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Title: Estimating Reduced Consumption for Dynamic Demand Response

Abstract

Growing demand is straining our existing electricity generation facilities and requires active participation of the utility and the consumers to achieve energy sustainability. One of the most effective and widely used ways to achieve this goal in the smart grid is demand response (DR), whereby consumers reduce their electricity consumption in response to a request sent from the utility whenever it anticipates a peak in demand. To successfully plan and implement demand response, the utility requires reliable estimate of reduced consumption during DR. This also helps in optimal selection of consumers and curtailment strategies during DR. While much work has been done on predicting normal consumption, reduced consumption prediction is an open problem that is under-studied. In this paper, we introduce and formalize the problem of reduced consumption prediction, and discuss the challenges associated with it. We also describe computational methods that use historical DR data as well as pre-DR conditions to make such predictions. Our experiments are conducted in the real-world setting of a university campus microgrid, and our preliminary results set the foundation for more detailed modeling.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Univ. of Southern California, Los Angeles, CA (United States)
Publication Date:
Research Org.:
City of Los Angeles Department, CA (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1332337
Report Number(s):
DOE-USC-00192-74
DOE Contract Number:  
OE0000192
Resource Type:
Conference
Resource Relation:
Conference: AAAI Workshop on Computational Sustainability , Austin, TX (United States), 25-30 Jan 2015
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats

Chelmis, Charalampos, Aman, Saima, Saeed, Muhammad Rizwan, Frincu, Marc, and Prasanna, Viktor K. Estimating Reduced Consumption for Dynamic Demand Response. United States: N. p., 2015. Web.
Chelmis, Charalampos, Aman, Saima, Saeed, Muhammad Rizwan, Frincu, Marc, & Prasanna, Viktor K. Estimating Reduced Consumption for Dynamic Demand Response. United States.
Chelmis, Charalampos, Aman, Saima, Saeed, Muhammad Rizwan, Frincu, Marc, and Prasanna, Viktor K. Fri . "Estimating Reduced Consumption for Dynamic Demand Response". United States. doi:. https://www.osti.gov/servlets/purl/1332337.
@article{osti_1332337,
title = {Estimating Reduced Consumption for Dynamic Demand Response},
author = {Chelmis, Charalampos and Aman, Saima and Saeed, Muhammad Rizwan and Frincu, Marc and Prasanna, Viktor K.},
abstractNote = {Growing demand is straining our existing electricity generation facilities and requires active participation of the utility and the consumers to achieve energy sustainability. One of the most effective and widely used ways to achieve this goal in the smart grid is demand response (DR), whereby consumers reduce their electricity consumption in response to a request sent from the utility whenever it anticipates a peak in demand. To successfully plan and implement demand response, the utility requires reliable estimate of reduced consumption during DR. This also helps in optimal selection of consumers and curtailment strategies during DR. While much work has been done on predicting normal consumption, reduced consumption prediction is an open problem that is under-studied. In this paper, we introduce and formalize the problem of reduced consumption prediction, and discuss the challenges associated with it. We also describe computational methods that use historical DR data as well as pre-DR conditions to make such predictions. Our experiments are conducted in the real-world setting of a university campus microgrid, and our preliminary results set the foundation for more detailed modeling.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jan 30 00:00:00 EST 2015},
month = {Fri Jan 30 00:00:00 EST 2015}
}

Conference:
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