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Sonar Inspired Optimization in Energy Problems Related to Load and Emission Dispatch

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Learning and Intelligent Optimization (LION 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11968))

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Abstract

One of the upcoming categories of Computational Intelligence (CI) is meta-heuristic schemes, which derive their intelligence from strategies that are met in nature, namely Nature Inspired Algorithms. These algorithms are used in various optimization problems because of their ability to cope with multi-objective problems and solve difficult constraint optimization problems. In this work, the performance of Sonar Inspired Optimization (SIO) is tested in a non-smooth, non-convex multi-objective Energy problem, namely the Economic Emissions Load Dispatch (EELD) problem. The research hypothesis was that this new nature-inspired method would provide better solutions because of its mechanisms. The algorithm manages to deal with constraints, namely Valve-point Effect and Multi-fuel Operation, and produces only feasible solutions, which satisfy power demand and operating limits of the system examined. Also, with a lot less number of agents manages to be very competitive against other meta-heuristics, such as hybrid schemes and established nature inspired algorithms. Furthermore, the proposed scheme outperforms several methods derived from literature.

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Notes

  1. 1.

    SOx stands for Sulphur Oxides and NOx for Nitrogen Oxides.

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Author information

Authors and Affiliations

  1. Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, School of Engineering, University of the Aegean, Kountouriotou 41, 82100, Chios, Greece

    Alexandros Tzanetos & Georgios Dounias

Authors
  1. Alexandros Tzanetos
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  2. Georgios Dounias
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Corresponding author

Correspondence to Alexandros Tzanetos .

Editor information

Editors and Affiliations

  1. Technical University of Crete, Chania, Greece

    Nikolaos F. Matsatsinis

  2. Technical University of Crete, Chania, Greece

    Yannis Marinakis

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos Pardalos

Appendix

Appendix

Table A1. System data of units with single-fuel cost coefficients considering VPE and emission coefficients.
Full size table

The loss coefficients matrix values:

$$ \begin{array}{*{20}l} {B_{ij} = \left[ {\begin{array}{*{20}c} {0.000049} & {0.000014} & {0.000015} & {0.000015} & {0.000016} & {0.000017} & {0.000017} & {0.000018} & {0.000019} & {0.000020} \\ {0.000014} & {0.000045} & {0.000016} & {0.000016} & {0.000017} & {0.000015} & {0.000015} & {0.000016} & {0.000018} & {0.000018} \\ {0.000015} & {0.000016} & {0.000039} & {0.000010} & {0.000012} & {0.000012} & {0.000014} & {0.000014} & {0.000016} & {0.000016} \\ {0.000015} & {0.000016} & {0.000010} & {0.000040} & {0.000014} & {0.000010} & {0.000011} & {0.000012} & {0.000014} & {0.000015} \\ {0.000016} & {0.000017} & {0.000012} & {0.000014} & {0.000035} & {0.000011} & {0.000013} & {0.000013} & {0.000015} & {0.000016} \\ {0.000017} & {0.000015} & {0.000012} & {0.000010} & {0.000011} & {0.000036} & {0.000012} & {0.000012} & {0.000014} & {0.000015} \\ {0.000017} & {0.000015} & {0.000014} & {0.000011} & {0.000013} & {0.000012} & {0.000038} & {0.000016} & {0.000016} & {0.000018} \\ {0.000018} & {0.000016} & {0.000014} & {0.000012} & {0.000013} & {0.000012} & {0.000016} & {0.000040} & {0.000015} & {0.000016} \\ {0.000019} & {0.000018} & {0.000016} & {0.000014} & {0.000015} & {0.000014} & {0.000016} & {0.000015} & {0.000042} & {0.000019} \\ {0.000020} & {0.000018} & {0.000016} & {0.000015} & {0.000016} & {0.000015} & {0.000018} & {0.000016} & {0.000019} & {0.000044} \\ \end{array} } \right]} \hfill \\ {B_{oi} = 0} \hfill \\ {B_{oo} = 0} \hfill \\ \end{array} $$
Table A2. System data of units considering multi-fuel cost coefficients with VPE.
Full size table

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Tzanetos, A., Dounias, G. (2020). Sonar Inspired Optimization in Energy Problems Related to Load and Emission Dispatch. In: Matsatsinis, N., Marinakis, Y., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2019. Lecture Notes in Computer Science(), vol 11968. Springer, Cham. https://doi.org/10.1007/978-3-030-38629-0_22

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