Optimal power flow incorporating stochastic wind and solar generation by metaheuristic optimizers
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ORIGINAL RESEARCH
Optimal power flow incorporating stochastic wind and solar generation by metaheuristic optimizers Mohd Herwan Sulaiman1
•
Zuriani Mustaffa2
Received: 6 August 2020 / Accepted: 23 September 2020 Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Optimal power flow (OPF) is one of the complex problems in power system operation that includes multi-modal, largescale, non-convex and non-linear constrained optimization problems. Due to these features, solving the OPF problem is becoming an active topic to be solved by power engineers and researchers. In this paper, recent metaheuristic algorithms namely Grasshopper Optimization Algorithm (GOA), Black Widow Optimization Algorithm, Grey Wolves Optimizer, Ant Lion Optimizer, Particles Swarm Optimization, Gravitational Search Algorithm, Moth-Flame Optimization and Barnacles Mating Optimizer (BMO) will be used to solve three objective functions of OPF problem viz. (1) cost minimization of the power generation that consists of thermal, stochastic wind and solar power generations, (2) power loss minimization, and (3) combined cost and emission minimization of power generations. To assess the performance of these selected metaheuristic algorithms on OPF, a modified IEEE 30-bus system that incorporate the stochastic wind and solar power generators will be used. Statistical studies are performed to identify the effectiveness of algorithms under consideration. Test results suggest that BMO performs better compared to the rest of algorithms and demonstrate that it can be effective alternative for the OPF problem solution.
1 Introduction Optimal power flow (OPF) is one of the most power system operation and planning issues that received much attention by researchers all over the world for the last few decades. The OPF problem solution aims to find the optimal control variables of power system components that optimize the selected objective function while satisfying all the constraints in a particular power system network such as generators capability, line capacity, bus voltage and power flow balance. Classical OPF deals with thermal power generators that run on fossil fuels and with the increment of renewable energy penetration into the power system network, the study of current OPF that includes the renewable energy sources become necessity (Biswas et al. 2017).
& Mohd Herwan Sulaiman [email protected]; [email protected] 1
Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang (UMP), 26600 Pekan Pahang, Malaysia
2
Faculty of Computing, Universiti Malaysia Pahang (UMP), 26600 Pekan Pahang, Malaysia
In order to overcome the limitation faced by the conventional techniques in terms of accuracy, convergence performance and robustness, such as in linear programming (LP) (Mota-Palomino and Quintana 1986), non-linear programming (NLP) (Habibollahzadeh et al. 1989) and quadratic programming (QP) (Burchett et al. 1984), researchers tend to use metaheuristic approache
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