A novel single/multi-objective frameworks for techno-economic operation in power systems using tunicate swarm optimization technique

This paper presents an efficient and reliable bio-inspired based meta-heuristic optimization algorithm to solve the optimal power flow (OPF) problem in modern power systems. The proposed algorithm is called, in the sequel, tunicate swarm algorithm (TSA). TSA combines the jet propulsion and swarm behaviors of tunicates during the navigation and foraging process for finding the optimal solution of the OPF problem. In the OPF problem, single and multi objective frameworks are developed and discussed. The developed frameworks aim to achieve individual and multidimensional economic and technical and environmental benefits to achieve the previous benefits, it is applied to single and multi-objective objective functions such as generation cost minimization, active power loss minimization, reactive power loss minimization, voltage profile improvement, and voltage stability enhancement. For the multi-objective function, the Pareto concept is implemented to generate a set of non-dominated solutions. The best compromise solution is extracted using fuzzy set theory. The performance of the developed frameworks are evaluated on two standard systems with 19 studied cases. The simulation results are compared with those obtained by other competitive algorithms in the literature. The simulation results are promising and show the high effectiveness and robustness of the proposed TSA in terms of the the low statistical indices, standard deviation, relative errors, maximum absolute error and root mean square error levels, for all tested cases. In addition, the sensitivity analysis study confirms that the proposed hybrid method produces robust results against parameter variations.