Method for evaluating day-ahead load adjustable capability of multifunctional washing machine

Abstract

The invention discloses a multifunctional washing machine load day-ahead adjustable capability evaluation method. The method comprises the steps that multifunctional washing machine load operation information and weather information are collected; constructing a multifunctional washing machine behavior characteristic prediction model considering weather factors, and predicting behavior characteristics of the multifunctional washing machine; constructing a multifunctional washing machine load correction model considering weather factors, and correcting behaviors of the multifunctional washing machine under different weather factors and electricity price information according to behavior characteristics of the multifunctional washing machine to obtain a corrected load of the multifunctional washing machine; and based on the corrected load of the multifunctional washing machine, constructing a multifunctional washing machine load day-ahead adjustable capability evaluation model considering weather factors, and evaluating the multifunctional washing machine load day-ahead adjustable capability. According to the method, the multifunctional washing machine is modeled finely, the load prediction accuracy of the multifunctional washing machine is improved, and the day-ahead adjustable capacity of the load of the multifunctional washing machine is effectively evaluated.

Description

Technical Field

[0001] The invention relates to the technical field of power system regulation and control, and in particular to a method for evaluating the adjustable capacity of a multifunctional washing machine load. Background Art

[0002] By evaluating and utilizing the day-ahead adjustable capacity of multifunctional washing machine loads, the power grid can better optimize energy distribution, improve the utilization efficiency of renewable energy, enhance demand response capabilities, and reduce operating costs. This is of great significance for coping with the complexity of future electricity demand, promoting the popularization of green energy, and promoting the development of smart grids. The existing methods do have the following shortcomings: 1) Lack of refined modeling. Multifunctional washing machines with drying functions involve multiple working modes, such as washing, rinsing, dehydration, and drying. The energy consumption and load characteristics of each mode are different. Existing methods often fail to refine these functions and usually use a single model to predict the entire process. It may not be able to accurately capture the load characteristics of each stage, resulting in insufficient accuracy of the overall load forecast and inability to effectively utilize the dispatchability of the equipment. 2) The influence of external environmental factors is not considered. External factors such as weather, temperature, humidity, and electricity prices have a significant impact on the use behavior of washing machines. For example, when the humidity is high, users may be more inclined to use the drying function; during the low electricity price period, users may choose to postpone washing. Existing models usually ignore these key factors, resulting in inaccurate predictions of user behavior, thereby affecting the accuracy and reliability of washing machine load prediction. 3) There is a lack of evaluation methods for adjustable capacity. The adjustable capacity of multi-function washing machines, that is, their flexibility in operation in different time periods and their ability to respond to grid demand, has not been fully evaluated. Existing methods often lack targeted evaluation tools or indicators to quantify this capacity. It is difficult for grid operators to formulate optimized dispatching strategies and they cannot fully utilize the potential flexibility of multi-function washing machines to support grid load balancing and the absorption of renewable energy. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a method for evaluating the adjustable capacity of a multifunctional washing machine load by improving the accuracy of load prediction of the multifunctional washing machine.

[0004] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0005] The present invention is a method for evaluating the load-adjustable capacity of a multifunctional washing machine, comprising the following operations:

[0006] Collect multi-function laundry load operation information and weather information;

[0007] Construct a prediction model of the behavior characteristics of a multifunctional washing machine taking weather factors into account, and predict the behavior characteristics of the multifunctional washing machine;

[0008] Construct a load correction model for a multifunctional washing machine that takes weather factors into consideration, and correct the behavior of the multifunctional washing machine under different weather factors and electricity price information according to the behavior characteristics of the multifunctional washing machine to obtain the corrected load of the multifunctional washing machine;

[0009] Based on the corrected load of the multi-functional washing machine, a day-ahead load adjustable capacity evaluation model for the multi-functional washing machine considering weather factors is constructed, and the day-ahead load adjustable capacity of the multi-functional washing machine is evaluated.

[0010] A further improvement of the present invention is that the collected multifunctional laundry load operation information includes the washing machine operating time interval, washing machine startup time, washing machine shutdown time, washing machine power, and drying operation power, and the collected weather information includes weather conditions, outdoor temperature, and humidity, and the expression is:

[0011]

[0012] Among them, X i (k) is the basic data of multi-function washing machine load i on day k, is the startup time of multifunctional washing machine i on day k, is the shutdown time of multifunctional washing machine i on day k, is the end time of the drying load of the multifunctional washing machine on the kth day, is the operating time range of multifunctional washing machine i on day k, where is the earliest time that multifunctional washing machine i can be turned on on day k, is the latest time that multifunctional washing machine i can be turned on on day k, P i x is the washing power of multifunctional washing machine i, P i h is the drying power of multifunctional washing machine i, T(k) is the temperature on the kth day, R(k) is the humidity on the kth day, W(k) is the weather condition on the kth day, the value is 1 for sunny day, the value is 2 for cloudy day, the value is 3 for rainy day, the value is 4 for snowy day, ψ(k) is the electricity price information on the kth day.

[0013] A further improvement of the present invention is to construct a prediction model of the behavior characteristics of a multifunctional washing machine taking into account weather factors, specifically including: based on a BP neural network, training the collected multifunctional laundry load operation information and weather information to construct the multifunctional laundry load operation information and weather information, wherein the BP neural network includes 4 inputs and 2 outputs, the 4 inputs are: the temperature T(k) of the kth day, the humidity R(k) of the kth day, the weather state W(k) of the kth day, the electricity price information ψ(k) of the kth day, and the 2 outputs are: δ x =1 indicates that the multi-function washing machine will only start the washing function on the day before the forecast,h =1 indicates that the multifunctional washing machine has started the washing and drying functions on the predicted day. The hidden layer of the BP neural network uses the tansig function. The expression of the multifunctional washing machine behavior feature prediction model is:

[0014]

[0015] Among them, f x () is the BP neural network laundry behavior prediction model after training, f h () is the washing and drying behavior prediction model of the BP neural network after training.

[0016] The further improvement of the present invention is that: the load correction model of the multifunctional washing machine considering weather factors includes two cases of using and not using the dryer;

[0017] Among them, the expression of the load correction model of the multifunctional washing machine without using the dryer is:

[0018]

[0019]

[0020] The expression of the load correction model of a multifunctional washing machine using a dryer is:

[0021]

[0022]

[0023] Among them, δ x is the laundry behavior predicted based on weather factors and electricity price information, δ x =1 indicates that the multifunctional washing machine in the forecasted day only starts the washing function, P(t) is the power of multifunctional washing machine i, δ h is the washing and drying behavior predicted based on weather factors and electricity price information, δ h =1 indicates that the multi-function washing machine has started the washing and drying functions on the predicted day. i x (t) is the washing power of multifunctional washing machine i at time t, P i h (t) is the drying power of multifunctional washing machine i at time t.

[0024] A further improvement of the present invention is that the expression of the evaluation model of the load-adjustable capacity of the multifunctional washing machine is:

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] Among them, t x is the washing time, t h is the drying time, ΔP up (t) is the load increase, ΔP dn (t) is the load reduction, M is the number of washing machines involved in the increase, N is the number of washing machines involved in the decrease, is the startup time of the multifunctional washing machine i1 participating in the upward adjustment on the kth day, is the shutdown time of the multifunctional washing machine i1 participating in the upward adjustment on the kth day, is the end time of the drying load of the multifunctional washing machine i1 participating in the upward adjustment on the kth day, is the startup time of the multifunctional washing machine i2 participating in the downward adjustment on the kth day, is the shutdown time of the multifunctional washing machine i2 participating in the downward adjustment on the kth day, is the end time of drying load of multifunctional washing machine i2 participating in the downward adjustment on the kth day,

[0031] is the washing power of the multifunctional washing machine i1 at time t, is the drying power of the multifunctional washing machine i1 at time t, is the washing power of the multifunctional washing machine i2 at time t, is the drying power of the multifunctional washing machine i2 at time t, where multifunctional washing machines i1 and i2 belong to i.

[0032] The beneficial effects of the present invention are as follows: the present invention refines the modeling of the multifunctional washing machine, improves the accuracy of load prediction of the multifunctional washing machine, and realizes effective evaluation of the adjustable capacity of the multifunctional washing machine load in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a schematic diagram of a method flow of an embodiment of the present invention;

[0034] Figure 2 is a schematic diagram of a behavior characteristic prediction model of a multifunctional washing machine in an embodiment of the present invention;

[0035] Figure 3 is a schematic diagram of an uncorrected load in an embodiment of the present invention;

[0036] Figure 4 Schematic diagram of the corrected load in the embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0038] like Figure 1 As shown, this embodiment is a method for evaluating the load-adjustable capacity of a multifunctional washing machine, including the following operations:

[0039] Step 1, collect multifunctional laundry load operation information, weather information and electricity price information. In this embodiment, the multifunctional washing machine has the functions of washing clothes and drying clothes. The multifunctional laundry load operation information includes the operating time interval of the washing machine, the start and stop time of the washing machine, the washing machine power, the power during drying operation, etc. The weather information includes weather conditions, outdoor temperature, humidity and other information, wherein the weather conditions are sunny, cloudy, rainy, snowy, etc. The collected information is expressed by the following expression:

[0040]

[0041] Among them, X i (k) is the basic data of multi-function washing machine load i on day k, is the startup time of multifunctional washing machine i on day k, is the shutdown time of multifunctional washing machine i on day k, is the end time of the drying load of the multifunctional washing machine on the kth day, is the operating time range of multifunctional washing machine i on day k, where is the earliest time that multifunctional washing machine i can be turned on on day k, is the latest time that multifunctional washing machine i can be turned on on day k, P i x is the washing power of multifunctional washing machine i, P i h is the drying power of multifunctional washing machine i, T(k) is the temperature on the kth day, R(k) is the humidity on the kth day, W(k) is the weather condition on the kth day, the value is 1 for sunny day, the value is 2 for cloudy day, the value is 3 for rainy day, the value is 4 for snowy day, ψ(k) is the electricity price information on the kth day.

[0042] Step 2, construct a multifunctional washing machine behavior feature prediction model taking into account weather factors, and predict the behavior features of the multifunctional washing machine. Among them, weather factors and electricity prices mainly affect the operation behavior of the multifunctional washing machine. For example, on rainy days, when the temperature is low and the humidity is high, it is difficult to dry clothes outdoors. When the electricity price is low, the drying function of the washing machine will be increased, and the clothes will be dried by the multifunctional washing machine, the drying operation power will be increased, and the operation behavior of the washing machine will be changed. The multifunctional washing machine behavior feature prediction model constructed in this embodiment is based on the BP neural network to train the weather information and electricity price information collected in step 1 to predict the behavior features of the multifunctional washing machine.

[0043] Step 3: Construct a load correction model for the multifunctional washing machine taking weather factors into consideration. According to the behavior characteristics of the multifunctional washing machine predicted in step 2, the behavior of the multifunctional washing machine under different weather information and electricity price information is corrected, and further, a model is constructed to correct the load of the multifunctional washing machine.

[0044] Step 4: Construct a day-ahead load adjustable capacity assessment model for multifunctional washing machines taking into account weather factors. Based on the load of the multifunctional washing machine modified in step 3, a day-ahead load adjustable capacity assessment model for multifunctional washing machines is constructed. The day-ahead load adjustable capacity assessment model for multifunctional washing machines takes into account the impact of weather and electricity prices. If the dryer is not used according to weather and electricity prices, the dryer load P is set to i h (t h )=0. The multifunctional washing machine load day-ahead adjustable capacity evaluation model includes a maximum upward adjustable capacity evaluation model and a maximum downward adjustable capacity evaluation model, so as to realize the evaluation of the multifunctional washing machine load day-ahead adjustable capacity.

[0045] In step 2, the outdoor temperature, humidity, weather conditions, and electricity price information are mined based on the BP neural network, and the BP neural network is trained offline to build a multifunctional washing machine behavior feature prediction model based on the BP neural network to achieve effective prediction of the multifunctional washing machine behavior in the past few days. Figure 2 As shown in Figure 1, it includes 4 inputs and 2 outputs. The 4 inputs are: the temperature T(k) of the kth day, the humidity R(k) of the kth day, the weather conditions W(k) of the kth day, and the electricity price information ψ(k) of the kth day. The 2 outputs are: δ x =1 indicates that the multi-function washing machine will only start the washing function on the day before the forecast, h =1 indicates that the multifunctional washing machine has started the washing and drying functions on the predicted day. The hidden layer of the BP neural network uses the tansig function. The expression of the multifunctional washing machine behavior feature prediction model is:

[0046]

[0047] Among them, f x () is the BP neural network laundry behavior prediction model after training, f h () is the washing and drying behavior prediction model of the BP neural network after training.

[0048] In step 3, the load correction model of the multifunctional washing machine considering weather factors is constructed including two cases of using and not using a dryer.

[0049] In the case where the prediction result of the multifunctional washing machine behavior feature prediction model is that the dryer is not used, that is, δ x =1, then the load of the multifunctional washing machine is calculated according to expression (4);

[0050]

[0051] In the case where the prediction result of the multifunctional washing machine behavior feature prediction model is that the dryer is not used, that is, δ h =1, then the load of the multifunctional washing machine is calculated according to expression (6);

[0052]

[0053]

[0054] Among them, δ x is the laundry behavior predicted based on weather factors and electricity price information, δ x =1 indicates that the multifunctional washing machine in the forecasted day only starts the washing function, P(t) is the power of multifunctional washing machine i, δ h is the washing and drying behavior predicted based on weather factors and electricity price information, δ h =1 indicates that the multi-function washing machine has started the washing and drying functions on the predicted day. i x (t) is the washing power of multifunctional washing machine i at time t, P i h (t) is the drying power of multifunctional washing machine i at time t.

[0055] This embodiment corrects the load of the multifunctional washing machine according to the multifunctional washing machine load correction model, thereby improving the accuracy of the multifunctional washing machine load evaluation. Figure 3 and Figure 4 shown.

[0056] In step 4, the maximum adjustable capacity evaluation model is shown in expressions (7) and (8). Expression (7) represents the earliest possible startup time of all machines. The multifunctional washing machine that is before the current time t and is turned on after the current time t can move the load forward to achieve the load increase. The load that can be increased is calculated according to expression (8), which is determined by the washing power P i x (t x ) and drying power P i h (t h ) consists of two parts.

[0057] Expression (9) indicates that when the remaining time of the multi-function washing machine is Greater than the total time required for washing and drying When the load of the multifunctional washing machine is moved back, the load can be reduced, that is, the load is adjusted down. The load that can be adjusted down is calculated according to expression (10), which is determined by the washing power P i x (t x ) and drying power P i h (t h ) is composed of two parts. Expression (11) indicates that when the washing machine behavior predicted by weather factors and electricity price information is only washing but not drying, δ x =1, let the drying load be 0, that is, P i h (t) = 0; when the washing machine behavior predicted by weather factors and electricity price information is washing and drying h =1, calculate the drying load according to the actual situation, that is, P i h (t) = P i h (t).

[0058]

[0059]

[0060] Among them, t x is the washing time, t h is the drying time, ΔP up (t) is the load increase, ΔP dn (t) is the load reduction, M is the number of washing machines involved in the increase, N is the number of washing machines involved in the decrease, is the startup time of the multifunctional washing machine i1 participating in the upward adjustment on the kth day, is the shutdown time of the multifunctional washing machine i1 participating in the upward adjustment on the kth day, is the end time of the drying load of the multifunctional washing machine i1 participating in the upward adjustment on the kth day, is the startup time of the multifunctional washing machine i2 participating in the downward adjustment on the kth day, is the shutdown time of the multifunctional washing machine i2 participating in the downward adjustment on the kth day, is the end time of drying load of multifunctional washing machine i2 participating in the downward adjustment on the kth day,

[0061] is the washing power of the multifunctional washing machine i1 at time t, is the drying power of the multifunctional washing machine i1 at time t, is the washing power of the multifunctional washing machine i2 at time t, is the drying power of the multifunctional washing machine i2 at time t, where multifunctional washing machines i1 and i2 belong to i.

[0062] According to the above steps, the current adjustable capacity of the multifunctional washing machine can be effectively evaluated, and the accuracy of the adjustable capacity evaluation can be improved.

[0063] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have the same meaning as in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined similarly here.

[0064] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the load-adjustable capacity of a multifunctional washing machine, characterized in that: The following operations are included: collecting multi-function laundry load operation information and weather information; Construct a prediction model of the behavior characteristics of a multifunctional washing machine taking weather factors into account, and predict the behavior characteristics of the multifunctional washing machine; Construct a load correction model for a multifunctional washing machine that takes weather factors into consideration, and correct the behavior of the multifunctional washing machine under different weather factors and electricity price information according to the behavior characteristics of the multifunctional washing machine to obtain the corrected load of the multifunctional washing machine; Based on the corrected load of the multi-functional washing machine, a day-ahead load adjustable capacity evaluation model for the multi-functional washing machine considering weather factors is constructed, and the day-ahead load adjustable capacity of the multi-functional washing machine is evaluated.

2. The method for evaluating the load-adjustable capacity of a multifunctional washing machine according to claim 1, characterized in that: The collected multifunctional laundry load operation information includes the washing machine operating time interval, washing machine startup time, washing machine shutdown time, washing machine power, and drying operation power. The collected weather information includes weather conditions, outdoor temperature, and humidity. The expression is: X i (k)=[t i p (k),t i q (k),t i h (k),t i a (k),t i b (k),P i x ,P i h ,T(k),R(k),W(k),ψ(k)] Among them, X i (k) is the basic data of multi-function washing machine load i on day k, t i p (k) is the start-up time of multifunctional washing machine i on day k, t i q (k) is the shutdown time of multifunctional washing machine i on the kth day, t i h (k) is the end time of drying load of multifunctional washing machine i on day k, (t i a (k),t i b (k)) is the operating time range of multifunctional washing machine i on the kth day, where t i a (k) is the earliest time when multifunctional washing machine i can be turned on on day k, t i b (k) is the latest time that the multifunctional washing machine i can be turned on on the kth day, P i x is the washing power of multifunctional washing machine i, P i h is the drying power of multifunctional washing machine i, T(k) is the temperature on the kth day, R(k) is the humidity on the kth day, W(k) is the weather condition on the kth day, the value is 1 for sunny day, the value is 2 for cloudy day, the value is 3 for rainy day, the value is 4 for snowy day, ψ(k) is the electricity price information on the kth day.

3. The method for evaluating the load-adjustable capacity of a multifunctional washing machine according to claim 2, characterized in that: The construction of the multifunctional washing machine behavior feature prediction model taking into account weather factors specifically includes: based on the BP neural network, training the collected multifunctional washing machine load operation information and weather information to construct the multifunctional washing machine load operation information and weather information, wherein the BP neural network includes 4 inputs and 2 outputs, the 4 inputs are: the temperature T(k) of the kth day, the humidity R(k) of the kth day, the weather state W(k) of the kth day, and the electricity price information ψ(k) of the kth day, and the 2 outputs are: δ x =1 indicates that the multi-function washing machine will only start the washing function on the day before the forecast, h =1 indicates that the multifunctional washing machine has started the washing and drying functions on the predicted day. The hidden layer of the BP neural network uses the tansig function. The expression of the multifunctional washing machine behavior feature prediction model is: Among them, f x () is the BP neural network laundry behavior prediction model after training, f h () is the washing and drying behavior prediction model of the BP neural network after training.

4. The method for evaluating the load-adjustable capacity of a multifunctional washing machine according to claim 2, characterized in that: The constructed load correction model of multifunctional washing machine considering weather factors includes two cases: using and not using the dryer; Among them, the expression of the load correction model of the multifunctional washing machine without using the dryer is: The expression of the load correction model of the multifunctional washing machine when using a dryer is: Among them, δ x is the laundry behavior predicted based on weather factors and electricity price information, δ x =1 indicates that the multifunctional washing machine in the forecasted day only starts the washing function, P(t) is the power of multifunctional washing machine i, δ h is the washing and drying behavior predicted based on weather factors and electricity price information, δ h =1 means that the multi-function washing machine has started the washing and drying functions on the predicted day. is the washing power of multifunctional washing machine i at time t, is the drying power of multifunctional washing machine i at time t.

5. The method for evaluating the load-adjustable capacity of a multifunctional washing machine according to claim 2, characterized in that: The expression of the evaluation model of the load-adjustable capacity of the multifunctional washing machine is: Among them, t x is the washing time, t h is the drying time, ΔP up (t) is the load increase, ΔP dn (t) is the load reduction, M is the number of washing machines involved in the increase, N is the number of washing machines involved in the decrease, is the startup time of the multifunctional washing machine i1 participating in the upward adjustment on the kth day, is the shutdown time of the multifunctional washing machine i1 participating in the upward adjustment on the kth day, is the end time of the drying load of the multifunctional washing machine i1 participating in the upward adjustment on the kth day, is the startup time of the multifunctional washing machine i2 participating in the downward adjustment on the kth day, is the shutdown time of the multifunctional washing machine i2 participating in the downward adjustment on the kth day, is the end time of drying load of multifunctional washing machine i2 participating in the downward adjustment on the kth day, is the washing power of the multifunctional washing machine i1 at time t, is the drying power of the multifunctional washing machine i1 at time t, is the washing power of the multifunctional washing machine i2 at time t, is the drying power of the multifunctional washing machine i2 at time t, where multifunctional washing machines i1 and i2 belong to i.

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