Wind Driven Optimization With Smart Home Battery for Power Scheduling Problem in Smart Home

The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.

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A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home

Swarm and Evolutionary Computation ◽

10.1016/j.swevo.2020.100793 ◽

2021 ◽

Vol 60 ◽

pp. 100793

Author(s):

Sharif Naser Makhadmeh ◽

Ahamad Tajudin Khader ◽

Mohammed Azmi Al-Betar ◽

Syibrah Naim ◽

Ammar Kamal Abasi ◽

...

Keyword(s):

Smart Home ◽

Grey Wolf Optimizer ◽

Scheduling Problem ◽

Grey Wolf ◽

Power Scheduling

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The wind power scheduling problem

OPSEARCH ◽

10.1007/s12597-021-00510-y ◽

2021 ◽

Author(s):

T. R. Lalita ◽

G. S. R. Murthy

Keyword(s):

Wind Power ◽

Scheduling Problem ◽

Power Scheduling

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Optimization methods for power scheduling problems in smart home: Survey

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2019.109362 ◽

2019 ◽

Vol 115 ◽

pp. 109362 ◽

Cited By ~ 16

Author(s):

Sharif Naser Makhadmeh ◽

Ahamad Tajudin Khader ◽

Mohammed Azmi Al-Betar ◽

Syibrah Naim ◽

Ammar Kamal Abasi ◽

...

Keyword(s):

Smart Home ◽

Optimization Methods ◽

Scheduling Problems ◽

Power Scheduling

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Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i5.pp2349-2356 ◽

2017 ◽

Vol 7 (5) ◽

pp. 2349 ◽

Cited By ~ 7

Author(s):

Surender Reddy Salkuti

Keyword(s):

Power System ◽

Reactive Power ◽

Search Algorithm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Optimization Techniques ◽

Mixed Integer ◽

Integer Programming Problem ◽

Scheduling Problem ◽

Power Scheduling

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

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Short-term Hydro-Thermal-Wind-Solar Power Scheduling: A Case Study of Kanyakumari Region of India

International Journal of Renewable Energy Development ◽

10.14710/ijred.0.35558 ◽

2021 ◽

Author(s):

Sunimerjit Kaur ◽

Yadwinder Singh Brar ◽

Jaspreet Singh Dhillon

Keyword(s):

Solar System ◽

Power System ◽

Simplex Method ◽

Solar Power ◽

Test System ◽

Scheduling Problem ◽

Fuel Cost ◽

Thermal Wind ◽

Power Scheduling

In this paper, a multi-objective hydro-thermal-wind-solar power scheduling problem is established and optimized for the Kanyakumari (Tamil Nadu, India) for the 18th of September of 2020. Four contrary constraints are contemplated for this case study (i) fuel cost and employing cost of wind and solar power system, (ii) NOx emission, (iii) SO2 emission, and (iv) CO2 emission. An advanced hybrid simplex method named as-the -constrained simplex method (ACSM) is deployed to solve the offered problem. To formulate this technique three amendments in the usual simplex method (SM) are adopted (i) -level differentiation, (ii) mutations of the worst point, and (iii) the incorporation of multi-simplexes. The fidelity of the projected practice is trailed upon two test systems. The first test system is hinged upon twenty-four-hour power scheduling of a pure thermal power system. The values of total fuel cost and emissions (NOx, SO2, CO2) are attained as 346117.20 Rs, 59325.23 kg, 207672.70 kg, and 561369.20 kg, respectively. In the second test system, two thermal generators are reintegrated with renewable energy resources (RER) based power systems (hydro, wind, and solar system) for the same power demands. The hydro, wind, and solar data are probed with the Glimn-Kirchmayer model, Weibull Distribution Density Factor, and Normal Distribution model, respectively. For this real-time hydro-thermal-wind-solar power scheduling problem the values of fuel cost and emissions (Nox, SO2, CO2) are shortened to 119589.00 Rs, 24262.24 kg, 71753.80 kg, and 196748.20 kg, respectively for the specified interval. The outturns using ACSM are contrasted with the SM and evolutionary method (EM). The values of the operating cost of solar system, wind system, total system transmission losses, and computational time of test system-2 with ACSM, SM, and EM are evaluated as 620497.40 Rs, 1398340.00 Rs, 476.6948 MW & 15.6 seconds; 620559.45 Rs, 1398479.80 Rs, 476.7425 MW & 16.8 seconds; and 621117.68 Rs, 1399737.80 Rs, 477.1715 MW and 17.3 seconds, respectively. The solutions portray the sovereignty of ACSM over the other two methods in the entire process.

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