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Energies
Volume 15
Issue 24
10.3390/en15249301
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Article

Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems

by
Soheil Younesi
1,
Bahman Ahmadi
2,
Oguzhan Ceylan
3 and
Aydogan Ozdemir
1,*
1
Department of Electrical Engineering, Istanbul Technical University, 34467 Istanbul, Turkey
2
Department of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands
3
Department of Electrical and Electronics Engineering, Marmara University, 34722 Istanbul, Turkey
*
Author to whom correspondence should be addressed.
Energies 2022, 15(24), 9301; https://doi.org/10.3390/en15249301
Submission received: 4 November 2022 / Revised: 2 December 2022 / Accepted: 6 December 2022 / Published: 8 December 2022
(This article belongs to the Special Issue Modeling and Analysis of Active Distribution Networks and Smart Grids)
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Abstract

The optimum penetration of distributed generations into the distribution grid provides several technical and economic benefits. However, the computational time required to solve the constrained optimization problems increases with the increasing network scale and may be too long for online implementations. This paper presents a parallel solution of a multi-objective distributed generation (DG) allocation and sizing problem to handle a large number of computations. The aim is to find the optimum number of processors in addition to energy loss and DG cost minimization. The proposed formulation is applied to a 33-bus test system, and the results are compared with themselves and with the base case operating conditions using the optimal values and three popular multi-objective optimization metrics. The results show that comparable solutions with high-efficiency values can be obtained up to a certain number of processors.
Keywords: smart grid; DG penetration; parallel computing; loss minimization; multi-objective optimization smart grid; DG penetration; parallel computing; loss minimization; multi-objective optimization

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MDPI and ACS Style

Younesi, S.; Ahmadi, B.; Ceylan, O.; Ozdemir, A. Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems. Energies 2022, 15, 9301. https://doi.org/10.3390/en15249301

AMA Style

Younesi S, Ahmadi B, Ceylan O, Ozdemir A. Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems. Energies. 2022; 15(24):9301. https://doi.org/10.3390/en15249301

Chicago/Turabian Style

Younesi, Soheil, Bahman Ahmadi, Oguzhan Ceylan, and Aydogan Ozdemir. 2022. "Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems" Energies 15, no. 24: 9301. https://doi.org/10.3390/en15249301

APA Style

Younesi, S., Ahmadi, B., Ceylan, O., & Ozdemir, A. (2022). Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems. Energies, 15(24), 9301. https://doi.org/10.3390/en15249301

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