Assessing the performance and benefits of customer distributed generation developers under uncertain

In this paper, the performance of customer-owned distributed generation (DG) units is quantified from different perspectives through an uncertainty study. A Monte Carlo-based method is applied to assess the stochastic operation of the customer-owned DG units in the power distribution system. Several cases are studied to analyze the impact on system performance of using such generators, with the emphasis on benefits. The results of the studied cases show that proper operation of customer-owned DG units placed close to significant consumption centers offers several benefits which lead to significant energy savings and improvement in the performance indices while maintaining the cost-effectiveness. Furthermore, based on the energy demand, different electricity price scenarios considering a cost sensitivity analysis are performed to indicate how the variations in electricity price influence each scenario’s feasibility. It is concluded that implementation of a proper energy purchase policy, and allocating the benefits of DG units to the owners, improves the economic performance of their investments and encourages customer DG developers to connect DG to the distribution network.

[1]  Selcuk Cebi,et al.  A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process , 2009 .

[2]  Stein-Erik Fleten,et al.  Optimal Investment Strategies in Decentralized Renewable Power Generation Under Uncertainty , 2006 .

[3]  H Lee Willis,et al.  Power distribution planning reference book , 2000 .

[4]  Om P. Malik,et al.  Risk-based distributed generation placement , 2008 .

[5]  A. Piccolo,et al.  Exploring the Tradeoffs Between Incentives for Distributed Generation Developers and DNOs , 2007, IEEE Transactions on Power Systems.

[6]  Carmen L. T. Borges,et al.  Optimal distributed generation allocation for reliability, losses, and voltage improvement , 2006 .

[7]  W. El-khattam,et al.  Optimal investment planning for distributed generation in a competitive electricity market , 2004, IEEE Transactions on Power Systems.

[8]  G. Sundberg,et al.  Project financing consequences on cogeneration: industrial plant and municipal utility co-operation in Sweden , 2003 .

[9]  Poul Alberg Østergaard,et al.  Reviewing optimisation criteria for energy systems analyses of renewable energy integration , 2009 .

[10]  Caisheng Wang,et al.  Analytical approaches for optimal placement of distributed generation sources in power systems , 2004 .

[11]  R. Ramakumar,et al.  An approach to quantify the technical benefits of distributed generation , 2004, IEEE Transactions on Energy Conversion.

[12]  M.M.A. Salama,et al.  An integrated distributed generation optimization model for distribution system planning , 2005, IEEE Transactions on Power Systems.

[13]  P. Fraser Distributed generation in liberalised electricity markets , 2003 .

[14]  Rémi Bardenet,et al.  Monte Carlo Methods , 2013, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[15]  Shahram Jadid,et al.  Promotion strategy of clean technologies in distributed generation expansion planning , 2009 .

[16]  Haozhong Cheng,et al.  Technical and economic impacts of active management on distribution network , 2009 .

[17]  Kim-Leng Poh,et al.  Break-even price of distributed generation under uncertainty , 1999 .

[18]  Hamid Lesani,et al.  An approach to deterministic and stochastic evaluation of the uncertainties in distributed generation systems , 2009 .

[19]  Tomás Gómez,et al.  Impact of distributed generation on distribution investment deferral , 2006 .

[20]  F. Pilo,et al.  A multiobjective evolutionary algorithm for the sizing and siting of distributed generation , 2005, IEEE Transactions on Power Systems.

[21]  Chris Marnay,et al.  Distributed Generation Investment by a Microgrid under Uncertainty , 2006 .

[22]  Sanya Carley,et al.  Distributed generation: An empirical analysis of primary motivators , 2009 .

[23]  Dorota Kurowicka,et al.  Integration of stochastic generation in power systems , 2006 .

[24]  Brett Cohen,et al.  Ranking and selection of power expansion alternatives for multiple objectives under uncertainty , 2007 .

[25]  Ronnie Belmans,et al.  Distributed generation: definition, benefits and issues , 2005 .

[26]  Hamid Lesani,et al.  Uncertainty Assessment of Distribution Generation Placement in Distributed Power Systems , 2009 .

[27]  Angel A. Bayod-Rújula,et al.  Future development of the electricity systems with distributed generation , 2009 .

[28]  Mehdi Ehsan,et al.  A distribution network expansion planning model considering distributed generation options and techo-economical issues , 2010 .

[29]  Kyung Bin Song,et al.  Multiobjective distributed generation placement using fuzzy goal programming with genetic algorithm , 2008 .

[30]  Paulien M. Herder,et al.  Uncertainties in the design and operation of distributed energy resources: The case of micro-CHP systems , 2008 .

[31]  G. Joós,et al.  On the Quantification of the Network Capacity Deferral Value of Distributed Generation , 2006, IEEE Transactions on Power Systems.