Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks

Abstract

In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time.

Enhanced Multi-Verse Optimizer (TMVO) and Applying it in Test Data Generation for Path Testing

“…In order to get the space disparity, we need to first measure the distance that separates each of the test suites. Next, we need to calculate the path disparity by working backwards from the branch condition through the control flow graph [22], [23]. Because product testing must take into account both the long term and the cost-benefit analysis, extensive testing may not be carried out.…”

Effects of Atom Search-Optimized Thornthwaite Potential Evapotranspiration on Root and Shoot Systems in Controlled Carica papaya Cultivation

“…The MVO algorithm is inspired by the concept of multiverse theory, where the white hole and black hole can interact through a wormhole as a travel path [35,44]. This algorithm takes the solution as a universe; therefore, the population size is the total number of universes.…”

“…To address the challenge of an ineffective evapotranspiration model, this study optimized the Thornthwaite evapotranspiration model using atom search (ASO), differential evolution (DE), and multiverse (MVO) metaheuristics and applied the recommended best temperature in a controlled growth chamber for papaya seedling cultivation. ASO, DE, and MVO were the preferred computational intelligence algorithms as they did not result in premature convergence during the initial experiment and had fewer hyperparameters that could be easily reconfigured, unlike some recently developed optimization models [33][34][35].…”

Recursive convex approximations for optimal power flow solution in direct current networks

“…Note that these results are reached through the comparison between the proposed convex reformulations and the most common metaheuristic approaches reported in the current literature. The selected comparative metaheuristics are the following: MVO [19], PSO [17], BH [18], CGA [9], and ALO [13].…”

“…To validate the effectiveness and robustness of the proposed quadratic programming approximations in this work, it was used the DC 69 bus test system reported in [19]. This test systems presents 69 buses, 68 branches, an unique slack generator and multiple constant power loads connected in the different buses.…”