Abstract
Abstract This paper presents a methodology for the optimal placement and sizing of reactive power compensation devices in a distribution system (DS) with distributed generation. Quasi-static time series is embedded in an optimization method based on a genetic algorithm to adequately represent the uncertainty introduced by solar photovoltaic generation and electricity demand and its effect on DS operation. From the analysis of a typical DS, the reactive power compensation rating power results in an increment of 24.9% when compared to the classical genetic algorithm model. However, the incorporation of quasi-static time series analysis entails an increase of 26.8% on the computational time required.
Highlights
The cultural and technological development of human society is closely related to the idea of management and allocation of natural, human, and economic resources in an appropriate manner
This paper presents a methodology for the optimal placement and sizing of reactive power compensation devices in a distribution system (DS) with distributed generation
The proposed methodology reduces the computational complexity when compared to the traditional genetic algorithm (GA) implementation based on the discretization of the load duration curve (LDC), because it considers only binary variables
Summary
The cultural and technological development of human society is closely related to the idea of management and allocation of natural, human, and economic resources in an appropriate manner. Management of resources and budgets needed for developing social welfare requires feasible strategies, frequently based on optimization techniques. With the increasing complexity of industrial systems and human interactions, optimized management of real-life activities involves the incorporation of stochastic processes. The construction of a sustainable society requires an electricity network with smart capabilities to integrate renewable resources and mechanisms to their full exploitation. The topic under discussion in this paper is essentially a planning problem subject to the uncertain effects of renewable generation. Subsection 1.1 briefly presents a general perspective of the state-of-the-art optimization techniques under uncertainty, while subsection 1.2 explores particular issues of the electricity system operating under uncertain conditions. Subsection 1.3 carefully explains the main contributions of the approach developed in this work
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