Preference cone based multi-objective evolutionary algorithm applied to optimal management of distributed energy resources in microgrids

Willian M. Ferreira, Ivan R. Meneghini, Danilo I. Brandao and Frederico G. Guimarães

Applied Energy, 2020, vol. 274, issue C, No S0306261920308382

Abstract: The existence of unbalanced loads and high penetration of single-phase distributed energy resources connected unevenly throughout the phases of a microgrid causes unbalanced currents through the microgrid’s point of common coupling and then degradation of power factor. This paper proposes a multi-objective optimization model applied to optimization the use of distributed energy resources in an energy system. The centralized control strategy is divided into three levels integrated into a master controller. The primary level performs the local functions of distributed inverters. The secondary level is split into two layers: the first layer performed by the power-based control is responsible for sharing active/reactive power among distributed units, as well as dispatching power to the upstream network using a low-speed communication link; and the second layer employs a preference cone based multi-objective evolutionary algorithm based on decomposition for the solution of the proposed multi-objective optimization formulation to maximize the active power injection by single-phase units, and minimize the currents unbalance into the main grid. The preference cone based multi-objective evolutionary algorithm has obtained solutions with good repeatability, convergence and distribution leading the microgrid to operate at its optimal point while maintaining its stability. Finally, the tertiary level defines the constrains of active and reactive power based on the utility status. This paper focuses on the secondary level of control and the proposed method is assessed by computational simulations considering a three-phase four-wire microgrid operating in both islanded and grid-connected modes under realistic operational conditions in terms of load, generation and grid variations.

Keywords: Distributed generation; Multi-objective optimization approach; Hierarchical control; Microgrid; Power flow control; MOEA/D (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.apenergy.2020.115326

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