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

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

Sunanda Hazra, Provas Kumar Roy
DOI: 10.4018/978-1-7998-8561-0.ch003
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Due to the rising requirement on energy sources and the global doubts for using fossil fuel because of its consequences on the climate changes and the global warming caused by hazardous gases, the scientific research has shifted to the renewable energy. To minimize the usage of thermal power generation plants and to meet the rising load demand, a thermal-integrated wind-hydro-system is taking an important role in renewable power systems. A proficient nature-inspired optimization is proposed for solving economic and emission dispatch for the hydro-thermal-wind (HTW) scheduling problem. Further, the opposition-based learning have been incorporated with the chemical reaction optimization for improving the performance of the algorithm. To investigate the performance of oppositional chemical reaction optimization algorithm, the algorithm is tested on two different cases. Along with this, some statistical tests have also been performed. The results obtained by the OCRO algorithm are compared with other recently proposed methods to establish its robustness.
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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).

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