Monte Carlo simulation-based probabilistic assessment of DG penetration in medium voltage distribution networks |
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| Affiliation: | 1. Ecole Centrale Paris and Supelec, Paris, France;2. Department of Energy, Politecnico di Milano, Milan, Italy;3. Institute of Energy Technology, ETH Zurich, Zurich, Switzerland;1. Electric Power Engineering, Luleå University of Technology, 931 87, Skellefteå, Sweden;2. Electrical and Electronics Engineering, Balikesir University, Balikesir, Turkey;3. 15th of May Higher Institute of Engineering, Mathematical and Physical Sciences, Helwan, Cairo, Egypt;4. College of Engineering, Design & Physical Sciences, Brunel University London, Uxbridge, United Kingdom;1. Energy Department, Graduate University of Advanced Technology, Kerman, Iran;2. Energy & Industry Commission of Kerman Chamber of Commerce, Industry, Mines & Agriculture, Iran;3. Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran;4. Center of Excellence in Power System Management & Control (CEPSMC), Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran;1. Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Carrera 7 No. 40B – 53, Bogotá D.C 11021, Colombia;2. Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, km 1 vía Turbaco, Cartagena 131001, Colombia;3. Grupo GIIEN, Facultad de Ingeniería, Institución Universitaria Pascual Bravo, Campus Robledo, Medellín 050036, Colombia;4. Electrical and Electronic Engineering Department, Universidad del Norte, Barranquilla 080001, Colombia;1. Politecnico di Milano, Dipartimento di Elettronica Informazione e Bioingegneria, Piazza Leonardo da vinci, 32 I-20133 Milano, Italy;2. Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106, USA;3. Massachusetts Institute of Technology (MIT), Cambridge, CMA 02139, USA |
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| Abstract: | With the growing use of renewable energy sources, Distributed Generation (DG) systems are rapidly spreading. Embedding DG to the distribution network may be costly due to the grid reinforcements and control adjustments required in order to maintain the electrical network reliability. Deterministic load flow calculations are usually employed to assess the allowed DG penetration in a distribution network in order to ensure that current or voltage limits are not exceeded. However, these calculations may overlook the risk of limit violations due to uncertainties in the operating conditions of the networks. To overcome this limitation, related to both injection and demand profiles, the present paper addresses the problem of DG penetration with a Monte Carlo technique that accounts for the intrinsic variability of electric power consumption. The power absorbed by each load of a medium voltage network is characterized by a load variation curve; a probabilistic load flow is then used for computing the maximum DG power that can be connected to each bus without determining a violation of electric constraints. A distribution network is studied and a comparison is provided between the results of the deterministic load flow and probabilistic load flow analyses. |
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| Keywords: | Distributed generation Electrical distribution network Probabilistic load flow Monte Carlo simulation |
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