Efficient Scheduling of Smart Home Appliances for Energy Management by Cost and PAR Optimization Algorithm in Smart Grid

Shuja, Sahibzada Muhammad; Javaid, Nadeem; Khan, Sajjad; Akmal, Hina; Hanif, Murtaza; Fazalullah, Qazi; Khan, Zafar Hayat

doi:10.1007/978-3-030-15035-8_37

Cited by 10 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the Carbon emission is not taken into account in their study. The authors of [32] implemented the Firefly Algorithm (FA) for energy management under the pricing schemes such as Real-Time Price (RTP) and Critical Peak Price (CPP). However, user comfort is compromised.…”

Section: Stochastic Techniquesmentioning

confidence: 99%

Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids

et al. 2021

View full text Add to dashboard Cite

In smart grid, energy management is an indispensable for reducing energy cost of consumers while maximizing user comfort and alleviating the peak to average ratio and carbon emission under real time pricing approach. In contrast, the emergence of bidirectional communication and power transfer technology enables electric vehicles (EVs) charging/discharging scheduling, load shifting/scheduling, and optimal energy sharing, making the power grid smart. With this motivation, efficient energy management model for a microgrid with ant colony optimization algorithm to systematically schedule load and EVs charging/discharging of is introduced. The smart microgrid is equipped with controllable appliances, photovoltaic panels, wind turbines, electrolyzer, hydrogen tank, and energy storage system. Peak load, peak to average ratio, cost, energy cost, and carbon emission operation of appliances are reduced by the charging/discharging of electric vehicles, and energy storage systems are scheduled using real time pricing tariffs. This work also predicts wind speed and solar irradiation to ensure efficient energy optimization. Simulations are carried out to validate our developed ant colony optimization algorithm-based energy management scheme. The obtained results demonstrate that the developed efficient energy management model can reduce energy cost, alleviate peak to average ratio, and carbon emission.

show abstract

Section: Stochastic Techniquesmentioning

confidence: 99%

Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The non-interruptible appliances are clothes washer and spin dryer while the base appliances include oven, TV, PC, laptop, radio, and coffee maker. Shuja et al [62] classified fifteen appliances into seven shiftable, two non-shiftable, and six fixed appliances. Shiftable appliances include water pump, water heater, vacuum cleaner, dishwasher, steam iron, air conditioner, and refrigerator.…”

Section: Datasets For Appliances Classification and Their Energy Consmentioning

confidence: 99%

“…In [62], a new meta-heuristic algorithm is proposed for scheduling the consumption patterns of home appliances. This study has considered fifteen home appliances for scheduling.…”

Section: Meta-heuristics Algorithmsmentioning

confidence: 99%

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

et al. 2020

View full text Add to dashboard Cite

Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.

show abstract

“…This is the most important issue in the SG that needs special attention. In a smart home, the energy management system controls energy consumption by scheduling residential appliances [21]. In [22], a scheme has been proposed that reduces the cost of electricity consumption and, on the other hand, tries to increase consumer comfort.…”

Section: Problem Statementmentioning

confidence: 99%

A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home

2020

View full text Add to dashboard Cite

In recent years, energy demand has grown significantly relative to its production. The power companies have also offered a variety of schemes such as energy consumption management to meet this growing consumer demand. Energy consumption management is a set of strategies used to optimize energy consumption which includes a set of interconnected activities between the utility and customers to transfer the load from peak hours to off-peak hours. This reduces the electricity bill. This paper presents an optimal schedule for the consumption of residential appliances based on improved multi-objective antlion optimization algorithm to minimize the electrical cost and the user comfort. To prevent peaks, the peak-to-average ratio is considered as a constraint for the energy cost function. Also, two different tariff signals have been used to measure energy costs. The real-time pricing and critical peak pricing are considered as energy tariffs. The simulations results are compared with other meta-heuristic algorithms, including multi-objective particle swarm optimization, the second version of the non-dominated sorting genetic algorithm, and the basic antlion optimizer algorithm to show the superiority of the proposed algorithm. Final results show that using the proposed scheme reaches electricity bills less than 80%.

show abstract

Efficient Scheduling of Smart Home Appliances for Energy Management by Cost and PAR Optimization Algorithm in Smart Grid

Cited by 10 publications

References 19 publications

Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids

Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home

Contact Info

Product

Resources

About