Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models

Abstract

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

Performance Assessment User Interface to Enhance the Utilization of Grid-Connected Residential PV Systems

“…Specifically, the small-and medium-scale grid-tie PV systems have attracted more attention in electricity generation sectors in the last decade due to the technical advancement in the energy conversion technologies and the new environmental requirements [10]. Moreover, the PV-DGs play pivotal roles in the power grid reliability and efficiency enhancement [11,12]. For example, the studies in [13][14][15] showed the increasing rate of small-scale PV applications in existing and modern power networks, such as micro-grids and smart power systems.…”