A power system dispatching method and device coupled with multi-factor demand response

Abstract

The application discloses a kind of coupling multi-factor demand response power system scheduling method and device, the power system scheduling method is first based on consumer psychology theory and constructs the demand response uncertainty model of economic factor.Second, the difference between the output of demand response uncertainty model of economic factor and reality response is calculated, as the influence of non-economic factor on demand response participation degree.Then, difference is classified based on fuzzy C means clustering algorithm and realizes the interval quantization of the influence of non-economic factor on demand response participation degree.Finally, the demand response uncertainty model is realized to realize power system scheduling by coupling double factor construction.Lastly, the actual response capability of user can be more accurately described under the premise of the uncertainty of demand response, so as to obtain more reasonable regulating power, avoid the demand response control effect from being weakened due to the uncertainty of regulating power estimation being too small, and improve the reliability, flexibility and actual execution effect of demand response control.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization technology, specifically relating to a power system dispatching method and apparatus that couples multi-factor demand response. Background Technology

[0002] With a high proportion of renewable energy being integrated into the power system, the randomness and volatility of its output will have a certain impact on the voltage and frequency of the power system. Demand response, through price signals or incentive mechanisms, guides users to actively adjust their electricity consumption behavior, realizing load shifting and power regulation. It is an important technical means to tap into demand-side flexibility and improve the power system's regulation capabilities.

[0003] In practical applications, demand response capabilities are often idealized and overestimated, leading to significant deviations in scheduling results during the execution phase. Electricity price signals and expected revenue, as the dominant factors driving consumer participation in demand response, determine the overall strength of user willingness to participate. Demand response uncertainty models built based on consumer psychological factors can, to some extent, characterize user participation levels. However, influenced by non-economic psychological factors such as perceived comfort and concerns about control and privacy, the deviation between the evaluation results of demand response uncertainty models based on consumer psychological factors and actual response behavior is further amplified, significantly increasing the uncertainty of demand response regulation effects. This manifests as "imprecise power regulation" in the demand response regulation decision-making process under dual uncertainty. Therefore, it is urgent to construct a demand response uncertainty model that addresses the dual uncertainty caused by the combined effects of economic and non-economic psychological factors, thereby improving the reliability and accuracy of demand response execution. Summary of the Invention

[0004] The purpose of this invention is to provide a power system dispatching method and apparatus that couples multi-factor demand response.

[0005] In a first aspect, the present invention provides a power system dispatching method coupled with multi-factor demand response, the method comprising:

[0006] Construct a demand response uncertainty model based on economic factors;

[0007] Demand response participation is predicted using a demand response uncertainty model based on historical data to obtain economic factors. and predicting demand response participation Corresponding actual demand response participation To predict demand response participation Response to actual needs and participation The difference is considered as the demand response participation rate caused by non-economic factors. ;

[0008] Demand response participation at different times Perform clustering and, based on the clustering results, determine the participation level in each category of demand response. This produces an interval quantization result that includes the mean, maximum, and minimum values.

[0009] The interval quantization results are used as compensation for non-economic factor disturbances in the demand response uncertainty model of economic factors to obtain a coupled two-factor demand response uncertainty model.

[0010] Based on the set confidence level and the coupled two-factor demand response uncertainty model, the confidence interval boundary of the demand response participation degree is solved in reverse, and the regulation power range is generated based on the planned regulation power to realize the demand response regulation of the power system.

[0011] Preferably, the demand response uncertainty model for economic factors includes an economic incentive / demand response maximum error function, an economic incentive / demand response participation function, and a demand response participation / probability density function; wherein, the economic incentive / demand response maximum error function is used to determine the demand response participation error level corresponding to the economic incentive; the economic incentive / demand response participation function is used to determine the mean and upper and lower boundaries of the demand response participation corresponding to the economic incentive; and the demand response participation / probability density function is used to characterize the probability distribution of demand response participation under economic factors.

[0012] Preferably, the economic incentive / demand response maximum error function uses a piecewise linear form to describe the relationship between economic incentives and the demand response participation error level, and uses the incentive value corresponding to the maximum error level of demand response participation as the piecewise inflection point.

[0013] Preferably, the method for clustering the demand response participation is the fuzzy C-means clustering algorithm; the fuzzy C-means clustering algorithm constructs an objective function based on the membership degree of the sample to each cluster category, the distance between the sample and the cluster center, and the fuzzy weighting index; the fuzzy weighting index is 2.

[0014] As a preferred embodiment, the compensation method for the interval quantification results is as follows: the mean value in the interval quantification results is used as the disturbance amount of non-economic factors on the mean value of demand response participation, and the maximum and minimum values ​​in the interval quantification results are used as the disturbance amount of non-economic factors on the error level of demand response participation. A normal distribution function with expectation as the center and different variances on the left and right sides is constructed to form a demand response uncertainty model with coupled two factors.

[0015] Preferably, the adjustment power range includes a maximum adjustment power and a minimum adjustment power; the maximum adjustment power and the minimum adjustment power are obtained by multiplying the planned adjustment power by the confidence interval.

[0016] Preferably, the confidence level is set based on the degree of security constraints of the power system; the stronger the security constraints of the power system, the higher the confidence level and the wider the generated regulation power range.

[0017] Secondly, the present invention provides a power system dispatching device coupled with multi-factor demand response, which is used to execute the above-mentioned power system dispatching method; the power system dispatching device includes a human-machine interaction module, a data acquisition module, a multi-factor demand response module, and a system dispatching module; the human-machine interaction module is used to input the set economic incentives and confidence levels; the data acquisition module is used to collect historical data; the multi-factor demand response module is used to determine the non-economic factor response based on the historical data, and compensate the non-economic factor response into the economic factor demand response uncertainty model to obtain a coupled two-factor demand response uncertainty model; the system dispatching module is used to perform demand response regulation of the power system according to the regulation power range output by the multi-factor demand response module.

[0018] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the above-described power system dispatching method.

[0019] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-described power system dispatching method.

[0020] The beneficial effects of this invention are:

[0021] 1. This invention constructs an uncertain demand response model based on economic factors and uses the difference between predicted and actual demand response participation in historical data to characterize the impact of non-economic factors on demand response participation. This can transform non-economic factors, such as comfort perception, control concerns, and privacy concerns, which are difficult to model directly, into calculable response biases, thereby solving the problem of estimation bias in actual response capability when characterizing demand response behavior based solely on economic factors.

[0022] 2. This invention classifies the response deviations of non-economic factors at different times and compensates the interval quantification results of the impact of non-economic factors constructed based on the classification results into the demand response uncertainty model of economic factors. This results in the construction of a demand response uncertainty model that simultaneously couples economic and non-economic factors, thereby improving the accuracy and interpretability of the demand response model in characterizing the actual response behavior of users and its fluctuation range.

[0023] 3. This invention constructs a coupled two-factor demand response uncertainty model, solves the confidence interval boundary of demand response participation under a set confidence level, and generates the maximum and minimum values ​​of the regulation power by combining the planned regulation power, thus outputting the demand response regulation power in interval form. At the same time, by selecting different confidence levels to adapt to different scheduling reliability requirements, it improves the reliability, flexibility and actual execution effect of power system demand response scheduling in uncertain environments. Attached Figure Description

[0024] Figure 1 This is the overall flowchart of the present invention.

[0025] Figure 2 This is a schematic diagram of the demand response uncertainty model of economic factors in this invention; where (a) is the mapping relationship between the probability interval of user participation and economic incentives; (b) is the variation law of the maximum error level affected by economic incentives; and (c) is the probability density curve of user participation at the inflection point of economic incentives.

[0026] Figure 3 This is a schematic diagram of the demand response uncertainty model for non-economic factors in this invention; where (a) is the mapping relationship between the probability interval of user participation and the incentive of non-economic factors; (b) is the mapping relationship between the probability interval of user participation and the incentive of two factors; and (c) is the probability density curve of user participation at the inflection point of the incentive of two factors.

[0027] Figure 4 The demand response regulation power generated by this invention. Detailed Implementation

[0028] The present invention will be further described below with reference to the accompanying drawings.

[0029] A power system dispatching method coupled with multi-factor demand response is disclosed. The power system dispatching device includes a human-machine interaction module, a data acquisition module, a multi-factor demand response module, and a system dispatching module. The human-machine interaction module is used to input the set economic incentives and confidence levels. The data acquisition module collects historical data and inputs it to the multi-factor demand response module to correct the uncertainty model of the demand response to economic factors. The multi-factor demand response module obtains the maximum and minimum values ​​of the regulating power based on the parameters set by the human-machine interaction module. The system dispatching module dispatches the power system based on the maximum and minimum values ​​of the regulating power output by the multi-factor demand response module.

[0030] like Figure 1 As shown, a power system dispatching method coupling multiple factors and demand response includes the following steps:

[0031] S1. Constructing a demand response uncertainty model based on economic factors

[0032] like Figure 2 As shown, the demand response uncertainty model based on economic factors is constructed using consumer psychology theory. This model includes the maximum error function of economic incentives / demand response, the participation function of economic incentives / demand response, and the participation / probability density function of demand response. Its specific construction method is as follows:

[0033] (1) The maximum error function of economic incentives / demand response reflects the changing pattern of the error level of demand response participation under the influence of economic incentives, such as Figure 2 As shown in (b) above, the maximum error function of economic incentive / demand response can be approximated by a piecewise linear form to reflect the differences in response uncertainty within different incentive intervals, as expressed below:

[0034]

[0035] in, The error level of demand response participation at time t; This represents the maximum error level for demand response participation. The maximum error level d for demand response participation max The corresponding incentive value; and These are the minimum and maximum incentive values, respectively. Let t be the economic incentive at time t.

[0036] When the target of the demand response consists of a large number of users, the overall user participation can be approximately distributed normally in a statistical sense.

[0037] (2) The economic incentive / demand response participation function reflects the range of demand response participation. With economic incentives Mapping relationships between them, such as Figure 2 As shown in (a); the curve in it and The curves represent the upper and lower bounds of user engagement, respectively. This represents the expected value curve of user engagement (demand response engagement). The economic incentive / demand response engagement function is expressed as follows:

[0038]

[0039]

[0040] in, Let t be the mean of demand response participation (user participation) under economic incentives at time t; Maximum demand response participation means that all demand response adjustment power is executed by the user. and These represent the upper and lower limits of demand response participation under economic incentives at time t; Let t be the error level of demand response participation under economic incentives.

[0041] (3) The demand response participation / probability density function reflects the probability density function curve of demand response participation at the economic incentive, and it is expressed as follows:

[0042]

[0043] in, The demand response participation rate at time t; Demand response participation The probability density.

[0044] Therefore, based on the demand response participation range ( This allows us to obtain approximate confidence intervals. For example... Figure 2 As shown in (c), the interval width varies depending on the confidence level. The size of this probability interval mainly reflects the degree to which economic factors influence user participation behavior. The approximate confidence interval is represented as follows:

[0045]

[0046] in, The confidence level represents the degree of participation in the demand response. Falling in the range The probability of; and These are the lower and upper bounds of the confidence interval, respectively.

[0047] S2. Cluster Analysis

[0048] like Figure 3 As shown, the constructed economic factor demand response uncertainty model is corrected using actual demand response participation. Specifically, the difference between the predicted demand response participation caused by economic factors, calculated based on historical data, and the actual user demand response participation is used as the demand response participation caused by non-economic factors. ,like Figure 3 The solid blue line in (a) represents demand response participation. The expression is:

[0049]

[0050] in, Let be the predicted demand response participation at time t; The actual demand response participation rate at time t.

[0051] Obtain demand response participation caused by non-economic factors at different times Constructing a sample set ;in, The sample size is given; the fuzzy C-means clustering algorithm is used to cluster the sample set. Classify ( Figure 3 (a) represents 5 classes. During the clustering process, the objective function is constructed. It is expressed as follows:

[0052]

[0053] Where n is the number of samples; c is the number of clusters; Let be the membership degree of the i-th sample to the j-th class; It is the cluster center of the j-th class; is the squared distance from the sample to the cluster center; m is the fuzzy weighting index, preferably 2.

[0054] Calculate the mean demand response participation caused by non-economic factors for each category based on the classification results. (Orange dashed line) and maximum value and minimum value (Brown dashed line) enables interval quantization for each category. The mean serves as the uniform demand response participation rate for that category, while the maximum and minimum values ​​serve as uniform upper and lower boundary envelopes.

[0055] S4. Compensate for the demand response participation caused by non-economic factors to the demand response participation / probability density function in the demand response uncertainty model of economic factors. When the coupled demand response participation exhibits different fluctuation characteristics on both sides of the mean, respectively, refer to the mean mentioned above. and maximum value and minimum value The probability density function curve, added as a disturbance to the economic incentive, is used to integrate the demand response participation / probability density functions of economic and non-economic factors. This allows for the construction of a normal distribution function with different variances on both sides, centered on the expectation, to represent the coupled two-factor demand response uncertainty model, as shown below:

[0056]

[0057] in, and These represent the disturbances to the standard deviation caused by non-economic factors to the right and left of the mean at time t, respectively. Let t be the amount of non-economic factors that affect the mean at time t. and They represent the same meaning, namely the average participation rate at time t.

[0058] S5. To balance control reliability and resource utilization efficiency, different confidence levels can be selected based on scheduling requirements: when the system has high requirements for control reliability, a higher confidence level (e.g., 95% or 99%) should be selected; when the system has a certain risk tolerance, the confidence level can be appropriately reduced (e.g., 80% or 75%) to improve the utilization rate of control potential. The corresponding confidence interval boundaries are then solved in reverse based on the given confidence level and the coupled two-factor demand response uncertainty model in S4. and Based on confidence intervals The demand response regulation power is obtained, and it is expressed as:

[0059]

[0060] in, To adjust power according to plan; and These are the maximum and minimum regulating power, respectively.

[0061] Demand response regulation of the power system is achieved by using the obtained maximum and minimum regulation power.

[0062] Based on the demand response power adjustment of the present invention, such as Figure 3 As shown. The planned regulation power is represented by a solid black line, and the regulation power ranges for each confidence interval are represented by different shades of blue. The regulation power reaches its maximum value at 10:58, with a planned regulation power of 100.7564 kW. The regulation power ranges at 95% confidence level are: [59.4136, 142.0991] kW, at 90% confidence level: [66.0604, 135.4523] kW, at 80% confidence level: [73.7238, 127.7889] kW, and at 60% confidence level: [83.0035, 118.5092] kW. At 19:29, the regulating power reached its minimum value. The planned regulating power was -70.3493 kW. The regulating power range at 95% confidence level is [-114.2617, -26.4368] kW, the regulating power range at 90% confidence level is [-107.2017, -33.4968] kW, the regulating power range at 80% confidence level is [-99.0621, -41.6364] kW, and the regulating power range at 60% confidence level is [-89.2055, -51.4930] kW.

[0063] Based on confidence intervals, in scenarios with strong power system security constraints, a high-confidence interval can be selected to avoid the risk of insufficient regulation. In scenarios oriented towards renewable energy consumption or economic optimization, the confidence level can be appropriately reduced to improve the utilization of load regulation potential. Furthermore, expressing regulation power in interval form improves the accuracy of characterizing demand response regulation power under uncertain power system environments, and enhances the adaptability and interpretability of control strategies, providing strong support for achieving safe, flexible, and efficient demand response control.

Claims

1. A power system dispatching method coupling multiple factors and demand response, characterized in that: The method includes: Construct a demand response uncertainty model based on economic factors; Demand response participation is predicted using a demand response uncertainty model based on historical data to obtain economic factors. and predicting demand response participation Corresponding actual demand response participation To predict demand response participation Response to actual needs and participation The difference is considered as the demand response participation rate caused by non-economic factors. ; Demand response participation at different times Perform clustering and, based on the clustering results, determine the participation level in each category of demand response. This produces an interval quantization result that includes the mean, maximum, and minimum values. The interval quantization results are used as compensation for non-economic factor disturbances in the demand response uncertainty model of economic factors to obtain a coupled two-factor demand response uncertainty model. Based on the set confidence level and the coupled two-factor demand response uncertainty model, the confidence interval boundary of the demand response participation degree is solved in reverse, and the regulation power range is generated based on the planned regulation power to realize the demand response regulation of the power system.

2. The power system dispatching method coupled with multi-factor demand response as described in claim 1, characterized in that: The economic factor-driven demand response uncertainty model includes an economic incentive / demand response maximum error function, an economic incentive / demand response participation function, and a demand response participation / probability density function. The economic incentive / demand response maximum error function is used to determine the demand response participation error level corresponding to the economic incentive. The economic incentive / demand response participation function is used to determine the mean and upper and lower boundaries of the demand response participation corresponding to the economic incentive. The demand response participation / probability density function is used to characterize the probability distribution of demand response participation under economic factors.

3. The power system dispatching method coupled with multi-factor demand response as described in claim 2, characterized in that: The maximum error function of economic incentives / demand response uses a piecewise linear form to describe the relationship between economic incentives and the error level of demand response participation, and uses the incentive value corresponding to the maximum error level of demand response participation as the piecewise inflection point.

4. The power system dispatching method coupled with multi-factor demand response as described in claim 1, characterized in that: The method for clustering the demand response participation is the fuzzy C-means clustering algorithm; the fuzzy C-means clustering algorithm constructs an objective function based on the membership degree of the sample to each cluster category, the distance between the sample and the cluster center, and the fuzzy weighting index; the fuzzy weighting index is 2.

5. A power system dispatching method coupled with multi-factor demand response as described in claim 1, characterized in that: The compensation method for the interval quantification results is as follows: the mean value in the interval quantification results is used as the disturbance amount of non-economic factors on the mean value of demand response participation, and the maximum and minimum values ​​in the interval quantification results are used as the disturbance amount of non-economic factors on the error level of demand response participation. A normal distribution function with expectation as the center and different variances on the left and right sides is constructed to form a demand response uncertainty model with coupled two factors.

6. The power system dispatching method coupled with multi-factor demand response according to claim 1, characterized in that: The regulation power range includes the maximum regulation power and the minimum regulation power; the maximum regulation power and the minimum regulation power are obtained by multiplying the planned regulation power by the confidence interval.

7. The power system dispatching method coupled with multi-factor demand response as described in claim 1, characterized in that: The confidence level is set based on the degree of security constraints of the power system; the stronger the security constraints of the power system, the higher the confidence level and the wider the generated regulation power range.

8. A power system dispatching device that couples multi-factor demand response, characterized in that: A power system dispatching method for coupled multi-factor demand response as described in claim 1 is used to execute the power system dispatching device comprising a human-machine interaction module, a data acquisition module, a multi-factor demand response module, and a system dispatching module. The human-machine interaction module is used to input set economic incentives and confidence levels. The data acquisition module is used to acquire historical data. The multi-factor demand response module is used to determine non-economic factor responses based on historical data and compensate for these non-economic factor responses in the economic factor demand response uncertainty model, thereby obtaining a coupled two-factor demand response uncertainty model. The system dispatching module is used to perform demand response regulation of the power system based on the regulating power range output by the multi-factor demand response module.

9. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that: When the processor executes the computer program, it implements a power system dispatching method with coupled multi-factor demand response as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements a power system dispatching method with coupled multi-factor demand response as described in any one of claims 1-7.

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