Residential energy demand management in smart grids

Oviedo, Raul J. Martinez; Fan, Zhong; Görmüş, Sedat; Kulkarni, Parag; Kaleshi, Dritan

doi:10.1109/tdc.2012.6281573

Cited by 20 publications

(20 citation statements)

References 8 publications

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“…Their model only considers solar energy source. A similar work is reported in [11], in which an enhanced model of a distribution system is adopted to include additional renewable sources. Also, the authors evaluate the effect failures in the communication infrastructure have on the survivability of the distribution system.…”

Section: Related Workmentioning

confidence: 97%

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A residential PHEV load coordination mechanism with renewable sources in smart grids

Oviedo

Fan

Görmüş

et al. 2014

International Journal of Electrical Power & Energy Systems

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A high penetration of Plug-in Hybrid Electric Vehicles (PHEVs) can lead to stress and overload in distribution systems when uncoordinated charging is considered. Thus, coordination mechanisms to manage PHEV charging during peak periods are needed. This paper evaluates the impact of PHEVs in the residential distribution grid and proposes a coordination mechanism based on heuristic rules as a load shaping tool. The impact of the mechanism on user convenience is also studied, and the benefits of having a renewable generation system is examined. Results show that our coordination mechanism is able to prevent the overloading of the distribution transformer. Further, it has been found that the performance of the renewable system (e.g., renewable fraction and renewable cost ratio) depend on many factors such as weather, region, equipment specification, and user preference.

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Section: Related Workmentioning

confidence: 97%

“…Communication links are assumed to work ideally without disruption or failures. Communications requirements of the coordination mechanism, including the impact of failures, are addressed in our previous work [11].…”

Section: System Studiedmentioning

confidence: 99%

A residential PHEV load coordination mechanism with renewable sources in smart grids

Oviedo

Fan

Görmüş

et al. 2014

International Journal of Electrical Power & Energy Systems

Self Cite

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“…We also define the vector and domain of variables for all elastic appliances as x E = (x a ) a∈A E and X E = {x E | x a ∈ X a , ∀a ∈ A E }, respectively, and those of variables for all inelastic appliances as x IE = (x a ) a∈A IE and X IE = {x IE | x a ∈ X a , ∀a ∈ A IE }, respectively. In addition, Algorithm 1: GBD Procedure to Solve the Problem in (28) Input : X a , ∀a ∈ A and E t th , ∀t ∈ T Output: x a , ∀a ∈ A 1 Initialization: p ← 0, q ← 0, UBD ← ∞, LBD ← −∞; 2 Select the parameter γ > 0 andx IE ∈ X IE ; 3 Step (1) 4 Solve problem SP(x IE ) in (31) for givenx IE ; 5 if problem SP(x IE ) is feasible then 6 if v(x IE ) ≥ UBD − γ then obtain optimal solutionx E and terminate; 7 else 8 Obtain optimal multiplier vectorμ and function L * (x IE ;μ); 9 p ← p + 1 and μ p ←μ;…”

Section: B Generalized Benders Decompositionmentioning

confidence: 99%

Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid

Roh

Lee

2016

IEEE Trans. Smart Grid

139

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In this paper, we study an electricity load scheduling problem in a residence. Compared with previous works in which only limited sets of appliances are considered, we classify various appliances into five sets considering their different energy consumption and operation characteristics, and provide mathematical models for them. With these appliance models, we propose an electricity load scheduling algorithm that controls the operation time and energy consumption level of each appliance adapting to time-of-use pricing in order to maximize the overall net utility of the residence while satisfying its budget limit. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is in general, difficult to solve. In order to solve the problem, we use the generalized Benders decomposition approach with which we can solve the MINLP problem easily with low computational complexity. By solving the problem, we provide an algorithm to obtain the optimal electricity load scheduling of various appliances with different energy consumption and operation characteristics in a unified way. Index Terms-Demand response (DR), electricity load scheduling, generalized Benders decomposition (GBD), multiclass appliances, smart grid, time-of-use (TOU) pricing. NOMENCLATURE Indices aIndex for appliance. tIndex for sub-interval. SetsA Set of electric appliances. A E Set of elastic appliances. A EML Set of elastic appliances with a memoryless property. A EFM Set of elastic appliances with a full memory property. A EPM Set of elastic appliances with a partial memory property. . Since 2005, he has been with the School of Electrical and Electronic Engineering, Yonsei University, where he is currently an Associate Professor. His current research interests include resource allocation, quality of service and pricing issues, optimization, and performance analysis in communication networks and smart grid.

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“…An example residential smart grid consists of 4 to 7 houses served by a single transformer that steps down grid power to domestic voltage. Each house also has photovoltaic cells and a wind turbine [18]. Additionally, each house may have a plug-in electric hybrid vehicle (PHEV) which has a battery which is used both to power the vehicle and to store excess renewable energy.…”

Section: Smart Gridsmentioning

confidence: 99%

Quantitative Modelling of Residential Smart Grids

Galpin

2015

Software Engineering and Formal Methods

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Generation of electricity has traditionally taken place at a small number of power stations but with advances in generating technology, small-scale generation of energy from wind and sun is now possible at individual buildings. Additionally, the integration of information technology into the generation and consumption process provides the notion of smart grid. Formal modelling of these systems allows for an understanding of their dynamic behaviour without building or interacting with actual systems. This paper reports on using a quantitative process algebra HYPE to model a residential smart grid (microgrid) for a spatiallyextensive suburb of houses where energy is generated by wind power at each house and where excess energy can be shared with neighbours and between neighbourhoods. Both demand and wind availability are modelled stochastically, and the goal of the modelling is to understand the behaviour of the system under different redistribution policies that use local knowledge with spatial heterogeneity in wind availability.

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Residential energy demand management in smart grids

Cited by 20 publications

References 8 publications

A residential PHEV load coordination mechanism with renewable sources in smart grids

A residential PHEV load coordination mechanism with renewable sources in smart grids

Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid

Quantitative Modelling of Residential Smart Grids

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