Optimal PV Placement to Reduce Power Loss and Improve Voltage in Distribution Network System Using K-means Clustering Method

Yunus, Syukri; Latif, Melda; Darwison, Darwison

doi:10.25077/ajeeet.v2i1.19

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…The work by [23] addressed the optimal PV plant location and sizing problem with the aim to reduce the expected grid power losses and improve voltage profiles. The k-means clustering method was selected to determine the best nodal locations for the PV plants.…”

Section: Literature Reviewmentioning

confidence: 99%

A Robust Conic Programming Approximation to Design an EMS in Monopolar DC Networks with a High Penetration of PV Plants

Montoya,

Serra,

Gil-González

2023

Energies

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This research addresses the problem regarding the efficient operation of photovoltaic (PV) plants in monopolar direct current (DC) distribution networks from a perspective of convex optimization. PV plant operation is formulated as a nonlinear programming (NLP) problem while considering two single-objective functions: the minimization of the expected daily energy losses and the reduction in the expected CO2 emissions at the terminals of conventional generation systems. The NLP model that represents the energy management system (EMS) design is transformed into a convex optimization problem via the second-order cone equivalent of the product between two positive variables. The main contribution of this research is that it considers the uncertain nature of solar generation and expected demand curves through robust convex optimization. Numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness and robustness of the proposed second-order cone programming model in defining an EMS for a monopolar DC distribution network. A comparative analysis with four different combinatorial optimizers is carried out, i.e., multiverse optimization (MVO), the salp swarm algorithm (SSA), the particle swarm optimizer (PSO), and the crow search algorithm (CSA). All this is achieved while including an iterative convex method (ICM). This analysis shows that the proposed robust model can find the global optimum for two single-objective functions. The daily energy losses are reduced by 44.0082% with respect to the benchmark case, while the CO2 emissions (kg) are reduced by 27.3771%. As for the inclusion of uncertainties, during daily operation, the energy losses increase by 22.8157%, 0.2023%, and 23.7893% with respect to the benchmark case when considering demand uncertainty, PV generation uncertainty, and both. Similarly, CO2 emissions increase by 11.1854%, 0.9102%, and 12.1198% with regard to the benchmark case. All simulations were carried out using the Mosek solver in the Yalmip tool of the MATLAB software.

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Section: Literature Reviewmentioning

confidence: 99%

A Robust Conic Programming Approximation to Design an EMS in Monopolar DC Networks with a High Penetration of PV Plants

Montoya,

Serra,

Gil-González

2023

Energies

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Exploring the Effect of Environmental and Meteorological Factors on Photovoltaic (PV) Power Generation through Clustering Analysis

Hidayat,

Fathoni,

Hasanah

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Photovoltaic (PV) is a renewable energy solution that offers environmental sustainability and the potential to reduce greenhouse gas emissions. As PV systems become increasingly prevalent, the need for efficient monitoring, control, and optimization becomes paramount. Understanding the significance of environmental and meteorological factors, such as First Hour of Period, Distance to Solar Noon, Average Temperature, Average Wind Direction, Average Wind Speed, Sky Cover, Visibility, Relative Humidity, Average Wind Speed, Average Barometric Pressure, and Power Generated, on PV efficiency is essential for optimizing PV system design and operation. Clustering analysis is applied to identify distinct operational patterns and correlations among these parameters, providing valuable insights into PV system performance under varying conditions. The clustering analysis results in two distinct clusters, each representing specific operational characteristics of PV systems. Cluster 0 demonstrates peak generation capacity during mid-morning and afternoon hours, while Cluster 1 experiences peak generation during morning and evening hours. The silhouette coefficient of 0.708 validates the clustering results’ quality, signifying well-defined clusters and the relevance of the selected features. The findings can aid in optimizing PV system performance, guiding design decisions, and promoting the adoption of renewable energy solutions.

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Robust Next-Day Scheduling of PV Generation Sources Supplying a Standalone DC Microgrid via a Semi-Definite Programming Model

Gil-González,

Montoya,

Grisales-Noreña

et al. 2024

IEEE Open J. Power Energy

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Optimal PV Placement to Reduce Power Loss and Improve Voltage in Distribution Network System Using K-means Clustering Method

Cited by 3 publications

References 8 publications

A Robust Conic Programming Approximation to Design an EMS in Monopolar DC Networks with a High Penetration of PV Plants

A Robust Conic Programming Approximation to Design an EMS in Monopolar DC Networks with a High Penetration of PV Plants

Exploring the Effect of Environmental and Meteorological Factors on Photovoltaic (PV) Power Generation through Clustering Analysis

Robust Next-Day Scheduling of PV Generation Sources Supplying a Standalone DC Microgrid via a Semi-Definite Programming Model

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