A virtual power grid intelligent scheduling method for an air conditioning cold storage system

Abstract

The present application relates to the field of virtual power grid, especially to a kind of virtual power grid intelligent scheduling method for air conditioner cold storage system, comprising: in response to the first time node, the predicted price of photovoltaic power, wind power and thermal power in the first prediction period is respectively acquired;The predicted energy consumption of energy consumption end in the first prediction period is calculated, and the predicted energy consumption includes basic demand energy consumption and fluctuation demand energy consumption;Determine the electricity purchase quantity according to the predicted energy consumption, and construct the electricity purchase quantity scheme based on each predicted price;The residual proportion of single fluctuation demand energy consumption is acquired, and whether the scheduling of single predicted energy consumption meets the preset standard is determined according to the residual proportion and fluctuation demand quantity characteristic value;In response to the scheduling of single predicted energy consumption does not meet the preset standard, reduce the fluctuation demand energy consumption of next prediction period, or based on the prediction deviation characteristic value, whether the scheduling of predicted energy consumption meets the preset standard is determined again, or increase the fluctuation demand energy consumption of next prediction period.The present application realizes the accurate scheduling of power.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of virtual power grid, and particularly relates to a virtual power grid intelligent scheduling method for air-conditioning cold storage system. BACKGROUND

[0002] As a new type of power grid organization form, virtual power grid integrates and optimally operates distributed power sources, energy storage devices, loads and other elements through advanced information communication technology and control technology. Virtual power grid can realize the complementation and optimal configuration of different energies, improve energy utilization efficiency, and enhance the flexibility and reliability of power systems. In the virtual power grid environment, the intelligent scheduling of air-conditioning cold storage system, as an important load and energy storage unit, is of great significance to the stable operation and optimal scheduling of virtual power grid.

[0003] However, the existing virtual power grid scheduling method mainly focuses on the generation scheduling of distributed power sources, and the scheduling research on the load side is relatively less, especially for air-conditioning cold storage system which has special energy consumption characteristics. The energy consumption demand of air-conditioning cold storage system has basic demand and fluctuation demand. The basic demand is the energy consumption necessary for maintaining the normal operation of the system and the energy consumption end, while the fluctuation demand is closely related to the energy storage state of the system, environmental changes and other factors. How to accurately predict the energy consumption demand of air-conditioning cold storage system and develop a reasonable power purchase scheme and scheduling strategy according to the real-time power price and environmental conditions is a problem to be solved in the current virtual power grid scheduling field. SUMMARY

[0004] Therefore, the present application provides a virtual power grid intelligent scheduling method for air-conditioning cold storage system to overcome the problem that the existing technology cannot dynamically adjust according to the real-time energy consumption fluctuation and environmental changes. When there is a prediction deviation or environmental mutation, the scheduling strategy cannot respond in time, which easily leads to unstable system operation or energy waste.

[0005] To achieve the above-mentioned purpose, the present application provides a virtual power grid intelligent scheduling method for air-conditioning cold storage system, comprising:

[0006] Step S1, in response to a first time node, respectively acquiring the predicted prices of photovoltaic power, wind power and thermal power in a first prediction period;

[0007] Step S2, in response to completing the acquisition of the predicted prices, calculating the predicted energy consumption of the energy consumption end in the first prediction period, the predicted energy consumption including basic demand energy consumption and fluctuation demand energy consumption;

[0008] Step S3, determining the power purchase amount of the first prediction period according to the predicted energy consumption, and constructing a power purchase amount scheme based on each predicted price;

[0009] In step S4, the remaining proportion of the single fluctuation demand energy consumption is obtained, and it is determined whether the scheduling of the single predicted energy consumption meets the preset standard according to the remaining proportion and the fluctuation demand quantity characteristic value.

[0010] In step S5, in response to the scheduling of the single predicted energy consumption not meeting the preset standard, the fluctuation demand energy consumption of the next prediction period is reduced, or the scheduling of the predicted energy consumption is determined again based on the prediction deviation characteristic value whether it meets the preset standard, or the fluctuation demand energy consumption of the next prediction period is increased.

[0011] The process of constructing the electricity purchase quantity scheme based on the predicted prices comprises:

[0012] The first prediction period 24h is divided into 24 sub-periods,

[0013] The base demand energy consumption is constructed using thermal power and / or photovoltaic power and selecting the power category with a low predicted price in a single sub-period;

[0014] The fluctuation demand energy consumption is constructed using photovoltaic power and / or wind power and selecting the power category with a low predicted price in a single sub-period;

[0015] The electricity quantity of each power category determined in each sub-period is fitted to construct the electricity purchase quantity scheme.

[0016] Further, the base demand energy consumption comprises the minimum energy consumption required by the energy consumption end operation, the minimum energy consumption required by the air conditioning cold storage system maintenance operation, and the base energy storage energy consumption of the air conditioning cold storage system;

[0017] The fluctuation demand energy consumption is selected from the difference between the full-power energy storage energy consumption and the base energy storage energy consumption of the air conditioning cold storage system.

[0018] Further, the process of constructing the electricity purchase quantity scheme based on the predicted prices comprises:

[0019] The first prediction period is divided into a plurality of sub-periods,

[0020] The base demand energy consumption is constructed using thermal power and / or photovoltaic power and selecting the power category with a low predicted price in a single sub-period;

[0021] The fluctuation demand energy consumption is constructed using photovoltaic power and / or wind power and selecting the power category with a low predicted price in a single sub-period;

[0022] The electricity quantity of each power category determined in each sub-period is fitted to construct the electricity purchase quantity scheme.

[0023] Further, in the step S4, the determination of whether the scheduling of the single predicted energy consumption is qualified is performed in response to the remaining proportion being greater than the preset remaining proportion.

[0024] Further, in the step S4, it is preliminarily determined whether the scheduling of the single predicted energy consumption conforms to the preset standard according to the fluctuation demand characteristic value, wherein,

[0025] If the fluctuation demand characteristic value is less than the first preset fluctuation threshold, it is preliminarily determined that the scheduling of the single predicted energy consumption does not conform to the preset standard, and the fluctuation demand energy consumption of the next prediction period is reduced according to the difference between the first preset fluctuation threshold and the fluctuation demand characteristic value.

[0026] If the fluctuation demand characteristic value is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold, it is preliminarily determined that the scheduling of the single predicted energy consumption conforms to the preset standard.

[0027] If the fluctuation demand characteristic value is greater than or equal to the second preset fluctuation threshold and less than the third preset fluctuation threshold, it is preliminarily determined that the scheduling of the single predicted energy consumption does not conform to the preset standard, and it is verified whether the scheduling of the single predicted energy consumption conforms to the preset standard according to the environmental fluctuation evaluation value.

[0028] If the fluctuation demand characteristic value is greater than or equal to the third preset fluctuation threshold, it is preliminarily determined that the scheduling of the single predicted energy consumption does not conform to the preset standard, and the fluctuation demand energy consumption of the next prediction period is increased according to the difference between the third preset fluctuation threshold and the fluctuation demand characteristic value.

[0029] Further, the fluctuation demand characteristic value is a variance value of a plurality of historical residual proportions including the residual proportion.

[0030] Further, the reduction degree of the fluctuation demand energy consumption of the next prediction period is positively correlated with the difference between the first preset fluctuation threshold and the fluctuation demand characteristic value.

[0031] Further, it is verified whether the scheduling of the single predicted energy consumption conforms to the preset standard according to the environmental fluctuation evaluation value, wherein,

[0032] If the environmental fluctuation evaluation value is less than a preset environmental fluctuation threshold, it is verified that the scheduling of the single predicted energy consumption does not conform to the preset standard, and the basic demand energy consumption of the next prediction period is increased according to the difference between the preset environmental fluctuation threshold and the environmental fluctuation evaluation value.

[0033] If the environmental fluctuation evaluation value is greater than or equal to the preset environmental fluctuation threshold, it is verified that the scheduling of the single predicted energy consumption conforms to the preset standard.

[0034] Further, the environmental fluctuation evaluation value is determined based on the temperature data and the humidity data.

[0035] Further, the increase amplitude of the basic demand energy consumption of the next prediction period is positively correlated with the difference between the preset environmental fluctuation threshold and the environmental fluctuation evaluation value.

[0036] Compared with the prior art, the present application has the beneficial effects that: the present application obtains the predicted prices of multiple power sources at the first time node, calculates the predicted energy consumption at the energy consumption end, and then determines the electricity purchase quantity and constructs the electricity purchase quantity scheme, and finally flexibly adjusts the energy consumption of the subsequent period according to the scheduling of fluctuating demand energy consumption. This method comprehensively considers the price fluctuations of different power sources and the actual demand of the energy consumption end, can dynamically adjust the electricity purchase strategy according to the real-time situation, effectively reduces the electricity purchase cost, improves the energy utilization efficiency, and realizes the intelligent and economic operation of the air-conditioning cold storage system in the virtual power grid.

[0037] Further, the present application clearly defines the basic demand energy consumption and the fluctuating demand energy consumption. The basic demand energy consumption covers the minimum energy consumption required for the energy consumption end operation, air-conditioning cold storage system maintenance operation and basic energy storage, which provides a basic guarantee for stable operation of the system; the fluctuating demand energy consumption is selected from the difference between the full-power energy storage energy consumption of the air-conditioning cold storage system and the basic energy storage energy consumption, which clearly reflects the flexibility of the system energy storage. This clear energy consumption classification helps to more accurately predict and control energy consumption, provides accurate basis for subsequent development of reasonable electricity purchase quantity scheme and scheduling strategy, and further improves the economy and stability of the system.

[0038] Further, the present application elaborates the process of constructing the electricity purchase quantity scheme based on each predicted price. The first prediction period is divided into several sub-periods, and the power with low predicted price in different power source categories is selected for construction according to the basic demand energy consumption and the fluctuating demand energy consumption, and finally the electricity quantity determined in each sub-period is fitted to form the electricity purchase quantity scheme. This construction method of selecting low-price power sources in different periods and different demand types can fully utilize the price advantage of different power sources in different periods, maximize the reduction of electricity purchase cost, and at the same time ensure that the system can obtain appropriate power supply under different demand scenarios, improving the flexibility and economy of energy procurement.

[0039] Further, in step S4, the present application determines whether the scheduling of the single predicted energy consumption is qualified only when the remaining proportion is greater than the preset remaining proportion. This setting avoids invalid determination in unreasonable cases such as too low remaining proportion, ensures that the scheduling determination process is carried out within a reasonable energy consumption range, improves the accuracy and effectiveness of the determination, and makes the scheduling strategy more accurately adjust to the actual energy consumption, thereby optimizing the operation efficiency and energy utilization effect of the system.

[0040] Further, the application preliminarily determines whether the scheduling of the single predicted energy consumption meets the preset standard according to the comparison between the fluctuation demand characteristic value and different preset fluctuation thresholds, and formulates corresponding adjustment strategies for different situations. By setting multiple threshold intervals, the scheduling situation can be more carefully evaluated, and measures such as reducing, maintaining or increasing the fluctuation demand energy consumption of the next prediction period are taken according to the specific range of the fluctuation demand characteristic value. This hierarchical processing method makes the scheduling strategy more flexible and accurate, and can better adapt to different energy consumption fluctuations, improving the stability and economy of the system.

[0041] Further, the application defines the fluctuation demand characteristic value as the variance value of a plurality of historical residual proportions including the residual proportion. The variance value can reflect the dispersion degree of the historical residual proportion, and by analyzing this characteristic value, the historical changes of the fluctuation demand energy consumption can be more comprehensively understood, so that whether the scheduling of the current single predicted energy consumption is reasonable can be more accurately evaluated. This analysis method based on historical data statistics provides a more scientific basis for scheduling decisions, and helps to improve the accuracy and reliability of the scheduling strategy.

[0042] Further, the application points out that the reduction degree of the fluctuation demand energy consumption of the next prediction period is positively correlated with the difference between the first preset fluctuation threshold and the fluctuation demand characteristic value. This positive correlation makes the adjustment range of the fluctuation demand energy consumption be reasonably determined according to the difference between the actual fluctuation and the preset standard. When the difference is large, the reduction range is appropriately increased, which can more effectively correct the scheduling deviation; when the difference is small, the reduction range is also correspondingly reduced, avoiding excessive adjustment leading to new energy consumption problems. This dynamic adjustment method improves the self-adaptation ability of the system, and helps to maintain the stability and reasonableness of the energy consumption.

[0043] Further, the application verifies whether the scheduling of the single predicted energy consumption meets the preset standard according to the environmental fluctuation evaluation value, and formulates corresponding basic demand energy consumption adjustment strategies for different verification results. By introducing the environmental fluctuation evaluation value, the influence of environmental factors on energy consumption is comprehensively considered, making the scheduling decision more comprehensive and scientific. When the environmental fluctuation evaluation value is less than the preset environmental fluctuation threshold, the basic demand energy consumption of the next prediction period is increased to cope with possible environmental changes; when it is greater than or equal to the threshold, the current scheduling meets the preset standard. This verification and adjustment mechanism based on environmental factors improves the adaptability of the system to environmental changes, and guarantees the stable operation of the system.

[0044] Further, the environmental fluctuation evaluation value of the present application is determined based on temperature data and humidity data. Temperature and humidity are important environmental factors affecting the energy consumption of air conditioning systems. By comprehensively considering both data to determine the environmental fluctuation evaluation value, the actual impact of environmental changes on the energy consumption of the air conditioning cold storage system can be more accurately reflected. This comprehensive consideration of multiple environmental factors provides more comprehensive and accurate environmental information for the development of scheduling strategies, which helps to improve the scientificity and rationality of scheduling decisions and further improve the energy utilization efficiency and operation stability of the system.

[0045] Further, the present application is positively related to the increase range of the basic demand energy consumption of the next predicted period and the difference between the preset environmental fluctuation threshold and the environmental fluctuation evaluation value. This positive correlation enables the adjustment range of the basic demand energy consumption to be reasonably determined according to the difference between the environmental fluctuation and the preset standard. When the difference is large, the increase range is appropriately increased, which can better cope with large environmental fluctuations and ensure that the system has sufficient energy storage. When the difference is small, the increase range is also correspondingly reduced, avoiding excessive increase of energy consumption leading to cost increase. This dynamic adjustment method improves the response ability of the system to environmental changes, which helps to ensure stable operation of the system while realizing reasonable utilization of energy and cost control. BRIEF DESCRIPTION OF DRAWINGS

[0046] Fig. 1 A flowchart of a virtual power grid intelligent scheduling method for an air conditioning cold storage system according to an embodiment of the present application;

[0047] Fig. 2 A flowchart of preliminary determination of whether the scheduling of the single predicted energy consumption meets the preset standard according to an embodiment of the present application;

[0048] Fig. 3 A flowchart of verification of whether the scheduling of the single predicted energy consumption meets the preset standard according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the objects and advantages of the present application clearer, the present application will be further described below with reference to embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0050] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and do not limit the protection scope of the present application.

[0051] Please refer to Figs. 1-3The application discloses a virtual power grid intelligent scheduling method for an air-conditioning cold storage system.

[0052] The application discloses a virtual power grid intelligent scheduling method for an air-conditioning cold storage system, which comprises the following steps:

[0053] In step S1, the predicted prices of photovoltaic power, wind power and thermal power in a first prediction period of 24 hours are respectively acquired at a first time node 0.

[0054] In step S2, the predicted energy consumption of an energy consumption end in the first prediction period of 24 hours is calculated after the acquisition of the predicted prices is completed, wherein the predicted energy consumption comprises basic demand energy consumption and fluctuation demand energy consumption.

[0055] In step S3, the electricity purchase quantity in the first prediction period of 24 hours is determined according to the predicted energy consumption, and an electricity purchase quantity scheme is constructed based on the predicted prices.

[0056] In step S4, the residual proportion of single fluctuation demand energy consumption is acquired, and whether the scheduling of single predicted energy consumption conforms to a preset standard is determined according to the residual proportion and a fluctuation demand quantity characteristic value.

[0057] In step S5, the fluctuation demand energy consumption of a next prediction period is reduced, or whether the scheduling of predicted energy consumption conforms to the preset standard is determined again based on a prediction deviation characteristic value, or the fluctuation demand energy consumption of the next prediction period is increased, in response to the scheduling of single predicted energy consumption not conforming to the preset standard.

[0058] Specifically, the predicted prices of photovoltaic power, wind power and thermal power are preferentially inquired from a provincial power transaction platform, real-time quotation and day-ahead market declaration data are acquired, and third-party tools such as Beixing star power grid and Wande are additionally used to improve the prediction accuracy through bidding space analysis and segmented function fitting.

[0059] Specifically, the basic demand energy consumption comprises minimum energy consumption required by energy consumption end operation, minimum energy consumption required by air-conditioning cold storage system maintenance operation and basic energy storage energy consumption of the air-conditioning cold storage system.

[0060] The fluctuation demand energy consumption is selected from the difference between full-power energy storage energy consumption and basic energy storage energy consumption of the air-conditioning cold storage system.

[0061] The basic energy storage energy consumption usually accounts for 20% of the full-power energy storage energy consumption of the air-conditioning cold storage system.

[0062] The basic demand energy consumption covers the energy consumption end operation, the air conditioning cold storage system maintenance operation and the minimum energy consumption required by the basic energy storage, thereby providing a basic guarantee for stable operation of the system; the fluctuation demand energy consumption is selected from the difference between the full-power energy storage energy consumption of the air conditioning cold storage system and the basic energy storage energy consumption, thereby clearly reflecting the flexible space of the system energy storage. The clear energy consumption classification helps to more accurately predict and control the energy consumption, thereby providing an accurate basis for subsequent development of a reasonable electricity purchase quantity scheme and a scheduling strategy, and further improving the economy and stability of the system.

[0063] Specifically, the process of constructing the electricity purchase quantity scheme based on each predicted price includes:

[0064] dividing the first prediction period 24h into 24 sub-periods,

[0065] constructing the basic demand energy consumption using thermal power and / or photovoltaic power and selecting a power category with a low predicted price in a single sub-period;

[0066] constructing the fluctuation demand energy consumption using photovoltaic power and / or wind power and selecting a power category with a low predicted price in a single sub-period;

[0067] fitting the electricity quantity of each power category determined in each sub-period to construct the electricity purchase quantity scheme.

[0068] The first prediction period is divided into several sub-periods, a power with a low predicted price in different power categories is selected for the construction of the basic demand energy consumption and the fluctuation demand energy consumption, respectively, and finally the electricity quantity determined in each sub-period is fitted to form the electricity purchase quantity scheme. This construction method of selecting a low-price power in different periods and different demand types can fully utilize the price advantage of different powers in different periods, maximally reduce the electricity purchase cost, and at the same time ensure that the system can obtain appropriate power supply in different demand scenarios, thereby improving the flexibility and economy of energy procurement.

[0069] Specifically, in the step S4, the determination of whether the scheduling of the single-time predicted energy consumption is qualified is performed in response to the remaining proportion being greater than a preset remaining proportion 15%. The determination of whether the scheduling of the single-time predicted energy consumption is qualified is performed only when the remaining proportion is greater than the preset remaining proportion. This setting avoids invalid determination in an unreasonable case where the remaining proportion is too low, ensures that the scheduling determination process is performed within a reasonable energy consumption range, improves the accuracy and effectiveness of the determination, enables the scheduling strategy to be adjusted more accurately according to the actual energy consumption, and thereby optimizes the operation efficiency and energy utilization effect of the system.

[0070] Specifically, in the step S4, whether the scheduling of the single-time predicted energy consumption meets a preset standard is preliminarily determined according to a fluctuation demand quantity characteristic value, wherein,

[0071] if the fluctuation demand quantity characteristic value is less than a first preset fluctuation threshold If the fluctuation demand characteristic value is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold, the scheduling of the single predicted energy consumption is preliminarily determined to be in conformity with the preset standard.

[0072] If the fluctuation demand characteristic value is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold, the scheduling of the single predicted energy consumption is preliminarily determined to be in conformity with the preset standard. If the fluctuation demand characteristic value is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold, the scheduling of the single predicted energy consumption is preliminarily determined to be in conformity with the preset standard.

[0073] If the fluctuation demand characteristic value is greater than or equal to the second preset fluctuation threshold and less than the third preset fluctuation threshold, the scheduling of the single predicted energy consumption is preliminarily determined to be not in conformity with the preset standard, and whether the scheduling of the single predicted energy consumption conforms to the preset standard is verified according to the environmental fluctuation evaluation value. If the fluctuation demand characteristic value is greater than or equal to the second preset fluctuation threshold and less than the third preset fluctuation threshold, the scheduling of the single predicted energy consumption is preliminarily determined to be not in conformity with the preset standard, and whether the scheduling of the single predicted energy consumption conforms to the preset standard is verified according to the environmental fluctuation evaluation value.

[0074] If the fluctuation demand characteristic value is greater than or equal to the third preset fluctuation threshold, the scheduling of the single predicted energy consumption is preliminarily determined to be not in conformity with the preset standard, and the fluctuation demand energy consumption of the next prediction period is increased according to the difference between the fluctuation demand characteristic value and the third preset fluctuation threshold.

[0075] The scheduling of the single predicted energy consumption is preliminarily determined according to the comparison between the fluctuation demand characteristic value and different preset fluctuation thresholds, and corresponding adjustment strategies are formulated for different situations. By setting multiple threshold intervals, the scheduling situation can be evaluated more meticulously, and measures such as reducing, maintaining or increasing the fluctuation demand energy consumption of the next prediction period can be taken according to the specific range of the fluctuation demand characteristic value. This hierarchical processing method makes the scheduling strategy more flexible and accurate, and can better adapt to different energy consumption fluctuation situations, improving the stability and economy of the system.

[0076] Specifically, the fluctuation demand characteristic value is the variance value of 10 historical residual proportions containing the residual proportion. The fluctuation demand characteristic value is defined as the variance value of a plurality of historical residual proportions containing the residual proportion. The variance value can reflect the dispersion degree of the historical residual proportion, and by analyzing this characteristic value, the historical change of the fluctuation demand energy consumption can be more comprehensively understood, so that whether the scheduling of the current single predicted energy consumption is reasonable can be more accurately evaluated. This analysis method based on historical data statistics provides a more scientific basis for scheduling decisions, which helps to improve the accuracy and reliability of the scheduling strategy.

[0077] Specifically, the degree of reduction of the fluctuation demand energy consumption in the next prediction period is positively correlated with the difference between the first preset fluctuation threshold and the fluctuation demand quantity characteristic value. It can be understood that the positive correlation is, for example, a linear positive correlation and a nonlinear positive correlation, and is not specifically limited, as long as the greater the difference between the first preset fluctuation threshold and the fluctuation demand quantity characteristic value, the greater the degree of reduction of the fluctuation demand energy consumption in the next prediction period. The degree of reduction of the fluctuation demand energy consumption in the next prediction period is positively correlated with the difference between the first preset fluctuation threshold and the fluctuation demand quantity characteristic value. Such a positive correlation relationship enables the adjustment range of the fluctuation demand energy consumption to be reasonably determined according to the difference between the actual fluctuation and the preset standard. When the difference is large, the reduction range is appropriately increased, which can more effectively correct the scheduling deviation; when the difference is small, the reduction range is also correspondingly reduced, avoiding excessive adjustment to cause new energy consumption problems. Such a dynamic adjustment mode improves the adaptive ability of the system and helps to maintain the stability and reasonableness of energy consumption.

[0078] Specifically, whether the scheduling of the single-time prediction energy consumption conforms to the preset standard is verified according to the environmental fluctuation evaluation value, wherein,

[0079] If the environmental fluctuation evaluation value is less than the preset environmental fluctuation threshold 0.95, it is verified that the scheduling of the single-time prediction energy consumption does not conform to the preset standard, and the basic demand energy consumption in the next prediction period is increased according to the difference between the preset environmental fluctuation threshold and the environmental fluctuation evaluation value.

[0080] If the environmental fluctuation evaluation value is greater than or equal to the preset environmental fluctuation threshold, it is verified that the scheduling of the single-time prediction energy consumption conforms to the preset standard.

[0081] According to the environmental fluctuation evaluation value, whether the scheduling of the single-time prediction energy consumption conforms to the preset standard is verified, and corresponding basic demand energy consumption adjustment strategies are formulated for different verification results. By introducing the environmental fluctuation evaluation value, the influence of environmental factors on energy consumption is comprehensively considered, so that the scheduling decision is more comprehensive and scientific. When the environmental fluctuation evaluation value is less than the preset environmental fluctuation threshold, the basic demand energy consumption in the next prediction period is increased to cope with possible environmental changes; when it is greater than or equal to the threshold, the current scheduling conforms to the preset standard. Such a verification and adjustment mechanism based on environmental factors improves the adaptability of the system to environmental changes and ensures the stable operation of the system.

[0082] Specifically, the environmental fluctuation evaluation value is calculated by the following formula,

[0083]

[0084] In the formula, E represents the environmental fluctuation evaluation value, represents the first evaluation coefficient, which is set to 0.1 , U represents the highest temperature in the first prediction period, Ty represents a temperature threshold, and Ty is set to 16 °C, represents a second evaluation coefficient, and , RH represents the average humidity in the first prediction period, RHy represents a humidity threshold, and RHy is set to 50%.

[0085] Temperature and humidity are important environmental factors affecting the energy consumption of an air conditioning system. The comprehensive consideration of both data to determine the environmental fluctuation evaluation value can more accurately reflect the actual influence of environmental changes on the energy consumption of the air conditioning cold storage system. This comprehensive consideration of multiple environmental factors provides more comprehensive and accurate environmental information for the development of scheduling strategies, which helps to improve the scientificity and rationality of scheduling decisions and further improve the energy utilization efficiency and operation stability of the system.

[0086] Specifically, the increase range of the basic demand energy consumption in the next prediction period is positively correlated with the difference between the preset environmental fluctuation threshold and the environmental fluctuation evaluation value. It can be understood that the positive correlation is, for example, a linear positive correlation and a nonlinear positive correlation, and the specific limitation is not limited. It only needs to meet that the greater the difference between the preset environmental fluctuation threshold and the environmental fluctuation evaluation value, the greater the increase range of the basic demand energy consumption in the next prediction period. This positive correlation makes the adjustment range of the basic demand energy consumption be reasonably determined according to the difference between the environmental fluctuation and the preset standard. When the difference is large, the increase range is appropriately increased, which can better cope with large environmental fluctuations and ensure that the system has sufficient energy storage. When the difference is small, the increase range is also correspondingly reduced, avoiding excessive increase of energy consumption leading to cost increase. This dynamic adjustment method improves the response ability of the system to environmental changes, which helps to ensure stable operation of the system while realizing reasonable utilization of energy and cost control.

[0087] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to related technical features without departing from the principles of the present application, and the technical solutions after these changes or replacements will fall within the protection scope of the present application.

[0088] The above description is only the preferred embodiments of the present application and is not intended to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A virtual power grid intelligent dispatching method for air conditioning cold storage systems, characterized in that, include: Step S1: In response to the first time node, obtain the predicted prices of photovoltaic power, wind power and thermal power in the first prediction period respectively; Step S2: In response to obtaining the predicted price, calculate the predicted energy consumption of the energy consumption side during the first prediction period. The predicted energy consumption includes basic demand energy consumption and fluctuating demand energy consumption. Step S3: Determine the electricity purchase amount for the first forecast period according to the predicted energy consumption, and construct an electricity purchase plan based on each of the predicted prices; Step S4: Obtain the remaining percentage of energy consumption for a single fluctuation, and determine whether the scheduling of the predicted energy consumption for a single fluctuation meets the preset standard based on the remaining percentage and the characteristic value of the fluctuation demand. Step S5: In response to the fact that the scheduling of energy consumption in a single prediction does not meet the preset standard, reduce the fluctuating energy consumption of the next prediction period, or determine whether the scheduling of the predicted energy consumption meets the preset standard again based on the prediction deviation characteristic value, or increase the fluctuating energy consumption of the next prediction period. The process of constructing an electricity purchase plan based on the predicted prices includes: The first prediction period of 24 hours is divided into 24 sub-periods. The baseline demand energy consumption is constructed using thermal power and / or photovoltaic power, and by selecting the electricity category with the lowest predicted price within a single sub-period. The fluctuating demand energy consumption is constructed using photovoltaic power and / or wind power, and the electricity category with the lowest predicted price within a single sub-period is selected. The electricity consumption of each category determined in each sub-period is fitted to construct an electricity purchase plan; The basic energy consumption requirement includes the minimum energy consumption required for the operation of the energy consumption end, the minimum energy consumption required for the air conditioning cold storage system to maintain operation, and the basic energy storage energy consumption of the air conditioning cold storage system. The fluctuating energy demand is selected from the difference between the full-power energy consumption of the air conditioning cold storage system and the basic energy consumption.

2. The virtual power grid intelligent dispatching method for air conditioning cold storage systems according to claim 1, characterized in that, In step S4, a determination is made as to whether the scheduling of a single predicted energy consumption is qualified in response to the remaining percentage being greater than the preset remaining percentage.

3. The virtual power grid intelligent scheduling method for air conditioning cold storage systems according to claim 2, characterized in that, In step S4, based on the characteristic values ​​of fluctuating demand, it is initially determined whether the scheduling of a single predicted energy consumption meets the preset standards. If the characteristic value of the fluctuating demand is less than the first preset fluctuation threshold, it is initially determined that the scheduling of the single predicted energy consumption does not meet the preset standard, and the fluctuating demand energy consumption for the next prediction period is reduced according to the difference between the first preset fluctuation threshold and the characteristic value of the fluctuating demand. If the characteristic value of the fluctuating demand is greater than or equal to the first preset fluctuation threshold and less than the second preset fluctuation threshold, then it is preliminarily determined that the scheduling of a single predicted energy consumption meets the preset standard. If the characteristic value of the fluctuating demand is greater than or equal to the second preset fluctuation threshold and less than the third preset fluctuation threshold, it is initially determined that the scheduling of a single predicted energy consumption does not meet the preset standard, and the scheduling of a single predicted energy consumption is verified according to the environmental fluctuation evaluation value. If the characteristic value of the fluctuating demand is greater than or equal to the third preset fluctuation threshold, it is preliminarily determined that the scheduling of the single predicted energy consumption does not meet the preset standard, and the fluctuating demand energy consumption for the next prediction period is increased according to the difference between the characteristic value of the fluctuating demand and the third preset fluctuation threshold.

4. The virtual power grid intelligent dispatching method for air conditioning cold storage systems according to claim 3, characterized in that, The characteristic value of the fluctuating demand is the variance of a number of historical remaining percentages, including the remaining percentage.

5. The virtual power grid intelligent dispatching method for air conditioning cold storage systems according to claim 4, characterized in that, The degree of reduction in the energy consumption of the fluctuating demand in the next forecast period is positively correlated with the difference between the first preset fluctuation threshold and the characteristic value of the fluctuating demand.

6. The virtual power grid intelligent scheduling method for air conditioning cold storage systems according to claim 5, characterized in that, The scheduling of single-time predicted energy consumption is verified based on the environmental fluctuation assessment value to determine whether it meets the preset standards. If the environmental fluctuation evaluation value is less than the preset environmental fluctuation threshold, then the scheduling of the single predicted energy consumption does not meet the preset standard, and the basic demand energy consumption for the next prediction period is increased according to the difference between the preset environmental fluctuation threshold and the environmental fluctuation evaluation value. If the environmental fluctuation evaluation value is greater than or equal to the preset environmental fluctuation threshold, then the scheduling of the single predicted energy consumption is verified to meet the preset standard.

7. The virtual power grid intelligent dispatching method for air conditioning cold storage systems according to claim 6, characterized in that, The environmental fluctuation evaluation value is determined based on both temperature and humidity data.

8. The virtual power grid intelligent dispatching method for air conditioning cold storage systems according to claim 7, characterized in that, The increase in the basic energy demand for the next forecast period is positively correlated with the difference between the preset environmental fluctuation threshold and the environmental fluctuation assessment value.

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