KR-20260064093-A - Predictive home energy management system and bidirectional real-time pricing prediction method for demand response thereof

KR20260064093AKR 20260064093 AKR20260064093 AKR 20260064093AKR-20260064093-A

Abstract

A home energy management system and a bidirectional real-time price prediction method for demand response thereof are disclosed. The bidirectional real-time price prediction method for demand response comprises: (a) obtaining user-related information and real-time electricity prices for smart home users; (b) applying the user-related information and real-time electricity prices to a deep learning-based prediction model to generate future electricity price prediction information; (c) generating power consumption scheduling results for smart home devices based on the electricity price prediction information; (d) analyzing the power consumption scheduling results to calculate the hourly shifted power amount and flexible device consumption ratio, and using the hourly shifted power amount and flexible device consumption ratio to calculate a shift adjustment value and a consumption adjustment value, respectively; (e) calculating an incentive-penalty weight value according to the power consumption pattern using the shift adjustment value and the consumption adjustment value; and (f) deriving a bidirectional real-time electricity price by reflecting the incentive-penalty weight value in the electricity price prediction information.

Inventors

김문겸
김형준

Assignees

중앙대학교 산학협력단

Dates

Publication Date: 20260507
Application Date: 20241031

Claims (14)

(a) A step of obtaining user-related information and real-time power prices for smart home users; (b) a step of generating future power price prediction information by applying the above user-related information and real-time power prices to a deep learning-based prediction model; (c) a step of generating power consumption scheduling results for smart home devices based on the above power price prediction information; (d) a step of analyzing the power consumption scheduling results to calculate the hourly shifted power amount and the flexible device consumption ratio, and using the hourly shifted power amount and the flexible device consumption ratio to calculate the shift adjustment value and the consumption adjustment value, respectively; (e) a step of calculating an incentive-penalty weighting value according to the power consumption pattern using the above movement adjustment value and the above consumption adjustment value; and (f) A bidirectional real-time price forecasting method for demand response comprising the step of deriving a bidirectional real-time power price by reflecting the above incentive-penalty weighting values in the above power price forecasting information.
In Article 1, A bidirectional real-time price prediction method for demand response, characterized in that the deep learning-based prediction model inputs time series data regarding user-related information and real-time power prices into a first deep learning model to extract spatial features, and applies the spatial features to a second deep learning model to generate power price prediction information reflecting temporal patterns.
In Article 2, The above-mentioned first deep learning model is a USCNN (unsupervised shallow convolutional neural network) based model, and A bidirectional real-time price prediction method for demand response, characterized in that the second deep learning model is a nested LSTM-based model.
In Article 1, A bidirectional real-time price forecasting method for demand response, characterized in that the above-mentioned movement adjustment value is calculated using the following mathematical formula. Here, represents the actual real-time power price, and and represents the minimum and maximum values of bidirectional real-time power prices, and represents the amount of power transferred over time, and and represents the maximum credit score values in the positive and negative directions.
In Article 1, A bidirectional real-time price forecasting method for demand response, characterized in that the above consumption adjustment value is calculated using the following mathematical formula. Here, represents the actual real-time power price, and represents the minimum value of bidirectional real-time power prices, and It represents a credit score for the consumer's flexible device consumption ratio, and represents the default score, represents the maximum credit score for the flexible device consumption ratio.
In Article 1, A bidirectional real-time price prediction method for demand response, characterized in that the above user-related information includes load, device information, previous consumption patterns, and whether a flexible device is used.
In Article 1, A bidirectional real-time price forecasting method for demand response, characterized in that the above incentive-penalty weighting values are calculated using the following mathematical formula. Here, represents the movement adjustment value, represents the consumption adjustment value of the flexible device, and represents the weighting of changes in real-time power prices.
A computer-readable recording medium having recorded program code for performing a method according to any one of claims 1 to 7.
In home energy management systems, Memory for storing at least one instruction; and The instructions stored in the above memory include a processor, The instructions executed by the above processor are, respectively, (a) A step of obtaining user-related information and real-time power prices for smart home users; (b) a step of generating future power price prediction information by applying the above user-related information and real-time power prices to a deep learning-based prediction model; (c) a step of generating power consumption scheduling results for smart home devices based on the above power price prediction information; (d) a step of analyzing the power consumption scheduling results to calculate the hourly shifted power amount and the flexible device consumption ratio, and using the hourly shifted power amount and the flexible device consumption ratio to calculate the shift adjustment value and the consumption adjustment value, respectively; (e) a step of calculating an incentive-penalty weighting value according to the power consumption pattern using the above movement adjustment value and the above consumption adjustment value; and (f) A household energy management system characterized by performing a step of deriving a bidirectional real-time power price by reflecting the above incentive-penalty weighting value in the above power price prediction information.
In Article 9, A home energy management system characterized by the above-described deep learning-based prediction model inputting time series data regarding user-related information and real-time electricity prices into a first deep learning model to extract spatial features, and applying the spatial features to a second deep learning model to generate electricity price prediction information reflecting temporal patterns.
In Article 10, The above-mentioned first deep learning model is a USCNN (unsupervised shallow convolutional neural network) based model, and A home energy management system characterized in that the second deep learning model is a nested LSTM-based model.
In Article 9, A home energy management system characterized by the above-mentioned movement adjustment value being calculated using the following mathematical formula. Here, represents the actual real-time power price, and and represents the minimum and maximum values of bidirectional real-time power prices, and represents the amount of power transferred over time, and and represents the maximum credit score values in the positive and negative directions.
In Article 9, A household energy management system characterized by the above consumption adjustment value being calculated using the following mathematical formula. Here, represents the actual real-time power price, and represents the minimum value of bidirectional real-time power prices, and It represents a credit score for the consumer's flexible device consumption ratio, and represents the default score, represents the maximum credit score for the flexible device consumption ratio.
In Article 9, A household energy management system characterized by the above incentive-penalty weighting value being calculated using the following mathematical formula. Here, represents the movement adjustment value, represents the consumption adjustment value of the flexible device, and represents the weighting of changes in real-time power prices.

Description

Predictive home energy management system and bidirectional real-time pricing prediction method for demand response thereof The present invention relates to a household energy management system and a bidirectional real-time price prediction method for the demand response thereof. Recent advancements in Internet of Things (IoT) technology and smart metering infrastructure have enabled smart home users to schedule real-time (RT) power consumption through home energy management systems (HEMS). This power consumption scheduling via HEMS is known as residential demand response (DR), which is an effective method for changing power demand by adjusting mobile and controllable loads. Price-based DR has been regarded as a promising means of shifting users' peak loads, and reasonable electricity pricing mechanisms can directly influence customers' ability to participate in DR. To implement price-based DR, real-time pricing (RTP) has proven to be an effective pricing mechanism that reduces users' electricity bills and mitigates grid peaks through load shifting. While conventional technology by Wang and Paranjape et al. demonstrated that RTP could reduce loads and electricity rates during peak periods, it was found that another peak rebound could occur at other times, potentially increasing the PAR ratio. To address this, Anees and Chen reduced both electricity rates and PAR (peak-to-average ratio, hereinafter referred to as PAR) by integrating sloped block rates with RTP to set a user consumption threshold. Sloped block rates were also used to manage PAR and flatten power consumption while improving the social well-being of customers. However, these conventional technologies cannot easily accommodate the diverse electricity demands of end users due to the complexity of the residential sector and the presence of various appliances operating at different times of the day. Furthermore, because they are not directly related to actual prices, they can lead to inefficient behavioral changes and provide less flexible services. Considering these limitations, recently proposed pricing mechanisms for virtual power plants based on customized rebate packages, decentralized RTP frameworks based on compensation fairness, and hybrid pricing mechanisms that consider both RTP and RT incentives have been suggested. While there have been significant efforts to develop new pricing mechanisms that provide financial rewards to incentivize customer participation in DR, existing pricing mechanisms tend to offer the same energy price levels to specific user groups or regions. Furthermore, although these mechanisms may influence consumers' energy consumption patterns to some extent, they fall short in guiding them toward long-term, sustainable energy usage habits. This is because, even if consumers are influenced by RTP, their energy consumption behavior is not successfully reflected in energy prices. Consequently, the unidirectional nature of current electricity pricing mechanisms can dampen consumer motivation to participate in residential DR. Therefore, to encourage active consumer participation in residential DR, it is necessary to develop a new, customized bidirectional pricing mechanism that allows consumers to set their own electricity rates through bidirectional pricing. FIG. 1 is a flowchart illustrating a bidirectional real-time price prediction method for demand response in PHEMS according to one embodiment of the present invention. FIG. 2 is a drawing illustrating the overall architecture of PHEMS according to one embodiment of the present invention. FIG. 3 is a diagram illustrating the HSP and HFA segmentation section according to one embodiment of the present invention. FIG. 4 is a drawing illustrating the difference between a conventional model and a first deep learning model according to an embodiment of the present invention. FIG. 5 is a diagram illustrating an nLSTM architecture according to an embodiment of the present invention. FIG. 6 is a diagram illustrating pseudocode of a bidirectional real-time price prediction method for demand response in PHEMS according to an embodiment of the present invention. FIG. 7 is a diagram illustrating a power consumption determination timeline according to an embodiment of the present invention. FIG. 8 is a block diagram schematically illustrating the internal configuration of a household energy management system according to one embodiment of the present invention. As used in this specification, singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as "composed" or "comprising" should not be interpreted as necessarily including all of the various components or steps described in the specification, and should be interpreted as meaning that some of the components or steps may be excluded, or that additional components or steps may be included. Furthermore, terms such as "...part," "module," etc., as used in the speci