Ant Lion Optimization Based OPF Solution Incorporating Wind Turbines and Carbon Emissions

Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: Review and analysis

“…where a i , b i , g i , u i , and v i are emission coefficients of i th thermal generating units. Numerous countries have implemented carbon taxes on greenhouse gas emissions to encourage clean power production and save the environment (Maheshwari et al, 2021). Due to added expenses, the power generation sector is under tremendous pressure to minimize emissions and generate cleaner energy by utilizing RESs .…”

Boosting cuckoo optimization algorithm via Gaussian mixture model for optimal power flow problem in a hybrid power system with solar and wind renewable energies

“…Additionally, a power flow-based hydro-thermal-wind scheduling approach for hybrid power systems was developed, employing the sine-cosine algorithm (SCA) [ 33 , 34 ], slime mould algorithm (SMA) [ 35 ] the performance of the SMA was assessed on the IEEE 30-bus and Algerian DZA 114-bus networks. Comparative analysis involved four optimization algorithms, results demonstrated the SMA's superiority across diverse function landscapes, consistently ranking first among the compared algorithms, antlion optimization (ALO) [ 36 ] the OPF problem integrated real-time measurements and probabilistic wind speed models for anticipated active power generation from WT. Evaluation criteria included operational costs, voltage profile, and network-wide transmission power losses.…”

Optimal power flow analysis including stochastic renewable energy sources using modified ant lion optimization algorithm

“…Meanwhile, metaheuristic techniques are growing in popularity, particularly for handling OPF problems, because of their ability to escape local optima by utilizing simple notions that resembles nature and can be applied to a wide variety of challenges (Sulaiman and Mustaffa, 2021). The state-of-the-art of methodologies used to address the OPF problems are particle swarm optimization (PSO) (Hazra and Sinha, 2011), moth swarm optimization (Mohamed et al, 2017), artificial bee colony algorithm (Rezaei Adaryani and Karami, 2013), social spider optimization algorithm (Nguyen, 2019), teaching-learning based algorithm (Bouchekara et al, 2014), most valuable player algorithm (Bouchekara, 2020), krill herd algorithm (Roy and Paul, 2015), harmony search method (Bhamidi and Shanmugavelu, 2019), ant colony optimization (Maheshwari et al, 2021), and grey wolf optimization algorithm (Siavash et al, 2017). These approaches guide the search process toward a near-optimal solution by effectively investigating the solution space.…”