A freely configurable structure of virtual synchronous generator for grid-forming converters

Suvorov, Aleksey; Askarov, Alisher; Kievets, Аnton

doi:10.1007/s00202-023-01742-5

Cited by 4 publications

(2 citation statements)

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“…In this case, methods that do not require coding, such as swarm intelligence [147], should be used. The particle swarm method is simpler because it does not require coding, so its application is feasible when computational resources are limited [148]. Further reduction of computational costs is possible through the use of distributed metaheuristic algorithms, such as artificial immune systems that use only local information but have low accuracy compared to GAs and DE [149].…”

Section: Implementation Of Optimization Algorithms In Microgridsmentioning

confidence: 99%

“…Swarm intelligence methods do not require special coding for their application. For example, the particle swarm optimization method is quite simple to implement and, given its efficiency, is the optimal solution in the case of the limited computing power of the controller [147,148]. Artificial immune systems are formed on the basis of a distributed control model, when there is no single control center, and they require only local information.…”

Section: Implementation Of Optimization Algorithms In Microgridsmentioning

confidence: 99%

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Approaches to Building AC and AC–DC Microgrids on Top of Existing Passive Distribution Networks

et al. 2023

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The process of building microgrids on top of existing passive distribution networks warrants a multi-criteria analysis. Besides the calculation of the investment outlays needed for the modernization of distribution networks, such an analysis covers an assessment of the technological and economic effects of building microgrids. The resulting effects depend on the topology and configuration of distribution networks, specific microgrid features, the choice of the current type for the entire microgrid or its individual parts, the methods of connecting distributed energy resources (DERs), the availability and maturity of information and communications technology (ICT) infrastructure, and other factors. Comprehensive input data allow for designing an optimal microgrid configuration, but the main technological and economic effects are determined by the algorithms of operation and the parameter settings of the automatic control system (ACS) and the protection system. The known approaches to designing microgrids focus on addressing basic tasks while minimizing the investment required for their implementation. The above is fully justified when constructing new microgrids, but building microgrids on top of existing distribution networks, given the uniqueness of their topology and configuration, does not allow the use of standardized solutions. The development of approaches to the design of microgrids under such constraints, with minimized investment in the modernization of existing distribution networks, is an urgent task. The use of different types of current for individual microgrid segments determines the choice of the particular ACS and protection system, which depends on the availability of information and communications technology infrastructure. This article contributes a review of approaches to designing AC and AC–DC microgrids so as to maximize their technological and economic effects. We review techniques for analyzing the existing distribution networks aimed at choosing the type of current for the entire microgrid or its individual parts, the optimal points for the connection of microgrids to distribution networks, and the mix and capacity of DERs, with such choices informed by the conditions of the switching devices and information and communications technology infrastructure. This article presents the results of the analysis of approaches to choosing the optimal configuration of microgrids, microgrid ACS, and protection system, with an evaluation of the technological and economic effects subject to the minimization of investment in the modernization of the existing distribution networks.

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Section: Implementation Of Optimization Algorithms In Microgridsmentioning

confidence: 99%

Section: Implementation Of Optimization Algorithms In Microgridsmentioning

confidence: 99%