CN113904331B - Auxiliary regulation and control method, device and system for variable-frequency air conditioner cluster participation power system - Google Patents
Auxiliary regulation and control method, device and system for variable-frequency air conditioner cluster participation power system Download PDFInfo
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Abstract
The embodiment of the invention provides a method, a device and a system for assisting in regulating and controlling a variable-frequency air conditioner cluster participating in a power system, and relates to the technical field of air conditioner regulation and control. The method comprises the following steps: coupling according to the time sequence of the power load and meteorological factors, and carrying out short-term prediction on the future load to obtain a predicted load curve; and formulating an optimized scheduling strategy according to the predicted load curve predicted in a short period and the operation strategy. According to the method, the frequency variable and power adjustability of the variable-frequency air conditioner can be utilized, the predicted load curve of the variable-frequency air conditioner is considered, an optimal scheduling strategy is given, the running stability of the power system is improved, and peak-to-valley load difference is effectively reduced.
Description
Technical Field
The invention relates to the technical field of air conditioner regulation and control, in particular to a method, a device and a system for auxiliary regulation and control of a variable-frequency air conditioner cluster participating in a power system.
Background
With the power electronization of electric loads, a large number of flexible adjustable loads such as variable frequency air conditioners, electric automobiles, communication loads and the like are emerging on the load side. These controllable loads can participate in auxiliary regulation of the power system through cluster optimization scheduling. In the process that the adjustable load cluster participates in auxiliary regulation of the power system, a daily load curve is an important basis for load cluster scheduling from the demand side. Therefore, short-term prediction is accurately performed on the power load, a daily load curve obtained through prediction is used as input to participate in demand side cluster optimization scheduling, and the method has important practical application value for regulating and controlling the behavior of the power load of the user.
Disclosure of Invention
The invention aims to provide a method, a device and a system for assisting and controlling a power system by using a variable frequency and adjustable power of a variable frequency air conditioner cluster, and provides an optimized scheduling strategy by considering a predicted load curve of the variable frequency air conditioner, so that the running stability of the power system is improved, and the peak-to-valley load difference is effectively reduced.
Embodiments of the invention may be implemented as follows:
in a first aspect, the invention provides a method for assisting and controlling a variable-frequency air conditioner cluster to participate in an electric power system, which comprises the following steps:
coupling according to the time sequence of the power load and meteorological factors, and carrying out short-term prediction on the future load to obtain a predicted load curve;
and formulating an optimized scheduling strategy according to the predicted load curve predicted in a short period and the operation strategy.
In alternative embodiments, the weather factors include historical maximum temperature, minimum temperature, average temperature, relative humidity, and precipitation.
In an alternative embodiment, the step of deriving the predicted load curve includes the steps of:
collecting a time sequence of the power load and meteorological factors;
establishing a BP neural network by using a MATLAB neural network tool box;
performing data training and data carding on the BP neural network, and establishing a load prediction model;
performing error analysis on the calculation result of the load prediction model;
and determining a calculation result meeting error convergence as an actual prediction value to obtain a prediction load curve.
In an alternative embodiment, the operation strategy comprises combining the grid load capacity of the platform region, personal electricity preference of a user, grid load peak clipping requirement, the clipping amount meeting the target clipping amount and the peak clipping effect reaching the standard.
In an alternative embodiment, the step of formulating an optimized scheduling strategy based on the short-term predicted load curve and the operating strategy comprises:
judging whether each adjustable load unit can meet the target reduction amount according to the predicted load curve;
if the target reduction amount is met, selecting an adjustable load unit with the reduction potential closest to the target reduction amount for regulation and control;
and if the target reduction amount is not met, sorting the adjustable load units according to the reduction potential, and sequentially selecting the load unit with the maximum reduction potential to participate in control until the reduction amount reaches the target reduction amount.
In a second aspect, the present invention provides an auxiliary regulation device for a variable frequency air conditioner cluster participating in a power system, the device comprising:
the load prediction module is used for coupling with meteorological factors according to the time sequence of the power consumption load, and performing short-term prediction on the future load to obtain a predicted load curve;
and the optimal scheduling center module is used for formulating an optimal scheduling strategy according to the predicted load curve predicted in a short period and the operation strategy.
In an alternative embodiment, the load prediction module is configured to collect a time series of electrical loads and meteorological factors; establishing a BP neural network by using a MATLAB neural network tool box; performing data training and data carding on the BP neural network, and establishing a load prediction model; performing error analysis on the calculation result of the load prediction model; and determining a calculation result meeting error convergence as an actual prediction value to obtain a prediction load curve.
In an alternative embodiment, the optimal scheduling center module is configured to determine, according to the predicted load curve, whether each adjustable load unit can meet the target reduction amount; if the target reduction amount is met, selecting an adjustable load unit with the reduction potential closest to the target reduction amount for regulation and control; and if the target reduction amount is not met, sorting the adjustable load units according to the reduction potential, and sequentially selecting the load unit with the maximum reduction potential to participate in control until the reduction amount reaches the target reduction amount.
In a third aspect, the present invention provides an auxiliary regulation system for a variable frequency air conditioner cluster participating in a power system, the system comprising: the terminal response layer comprises a variable frequency air conditioner unit, the intermediate control layer comprises a distributed controller and is used for coupling with meteorological factors according to a time sequence of power loads, short-term prediction is carried out on future loads to obtain a predicted load curve, the predicted load curve is uploaded to the system architecture layer, the system architecture layer comprises an electric power service cloud platform, the electric power service cloud platform formulates an optimal scheduling strategy according to the predicted load curve and an operation strategy uploaded by the distributed controller and sends the optimal scheduling strategy to the intermediate control layer, and the intermediate control layer schedules the variable frequency air conditioner unit according to the optimal scheduling strategy.
In an optional embodiment, the terminal response layer is further used for uploading the unit work information to the intermediate control layer in real time, the distributed controller is further used for storing the unit work information uploaded by the terminal response layer, and sequencing the variable-frequency air conditioner units after performing real-time analysis and calculation on the unit demand response capacity to obtain a time sequence of the power load.
The auxiliary regulation and control method, device and system for the variable-frequency air conditioner cluster participation power system provided by the embodiment of the invention have the beneficial effects that:
the method, the device and the system utilize variable frequency and power adjustability of the variable frequency air conditioner, obtain a predicted load curve through a short-term load prediction technology, give an active load adjustment control strategy, improve the running stability of the power system, effectively reduce peak-valley load difference, realize the optimal division and precise regulation of a large-scale air conditioner cluster, improve the utilization rate of the air conditioner cluster, greatly reduce the peak-valley difference of the power grid load, and enable the power grid load and the power system to run more stably.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for assisting in regulating and controlling a power system by a variable-frequency air conditioner cluster according to an embodiment of the invention;
fig. 2 is a schematic diagram of the components of the auxiliary regulation system of the variable-frequency air conditioner cluster participation power system provided by the embodiment of the invention;
fig. 3 is a schematic diagram of the auxiliary regulation device of the variable-frequency air conditioner cluster participating in the power system according to the embodiment of the invention;
fig. 4 is a schematic diagram of a specific operation flow of an optimized dispatch center module according to an embodiment of the present invention.
Icon: 100-the variable-frequency air conditioner cluster participates in an auxiliary regulation and control system of the power system; 110-a terminal response layer; 111-a variable frequency air conditioner unit; 120-an intermediate control layer; 121-a distributed controller; 130-a system architecture layer; 200-the auxiliary regulation and control device of the power system is participated in by the variable-frequency air conditioner cluster; 210-a load prediction module; 220-an optimized dispatch center module.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
Furthermore, the terms "first," "second," and the like, if any, are used merely for distinguishing between descriptions and not for indicating or implying a relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
The existing air conditioning cluster adopts a double-layer regulation and control mechanism, and because passive aggregation does not have an optimization concept, the requirements of the economic operation of the power system cannot be met in certain markets, and therefore, the mode of active aggregation is taken as the main development direction in the future. The active aggregation is to select air conditioner part clusters with load requirements from the whole power utilization system for optimal aggregation according to the consideration of various aspects such as performance parameters, power distribution, economy, peak-valley fluctuation and the like in a certain range, so that the large-scale air conditioner clusters are divided into a plurality of aggregation models.
On a macroscopic level, the aggregator provides quotations and outputs to the power system regulation and control departments according to economic indexes, load capacity, regulation and control plans and other factors. The power company dispatching department provides distribution indexes according to the economy and the self demand. And on a microscopic level, the aggregation business receives the dispatching task of the power company and sends out a regulating instruction of the power company, and the distributed air conditioner clusters are reasonably aggregated according to the economical efficiency and the load capacity of the aggregation business.
In the prior art, the regulation and control requirements are mainly provided by an aggregator according to the economic index and the load capacity of the aggregator. And the power company dispatching center makes dispatching plans such as the participated peak clipping time length, the peak clipping number and the like according to the load prediction result. In practical application, the economical efficiency of a polymerizer is taken as a starting point, and the whole centralized regulation and control are carried out on a large-area air-conditioning station area or the centralized regulation and control of a central air conditioner are carried out on a large-scale commercial user. Through distributed regulation and control of an aggregator, precise regulation and control of individualization and user desirability are difficult to carry out for each air conditioning single unit system in a platform region. In addition, utility companies typically extrapolate trend from historical load data based on historical incentive response data when formulating a regulatory scheme. For short-term load prediction, a time-series method is often used for prediction. However, due to fluctuation of the variation amplitude of random factors such as meteorological factors, accurate prediction results are difficult to obtain, so that when the air conditioning clusters participate in the response of the demand side of the power system, the regulation strategy is not targeted, the utilization rate of the air conditioning clusters is greatly reduced, and the advantage of great reduction potential of the load of the air conditioning clusters is not exerted.
Referring to fig. 1, an embodiment of the invention provides a method for assisting and controlling a variable-frequency air conditioner cluster in an electric power system, which utilizes variable frequency and adjustable power of the variable-frequency air conditioner to obtain a predicted load curve through a short-term load prediction technology, gives an active load adjustment control strategy, improves the running stability of the electric power system and effectively reduces peak-to-valley load difference.
Specifically, the auxiliary regulation and control method for the variable-frequency air conditioner cluster participating in the power system comprises the following steps:
s01: and coupling the time sequence of the power load with meteorological factors, and carrying out short-term prediction on the future load to obtain a predicted load curve.
Among other things, weather factors include historical maximum temperature, minimum temperature, average temperature, relative humidity, and precipitation.
The specific program flow for obtaining the predicted load curve is as follows:
1) Collecting a time sequence of the power load and meteorological factors;
2) Establishing a BP neural network by using a MATLAB neural network tool box;
3) Performing data training and data carding on the BP neural network, and establishing a load prediction model;
4) Performing error analysis on the calculation result of the load prediction model;
5) And determining a calculation result meeting error convergence as an actual prediction value to obtain a prediction load curve.
S02: and formulating an optimized scheduling strategy according to the predicted load curve predicted in a short period and the operation strategy.
The operation strategy comprises the steps of combining the power grid load capacity of the transformer area, personal electricity preference of a user, power grid load peak clipping requirement, and the reduction amount meeting the target reduction amount and the peak clipping effect reaching the standard.
The specific content of the optimized scheduling strategy comprises the following steps: judging whether each adjustable load unit can meet the target reduction amount according to the predicted load curve; if the target reduction amount is met, selecting an adjustable load unit with the reduction potential closest to the target reduction amount for regulation and control; and if the target reduction amount is not met, sorting the adjustable load units according to the reduction potential, and sequentially selecting the load unit with the maximum reduction potential to participate in control until the reduction amount reaches the target reduction amount.
Referring to fig. 2, the embodiment of the invention further provides an auxiliary regulation system 100 for a variable frequency air conditioner cluster participating in a power system, where the system includes a terminal response layer 110, an intermediate control layer 120 and a system architecture layer 130.
The terminal response layer 110 includes a variable frequency air conditioner unit 111, configured to upload unit operation information to the intermediate control layer 120 in real time, where the unit operation information includes real-time power of a compressor, an operating frequency, a user set temperature, a number of required response control times, a required response control variable, an indoor real-time temperature, an outdoor real-time temperature, an air conditioner operation status flag bit, and the like.
The intermediate control layer 120 includes a distributed controller 121, configured to store the unit work information uploaded by the terminal response layer 110, perform real-time analysis and calculation on the unit demand response capability, and then sequence the variable frequency air conditioner units 111 to obtain a time sequence of electricity loads, couple with meteorological factors according to the time sequence of the electricity loads, perform short-term prediction on future loads to obtain a predicted load curve, and upload platform area data to the system architecture layer 130, where the platform area data includes the predicted load curve, prepare to respond to the power system scheduling instruction at any time, and feed back the response result to the system architecture layer 130 in real time.
The system architecture layer 130 includes an electric power service cloud platform, where the electric power service cloud platform formulates an optimal scheduling policy according to the platform area data and the operation policy uploaded by the distributed controller 121, and issues an electric power system scheduling instruction to the intermediate control layer 120, and the intermediate control layer 120 schedules the variable frequency air conditioner unit 111 according to the optimal scheduling policy.
In this way, in the auxiliary regulation and control of the air conditioning clusters, the three-layer topological structure is utilized, the dispersed air conditioning loads are integrated to participate in the operation of the electric power system, the unit work information uploaded by the unit is stored, the response result is fed back to the electric power service cloud platform of the system architecture layer 130 in real time, the large-scale air conditioning clusters are divided according to the area and the load quantity, and the auxiliary regulation and control of the electric power system is carried out in a grading manner, so that the real-time performance of the large-scale air conditioning clusters in the auxiliary regulation and control of the electric power system is stronger and more targeted.
Referring to fig. 3, the embodiment of the invention further provides an auxiliary regulation device 200 for a variable-frequency air conditioner cluster participating in a power system, which comprises a load prediction module 210 and an optimized dispatching center module 220.
The load prediction module 210 is configured to couple with meteorological factors according to a time sequence of the power load, and perform short-term prediction on the future load. The weather factors comprise a historical highest temperature, a lowest temperature, an average temperature, relative humidity, precipitation and the like, and the future load is predicted in a short term under the condition of considering the weather factors, so that a predicted load curve is obtained.
The specific parameter establishment steps of the load prediction module 210 are as follows: input parameters of the load prediction model are first determined, the input parameters including weather characteristics, real-time weather factors, and historical data, for example, the input parameters including historical load data, maximum temperature, minimum temperature, average temperature, relative humidity, and precipitation, and the input parameters are input as neurons of the input layer. And establishing a BP neural network by using a MATLAB neural network tool box, performing excavation modeling and training, establishing a load prediction model, calculating errors through the load prediction model until convergence, and completing load prediction to obtain a predicted load curve.
In this way, the load is subjected to short-term prediction, the time sequence of the power consumption load is coupled with weather factors such as the highest historical temperature, the lowest temperature, the average temperature, the relative humidity and the precipitation amount as influence factors, so that a short-term load prediction result is obtained, and the air conditioning cluster of the district is regulated and controlled more effectively.
The specific program flow of the load prediction module 210 is as follows:
1) Collecting a time sequence of the power load and meteorological factors;
2) Establishing a BP neural network by using a MATLAB neural network tool box;
3) Performing data training and data carding on the BP neural network, and establishing a load prediction model;
4) Performing error analysis on the calculation result of the load prediction model;
5) And determining a calculation result meeting error convergence as an actual prediction value to obtain a prediction load curve.
The optimal scheduling center module 220 is configured to formulate an optimal scheduling policy according to the predicted load curve predicted in a short period and the operation policy. The operation strategy comprises the steps of combining the power grid load capacity of the transformer area, personal electricity utilization preference of users, peak clipping requirement of the power grid load, meeting the target reduction amount of the reduction amount, achieving the peak clipping effect, and the like.
The specific program flow of the optimized dispatch center module 220 is as follows:
the method comprises the steps of making a demand side response and formulating an optimized scheduling strategy by predicting load curves, load capacity of a platform area and electricity consumption preference of a user of a single unit system of a platform area and aiming at the peak clipping effect which is the most, the maximum peak value is smaller and no new load peak is generated.
The operation strategy of the optimized dispatch center module 220 is as follows:
after entering the controlled period, the optimized dispatch center module 220 first determines whether each adjustable load unit can meet the target reduction amount according to the predicted load curve. If the target reduction amount is met, in order to avoid the waste of the reduction potential, the optimal scheduling center module 220 selects the adjustable load unit with the reduction potential closest to the target reduction amount to regulate; if the target reduction amount is not met, the optimal scheduling center module 220 sorts the adjustable load units according to the reduction potential, and sequentially selects the load unit with the maximum reduction potential to participate in control until the reduction amount reaches the target reduction amount, so as to ensure that the peak reduction purpose is achieved by a small number of effective adjustable load units.
Therefore, according to the load quantity of the platform region and the electricity preference of the user, the optimal peak clipping effect is achieved by combining the load prediction curve, the target peak value is minimum, new load peaks are not generated as targets for optimal scheduling, the air conditioner clusters are ordered according to the reduction potential of the air conditioner, and the utilization rate of the air conditioner clusters in participation demand response is greatly improved.
Referring to fig. 4, the specific operation flow of the optimized dispatch center module 220 is as follows:
s1: the target reduction amount is input.
S2: it is determined whether to enter a controlled period.
In the case where the controlled period is not entered, S3 is performed: the normal control mode is performed.
In the case of entering the controlled period, S4 is performed: and judging whether each adjustable load unit can meet the reduction requirement according to the predicted load curve.
In the case that not every adjustable load unit can meet the clipping requirement, S5 is executed: the load cells are ordered in a manner that cuts down the potential from small to large.
S6: and sequentially selecting the load units with the maximum reduction potential to participate in control until the reduction amount reaches the target reduction amount.
S7: it is determined whether the next controlled period is reached.
In the event that the next controlled period is reached, execution returns to S4.
In the case where the next controlled period is not reached, S3 is performed.
In the case that each adjustable load unit can meet the clipping requirement, S8 is executed: and sequentially selecting adjustable load units which cut down the maximum potential and do not reach the upper limit of the controlled times to perform out-of-line regulation and control until the cut-down amount reaches the target cut-down amount.
After execution is completed S8, S7 is executed.
The detailed mathematical model in the optimized dispatch center module 220 is as follows:
setting a target reduction amount g, adjusting the load unit number n and adjusting the load reduction potential [ m ] 1 ，m 2 ，…，m n ]。
When the target reduction amount is less than the reduction potential of the single adjustable load unit:
g≤max[m n ]n＝1,2,3......n
selecting a controlled load cell m n The method meets the following conditions:
min[m n -g]n＝1,2,3......n
when the target reduction amount is less than the reduction potential of the single adjustable load unit:
g≥max[m n ]n＝1,2,3......n
array [ m ] 1 ，m 2 ，…，m n ]Ordered from big to small:
max[m n ]n＝1
min[m n ]n＝n
the adjustable load units with maximum reduction potential are sequentially selected to participate in control until the reduction amount reaches the target reduction amount, so that the peak reduction purpose is ensured to be achieved by a small number of effective adjustable load units:
at this time, the controlled load unit is [ m ] 1 ，m 2 ，…，m i ]。
It is easy to understand that the technical functions of the auxiliary regulation and control method, the auxiliary regulation and control device and the auxiliary regulation and control system for the variable-frequency air conditioner cluster provided by the embodiment of the invention are corresponding to each other and can be mutually fused. For example, the auxiliary regulation device 200 for the variable-frequency air conditioner cluster participating in the power system is used for realizing an auxiliary regulation method for the variable-frequency air conditioner cluster participating in the power system, and the technical features of the auxiliary regulation device and the auxiliary regulation method are mutually corresponding and can be mutually referred to.
The auxiliary regulation and control method, device and system for the variable-frequency air conditioner cluster participation power system provided by the embodiment of the invention have the beneficial effects that:
1. aiming at the problem that the user preference of a single-unit system is difficult to consider in the participation of the air-conditioning clusters in the auxiliary regulation of the electric power system, and the flexible and accurate regulation of the air-conditioning clusters in the transformer area cannot be realized, the embodiment obtains a predicted load curve through a short-term load prediction technology, and realizes the optimized division and accurate regulation of the large-scale air-conditioning clusters by utilizing a three-level layered auxiliary regulation system;
2. by utilizing the frequency adjustability and the variable power property of the variable frequency air conditioner and combining the variable frequency air conditioner clusters based on short-time power prediction to participate in the power system strategy, under the condition of considering personal electricity preference of users and peak clipping requirements of the power system, the air conditioner clusters in each area are effectively optimally scheduled, the utilization rate of the air conditioner clusters is improved, the peak-valley difference of the power grid load is greatly reduced, and the power grid load and the power system are enabled to run more stably.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The auxiliary regulation and control method for the variable-frequency air conditioner cluster participating in the power system is characterized by comprising the following steps of:
coupling according to the time sequence of the power load and meteorological factors, and carrying out short-term prediction on the future load to obtain a predicted load curve;
according to the predicted load curve and the operation strategy predicted in a short period, an optimized scheduling strategy is formulated, which comprises the following steps: judging whether each adjustable load unit can meet the reduction requirement according to the predicted load curve; under the condition that not every adjustable load unit can meet the reduction requirement, sequencing the load units according to the mode of reducing potential from small to large, and sequentially selecting the load unit with the largest reduction potential to participate in control until the reduction amount reaches the target reduction amount; under the condition that each adjustable load unit can meet the reduction requirement, the adjustable load units with the maximum reduction potential and not reaching the upper limit of the controlled times are sequentially selected for out-of-line regulation until the reduction amount reaches the target reduction amount, and the method comprises the following steps:
setting a target reduction amount g, adjusting the load unit number n and adjusting the load reduction potential [ m ] 1 ，m 2 ，…，m n ]；
When the target reduction amount is less than the reduction potential of the single adjustable load unit:
g≤max[m n ]n＝1,2,3......n
selecting a controlled load cell m n The method meets the following conditions:
min[m n -g]n＝1,2,3......n
when the target reduction amount is less than the reduction potential of the single adjustable load unit:
g≥max[m n ]n＝1,2,3......n
array [ m ] 1 ，m 2 ，…，m n ]Ordered from big to small:
max[m n ]n＝1
min[m n ]n＝n
and sequentially selecting an adjustable load unit with maximum reduction potential to participate in control until the reduction amount reaches the target reduction amount:
at this time, the controlled load unit is [ m ] 1 ，m 2 ，…，m i ]。
2. The method for assisting and controlling the variable frequency air conditioner clusters in the power system according to claim 1, wherein the meteorological factors comprise historical maximum temperature, minimum temperature, average temperature, relative humidity and precipitation.
3. The auxiliary regulation method of the variable frequency air conditioner cluster participating in the power system according to claim 1, wherein the step of coupling with meteorological factors according to the time sequence of the power load, and performing short-term prediction on the future load to obtain a predicted load curve comprises the following steps:
collecting the time sequence of the power load and the meteorological factors;
establishing a BP neural network by using a MATLAB neural network tool box;
performing data training and data carding on the BP neural network, and establishing a load prediction model;
performing error analysis on the calculation result of the load prediction model;
and determining a calculation result meeting error convergence as an actual prediction value to obtain the prediction load curve.
4. The method for assisting and controlling the variable-frequency air conditioner clusters in the electric power system according to claim 1, wherein the operation strategy comprises combining the power grid load capacity of a platform region, personal electricity utilization preference of a user, power grid load peak clipping requirement, and reduction amount meeting target reduction amount and peak clipping effect reaching standards.
5. Auxiliary regulation and control device for variable-frequency air conditioner cluster participation power system is characterized in that the device comprises:
the load prediction module is used for coupling with meteorological factors according to the time sequence of the power consumption load, and performing short-term prediction on the future load to obtain a predicted load curve;
the optimal scheduling center module is used for formulating an optimal scheduling strategy according to the predicted load curve and the operation strategy predicted in a short term, and comprises the following steps: judging whether each adjustable load unit can meet the reduction requirement according to the predicted load curve; under the condition that not every adjustable load unit can meet the reduction requirement, sequencing the load units according to the mode of reducing potential from small to large, and sequentially selecting the load unit with the largest reduction potential to participate in control until the reduction amount reaches the target reduction amount; under the condition that each adjustable load unit can meet the reduction requirement, the adjustable load units with the maximum reduction potential and not reaching the upper limit of the controlled times are sequentially selected for out-of-line regulation until the reduction amount reaches the target reduction amount, and the method comprises the following steps:
setting a target reduction amount g, adjusting the load unit number n and adjusting the load reduction potential [ m ] 1 ，m 2 ，…，m n ]；
When the target reduction amount is less than the reduction potential of the single adjustable load unit:
g≤max[m n ]n＝1,2,3......n
selecting a controlled load cell m n The method meets the following conditions:
min[m n -g]n＝1,2,3......n
when the target reduction amount is less than the reduction potential of the single adjustable load unit:
g≥max[m n ]n＝1,2,3......n
array [ m ] 1 ，m 2 ，…，m n ]Ordered from big to small:
max[m n ]n＝1
min[m n ]n＝n
and sequentially selecting an adjustable load unit with maximum reduction potential to participate in control until the reduction amount reaches the target reduction amount:
at this time, the controlled load unit is [ m ] 1 ，m 2 ，…，m i ]。
6. The auxiliary regulation device for the variable-frequency air conditioner cluster participating in the power system according to claim 5, wherein the load prediction module is used for collecting the time sequence of the power load and the meteorological factors; establishing a BP neural network by using a MATLAB neural network tool box; performing data training and data carding on the BP neural network, and establishing a load prediction model; performing error analysis on the calculation result of the load prediction model; and determining a calculation result meeting error convergence as an actual prediction value to obtain the prediction load curve.
7. Auxiliary regulation and control system of a variable frequency air conditioner cluster participation power system is characterized in that the system comprises: the system comprises a terminal response layer, an intermediate control layer and a system architecture layer, wherein the terminal response layer comprises a variable frequency air conditioner unit, the intermediate control layer comprises a distributed controller and is used for coupling with meteorological factors according to a time sequence of power loads, short-term prediction is carried out on future loads to obtain a predicted load curve and uploading the predicted load curve to the system architecture layer, the system architecture layer comprises an electric power service cloud platform, the electric power service cloud platform formulates an optimal scheduling strategy according to the predicted load curve and an operation strategy uploaded by the distributed controller and sends the optimal scheduling strategy to the intermediate control layer, and the variable frequency air conditioner unit is scheduled by the intermediate control layer according to the optimal scheduling strategy, wherein the optimal scheduling strategy comprises: judging whether each adjustable load unit can meet the reduction requirement according to the predicted load curve; under the condition that not every adjustable load unit can meet the reduction requirement, sequencing the load units according to the mode of reducing potential from small to large, and sequentially selecting the load unit with the largest reduction potential to participate in control until the reduction amount reaches the target reduction amount; under the condition that each adjustable load unit can meet the reduction requirement, the adjustable load units with the maximum reduction potential and not reaching the upper limit of the controlled times are sequentially selected for out-of-line regulation until the reduction amount reaches the target reduction amount, and the method comprises the following steps:
setting a target reduction amount g, adjusting the load unit number n and adjusting the load reduction potential [ m ] 1 ，m 2 ，…，m n ]；
When the target reduction amount is less than the reduction potential of the single adjustable load unit:
g≤max[m n ]n＝1,2,3......n
selecting a controlled load cell m n The method meets the following conditions:
min[m n -g]n＝1,2,3......n
when the target reduction amount is less than the reduction potential of the single adjustable load unit:
g≥max[m n ]n＝1,2,3......n
array [ m ] 1 ，m 2 ，…，m n ]Ordered from big to small:
max[m n ]n＝1
min[m n ]n＝n
and sequentially selecting an adjustable load unit with maximum reduction potential to participate in control until the reduction amount reaches the target reduction amount:
at this time, the controlled load unit is [ m ] 1 ，m 2 ，…，m i ]。
8. The auxiliary regulation and control system for the variable frequency air conditioner clusters participating in the power system according to claim 7, wherein the terminal response layer is further used for uploading unit work information to the intermediate control layer in real time, the distributed controller is further used for storing the unit work information uploaded by the terminal response layer, and sequencing the variable frequency air conditioner units after real-time analysis and calculation of unit demand response capacity, so as to obtain the time sequence of the electricity load.
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