Multiobjective Optimal Power Flow Using a Semidefinite Programming-Based Model

Abstract

This is a self-archived -parallel published version of this article in the publication archive of the University of Vaasa. It might differ from the original.

Multi-Criteria Decision Making in Optimal Operation Problem of Unbalanced Distribution Networks Integrated With Photovoltaic Units

“…The simulation results showed that the proposed model could effectively contribute to minimizing the two objective functions. In [35], a multiobjective optimal power flow program was presented and solved using the ε-constraint method. The mentioned model was tested on various systems, including the IEEE 30, 57, and 118 bus test systems, and the results confirmed the model's superiority over other similar methods.…”

Individual and collective objectives in an energy community with network constraints

“…To guarantee the global optimality of Pareto solutions within a deterministic time (Davoodi et al 2018(Davoodi et al , 2021Ding et al 2017) propose a convexified MO-OPF which minimizes a quadratic cost function at each node of the system. However, the semi-definite programming approach they propose was not extended to the problem of controlling a storage unit, and in particular to account for battery efficiency losses.…”

An Orthogonal Learning Bird Swarm Algorithm for Optimal Power Flow Problems

“…In the last five decades, numerous studies [1,[14][15][16][17][23][24][25][26][27][28][29][30][31][32][33][34][35][36] based on original metaheuristic approaches have been documented to find feasible solutions to OPF problems. In these studies different population-based original metaheuristic algorithms including binary backtracking search (BTS) algorithm [1], DE [14], gravitational search algorithm (GSA) [23], glowworm swarm optimization (GWSO) [24], stud krill herd (SKH) [25], tree-seed algorithm [26], biogeography based optimization (BBO) [27], symbiotic organisms search (SOS) [28], semidefinite programming (SDP) [29], salp swarm algorithm (SSA) [30], lightning attachment procedure optimization (LAPO) [31], harris hawk optimization (HHO) [32], multi-objective backtracking search (BTS) algorithm [33], gradient-based optimizer (GBO) [34], slime mould algorithm (SMA) [35], and marine predator algorithm (MPA) [36] have been applied to find feasible solutions of OPF problems.…”

“…In addition to the traditional mathematical methods, numerous nature-inspired and bio-inspired metaheuristic approaches have been proposed for solving optimization problems in research literature [23][24][25][26][27][28][29][30][31][32][33][34][35][36]. The bird swarm algorithm (BSA) [67] is a new population-based stochastic swarm intelligence approach.…”