Smart Home Energy Management Including Renewable Sources: A QoE-driven Approach

Pilloni, Virginia; Floris, Alessandro; Meloni, Alessio; Atzori, Luigi

doi:10.1109/tsg.2016.2605182

Cited by 84 publications

(64 citation statements)

References 37 publications

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“…AMI provides various interfaces to interact with both users and service providers by using ICT technologies. Figure 4 shows the typical architecture of an AMI [119,122] able to integrate SMs with other objects inside a smart home, such as energy consuming appliances and RES power generation [123]. These objects are connected within the home area network (HAN).…”

Section: Communication Technologies For Smart Distribution Networkmentioning

confidence: 99%

Multidisciplinary Approach for the Development of Smart Distribution Networks

Ghiani¹,

Serpi²,

Pilloni³

et al. 2018

Preprint

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Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such evolution is pushing for a paradigm shift: the management must move from traditional planning and manual intervention to full "smartization" of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system that includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks.

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Section: Communication Technologies For Smart Distribution Networkmentioning

confidence: 99%

Multidisciplinary Approach for the Development of Smart Distribution Networks

Ghiani¹,

Serpi²,

Pilloni³

et al. 2018

Preprint

Self Cite

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“…Authors of [29,30] have not considered time-shiftable appliances in their work. Quality of experience (QoE) aware smart HEMS proposed in [31], relies on annoyance suffered by users due to the change with respect to user preferences. Smart HEMS relies on two algorithm: QoE aware cost saving appliance scheduling (Q-CSAS) and QoE aware renewable source power allocation (Q-RSPA).…”

Section: Related Workmentioning

confidence: 99%

“…The Former handles the scheduling of controllable load, based on users' profile and electricity prices, whereas the latter changes the schedules according to availability of the renewable sources. Major contribution of [30,31] is to jointly optimize DER and main grid power usage by considering uncertainties caused by DER and cost variations of the power grid. However, the model in [31] is based on greedy approach which considers immediate solution, ignoring the long-term benefits.…”

Section: Related Workmentioning

confidence: 99%

“…Major contribution of [30,31] is to jointly optimize DER and main grid power usage by considering uncertainties caused by DER and cost variations of the power grid. However, the model in [31] is based on greedy approach which considers immediate solution, ignoring the long-term benefits. Therefore, the greedy algorithm does not always provide optimal solution.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes

et al. 2017

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Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers' bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.

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“…[5] proposed different scheduling strategies. From the perspective of demand-side multi-user energy coordination, the multi-user energy dispatching method is issued in paper [6], with the goal of reducing electricity consumption costs in the region. From the perspective of user experience, Ref.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Optimization Model for Smart House Daily Scheduling Considering User Perceived Benefits

Deng¹,

Zhang²,

Yang³

et al. 2018

Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)

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Abstract-Smart house scheduling is the grid terminal demand response based on the reference signal, the electricity market price. In this paper, the user consumption perceived benefits and the system running cost were considered. In the intelligent house system with energy storage device, the load control model and the energy storage control model were established, and the two-stage optimization scheduling was carried out for the smart house appliances to get rid of the disturbance of load uncertainty. The first stage took the flexible load as the control object, and the genetic algorithm was proposed to provide a schedule for smart home appliances. The second stage considered the energy storage device as the control object and a particle swarm algorithm was used to generate a charge/discharge rates schedule for the battery. The optimal solution of the first stage optimization control participated in the second stage optimization control in the form of the load curve. The fitness value of the optimal solution of the load control stage was taken as the minimum objective function constraint of the energy storage control stage, thus further reducing the electricity cost of the terminal-user. The simulation example in MATLAB verifies the effectiveness of models.

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Smart Home Energy Management Including Renewable Sources: A QoE-driven Approach

Cited by 84 publications

References 37 publications

Multidisciplinary Approach for the Development of Smart Distribution Networks

Multidisciplinary Approach for the Development of Smart Distribution Networks

An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes

Two-Stage Optimization Model for Smart House Daily Scheduling Considering User Perceived Benefits

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