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In this paper, the estimation of solar cell parameters in the Bishop model was developed. The parameter estimation was formulated as an optimization problem. Several techniques were selected and compared, such as, Ant Lion Optimizer (ALO), Arithmetic Optimization Algorithm (AOA), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Salp Swarm Algorithm (SSA), and Vortex Search Algorithm (VSA). All these techniques were selected because of their solutions' quality when applied to estimating parameters in photovoltaic models. The Bishop model was selected since it can represent the behavior of a photovoltaic cell in direct and reverse modes of operation (when producing and consuming power, respectively). For the validation stage, two technologies were selected (monocrystalline and polycrystalline). All the techniques were executed 100 times and compared based on their estimated objective function and computation time (mean value and standard deviation). Then, the four techniques with the best results were selected and further analyzed employing the I–V curve reconstructions and the results of some critical points, such as short-circuit current and maximum power point. • A procedure to obtain cell I–V curve that includes information in the first and second quadrants of a commercial panel. • A cell parameter estimation strategy based on optimization techniques applied to the Bishop model. • Analysis of estimation results based on response repeatability and execution times. • Analysis of the quality of some important points in the I–V curve. [ABSTRACT FROM AUTHOR]

This paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses associated with energy transport and that considers the set of constraints that compose DC networks in an environment of distributed generation. To solve this mathematical formulation, a master–slave methodology that combines the Salp Swarm Algorithm (SSA) and the Successive Approximations (SA) method was used here. The effectiveness, repeatability, and robustness of the proposed solution methodology was validated using two test systems (the 21- and 69-node systems), five other optimization methods reported in the specialized literature, and three different penetration levels of distributed generation: 20%, 40%, and 60% of the power provided by the slack node in the test systems in an environment with no DGs (base case). All simulations were executed 100 times for each solution methodology in the different test scenarios. The purpose of this was to evaluate the repeatability of the solutions provided by each technique by analyzing their minimum and average power losses and required processing times. The results show that the proposed solution methodology achieved the best trade-off between (minimum and average) power loss reduction and processing time for networks of any size. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20 % , 40 % , and 60 % of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation. [ABSTRACT FROM AUTHOR]