TY - JOUR
T1 - Optimal multi-operation energy management in smart microgrids in the presence of ress based on multi-objective improved de algorithm
T2 - Cost-emission based optimization
AU - Ghiasi, Mohammad
AU - Niknam, Taher
AU - Dehghani, Moslem
AU - Siano, Pierluigi
AU - Alhelou, Hassan Haes
AU - Al-Hinai, Amer
N1 - Funding Information:
Funding: The work of Hassan Haes Alhelou was supported in part by the Science Foundation Ireland (SFI) through the SFI Strategic Partnership Programme under Grant SFI/15/SPP/E3125, and in part by the UCD Energy Institute. The opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Science Foundation Ireland. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The work of Amer Al-Hinai was supported by SQU under grant SR/ENG/ECED/17/1.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/4/19
Y1 - 2021/4/19
N2 - Today, in various leading power utilities in developing countries, achieving optimal operational energy management and planning, taking into account the costs reduction of generation, transmission and distribution of electricity, and also reducing the emission of an environmental pollutant becomes more and more important. Optimal use of renewable energy sources (RESs) is an effective way to achieve these goals. In this regard, in this research article, an improved multi-objective differential evolutionary (IMODE) optimization algorithm is suggested and performed to dispatch electricity generations in a smart microgrid (MG) system, taking into account economy and emission as competitive issues. In this paper, a nonlinear equation of multi-objective optimization issue with various equality and inequality limitations is formulated in order to lower the total operational costs of the MG considering environmental pollution effects simultaneously. In order to address the issue of optimal operation of the MG in single-objective and multi-objective forms, an intelligent method according to the improved differential evolutionary (IDE) optimization is utilized and performed and the proposed algorithm is implemented on different problems. First, it is assumed that there is no limit to the exchange of power overhead, and secondly, the limitation of power exchange with the upstream grid is considered. In multi-objective mode, these two modes are also considered. In order to show the impact of renewable energy on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources. In the final section, the sensitivity analysis on the number of populations in this problem is performed. The obtained results of the simulation are compared to differential evolutionary (DE) and particle swarm optimization (PSO) techniques. The effectiveness of the suggested multi-operational energy management method is confirmed by applying a study case system.
AB - Today, in various leading power utilities in developing countries, achieving optimal operational energy management and planning, taking into account the costs reduction of generation, transmission and distribution of electricity, and also reducing the emission of an environmental pollutant becomes more and more important. Optimal use of renewable energy sources (RESs) is an effective way to achieve these goals. In this regard, in this research article, an improved multi-objective differential evolutionary (IMODE) optimization algorithm is suggested and performed to dispatch electricity generations in a smart microgrid (MG) system, taking into account economy and emission as competitive issues. In this paper, a nonlinear equation of multi-objective optimization issue with various equality and inequality limitations is formulated in order to lower the total operational costs of the MG considering environmental pollution effects simultaneously. In order to address the issue of optimal operation of the MG in single-objective and multi-objective forms, an intelligent method according to the improved differential evolutionary (IDE) optimization is utilized and performed and the proposed algorithm is implemented on different problems. First, it is assumed that there is no limit to the exchange of power overhead, and secondly, the limitation of power exchange with the upstream grid is considered. In multi-objective mode, these two modes are also considered. In order to show the impact of renewable energy on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources. In the final section, the sensitivity analysis on the number of populations in this problem is performed. The obtained results of the simulation are compared to differential evolutionary (DE) and particle swarm optimization (PSO) techniques. The effectiveness of the suggested multi-operational energy management method is confirmed by applying a study case system.
KW - Cost effective
KW - Gas emission
KW - IDE
KW - Multi-operational energy management
KW - Optimization
KW - RES
KW - Smart microgrid
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U2 - 10.3390/app11083661
DO - 10.3390/app11083661
M3 - Article
AN - SCOPUS:85104987214
SN - 2076-3417
VL - 11
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 8
M1 - 3661
ER -