研究目的
To define and quantify flexibility in the context of aggregate residential electricity demand, focusing on the variation in time of the overall demand aggregation.
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 quantify the flexibility achievable from aggregate loads in different time periods, aiding in the selection of suitable time slots for demand response programs. The study demonstrates the effects of aggregation level and time step duration on flexibility indicators, providing insights into the collective behavior of residential customers.
研究不足
The study focuses on residential demand aggregations and does not address real-time control of specific appliances. The approach does not require knowledge of individual user details, which limits the granularity of control but addresses privacy concerns.
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 to define flexibility indicators.
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 electrical appliance usage.
3:List of Experimental Equipment and Materials:
The study utilizes smart meters and advanced data analysis techniques for data collection and analysis.
4:Experimental Procedures and Operational Workflow:
The methodology involves calculating load variations, applying binomial probability models, and estimating flexibility indicators for different aggregation levels and time step durations.
5:Data Analysis Methods:
The analysis includes the use of maximum likelihood estimation for binomial proportions and the calculation of confidence intervals for flexibility indicators.
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