Scheduling of cogeneration plants considering electricity wheeling using enhanced immune algorithm

A new method based on immune algorithm (IA) is presented to solve the scheduling of cogeneration plants in a deregulated market. The objective function includes fuel cost, population cost, and electricity wheeling cost, subjective to the use of mixed fuels, operational limits, emissions constraints, and transmission line flow constraints. Enhanced immune algorithm (EIA) is proposed by an improved crossover and mutation mechanism with a competition and auto-adjust scheme to avoid prematurity. Table lists with heuristic rules are also employed in the searching process to enhance the performance. EIA is also compared with the original IA. Test results verify that EIA can offer an efficient way for cogeneration plants to solve the problem of economic dispatch, environmental protection, and electricity wheeling.

[1]  Kit Po Wong,et al.  Power markets analysis using genetic algorithm with population concentration , 2000, PowerCon 2000. 2000 International Conference on Power System Technology. Proceedings (Cat. No.00EX409).

[2]  Bin-Kwie Chen,et al.  Optimum operation for a back-pressure cogeneration system under time-of-use rates , 1996 .

[3]  J. W. Lamont,et al.  Emission dispatch models and algorithms for the 1990s , 1995 .

[4]  Shyh-Jier Huang,et al.  An immune-based optimization method to capacitor placement in a radial distribution system , 2000 .

[5]  Jong-Bae Park,et al.  A hybrid genetic algorithm/dynamic programming approach to optimal long-term generation expansion planning , 1998 .

[6]  Song-Yop Hahn,et al.  A study on comparison of optimization performances between immune algorithm and other heuristic algorithms , 1998 .

[7]  M. L. Baughman,et al.  Optimizing combined cogeneration and thermal storage systems , 1989 .

[8]  Jinyu Wen,et al.  Construction of power system load models and network equivalence using an evolutionary computation technique , 2003 .

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

[10]  Hiroshi Asano,et al.  Impacts of time-of-use rates on the optimal sizing and operation of cogeneration systems , 1992 .

[11]  Ming-Tong Tsay,et al.  Interactive best-compromise approach for operation dispatch of cogeneration systems , 2001 .

[12]  J. Yuryevich,et al.  Evolutionary-programming-based algorithm for environmentally-constrained economic dispatch , 1998 .

[13]  H. Puttgen,et al.  Optimization Topics Related to Small Power Producing Facilities Operating Under Energy Spot Pricing Policies , 1987, IEEE Power Engineering Review.

[14]  F. J. Rooijers,et al.  Static economic dispatch for co-generation systems , 1994 .

[15]  S. A. Farghal,et al.  Economic justification of cogeneration systems for industrial steam users and utility systems , 1989 .

[16]  L. L. Lai,et al.  Multitime-interval scheduling for daily operation of a two-cogeneration system with evolutionary programming , 1998 .

[17]  J. Chun,et al.  Shape optimization of electromagnetic devices using immune algorithm , 1997 .

[18]  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 .

[19]  S. A. Farghal,et al.  Optimum operation of cogeneration plants with energy purchase facilities , 1987 .