Abstract
In this research work, bio-inspired computational heuristic algorithms (BCHAs) integrated with active-set algorithms (ASA) were designed to study integrated economics load dispatch problems with valve point effects involving stochastic wind power. These BCHAs are developed through variants of genetic algorithms based on a different set of routines for reproduction operators in order to make exploration and exploitation in the entire search space for finding the global optima, while the ASA is used for rapid local refinements of the results. The designed schemes are estimated on different load dispatch systems consisting of a combination of thermal generating units and wind power plants with and without valve point loading effects. The accuracy, convergence, robustness and complexity of the proposed schemes has been examined through comparative studies based on a sufficiently large number of independent trails and their statistical observations in terms of different performance indices.
Highlights
Economic load dispatch (ELD) is a fundamental issue in power plant systems, design and analysis with the aim of optimal scheduling of generated power in order to satisfy the load demand by least probable cost, while fulfilling the constraints on power generators [1,2,3]
A number of studies have introduced a variety of optimization procedures for ELD problems with and without valve point loading effect (VPLE) based on conventional and recently introduced meta-heuristics schemes, such as Newton methods [4,5], genetic algorithms [6], biogeography-based optimization algorithms [7], teaching learning based optimization methods [8], grey-wolf optimization algorithms [9], ant lion optimization procedures [10], modified krill herd algorithms [11,12], natural updated harmony searches [13], improved differential evolution [14], mine blast algorithms [15], and crow-search algorithms [16]
As per our literature survey, evolutionary computing strategies based on variants of genetic algorithms (GAs) have yet not been exploited in integrated power dispatch problems, the objective of the present study is to investigate integrated bio-inspired computational heuristic algorithms (BCHAs) based on the variants of GAs aid with the active-set algorithm (ASA) for optimization of load dispatch problems
Summary
Economic load dispatch (ELD) is a fundamental issue in power plant systems, design and analysis with the aim of optimal scheduling of generated power in order to satisfy the load demand by least probable cost, while fulfilling the constraints on power generators [1,2,3]. The electricity generation cost with thermal power plants is excessively high and suitable planning is needed to minimize the cost within reasonable levels. The ELD optimization problem is in one of the difficult constraints-based optimization systems in the power sector that usually needs excessive computations because of the nature of the cost functions and inherent non-smooth properties. An additional aim in optimal load dispatch is to decrease or reduce the emissions that are dispersed due to the procedure of electricity generation. These environmental goals are conflicting with the economical nature of the systems, i.e., the decline in emission from generating units (GUs) results
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