TY - JOUR
T1 - Multi-Objective Optimal Planning of Virtual Synchronous Generators in Microgrids with Integrated Renewable Energy Sources
AU - Abid, Md Shadman
AU - Ahshan, Razzaqul
AU - Al-Abri, Rashid
AU - Al-Badi, Abdullah
AU - Albadi, Mohammed
N1 - Funding Information:
The authors acknowledge the funding support provided by Babol Noshirvani University of Technology through Grant programs No. BNUT/370123/00. Authors also aknowledged the editor and anynomous reviewers for their constructive comments.
Publisher Copyright:
© 2013 IEEE.
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DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Appropriate renewable distributed generation (RDG) placement is one of the most significant issues for the efficient operation of current power systems. Since the inverter-interfaced RDG lacks rotating mass to sustain the system's inertia, microgrids have low total system inertia, which impairs frequency stability and can yield significant frequency and voltage instability in severe disruptions. The virtual synchronous generator (VSG), which uses concepts that regulate the inverter to simulate a conventional synchronous generator, is one of the most promising solutions to address these challenges. Hence, this research proposes a unique technique of simultaneous optimal solution for RDG and VSG sizing and placement in distribution networks using a recent metaheuristic technique called the Multi-objective Salp Swarm Optimization Algorithm (MOSSA). The objective function was to minimize the frequency deviation and maximize the total annual energy savings and operational costs of the RDG and VSG units. This study assesses IEEE 33 bus, 69 bus distribution network, and practical Masirah network as the test systems. Moreover, the MOSSA Pareto fronts are superior to two recent metaheuristics employed in this research domain: Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The results demonstrate that the MOSSA Pareto fronts satisfied the frequency and energy-saving objectives. In addition, all Pareto fronts accurately prevented voltage limit infringements, and the overall energy losses were significantly reduced.
AB - Appropriate renewable distributed generation (RDG) placement is one of the most significant issues for the efficient operation of current power systems. Since the inverter-interfaced RDG lacks rotating mass to sustain the system's inertia, microgrids have low total system inertia, which impairs frequency stability and can yield significant frequency and voltage instability in severe disruptions. The virtual synchronous generator (VSG), which uses concepts that regulate the inverter to simulate a conventional synchronous generator, is one of the most promising solutions to address these challenges. Hence, this research proposes a unique technique of simultaneous optimal solution for RDG and VSG sizing and placement in distribution networks using a recent metaheuristic technique called the Multi-objective Salp Swarm Optimization Algorithm (MOSSA). The objective function was to minimize the frequency deviation and maximize the total annual energy savings and operational costs of the RDG and VSG units. This study assesses IEEE 33 bus, 69 bus distribution network, and practical Masirah network as the test systems. Moreover, the MOSSA Pareto fronts are superior to two recent metaheuristics employed in this research domain: Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The results demonstrate that the MOSSA Pareto fronts satisfied the frequency and energy-saving objectives. In addition, all Pareto fronts accurately prevented voltage limit infringements, and the overall energy losses were significantly reduced.
KW - Virtual synchronous generator
KW - distributed generation
KW - multi-objective algorithm
KW - optimization
KW - renewable energy
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U2 - 10.1109/ACCESS.2023.3289813
DO - 10.1109/ACCESS.2023.3289813
M3 - Article
AN - SCOPUS:85163450029
SN - 2169-3536
VL - 11
SP - 65443
EP - 65456
JO - IEEE Access
JF - IEEE Access
ER -
