Nature-Inspired Algorithm Applied to a Renewable Energy-Integrating Hydro-Thermal Power Plant

Ii Background

In a few decades ago, Newton’s method (Lee et al. 1998) and dynamic programming (Farag, et al. 1995) etc. are used for the cost minimization. Traditional techniques have the complexities in non-linear constraints as well as considerable time-consuming effect. For that reason, previously developed techniques do not deal with sufficiently for solving economic optimization problems. The methods mentioned above suffer from poor local optimal optimization and slow convergence rate. To overcome this drawback, many populations based methods such as chemical reaction optimization (CRO) (Hazra & Roy, 2015), krill herd algorithm (Mandal et al. 2014), oppositional moth flame optimization (OMFO) (Hazra & Roy, 2019), quasi-oppositional chemical reaction optimization (QOCRO) (Hazra & Roy, 2019), grasshopper optimisation algorithm (GOA) (Hazra & Roy, 2020) etc. are represented. Chen et al. (1993) proposed distribution management-oriented renewable energy generation using novel interval. Aghaei et al. (2013) suggested programming framework over the 24hour time span based on wind power in a scenario-based stochastic dynamic economic and emission load transmit problem. Hetzer et al. (2008) briefly discussed the wind power underestimation cost and overestimation cost of available generation for renewable power. Bai et al. (2016) projected an artificial bee colony (ABC) to compact with the uncertainty of wind power for solving load dispatch problem. Panigrahi et al. (2010) discuss about wind resources as the stochastic nature type and for that reason wind generation output is difficult to predict. Earlier, the problem has been measured for several times as the progress of load dispatch, but now a day’s research focuses on wind energy units together with exact cost functions. Most of these works are used as valid statistics distribution to characterize the inconsistency of wind and it is known as Weibull distribution (Shi et al. 2012). Moreover, Yu et al. (2007) explained different PSO techniques to state conditional hydrothermal scheduling (HTS) preparation. Hazra & Roy (2021) implemented MFO algorithm for solving the renewable energy integrating load transmit and HTS problem (Hazra et al. 2020).