Abstract
Solving the optimal power flow problems (OPF) is an important step in optimally dispatching the generation with the considered objective functions. A single-objective function is inadequate for modern power systems, required high-performance generation, so the problem becomes multi-objective optimal power flow (MOOPF). Although the MOOPF problem has been widely solved by many algorithms, new solutions are still required to obtain better performance of generation. Slime mould algorithm (SMA) is a recently proposed metaheuristic algorithm that has been applied to solve several optimization problems in different fields, except the MOOPF problem, while it outperforms various algorithms. Thus, this paper proposes solving MOOPF problems based on SMA considering cost, emission, and transmission line loss as part of the objective functions in a power system. The IEEE 30-, 57-, and 118-bus systems are used to investigate the performance of the SMA on solving MOOPF problems. The objective values generated by SMA are compared with those of other algorithms in the literature. The simulation results show that SMA provides better solutions than many other algorithms in the literature, and the Pareto fronts presenting multi-objective solutions can be efficiently obtained.
Highlights
In the competitive electricity market, optimal power flow (OPF) is one of the important tools to optimally dispatch generation with the considered objective function while satisfying system constraints [1,2,3]
The Slime mould algorithm (SMA) was applied to solve both single-objective and multi-objective OPF problems in the IEEE 30, 57, and 118-bus systems to investigate its performance in terms of fuel cost, emission, and transmission loss reductions where 14 different cases shown in Algorithm 1
The SMA was applied to solve both single-objective and multi-objective OPF problems in the IEEE 30, 57, and 118-bus systems to investigate its performance in terms of fuel cost, emission, and transmission loss reductions where 14 different cases shown in Table 1 were evaluated
Summary
In the competitive electricity market, optimal power flow (OPF) is one of the important tools to optimally dispatch generation with the considered objective function while satisfying system constraints [1,2,3]. Some traditional techniques such as quadratic programming [6], interior point method [7], and nonlinear programming [8] were used to solve the OPF problem These algorithms are usually trapped in the local optima, which returns a low-quality solution, and requires a large amount of computational time. The performance of the SMA has been evaluated for solving both single- and multi-objective OPF problems where fuel cost, emission, and transmission line loss are considered as part of the objective functions. The SMA is adopted to solve single- and multi-objective optimal power flow problems in the IEEE 30-, 57-, and 118-bus systems.
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