Abstract
Many thermal generating units of an electric power system are supplied with multi-fuel sources such as coal, natural gas and oil. These fuels represent irreplaceable natural resources and conservation is used as a way to increase energy efficiency. Economic dispatch (ED) is one of the significance optimization problems in power system operation for fuel cost savings. This paper proposes a new approach which is hybrid variant of mean-variance mapping optimization (MVMO-SH) for solving this problem. The MVMO-SH is the improvement of original mean-variance mapping optimization algorithm (MVMO). This method adopts a swarm scheme of MVMO and incorporates local search and multi-parent crossover strategies to enhance its global search ability and improve solution quality for optimization problems. The proposed MVMO-SH is tested on 10-unit and large-scale systems with multiple fuels and valve-point effects. The obtained results are compared to those from other optimization methods available in the literature. The comparisons show that the proposed method provides higher quality solutions than the others. Therefore, the MVMO-SH is a promising method for solving the complex ED problems in electric power system.
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Abbreviations
- N :
-
total number of generating units
- F :
-
total operation cost
- a ik , b ik , c ik ,:
-
fuel cost coefficients of generator i
- B ij , B 0i , B 00 :
-
total system load demand
- P D :
-
total system load demand
- P i :
-
power output of generator i
- P i,max :
-
maximum power output of generator i
- P i,min :
-
minimum power output of generator i
- P L :
-
total transmission loss
- K :
-
the penalty factor for the slack unit
- P s :
-
power output of slack unit
- n_var :
-
number of variable (generators)
- n_par :
-
number of particles
- mode :
-
variable selection strategy for offspring creation
- iter max :
-
the maximum number of iterations
- Np :
-
number of particles
- archive zize :
-
n-best individuals to be stored in the table
- \( \Delta d_{0}^{{\text{ini}}} \) :
-
initial smoothing factor increment
- \( \Delta d_{0}^{{\text{final}}} \) :
-
final smoothing factor increment
- g p_ini :
-
max percentage of good particles
- g p_ini :
-
min percentage of good particles
- m ini :
-
initial number of variables selected for mutation
- m final :
-
final number of variables selected for mutation
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Acknowledgement
The researchers would like to sincerely thank Universiti Teknologi PETRONAS for providing the research laboratory facilities under Graduate Assistance Scheme. This work is supported by the Centre of Graduate Studies with the help of the Department of Fundamental & Applied Sciences, Universiti Teknologi PETRONAS.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Truong, K.H., Vasant, P., Balbir Singh, M.S., Vo, D.N. (2016). Hybrid Mean-Variance Mapping Optimization for Economic Dispatch with Multiple Fuels Considering Valve-Point Effects. In: Vinh, P., Barolli, L. (eds) Nature of Computation and Communication. ICTCC 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 168. Springer, Cham. https://doi.org/10.1007/978-3-319-46909-6_19
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