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Arya, A., Verma, S.S., Mehroliya, S., Tomar, S. and Rajeshwari, C.S. (2021) Optimal Placement of Distributed Generators in Power System Using Sensitivity Analysis. In: Bansal, R.C., Agarwal, A. and Jadoun, V.K., Eds., Advances in Energy Technology, Springer, 749-759.
https://doi.org/10.1007/978-981-16-1476-7_67

has been cited by the following article:

• TITLE: Probabilistic Assessment of PV-DG for Optimal Multi-Locations and Sizing Using Genetic Algorithm and Sequential-Time Power Flow

  AUTHORS: A. Elkholy

  KEYWORDS: Photovoltaic Distributed Generation, Probability, Genetic Algorithm, Radial Distribution Systems, Time Series Power Flow

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.13 No.2, February 27, 2025

  ABSTRACT: This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal allocation and sizing of multiple PV-DG units to minimize power losses using a probabilistic PV model and time-series power flow analysis. Addressing the uncertainties in PV output due to weather variability and diurnal cycles is critical. A probabilistic assessment offers a more robust analysis of DG integration’s impact on the grid, potentially leading to more reliable system planning. The presented approach employs a genetic algorithm (GA) and a determined PV output profile and probabilistic PV generation profile based on experimental measurements for one year of solar radiation in Cairo, Egypt. The proposed algorithms are validated using a co-simulation framework that integrates MATLAB and OpenDSS, enabling analysis on a 33-bus test system. This framework can act as a guideline for creating other co-simulation algorithms to enhance computing platforms for contemporary modern distribution systems within smart grids concept. The paper presents comparisons with previous research studies and various interesting findings such as the considered hours for developing the probabilistic model presents different results.