Automation of residential load in power distribution systems with focus on demand response

Deese, Anthony S.; Carrigan, Brian; Stein, Elliot; Klein, Elie

doi:10.1049/iet-gtd.2012.0145

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…In addition, we assume that the PHEV is available from 18:00 pm to next 7:00 am. Parameters of CHP and boiler are set based on the versatile energy resource allocation (VERA) software [16].…”

Section: Simulation Setupmentioning

confidence: 99%

Model predictive control‐based operation management for a residential microgrid with considering forecast uncertainties and demand response strategies

Zhang

Wang

Zhang

et al. 2016

IET Generation, Transmission & Distribution

110

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Section: Simulation Setupmentioning

confidence: 99%

Model predictive control‐based operation management for a residential microgrid with considering forecast uncertainties and demand response strategies

Zhang

Wang

Zhang

et al. 2016

IET Generation, Transmission & Distribution

110

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“…Thermostatically controlled loads (TLCs) in households are examined with the aim of providing regulation services in [23]. The DR participation of single-family houses is also investigated in [24], [25], [26].…”

Section: B Residential Demand Responsementioning

confidence: 99%

Demand response in smart grids: Participants, challenges, and a taxonomy

Hansen

Knudsen

Annaswamy

2014

53rd IEEE Conference on Decision and Control

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In recent decades, moves toward higher integration of Renewable Energy Resources have called for fundamental changes in both the planning and operation of the overall power grid. One such change is the incorporation of Demand Response (DR), the process by which consumers can adjust their demand in a flexible manner. This paper presents a survey of various aspects of DR including the different types of participants, as well as the underlying challenges and the overall potential of DR when it comes to large-scale implementations. Benefits of DR as reported in the literature for performance metrics such as frequency control and price control, as well as methods for ensuring privacy are discussed. A quantitative taxonomy of DR recently proposed in the literature based on the inherent magnitude, run-time, and integral constraints is discussed and its integration with economic dispatch is explored.

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“…For scheduling the residential loads, home energy management systems (HEMSs), based on smart metering and two‐way communication, have been proposed in the literature. The scheduler unit, based on data collected by smart meters, sets the operation interval of each appliance within the scheduling window [7, 8]. The authors in [9] studied the optimal operation of smart buildings using HEMS, considering the availability of plug‐in hybrid electric vehicles (PHEVs) and renewable energy production.…”

Section: Introductionmentioning

confidence: 99%