A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

Mostafa Rezaeimozafar, Mohsen Eskandari, Mohammad Hadi Amini, Mohammad Hasan Moradi and Pierluigi Siano
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Mostafa Rezaeimozafar: Department of Electrical Engineering, Hamedan Branch, Islamic Azad University, Hamedan 65181-15743, Iran
Mohsen Eskandari: Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, Australia
Mohammad Hadi Amini: School of Computing and Information Science, College of Engineering and Computing, Florida International University, Miami, FL 33199, USA
Mohammad Hasan Moradi: Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan 65167-38695, Iran
Pierluigi Siano: Department of Management and Innovation Systems, University of Salerno, 84084 Fisciano (SA), Italy

Energies, 2020, vol. 13, issue 7, 1-25

Abstract: The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.

Keywords: microgrid; renewable energy; electric vehicle; energy storage; demand response (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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