Clean technologies developments based on a grant function in distributed generation planning

Distributed generation expansion planning (DGEP) has been frequently reported in the literature around the world. However, no studies have been published yet that include type of distributed generation technologies and environmental constraints in the planning process. In this paper, a multiobjective optimization algorithm is applied to produce a Pareto set of optimal planning schemes by taking into account different objective functions. The best planning scheme among the Pareto set is chosen by using a Monte Carlo simulation of uncertain situations in the planning process. The major contribution of this paper is to develop a distributed generation expansion planning considering various technologies and environmental issues. The proposed promotion strategy includes an encouraging mechanism in favor of clean technologies. To assess the ability of the proposed method, a grant function of pollution not emanated as well as emission constraints are applied under three case studies.

[1]  S. Jadid,et al.  Normal boundary intersection for generating Pareto set in distributed generation planning , 2007, 2007 International Power Engineering Conference (IPEC 2007).

[2]  Chanan Singh,et al.  Dispersed generation planning using improved Hereford ranch algorithm , 1998 .

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

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

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

[6]  C. Wieckert,et al.  Economic evaluation of the solar carbothermic reduction of ZnO by using a single sensitivity analysis and a Monte-Carlo risk analysis , 2007 .

[7]  Fabrizio Giulio Luca Pilo,et al.  Embedded Generation Planning under Uncertainty including Power Quality Issues , 2003 .

[8]  A. Keane,et al.  Optimal allocation of embedded generation on distribution networks , 2005, IEEE Transactions on Power Systems.

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

[10]  Lennart Söder,et al.  Distributed generation : a definition , 2001 .

[11]  Kadir Erkan,et al.  Power generation expansion planning with adaptive simulated annealing genetic algorithm , 2006 .

[12]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[13]  Magdy M. A. Salama,et al.  Distributed generation technologies, definitions and benefits , 2004 .

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

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

[16]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[17]  Tsutomu Oyama,et al.  Generation planning including distributed generators under uncertainty of demand growth , 2004 .

[18]  Kamran Rezaie,et al.  Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties , 2007, Appl. Math. Comput..