Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow

Abstract

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 CO2 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.

Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions

“…This work highlights the advantage of using modified conventional continuous optimization methods (discrete versions) in order to solve problems involving discrete and binary variables. In the same way, different authors have recently addressed the problem regarding the selection and location of BS in GCNs [23][24][25].…”

“…By combining the three optimization methods proposed in master stage and PSO, it was possible to obtain three new master-slave methodologies for solving the problem under study, namely, PMC/PSO, PDGA/PSO, and PDCSA/PSO (hereinafter called PMC, PDGA, and PDCSA for the sake of simplicity). In addition, with the aim to evaluate the objective function and constraints related to each solution offered by these strategies, aiming for the shortest processing times and the best convergence, this study used the hourly power flow matrix based on successive approximations, as proposed in [23], which allows considering variations in distributed generation and power demand.…”

“…A power flow evaluation within a multi-hour scenario is known as an hourly power flow. In this work, a matrix hourly power flow based on successive approximations (MHPF) was selected, given the excellent results reported in [23]. Algorithm 1 describes this method: Algorithm 1: Algorithm proposed for the matrix hourly power flow based on successive approximations Data: Load the electrical system data; Load the BS information provided by the master-slave strategy: types of BS, location, and operation; Load V t dh (with t = 0), , and t max data; for t = 0 : t max do Evaluate the MHPF using Equation (17);…”

“…The parallel-discrete version of the genetic algorithm performs the selection, recombination, and mutation steps of traditional genetic algorithms (GA). However, this discrete version of the GA uses the codification proposed in Figure 2 and adapts the recombination and mutation steps to work with discrete variables, but the nature of the process is the same [23]. Algorithm 3 describes the iterative process of the PDGA.…”

Enhancing PV distributed generator planning in medium‐voltage DC distribution networks: A multi‐design techno‐economic analysis with load demand response

“…Applied various algorithms for the mitigating of active power losses in MVDC systems, including the Salp swarm algorithm (SSA) [19], arithmetic optimization algorithm (AOA) [20], and the multi-verse optimization (MVO) algorithm [21]. Recently, the ant-lion optimizer (ALO) algorithm was used for the reduction of the energy costs from the convectional generators and the photovoltaic (PV) source's maintenance costs [22], the vortex search algorithm (VSA) to minimization operating costs, energy losses, and pollution emissions [23], the search optimization algorithm (COA) to reduce the active power losses for EDN and DC soft open points converters [24].…”