NDLI: Aggregate domestic demand modelling for the next day direct load control applications

Content Provider	IET Digital Library
Author	Bina, Mohammad Tavakoli Ahmadi, Danial
Abstract	A large group of residential appliances can be controlled by an aggregator as a single entity to reduce their aggregate energy consumption at peak periods. The target of this study is concentrated on proposing a new strategy from the perspective of an aggregator that optimally schedules residential loads during the next day. In this research, Gaussian copula (GC) function and Gaussian mixture model (GMM) are investigated as new efficient tools to estimate the aggregate power demand of specific domestic appliances. Since copula function considers the correlation of the exact data in generating new data, the estimation would lead to much more accurate solutions in practice compared to other estimating functions. However, the GMM estimates data with about 5% reduction in prediction error compared to those of the GC. A semi-automated demand response is required for the aggregator to apply the proposed strategy to the flexible loads, including dish washer, washing machine and electrical vehicle. Moreover, a mathematical formulation for the objective function is also introduced in which both the peak demand and energy cost are treated by a stochastic non-linear programming. Finally, the proposed strategy was simulated with the GAMS as an optimisation tool.
Starting Page	1306
Ending Page	1317
Page Count	12
ISSN	17518687
Volume Number	8
e-ISSN	17518695
Issue Number	Issue 7, Jul (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/8/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2013.0567
Journal	IET Generation, Transmission & Distribution
Publisher Date	2014-02-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Aggregate Domestic Demand Modelling Aggregate Energy Consumption Reduction Aggregate Power Demand Estimation Control of Electric Power System Data Generation Demand Side Management Dish Washer Domestic Appliances DSM Electrical Vehicle Energy Cost GAMS Gaussian Copula Function Gaussian Mixture Model Gaussian Processes Load Regulation Mathematical Formulation Next Day Direct Load Control Application Nonlinear Programming Optimal Residential Load Scheduling Optimisation Technique Optimisation Tool Power Consumption Power System Control Power System Economics Power System Managemen Power System Operation Prediction Error Reduction Residential Appliances Scheduling Semiautomated Demand Response Statistics Stochastic Nonlinear Programming Stochastic Programming Washing Machine
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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