Economic Load Dispatch Incorporating Wind Power Using Hybrid Biogeography-Based Optimization: Salp Swarm Algorithm

Economic Load Dispatch Incorporating Wind Power Using Hybrid Biogeography-Based Optimization: Salp Swarm Algorithm

Bikram Saha, Provas Kumar Roy, Barun Mandal
Copyright: © 2021 |Pages: 27
DOI: 10.4018/IJAMC.2021070103
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Abstract

This article represents salp swarm algorithm (SSA) for the most favourable operating solution of economic load dispatch (ELD). For making the convergence first along with SSA, another optimization algorithm (i.e., BBO [biogeography;based optimization]) is also used. For lowering the operational cost, wind power is employed with thermal units. SSA is inspired by swarming behaviour of salp, which belongs to salpiside family. Salp possess a special kind of swarm while hunting for food and navigating. The recommended algorithm is executed on two systems of SIX units and 40 units. In both of the cases, load dispatch problem is carried out with renewable sources and also without renewable sources. Individually, BBO, SSA, and hybrid BBO-SSA are applied to all the test systems to justify effectiveness of hybrid BBO-SSA. Obtained results assure the prospective and advantages of recommended algorithm in contrast to algorithms mentioned in the article. Results come out to be very satisfying and reveal that hybrid BBO-SSA is a powerful algorithm to solve ELD problems.
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Introduction

Economic Load Dispatch plays a crucial part in working and planning of present day complicated power system network (Chakrabarti A, Halder S,2006). It is not easy to resolve ELD as it possess nonlinear objective functions with a various constraints. ELD deals with the obtaining of ideal generation to be scheduled to accessible alternators in such a way that total cost for generation gets reduced while following the system constraints (Kothari DP.2006; Aydin G.2014). Previously already accepted techniques like gradient method (Dodu JC, Martin P), lambda iteration technique (Chen CL, Wang SC; Aravindhababu P), linear programming(Parikh J, Chattopadhyay D.1996), quadratic programming (Fan JY, Zhang L.1998), lagrangian multiplier method (Nanda J.1994) and classical method on the basis of co-ordination equations (El-Keib AA.1994) are excessively utilised to find solution for ELD difficulties. But these traditional techniques need either monotonically increasing or linear incremental cost curves. In practical the input with respect to output characteristics of operating unit is not smooth and excessively non-linear. ELD problems had been dealing by various researchers during last couple of years. FLC (Fuzzy Logic Control) has vast impact in controlling related applications. Unlike the traditional methods, FLC composes the controlling action with respect to semantic orders drawn instead of in terms of a technique synthesized from system (Brar YS.2002) model. But before came into operation it requires more fining, simulation. Another algorithm ANN (Artificial Neural Network) also has its merits and as well as shortcomings. Though ANN enhances the system characteristics, but it has main shortcoming that it takes much time in selecting no of layers, no of layers of each neurons (Yalcinoz T.199).

Introduction of renewable energy resources with the traditional generation sources helps in cope up with the ever increasing economic; technological; operational confronts in case of a power utility (IEA Wind Task 25), despite encouraging environmental gains, the challenges come out from either or both of the following (EnerNex Corporation):

  • 1.

    Inconsistency of generated power from renewable sources makes it meteorologically dependent.

  • 2.

    There are shortcomings of Transmission and distribution foundation, which limits the power flow towards load centres without depending upon the demand of the load.

“1-2” can affect total optimal generation cost in power system; that is why ELD serves as a foremost indicator. ELD technique may be widely classified as:

  • ELD. Type i.e. static (or single-period) or dynamic (or multiperiod);

  • Different kind of renewable energy sources i.e. wind or solar-photovoltaic; bio gas;

  • Power fluctuation representation: probability density functions or interval averages.

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