To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and ...To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.展开更多
Demand Response(DR)is a critical strategy for managing the integration of renewable energy sources into the power grid,addressing the challenges posed by their intermittent and unpredictable nature.This study introduc...Demand Response(DR)is a critical strategy for managing the integration of renewable energy sources into the power grid,addressing the challenges posed by their intermittent and unpredictable nature.This study introduces a rapid evaluation method for assessing the DR potential of large-scale Heating,Ventilation,and Air Conditioning(HVAC)systems,focusing on the significant role these systems play in energy consumption and grid flexibility.Firstly,the methodology involves constructing a simulation model library that encompasses three dimensions including room type,room location,and internal heat gain mode to reflect the dynamic characteristics of cooling load.Additionally,batch simulations generate DR profiles under various typical weather conditions,and surrogate models are trained for each simulation model,leveraging feature engineering and cross-validation to enhance accuracy.The Multi-Layer Perceptron(MLP)surrogate models achieve high accuracy in predicting DR potential under various scenarios,with R^(2) values exceeding 0.95.This study provides a robust framework that enables load aggregators to accurately estimate the demand response potential of large-scale HVAC systems.It supports the quantification of response capabilities and facilitates participation in bidding processes.Furthermore,it highlights the potential of data-driven models to enable rapid and scalable energy management.展开更多
Public buildings present substantial demand re sponse(DR)potential,which can participate in the power sys tem operation.However,most public buildings exhibit a high degree of uncertainties due to incomplete informatio...Public buildings present substantial demand re sponse(DR)potential,which can participate in the power sys tem operation.However,most public buildings exhibit a high degree of uncertainties due to incomplete information,varying thermal parameters,and stochastic user behaviors,which hin ders incorporating the public buildings into power system oper ation.To address the problem,this paper proposes an interval DR potential evaluation method and a risk dispatch model to integrate public buildings with uncertainties into power system operation.Firstly,the DR evaluation is developed based on the equivalent thermal parameter(ETP)model,actual outdoor tem perature data,and air conditioning(AC)consumption data.To quantify the uncertainties of public buildings,the interval evalu ation is given employing the linear regression method consider ing the confidence bound.Utilizing the evaluation results,the risk dispatch model is proposed to allocate public building re serve based on the chance constrained programming(CCP).Fi nally,the proposed risk dispatch model is reformulated to a mixed-integer second-order cone programming(MISOCP)for its solution.The proposed evaluation method and the risk dis patch model are validated based on the modified IEEE 39-bus system and actual building data obtained from a southern city in China.展开更多
基金the National Natural Science Foundation of China Youth Fund,Research on Security Low Carbon Collaborative Situation Awareness of Comprehensive Energy System from the Perspective of Dynamic Security Domain(52307130).
文摘To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.
基金supported by the State Grid Headquarters Science and Technology Fund(5400-202340383A-2-3-XG).
文摘Demand Response(DR)is a critical strategy for managing the integration of renewable energy sources into the power grid,addressing the challenges posed by their intermittent and unpredictable nature.This study introduces a rapid evaluation method for assessing the DR potential of large-scale Heating,Ventilation,and Air Conditioning(HVAC)systems,focusing on the significant role these systems play in energy consumption and grid flexibility.Firstly,the methodology involves constructing a simulation model library that encompasses three dimensions including room type,room location,and internal heat gain mode to reflect the dynamic characteristics of cooling load.Additionally,batch simulations generate DR profiles under various typical weather conditions,and surrogate models are trained for each simulation model,leveraging feature engineering and cross-validation to enhance accuracy.The Multi-Layer Perceptron(MLP)surrogate models achieve high accuracy in predicting DR potential under various scenarios,with R^(2) values exceeding 0.95.This study provides a robust framework that enables load aggregators to accurately estimate the demand response potential of large-scale HVAC systems.It supports the quantification of response capabilities and facilitates participation in bidding processes.Furthermore,it highlights the potential of data-driven models to enable rapid and scalable energy management.
基金supported by the National Science Fund for Distinguished Young Scholars(No.52125702)the Key Science and Technology Project of China Southern Power Grid Corporation(No.090000KK52220020).
文摘Public buildings present substantial demand re sponse(DR)potential,which can participate in the power sys tem operation.However,most public buildings exhibit a high degree of uncertainties due to incomplete information,varying thermal parameters,and stochastic user behaviors,which hin ders incorporating the public buildings into power system oper ation.To address the problem,this paper proposes an interval DR potential evaluation method and a risk dispatch model to integrate public buildings with uncertainties into power system operation.Firstly,the DR evaluation is developed based on the equivalent thermal parameter(ETP)model,actual outdoor tem perature data,and air conditioning(AC)consumption data.To quantify the uncertainties of public buildings,the interval evalu ation is given employing the linear regression method consider ing the confidence bound.Utilizing the evaluation results,the risk dispatch model is proposed to allocate public building re serve based on the chance constrained programming(CCP).Fi nally,the proposed risk dispatch model is reformulated to a mixed-integer second-order cone programming(MISOCP)for its solution.The proposed evaluation method and the risk dis patch model are validated based on the modified IEEE 39-bus system and actual building data obtained from a southern city in China.
