Intelligent Residential Energy Management in Smart Grid

Ranjini, A.; Zoraida, B.S.E.

doi:10.17485/ijst/2016/v9i45/91453

Cited by 8 publications

(4 citation statements)

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“…In [10], Author considered different aspects of appliances including time duration, the priority of the appliance, and resource availability. In [11], Bacterial Foraging Optimization Algorithm (BFOA), Wind Driven Optimization (WDO), Genetic Wind-Driven (GWD), BPSO, and GA were used to manage home energy management systems by scheduling smart appliances.…”

Section: Related Workmentioning

confidence: 99%

“…RTP pricing schemes were used by [6,8,10,11]. GA algorithm was used with RTP in [6,10] with different purposes.…”

Section: Related Workmentioning

confidence: 99%

“…In [8], RTP was used with HSA and HDE to achieve the objective of minimizing total startup and generation costs. In [11], the RTP pricing scheme was used with BFOA, GWD, BPSO, and GA, to retain the UC, PAR reduction and decrease in electricity cost.…”

Section: Related Workmentioning

confidence: 99%

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Smart Grid Energy Optimization and Scheduling Appliances Priority for Residential Buildings through Meta-Heuristic Hybrid Approaches

et al. 2022

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Smart grid technology has given users the ability to regulate their home energy use more efficiently and effectively. Home Energy Management (HEM) is a difficult undertaking in this regard, as it necessitates the optimal scheduling of smart appliances to reduce energy usage. In this research, we introduce a metaheuristic-based HEM system which incorporates Earth Worm Algorithm (EWA) and Harmony Search Algorithms (HSA). In addition, a hybridization based on the EWA and HSA operators is used to optimize energy consumption in terms of electricity cost and Peak-to-Average Ratio (PAR) reduction. Hybridization has been demonstrated to be beneficial in achieving many objectives at the same time. Extensive simulations in MATLAB were used to test the performance of the proposed hybrid technique. The simulations were run for multiple homes with multiple appliances, which were categorized according to the usage and nature of the appliance, taking advantage of appliance scheduling in terms of the time-varying retail pricing enabled by the smart grid two-way communication infrastructure algorithms EWA and HSA, along with a Real-Time Price scheme. These techniques helped us to find the best usage pattern for energy consumption to reduce electricity costs. These metaheuristic techniques efficiently reduced and shifted the load from peak hours to off-peak hours and reduced electricity costs. In comparison to HSA, the simulation results suggest that EWA performed better in terms of cost reduction. In comparison to EWA and HSA, HSA was more efficient in terms of PAR. However, the proposed hybrid approach EHSA gave the maximum reduction in cost which was 2.668%, 2.247%, and 2.535% in the case of 10, 30, and 50 homes, respectively, as compared to EWA and HSA.

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Section: Related Workmentioning

confidence: 99%

“…RTP pricing schemes were used by [6,8,10,11]. GA algorithm was used with RTP in [6,10] with different purposes.…”

Section: Related Workmentioning

confidence: 99%

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Smart Grid Energy Optimization and Scheduling Appliances Priority for Residential Buildings through Meta-Heuristic Hybrid Approaches

et al. 2022

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“…The total EB is calculated with the time of use pricing mechanism in the scheduling tasks for the executions of smart home appliances for a day. The author [37] presents a home energy management system to deficiently control home appliances. The objective of the system reduces EB while peak periods.…”

Section: Related Workmentioning

confidence: 99%

Environment Optimization Scheme Based on Edge Computing Using PSO for Efficient Thermal Comfort Control in Resident Space

Jin

Kim

2021

Actuators

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With the fast development of infrastructure and communication technology, the Internet of Things (IoT) has become a promising field. Ongoing research is looking at the smart home environment as the most promising sector that adopts IoT and cloud computing to improve resident live experiences. The IoT and cloud-dependent smart home services related to recent researches have security, bandwidth issues, and a lack of concerning thermal comfort of residents. In this paper, we propose an environment optimization scheme based on edge computing using Particle Swarm Optimization (PSO) for efficient thermal comfort control in resident space to overcome the aforementioned limitations of researches on smart homes. The comfort level of a resident in a smart home is evaluated by Predicted Mean Vote (PMV) that represents the thermal response of occupants. The PSO algorithm combined with PMV to improve the accuracy of the optimization results for efficient thermal comfort control in a smart home environment. We integrate IoT with edge computing to upgrade the capabilities of IoT nodes in computing power, storage space, and reliable connectivity. We use EdgeX as an edge computing platform to develop a thermal comfort considering PMV-based optimization engine with a PSO algorithm to generate the resident’s friendly environment parameters and rules engine to detects the environmental change of the smart home in real-time to maintain the indoor environment thermal comfortable. For evaluating our proposed system that maintenance resident environment with thermal comfort index based on PSO optimization scheme in smart homes, we conduct the comparison between the real data with optimized data, and measure the execution times of optimization function. From the experimental results, when our proposed system is applied, it satisfies thermal comfort and consumes energy more stably.

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Energy Optimization in Smart Grid Using Grey Wolf Optimization Algorithm and Bacterial Foraging Algorithm

Charaf

Khan

Ghafar

et al. 2017

Lecture Notes on Data Engineering and Communications Technologies

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Intelligent Residential Energy Management in Smart Grid

Cited by 8 publications

References 0 publications

Smart Grid Energy Optimization and Scheduling Appliances Priority for Residential Buildings through Meta-Heuristic Hybrid Approaches

Smart Grid Energy Optimization and Scheduling Appliances Priority for Residential Buildings through Meta-Heuristic Hybrid Approaches

Environment Optimization Scheme Based on Edge Computing Using PSO for Efficient Thermal Comfort Control in Resident Space

Energy Optimization in Smart Grid Using Grey Wolf Optimization Algorithm and Bacterial Foraging Algorithm

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