Short-term real-power scheduling considering fuzzy factors in an autonomous system using genetic algorithms

Distributed generation has several advantages such as reducing required transmission capacity and real power losses, and in terms of traditional generation expansion etc. An autonomous system is an independent system that may include diesel generators, a wind park, solar photovoltaic (PV) modules and/or batteries etc. serving its loads. The paper presents a new method for dealing with short-term power scheduling of an autonomous system. The fuel cost of diesel units is required to be smaller than an expected value and all operational constraints are satisfied. In particular, wind and solar PV power generation with uncertainties are modelled by a fuzzy set. The genetic algorithm is used to solve this problem formulated as symmetrical fuzzy programming. Simulation results based on an autonomous system show the applicability of the proposed method.

[1]  R. Yokoyama,et al.  Improved genetic algorithms for optimal power flow under both normal and contingent operation states , 1997 .

[2]  Yong-Hua Song,et al.  Fault diagnosis of electric power systems based on fuzzy Petri nets , 2004 .

[3]  Y. Y. Hong,et al.  Development of Energy Loss Formula for Distribution Systems Using FCN Algorithm and Cluster-Wise Fuzzy Regression , 2002, IEEE Power Engineering Review.

[4]  J. Kabouris,et al.  Short term scheduling in a wind/diesel autonomous energy system , 1991 .

[5]  M. Shahidehpour,et al.  Short-term scheduling of battery in a grid-connected PV/battery system , 2005, IEEE Transactions on Power Systems.

[6]  S. Gerbex,et al.  Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms , 2001, IEEE Power Engineering Review.

[7]  Kit Po Wong,et al.  Combined genetic algorithm/simulated annealing/fuzzy set approach to short-term generation scheduling with take-or-pay fuel contract , 1996 .

[8]  A. El-Keib,et al.  A fuzzy branch and bound-based transmission system expansion planning for the highest satisfaction level of the decision maker , 2005, IEEE Transactions on Power Systems.

[9]  R.K. Aggarwal,et al.  Genetic algorithms for optimal reactive power compensation on the National Grid system , 2005, IEEE Power Engineering Society Summer Meeting,.

[10]  Leehter Yao,et al.  An iterative deepening genetic algorithm for scheduling of direct load control , 2005 .

[11]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[12]  J.A.P. Lopes,et al.  On the optimization of the daily operation of a wind-hydro power plant , 2004, IEEE Transactions on Power Systems.

[13]  L. L. Lai,et al.  Comparison between evolutionary programming and a genetic algorithm for fault-section estimation , 1998 .

[14]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[15]  A. Vargas,et al.  Fuzzy-heuristic methodology to estimate the load restoration time in MV networks , 2005, IEEE Transactions on Power Systems.

[16]  Ying-Yi Hong,et al.  Determination of transformer capacities in an industrial factory with intermittent loads , 2004 .

[17]  Y. Y. Hong,et al.  Genetic Algorithms Based Economic Dispatch for Cogeneration Units Considering Multiplant Multibuyer Wheeling , 2002, IEEE Power Engineering Review.

[18]  Ying-Yi Hong,et al.  Interactive multiobjective passive filter planning with fuzzy parameters in distribution systems using genetic algorithms , 2003 .

[19]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[20]  Kitpo Wong,et al.  Advanced constrained genetic algorithm load flow method , 1999 .

[21]  A. G. Expósito,et al.  Path-based distribution network modeling: application to reconfiguration for loss reduction , 2005, IEEE Transactions on Power Systems.

[22]  G. Sheblé,et al.  Power generation operation and control — 2nd edition , 1996 .

[23]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[24]  Ying-Yi Hong,et al.  Determination of network configuration considering multiobjective in distribution systems using genetic algorithms , 2005 .

[25]  V. Miranda,et al.  Fuzzy inference systems applied to LV substation load estimation , 2005, IEEE Transactions on Power Systems.

[26]  Anastasios G. Bakirtzis,et al.  Short term generation scheduling in a small autonomous system with unconventional energy sources , 1988 .

[27]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[28]  S. M. Shahidehpour,et al.  Short term generation scheduling in photovoltaic-utility grid with battery storage , 1997 .

[29]  Hong-Tzer Yang,et al.  Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions , 1996 .