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contributor authorChanpiwat, Pattanun
contributor authorGabriel, Steven A.
contributor authorMoglen, Rachel L.
contributor authorSiemann, Michael J.
date accessioned2022-02-04T14:38:56Z
date available2022-02-04T14:38:56Z
date copyright2020/01/03/
date issued2020
identifier issn2642-6641
identifier otherjesbc_1_1_011006.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4274096
description abstractThis paper develops means to analyze and cluster residential households into homogeneous groups based on the electricity load. Classifying customers by electricity load profiles is a top priority for retail electric providers (REPs), so they can plan and conduct demand response (DR) effectively. We present a practical method to identify the most DR-profitable customer groups as opposed to tailoring DR programs for each separate household, which may be computationally prohibitive. Electricity load data of 10,000 residential households from 2017 located in Texas was used. The study proposed the clustered load-profile method (CLPM) to classify residential customers based on their electricity load profiles in combination with a dynamic program for DR scheduling to optimize DR profits. The main conclusions are that the proposed approach has an average 2.3% profitability improvement over a business-as-usual heuristic. In addition, the proposed method on average is approximately 70 times faster than running the DR dynamic programming separately for each household. Thus, our method not only is an important application to provide computational business insights for REPs and other power market participants but also enhances resilience for power grid with an advanced DR scheduling tool.
publisherThe American Society of Mechanical Engineers (ASME)
titleUsing Cluster Analysis and Dynamic Programming for Demand Response Applied to Electricity Load in Residential Homes
typeJournal Paper
journal volume1
journal issue1
journal titleASME Journal of Engineering for Sustainable Buildings and Cities
identifier doi10.1115/1.4045704
page11006
treeASME Journal of Engineering for Sustainable Buildings and Cities:;2020:;volume( 001 ):;issue: 001
contenttypeFulltext


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