Common failures of demand response

By Jin-ho Kim, Anastasia Shcherbakova. 2011

336 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/874427de9925799acd74d6e7e42234430c6b1596

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In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Iot-based smart cities: A survey (2016, background): “6, such barriers are classified into three main sets, namely; consumers’ barriers, producers’ barriers, and structural barriers that are completely investigated in [57].”
Challenges and barriers to demand response deployment and evaluation (2015, background): “Historically, demand shedding was used for emergency contingency response and, in more recent times, DR programs have been targeted at large industrial energy users [2].”
A Review of Smart Cities Based on the Internet of Things Concept (2017, background): “In big It is clear that the IoT substructures would be some of these signiﬁcant sources of big data. data problems, three main specifications are highlighted, consisting of the number, speed as In big data problems, three main speciﬁcations are highlighted, consisting of the number, speed as well…”
Benefits and challenges of electrical demand response: A critical review (2014, background): “A comparison between buying a new car and paying for electricity is made by Kim and Shcherbakova [56].”
Microgrid to enable optimal distributed energy retail and end-user demand response (2018, background): “Importantly, it allows consumers sufficient time to schedule their consumption, while not being ‘‘fatigued” by hourly rate changes [54].”
Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting (2012, methodology): “First is that can we forecast energy load using the existing short term load forecasting model? Second question is that is the knowledge used for existing forecasting models sufficient? Kim and Shcherbakova point out at the lack of data about user as one of the major reason failure for DSM and DR pr”
On demand: Can demand response live up to expectations in managing electricity systems? (2019, background): “For example, [14] suggest that consumers”
A systematic review of the smart home literature: A user perspective (2019, background): “For instance, a study examining the perception of smart meters indicated that people are used to traditional flat electricity rates and that there is a lack of knowledge regarding the benefits that smart technologies could create [89].”