Multi-objective Optimal Planning of Virtual Synchronous Generators in Microgrids with Integrated Renewable Energy Sources

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Abid, Md. Shadman, Ahshan, Razzaqul, Abri, Rashid Al, Al-Badi, Abdullah, Albadi, Mohammed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Distributed Generation; Electric potential; Energy costs; Frequency control; Frequency deviation; Frequency stability; Genetic algorithms; Heuristic methods; Inertia; Inverters; Metaheuristics; Microgrids; Multi-objective Algorithm; Multiple objective analysis; Optimization; Particle swarm optimization; Placement; Power system stability; Reactive power; Renewable Energy; Renewable energy sources; Sorting algorithms; Synchronous machines; Virtual Synchronous Generator; Voltage
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3289813

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. Moreover, this study assesses the IEEE 33-bus, 69-bus distribution network, and practical Masirah Island network as the test systems. Furthermore, the proposed method's 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). According to the findings, the MOSSA optimal Pareto solution candidates (PSC) for the three test systems satisfied the frequency constraints and cost objectives. In addition, all PSCs accurately prevented voltage limit infringements, and the overall energy losses and the active power losses during each optimization period were significantly reduced.