Evolutionary Algorithms for Economic Load Dispatch Having Multiple Types of Cost Functions

Evolutionary Algorithms for Economic Load Dispatch Having Multiple Types of Cost Functions

Provas Kumar Roy, Moumita Pradhan, Tandra Pal
ISBN13: 9781522500582|ISBN10: 1522500588|EISBN13: 9781522500599
DOI: 10.4018/978-1-5225-0058-2.ch009
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MLA

Roy, Provas Kumar, et al. "Evolutionary Algorithms for Economic Load Dispatch Having Multiple Types of Cost Functions." Handbook of Research on Natural Computing for Optimization Problems, edited by Jyotsna Kumar Mandal, et al., IGI Global, 2016, pp. 201-226. https://doi.org/10.4018/978-1-5225-0058-2.ch009

APA

Roy, P. K., Pradhan, M., & Pal, T. (2016). Evolutionary Algorithms for Economic Load Dispatch Having Multiple Types of Cost Functions. In J. Mandal, S. Mukhopadhyay, & T. Pal (Eds.), Handbook of Research on Natural Computing for Optimization Problems (pp. 201-226). IGI Global. https://doi.org/10.4018/978-1-5225-0058-2.ch009

Chicago

Roy, Provas Kumar, Moumita Pradhan, and Tandra Pal. "Evolutionary Algorithms for Economic Load Dispatch Having Multiple Types of Cost Functions." In Handbook of Research on Natural Computing for Optimization Problems, edited by Jyotsna Kumar Mandal, Somnath Mukhopadhyay, and Tandra Pal, 201-226. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0058-2.ch009

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Abstract

This chapter presents various novel evolutionary algorithms, namely Real Coded Genetic Algorithm (RGA), two variants of Biogeography-Based Optimization (BBO), and three variants of Particle Swarm Optimization (PSO) in order to find the optimal power generation scheduling to simultaneously optimize fuel cost and power loss for solving constrained economic load dispatch problems of all thermal systems, considering multiple fuel operation and valve point effect. The effectiveness of the proposed algorithms is demonstrated in five different ELD problems, considering different constraints such as transmission losses, ramp rate limits, multi-fuel options and valve point loading. Comparative studies are carried out to examine the effectiveness and superiority of the proposed approaches. A comparison of simulation results reveals optimization usefulness of the proposed BBO scheme over other well established population based optimization techniques. It is also found that the convergence characteristics of the BBO algorithm are better than other optimization methods.

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