides a thermal storage peak-shaving power generation optimization method and system, which includes: obtaining historical power generation control data and historical thermal storage peak-shaving power generation data of the thermal storage device, analyzing them, and determining characteristic parameters that affect thermal storage peak-shaving power generation; constructing a prediction model based on the characteristic parameters and a preset neural network model, and predicting to obtain thermal storage peak-shaving power generation prediction data based on them; obtaining power consumption data of the target area, and analyzing the thermal storage peak-shaving power generation prediction data and power consumption data to determine the supply and demand difference time period and its supply and demand difference characteristics; analyzing and evaluating the power supply and demand difference of the thermal storage device in the supply and demand difference time period based on the supply and demand difference characteristics to obtain a power supply and demand difference evaluation value; determining the thermal storage peak-shaving power generation optimization coefficient according to the power supply and demand difference evaluation value, and optimizing the power generation control parameters according to the thermal storage peak-shaving power generation optimization coefficient. The present invention can improve the operating efficiency and economic benefits of thermal storage equipment and optimize the utilization of energy.

[0069] Finally, it should be noted that: Obviously, a person skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, the present invention is also intended to include these modifications and variations.

[0070] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0071] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0072] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it is easy for a person skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, a person skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A thermal storage peak load generation optimization method, characterized in that: include: Acquire historical power generation control data and historical thermal storage peak-shaving power generation data of the thermal storage device, analyze the historical power generation control data and historical thermal storage peak-shaving power generation data, and determine power generation control characteristic parameters that affect thermal storage peak-shaving power generation of the thermal storage device; A thermal storage peak-shaving power generation prediction model for the thermal storage device is constructed based on the power generation control characteristic parameters and the preset neural network model, and prediction is performed according to the thermal storage peak-shaving power generation prediction model to obtain thermal storage peak-shaving power generation prediction data for the thermal storage device; Obtain the electricity consumption data of the target area, and analyze the thermal storage peak-shaving power generation forecast data and electricity consumption data to determine the supply and demand difference time period and the corresponding supply and demand difference characteristics; Analyze and evaluate the power supply and demand difference of the thermal storage equipment in the supply and demand difference time period based on the supply and demand difference characteristics, and obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period; The thermal storage peak-shaving power generation optimization coefficient of the thermal storage equipment in each supply and demand difference time period is determined according to the power supply and demand difference evaluation value, and the power generation control parameters of the thermal storage equipment in each supply and demand difference time period are optimized according to the thermal storage peak-shaving power generation optimization coefficient.

2. A thermal storage peak load generation optimization method according to claim 1, characterized in that: The acquiring of historical power generation control data and historical thermal storage peak-shaving power generation data of the thermal storage device, analyzing the historical power generation control data and the historical thermal storage peak-shaving power generation data, and determining power generation control characteristic parameters affecting thermal storage peak-shaving power generation of the thermal storage device include: Acquire historical power generation control data of the thermal storage device after preprocessing, and divide the historical power generation control data into several groups of power generation control parameter data groups according to parameter types; Determine the i-th power generation control parameter data group from a plurality of power generation control parameter data groups, and select historical thermal storage peak-shaving power generation data when other control parameters are the same; Analyze the change in historical thermal storage peak-shaving power generation data corresponding to the i-th power generation control parameter data group, and determine the power generation control parameter corresponding to the i-th power generation control parameter data group whose change is greater than a preset change threshold as the power generation control characteristic parameter that affects the thermal storage peak-shaving power generation of the thermal storage equipment.

3. A thermal storage peak load generation optimization method according to claim condition 2, characterized in that: The heat storage peak-shaving power generation prediction model of the heat storage device is constructed based on the power generation control characteristic parameters and the preset neural network model, and prediction is performed according to the heat storage peak-shaving power generation prediction model to obtain the heat storage peak-shaving power generation prediction data of the heat storage device, including: A data set is constructed based on the power generation control characteristic parameters and the corresponding historical thermal storage peak-shaving power generation data, and the data set is input into a preset neural network model to construct an initial model for thermal storage peak-shaving power generation prediction; The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial model for thermal storage peak-shaving power generation prediction; The initial model for predicting thermal storage peak-shaving power generation is trained and tested until the initial model for predicting thermal storage peak-shaving power generation meets a preset convergence condition, thereby obtaining a thermal storage peak-shaving power generation prediction model; The power generation control parameters corresponding to the power generation control characteristic parameters in the thermal storage device are obtained, and the power generation control parameters are input into the thermal storage peak-shaving power generation prediction model. The thermal storage peak-shaving power generation prediction model performs prediction to obtain the thermal storage peak-shaving power generation prediction data of the thermal storage device.

4. A thermal storage peak load generation optimization method according to claim condition 3, characterized in that: The obtaining of the electricity consumption data of the target area, and analyzing the thermal storage peak-shaving power generation forecast data and the electricity consumption data to determine the supply and demand difference time period and the corresponding supply and demand difference characteristics include: Obtain the electricity consumption data of the target area, and compare the thermal storage peak-shaving power generation forecast data with the electricity consumption data to determine the time period when there is a difference between power generation and power consumption, and use this time period as the supply-demand difference time period; Subtract the thermal storage peak-shaving power generation forecast data and the power consumption data corresponding to the supply-demand difference time period to obtain supply-demand difference data, and draw the supply-demand difference data into a supply-demand difference data curve graph; Determine the first time period when electricity consumption is greater than power generation and the second time period when electricity consumption is less than power generation from the supply and demand difference data curve diagram, and determine the duration and number of the first time period and the second time period respectively; The duration and number of the first-category time period and the second-category time period corresponding to each supply-demand difference time period are determined as the supply-demand difference characteristics of the supply-demand difference time period.

5. A thermal storage peak load generation optimization method according to claim 4, characterized in that: The power supply and demand difference of the thermal storage equipment in the supply and demand difference time period is analyzed and evaluated based on the supply and demand difference characteristics to obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period, including: Obtain the duration and number of the first-type time period and the second-type time period corresponding to each supply-demand difference time period, and set the evaluation coefficients of the first-type time period and the second-type time period respectively; A comprehensive calculation is performed based on the duration, number and evaluation coefficient corresponding to the first and second time periods to obtain an evaluation value of the power supply and demand difference of the thermal storage equipment in each supply and demand difference time period.

6. A thermal storage peak load generation optimization method according to claim 5, characterized in that: The calculation formula for the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period is: Among them, L is the evaluation value of each type of curve segment, α is the evaluation coefficient of the first type of time period, Pi is the duration corresponding to the i-th first type of time period, xi is the number corresponding to the i-th first type of time period, β is the evaluation coefficient of the second type of time period, Qi is the duration corresponding to the i-th second type of time period, and yi is the number corresponding to the i-th second type of time period.

7. A thermal storage peak load generation optimization method according to claim 5, characterized in that: The step of determining the thermal storage peak-shaving power generation optimization coefficient of the thermal storage equipment in each supply and demand difference time period according to the power supply and demand difference evaluation value, and optimizing the power generation control parameters of the thermal storage equipment in each supply and demand difference time period according to the thermal storage peak-shaving power generation optimization coefficient, includes: Preset the correspondence between the thermal storage peak-shaving power generation optimization coefficient and the power supply and demand difference evaluation value interval. For each power supply and demand difference evaluation value interval, the corresponding thermal storage peak-shaving power generation optimization coefficient is associated with the corresponding thermal storage peak-shaving power generation optimization coefficient. Obtaining an electric power supply and demand difference assessment value, and based on a mapping relationship between an electric power supply and demand difference assessment value interval to which the electric power supply and demand difference assessment value belongs and a corresponding relationship between a thermal storage peak-shaving power generation optimization coefficient and an electric power supply and demand difference assessment value interval, selecting a thermal storage peak-shaving power generation optimization coefficient corresponding to the electric power supply and demand difference assessment value interval; The power generation control characteristic parameters are optimized according to the thermal storage peak-shaving power generation optimization coefficient, and the thermal storage equipment works according to the optimized power generation control characteristic parameters to complete the optimization of the thermal storage peak-shaving power generation of the thermal storage equipment.

8. A thermal storage peak-shaving power generation optimization system, characterized in that: include: An acquisition module is used to acquire historical power generation control data and historical thermal storage peak-shaving power generation data of the thermal storage device, analyze the historical power generation control data and historical thermal storage peak-shaving power generation data, and determine power generation control characteristic parameters that affect thermal storage peak-shaving power generation of the thermal storage device; A prediction module is used to construct a thermal storage peak-shaving power generation prediction model of the thermal storage device based on the power generation control characteristic parameters and the preset neural network model, and to perform predictions according to the thermal storage peak-shaving power generation prediction model to obtain thermal storage peak-shaving power generation prediction data of the thermal storage device; A determination module is used to obtain the electricity consumption data of the target area, analyze the thermal storage peak-shaving power generation forecast data and electricity consumption data, and determine the supply and demand difference time period and the corresponding supply and demand difference characteristics; An evaluation module, used to analyze and evaluate the power supply and demand difference of the thermal storage equipment in the supply and demand difference time period based on the supply and demand difference characteristics, and obtain the power supply and demand difference evaluation value of the thermal storage equipment in each supply and demand difference time period; The optimization module is used to determine the thermal storage peak-shaving power generation optimization coefficient of the thermal storage equipment in each supply and demand difference time period according to the power supply and demand difference evaluation value, and optimize the power generation control parameters of the thermal storage equipment in each supply and demand difference time period according to the thermal storage peak-shaving power generation optimization coefficient.

Images