Impact of stochastically distributed renewable PV generation on distribution network

Small-scale and decentralized renewable distributed generation (DG) systems, especially PV systems, will soon be dispersed in distribution networks. The objective of this study is to present tools and algorithms useful for planning distribution networks enhanced by randomly distributed DG. To remove the uncertainties of the location, the capacity, and the field orientation of randomly distributed PV systems, a stochastic simulation algorithm is implemented. The stochastic simulation analyzes the impact of stochastically dispersed residential PV systems from the perspective of energy, especially regarding peak power, electricity generation costs, and emissions, and quantifies the effects of the method of variance reduction, including importance sampling.

[1]  Paul Glasserman,et al.  Monte Carlo Methods in Financial Engineering , 2003 .

[2]  N.N. Schulz,et al.  Development of Three-Phase Unbalanced Power Flow Using PV and PQ Models for Distributed Generation and Study of the Impact of DG Models , 2007, IEEE Transactions on Power Systems.

[3]  M. Begovic,et al.  Quantitative techniques for analysis of large data sets in renewable distributed generation , 2004, IEEE Transactions on Power Systems.

[4]  John Dagpunar,et al.  Simulation and Monte Carlo , 2007 .

[5]  Ajeet Rohatgi,et al.  Impact of renewable distributed generation on power systems , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[6]  Benjamin K. Sovacool,et al.  Valuing the Greenhouse Gas Emissions from Nuclear Power: A Critical Survey , 2008 .

[7]  Arnold Leitner.  Fuel from the Sky: Solar Power's Potential for Western Energy Supply , 2008 .

[8]  Insu Kim,et al.  Distributed renewable PV generation in urban distribution networks , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[9]  J. Dagpunar.  Simulation and Monte Carlo: With Applications in Finance and MCMC , 2007 .

[10]  D. S. Shugar,et al.  Photovoltaics in the utility distribution system: The evaluation of system and distributed benefits , 1990, IEEE Conference on Photovoltaic Specialists.

[11]  A. Sangswang,et al.  Effects of PV Grid-Connected System Location on a Distribution System , 2006, APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems.

[12]  Nikos D. Hatziargyriou,et al.  Probabilistic load flow in distribution systems containing dispersed wind power generation , 1993 .

[13]  Christopher Timmins,et al.  Solar Photovoltaic Installation in California: Understanding the Likelihood of Adoption Given Incentives, Electricity Pricing and Consumer Characteristics , 2010 .

[14]  D. S. Shugar,et al.  The value of grid-support photovoltaics in reducing distribution system losses , 1995 .

[15]  J. S. Christensen,et al.  Probabilistic load flow calculation using Monte Carlo techniques for distribution network with wind turbines , 1998, 8th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.98EX227).

[16]  Barbara T. Fichman.  Annual Energy Review 2009 , 2010 .

[17]  R. Judkoff,et al.  Consumptive Water Use for U.S. Power Production: Preprint , 2003 .

[18]  S. Conti,et al.  Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators , 2007 .

[19]  W. H. Kersting,et al.  Radial distribution test feeders , 1991, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).