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Optimal Unit Commitment for Thermal Power Systems Using Penguin Search Algorithm

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Advances in Decision Sciences, Image Processing, Security and Computer Vision (ICETE 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 3))

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Abstract

Electricity networks day to day delivers hundreds of giga watts per hour (GWH) to consumers. Those interconnected systems became hard enough challenging to maintain and run in the operating mode. So need arises to generate and supply electricity in a smart methodology which is expected to be highly economical. Unit Commitment, an important economic problem through optimization to be solved for obtaining an optimal cost saving in a specific period of time which is purely based on the determination of the combination of available generators. An electrical industry which deals mainly with meeting the load demand for all the generators. A generating unit has various limitations such as minimum uptime, minimum downtime, minimum and maximum power generation limits, prohibited zones etc. This research implementation paper presents a general idea of scheduling the generating units using the Bio-inspired algorithm PeSOA - Penguin Search Optimization Algorithm. PeSOA is a latest metaheuristic method through penguins collaborative hunting strategies, specially designed for optimizing non-linear systems. Significance of PeSOA is penguins Distribution is balanced through local minima and global minimum. Implementation carried out for various types of systems include problem instances of 10 units - 24 h system, 3 units – 24 h system, 4 units - 8 h system and 4 units – 24 h system, each of all the cases has compared with the results from the literature.

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Correspondence to C. Chitra .

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Chitra, C., Amudha, T. (2020). Optimal Unit Commitment for Thermal Power Systems Using Penguin Search Algorithm. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_69

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