研究目的
To characterize the demand flexibility for aggregate residential loads by analyzing time-variable patterns and introducing mathematical definitions based on assessing positive and negative pattern variations.
研究成果
The paper introduces novel demand flexibility indicators (FIAD and PFL) for residential demand aggregations, based on binomial probability models of demand variations. These indicators help in quantifying the flexibility achievable from aggregate loads and can assist system operators in selecting suitable time slots for demand response programs.
研究不足
The approach does not operate in real time and does not account for the immediate effects of controls on specific appliances. The flexibility indicators are based on statistical analysis of aggregate demand patterns, which may not capture individual appliance dynamics.
1:Experimental Design and Method Selection:
The study uses a statistical approach to analyze time-variable patterns of aggregate residential customers, employing binomial probability models and maximum likelihood estimation.
2:Sample Selection and Data Sources:
Data are generated for extra-urban residential consumers using Monte Carlo simulations, based on information about family composition, lifestyle, house characterization, and usage of electrical appliances.
3:List of Experimental Equipment and Materials:
Not explicitly mentioned in the paper.
4:Experimental Procedures and Operational Workflow:
The methodology involves calculating load variations, applying binomial probability models, and estimating flexibility indicators.
5:Data Analysis Methods:
The analysis includes maximum likelihood estimation for binomial proportions, confidence interval calculations, and the formulation of flexibility indicators.
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