Search

This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimization of three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO 2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid-connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín-Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times. [ABSTRACT FROM AUTHOR]

This article reviews the main methodologies employed for the optimal location, sizing, and operation of Distributed Generators (DGs) and Energy Storage Systems (ESSs) in electrical networks. For such purpose, we first analyzed the devices that comprise a microgrid (MG) in an environment with Distributed Energy Resources (DERs) and their modes of operation. Following that, we examined the planning and operation of each DER considered in this study (DGs and ESSs). Finally, we addressed the joint integration of DGs and ESSs into MGs. From this literature review, we were able to identify both the objective functions and constraints that are most commonly used to formulate the problem of the optimal integration and operation of DGs and ESSs in MGs. Moreover, this review allowed us to identify the methodologies that have been employed for such integration, as well as the current needs in the field. With this information, the purpose is to develop new mathematical formulations and approaches for the optimal integration and operation of DERs into MGs that provide financial and operational benefits. [ABSTRACT FROM AUTHOR]

The problem of the optimal location and sizing of photovoltaic (PV) sources in electrical distribution systems is addressed in this article through the application of the black widow optimization algorithm (BWOA). This problem is of mixed-integer nonlinear nature and is addressed by a master-slave type optimization strategy. In the master stage, the BWOA defines the location and size of the PV generators through discrete-continuous coding, and with this information, the slave stage (power flow for distribution) determines the electrical variables of the system, with which is evaluated the objective function and the constraints of the problem. As an objective function, the minimization of the annual costs of operation and maintenance of the system is considered, added to the total costs of purchasing energy in the electrical network for a planning period of 20 years. The numerical results in the IEEE 34- and IEEE 85-node systems show that with the proposed optimization methodology it is possible to reduce around 27% of the annual operating costs in both systems with the optimal location of three photovoltaic sources. Comparisons with metaheuristics and exact methodologies reported in the specialized literature confirm the efficiency and robustness of the proposed methodology. [ABSTRACT FROM AUTHOR]