A new heuristically optimized Home Energy Management controller for smart grid

Javaid, Nadeem; Naseem, Mudassar; Rasheed, Muhammad Babar; Mahmood, Danish; Khan, Shahid A.; Alrajeh, Nabil; Iqbal, Zafar

doi:10.1016/j.scs.2017.06.009

Cited by 104 publications

(36 citation statements)

References 29 publications

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“…Jon the other hand, Avaid et al in [3] presented an electric consumption model that consider houses and devices mount; the kind of devices are interruptible, not interruptible and regular application use. This model is for a demand-side management to reduce peak average to ratio, costs and renewable energy that are considered tariff and time of use.…”

Section: Related Workmentioning

confidence: 99%

“…Not-interruptible devices stop their function once they are finished, their consume could be variable or constant [7,10,13,16,17]. Flexible devices, their functions could be stopped and continue at another time, could be on standby too [3,7,13,17] Not-flexible devices, they could not be turned off because is necessary that they have a constant function [7,13].…”

Section: Consumption and Context Of Usementioning

confidence: 99%

“…The works focused in the search of the improvement of the electric consumption are about devices or control variables [2][3][4][5] buildings consumption [3], houses [6], costs [2,[7][8][9][10] sensor infrastructure [3] and control algorithms [4,10]. Few works focus on the current challenge of developing solutions supported by IoT.…”

Section: Introductionmentioning

confidence: 99%

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Methodology for the Analysis of Electrical Consumption

Perez-Camacho¹,

González-Calleros²,

Olmos-Pineda³

2019

RCS

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A demand system is designed in order to analyze and control the way in which electricity consumption occurs in a scenario, this type of system has four objectives to reduce consumption, reduce costs, reduce peak average to ratio and maximize the comfort of the users. The design of these systems can be based on the implementation of IoT. When using IoT for the development of demand systems, a layer of information is integrated which generates a multiobjective optimization problem. Metaheuristics are used to solve multiobjective problems. This paper introduces a proposed methodology to develop demand systems and the first products implemented.

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Section: Related Workmentioning

confidence: 99%

Section: Consumption and Context Of Usementioning

confidence: 99%

See 1 more Smart Citation

Methodology for the Analysis of Electrical Consumption

Perez-Camacho¹,

González-Calleros²,

Olmos-Pineda³

2019

RCS

View full text Add to dashboard Cite

show abstract

“…However, the authors achieved these objectives at the cost of PAR. Heuristic-based home EMC for residential load scheduling is proposed in [22]. Authors formulate load scheduling using multiple knapsack, and use RTP as a reference signal for cost calculation.…”

Section: Related Workmentioning

confidence: 99%

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

et al. 2018

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Abstract:With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

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“…In [10], three optimization algorithms, EDE, binary particle swarm optimization (BPSO) and GA, were implemented to achieve cost minimization and load shifting, while carbon emission was reduced by integrating RES. DHP is used as a pricing tariff.…”

Section: Related Workmentioning

confidence: 99%

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

et al. 2018

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With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30-and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

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A new heuristically optimized Home Energy Management controller for smart grid

Cited by 104 publications

References 29 publications

Methodology for the Analysis of Electrical Consumption

Methodology for the Analysis of Electrical Consumption

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

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