Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems

Introduction

The addition of renewable energy sources in power systems is rising, with the increase in fossil fuel prices and due to excessive environmental degradation from the greenhouse gases i.e. global warming. Wind energy is a nondepleting, low cost and environment friendly source of renewable energy.

Economic load dispatch (ELD) is a technique to allocate the generating units according to the load demand and to minimize operating cost. ELD (Hazra et al. 2015) with consideration of carbon emission tax and integration of renewable source is a recent trend and an emerging technology. In this paper, economic load dispatch of six and fourteen conventional thermal generators under different loading condition is performed, with and without tax imposed on carbon emission. Afterward, two windmills are included in the system and ELD is performed with and without considering carbon emission tax. Wind power is readily available in nature, but due to its uncertain and stochastic characteristics, it creates challenges in the load dispatch model. As wind speed variation controls windmill outputs, so wind power forecasting errors will bring a major problem while estimating system reserve margin to provide the guarantee of secure and reliable operation. The uncontrolled penetration of wind power is risky for a power system as it may bring out difficulties. If the errors of forecasting generated by different methods have a low degree of correlation among each other, the random error from the individual forecasts will tend to offset each other with the result thus forecasting will have very fewer errors than individual forecast. Wind power generally follows Weibull distribution shown in so many papers (Roy et al. 2015).In several articles (Liu, and Xu, 2010; Hetzer et al. 2008), probabilistic optimization strategies are used to deal with wind power uncertainty.

Ero glu et al. (2013) implemented wind farm layout optimization by using particle filtering (PF) and ant colony optimization (ACO) approach accordingly. Chen et al. (2015) propose distribution management oriented renewable energy generation using different interval optimization. Hazra et al. (2019) approaches towards the economical operation of a hybrid systems which consist of conventional thermal generators and renewable energy sources. Jin et al. (2016) proposed optimal day-ahead scheduling of integrated urban energy systems considering the reconfigurable capability.

A meta-heuristic is an iterative technique that helps to find out the near-optimal solution in a more efficient way. The objective of this method is to enlarge the aptitude of heuristics by joining more and more heuristic method. Due to the significant achievements of meta-heuristics approaches in solving different kinds of non-linear optimization problems, interest has been gradually shifted to applications of population-based approaches to handling the complexity involved in the nonlinear problem. It is proved that population based metaheuristics method is used to speed up the search process as well as to get optimal solution. Recently, so many researchers have expressed their interest in solving load dispatch problems with constraint using evolutionary algorithms such as differential evolution (DE) (Bhattacharya & Chattopadhyay, 2010),genetic algorithm(GA) (Chung, & Chan, 2012), Particle swarm optimization PSO (Meng et al. 2010), evolutionary programming (EP) (Vlachogiannis, & Lee, 2008), pattern search(PS) (Al-Sumait et al. 2007), simulated annealing (SA) (Precup et al. 2012) and tabu search(TS) (Lin, 2010).