Notice of Violation of IEEE Publication PrinciplesA Benders Decomposition and Fuzzy Multicriteria Approach for Distribution Networks Remuneration Considering DG

In this paper, the remuneration of fixed costs of distribution networks with distributed generation is evaluated by means of an efficient planning strategy that includes the concept of fuzzy robustness of a given solution plan. As a key contribution, it is included the possibility of choice among different conductor sizes in order to assess the deep costs by overcapacity introduced by distributed generation. A single-stage multicriteria nonlinear problem is stated using three different criteria: investment cost in lines and transformers or distributed generators, power losses cost and unserved energy cost. Pareto or efficient plans are identified using the epsiv-Constraint/Weighting method and solved by Benders decomposition algorithm. Uncertainty associated to load demand and power injections of distributed resources are integrated using a fuzzy power flow in order to obtain the robustness indexes of each Pareto solution. The annualized fixed charge rate (AFCR) associated to new and existing distribution lines and transformers or distributed generators is assessed through a with-without analysis permitting to compute the annual avoided charge rate (AACR) in order to send incentives or charges to distributed generation promoters by the avoided or added investment costs on the system. The model is programmed in GAMS mathematical modeling language. The effectiveness of the proposal is demonstrated through a real 201-node distribution network.

[1]  A. M. Geoffrion Generalized Benders decomposition , 1972 .

[2]  M.T.P. de Leao,et al.  Remuneration of Distribution Networks using a Fuzzy Multicriteria Planning Algorithm , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[3]  J. T. Saraiva,et al.  Solving the Revenue Reconciliation Problem of Distribution Network Providers Using Long-Term Marginal Prices , 2002, IEEE Power Engineering Review.

[4]  Ignacio J. Ramirez-Rosado,et al.  Genetic algorithms applied to the design of large power distribution systems , 1998 .

[5]  H.M. Khodr,et al.  Ant colony system algorithm for the planning of primary distribution circuits , 2004, IEEE Transactions on Power Systems.

[6]  R. Shoults,et al.  Optimal Distribution Substation and Primary Feeder Planning VIA the Fixed Charge Network Formulation , 1982, IEEE Transactions on Power Apparatus and Systems.

[7]  I.J. Ramirez-Rosado,et al.  Pseudodynamic planning for expansion of power distribution systems , 1991, IEEE Power Engineering Review.

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

[9]  Jacques F. Benders,et al.  Partitioning procedures for solving mixed-variables programming problems , 2005, Comput. Manag. Sci..

[10]  Z. Vale,et al.  Transmission price simulator in a liberalized electricity market , 2008, 2008 5th International Conference on the European Electricity Market.

[11]  G. Strbac Impact of dispersed generation on distribution systems: a European perspective , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[12]  Hyde M. Merrill,et al.  Risk and uncertainty in power system planning , 1991 .

[13]  J. Cardoso,et al.  A Congestion Management and Transmission Price Simulator for Competitive Electricity Markets , 2007, 2007 IEEE Power Engineering Society General Meeting.

[14]  N. Fonseca,et al.  Fuzzy power flow-an AC model addressing correlated data , 2004, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[15]  Chun-Chang Liu,et al.  Multi-objective VAR Planning Using An Interactive Satisfying Method , 1995 .

[16]  H. Happ Cost of wheeling methodologies , 1994 .