Optimal scheduling of uncertain wind energy and demand response in unit commitment using binary grey wolf optimizer (BGWO)

The augment of ecological shield and the progressive exhaustion of traditional fossil energy sources have increased the interests in integrating renewable energy sources into existing power system. Wind power is becoming worldwide a significant component of the power generation portfolio. Profuse literature have been reported for the thermal Unit Commitment (UC) solution. In this work, the UC problem has been formulated by integrating wind power generators along with thermal power system. The Wind Generator Integrated UC (WGIUC) problem is more complex in nature, that necessitates a promising optimization tool. Hence, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm has been chosen as the main optimization tool and real coded scheme has been incorporated to handle the operational constraints. The standard test systems are used to validate the potential of the GWO algorithm. Moreover, the ramp rate limits are also included in the mathematical WGIUC formulation. The simulation results prove that the intended algorithm has the capability of obtaining economical resolutions with good solution quality.

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Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems

Applied Soft Computing ◽

10.1016/j.asoc.2018.05.006 ◽

2018 ◽

Vol 69 ◽

pp. 504-515 ◽

Cited By ~ 37

Author(s):

Mohammed H. Qais ◽

Hany M. Hasanien ◽

Saad Alghuwainem

Keyword(s):

Wind Energy ◽

Energy Conversion ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Wind Energy Conversion ◽

Wind Energy Conversion Systems ◽

Energy Conversion Systems

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Incentive-based demand response in grid-connected microgrid using quasi-opposed grey wolf optimizer

International Journal of Engineering Science and Technology ◽

10.4314/ijest.v13i2.1 ◽

2021 ◽

Vol 13 (2) ◽

pp. 1-14

Author(s):

Salil Madhav Dubey ◽

Hari Mohan Dubey ◽

Manjaree Pandit

Keyword(s):

Demand Response ◽

Research Field ◽

Grey Wolf Optimizer ◽

Demand Side ◽

Paradigm Shifts ◽

Solar Pv ◽

Grey Wolf ◽

Power Performance ◽

Portable Power ◽

Search Capability

The paradigm shifts in the electrical industry from demand-driven generation to supply-driven generation due to the incorporation of renewable generating sources is a growing research field. Implementing demand response in present-day distribution schemes is anattractive approach often adopted by microgrid (MiG) operator.This paper incorporates an incentivebased demand response (IBDR) method in a grid-connected microgrid (MiG) comprising of conventional generators (CGs), wind turbines (WTs), and solar PV units. The main aim is to collectively minimize the fossil fuel cost of CGs, lower the transaction cost of portable power from the grid, and maximize theMiG operator's profitafter implementing demand response. This multi-objective problem combining optimal economic load dispatch of MiG with an efficient demand-side response is solved using a proposed Quasi-opposed Grey Wolf Optimizer (QOGWO) algorithm. The effect of the proposed algorithm on demand-side management (DSM) is analyzed for two cases, (i) varying the value of power interruptibility (ii) varying the maximum limit of curtained power. Performance of QOGWO is compared with original GWO and a variant of GWO, Intelligent Grey Wolf Optimizer (IGWO). Results show the superior global search capability and complex constrained handling capability of QOGWO.

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hGWO-SA: A Novel Hybrid Grey Wolf Optimizer-Simulated Annealing algorithm for Engineering and Power System Optimization Problems

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200721010231 ◽

2020 ◽

Vol 13 ◽

Author(s):

Vikram Kumar Kamboj

Keyword(s):

Simulated Annealing ◽

Power System ◽

Simulated Annealing Algorithm ◽

Unit Commitment ◽

System Optimization ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Unit Commitment Problem ◽

Commitment Problem ◽

Annealing Algorithm

: The improved variants of Grey wolf optimizer has good exploration capability for global optimum solution. However, the exploitation competence of the existing variants of grey wolf optimizer is unfortunate. Researchers are continuously trying to improve the exploitation phase of the existing grey wolf optimizer, but still the improved variants of grey wolf optimizer are lacking in local search capability. In the proposed research, the exploitation phase of the existing grey wolf optimizer has been further improved using simulated annealing algorithm and the proposed hybrid optimizer has been named as hGWO-SA algorithm. The effectiveness of the proposed hybrid variant has been tested for various benchmark problems including multi-disciplinary optimization and design engineering problems and unit commitment problem of electric power system and it has been experimentally found that the proposed optimizer performs much better than existing variants of grey wolf optimizer. The feasibility of hGWO-SA algorithm has been tested for small & medium scale power systems unit commitment problem. In which, the results for 4 unit, 5 unit, 6 unit, 7 unit, 10 units, 19 unit, 20 unit, 40 unit and 60 units are evaluated. The 10-generating units are evaluated with 5% and 10% spinning reserve. The results obviously show that the suggested method gives the superior type of solutions as compared to other algorithms.

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