Sintonización del D-Statcom por medio del método de optimización PSO

En los sistemas eléctricos de distribución siempre se ha tenido que trabajar con las perturbaciones producidas por algún tipo de fallo o una elevada variación en la carga, afectando la calidad del servicio suministrado a los usuarios que están conectados a dicha red. Gracias a los grandes avances que se han dado en los dispositivos de electrónica de potencia, se ha encontrado una forma de mitigar dichas perturbaciones, a través de la utilización del D-STATCOM, el cual es un inversor DC/AC que cuenta con un controlador de tensión encargado de controlar el flujo de potencia reactiva desde el D-STATCOM hacia el sistema. A través de los años se han implementado varios métodos de diseño y sintonización del control de tensión, entre ellos el método de ubicación de polos desarrollado por Persson [1] y el método de optimización por cumulo de partículas (PSO), el cual fue originalmente desarrollado en Estados Unidos por el sociólogo James Kennedy y por el ingeniero Russ C. Eberhart en 1995 [2]. Con el desarrollo de este proyecto de investigación, se pretende realizar un análisis comparativo entre estos dos métodos de control, por medio del simulador PSCAD, en el cual se diseña un D-STATCOM conectado al sistema de distribución con su respectiva carga. Al sistema se aplican fallos que varían en determinados instantes de tiempo con el fin de provocar perturbaciones en el sistema y así poder analizar la acción de compensación realizada por el D-STATCOM en ambos casos de sintonización, estos resultados son necesarios para concluir que método de control presenta mayor eficiencia y robustez

Optimal Selection of Conductor Sizes in Three-Phase Asymmetric Distribution Networks Considering Optimal Phase-Balancing: An Application of the Salp Swarm Algorithm

This paper presents a new methodology to simultaneously solve the optimal conductor selection and optimal phase-balancing problems in unbalanced three-phase distribution systems. Both problems were represented by means of a mathematical model known as the Mixed-Integer Nonlinear Programming (MINLP) model, and the objective function was the minimization of the total annual operating costs. The latter included the costs associated with energy losses, investment in conductors per network segment, and phase reconfiguration at each node in the system. To solve the problem addressed in this study, a master–slave methodology was implemented. The master stage employs a discrete version of the Salp Swarm Algorithm (SSA) to determine the set of conductors to be installed in each line, as well as the set of connections per phase at each of the nodes that compose the system. Afterward, the slave stage uses the three-phase version of the backward/forward sweep power flow method to determine the value of the fitness function of each individual provided by the master stage. Compared to those of the Hurricane-based Optimization Algorithm (HOA) and the Sine Cosine Algorithm (SCA), the numerical results obtained by the proposed solution methodology in the IEEE 8- and 25-node test systems demonstrate its applicability and effectiveness. All the numerical validations were performed in MATLAB

Efficient Day-Ahead Dispatch of Photovoltaic Sources in Monopolar DC Networks via an Iterative Convex Approximation

The objective of this research is to propose an efficient energy management system for photovoltaic (PV) generation units connected to monopolar DC distribution networks via convex optimization while considering a day-ahead dispatch operation scenario. A convex approximation is used which is based on linearization via Taylor’s series expansion to the hyperbolic relations between voltages and powers in the demand nodes. A recursive solution methodology is introduced via sequential convex programming to minimize the errors introduced by the linear approximation in the power balance constraints. Numerical results in the DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed convex model when compared to different combinatorial optimization methods, with the main advantage that the optimal global solution is found thanks to the convexity of the solution space and the reduction of the error via an iterative solution approach. Different objective functions are analyzed to validate the effectiveness of the proposed iterative convex methodology (ICM), which corresponds to technical (energy losses reduction), economic (energy purchasing and maintenance costs), and environmental (equivalent emissions of CO (Formula presented.) to the atmosphere in conventional sources) factors. The proposed ICM finds reductions of about (Formula presented.) in daily energy losses, (Formula presented.) in energy purchasing and operating costs, and (Formula presented.) in CO (Formula presented.) emissions when compared to the benchmark case in the DC version of the IEEE 33-bus grid. All numerical validations were carried out in the MATLAB programming environment using the SEDUMI and SDPT3 tools for convex programming and our own scripts for metaheuristic methods. © 2023 by the authors

Hybrid ga-socp approach for placement and sizing of distributed generators in DC networks

This research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master–slave optimization approach in order to solve the problems of placement and location of DGs, respectively. The master stage applies to the classical Chu & Beasley genetic algorithm (GA), while the slave stage resolves a second-order cone programming reformulation of the optimal power flow problem for DC grids. This master–slave approach generates a hybrid optimization approach, named GA-SOCP. The main advantage of optimal dimensioning of DGs via SOCP is that this method makes part of the exact mathematical optimization that guarantees the possibility of finding the global optimal solution due to the solution space’s convex structure, which is a clear improvement regarding classical metaheuristic optimization methodologies. Numerical comparisons with hybrid and exact optimization approaches reported in the literature demonstrate the proposed hybrid GA-SOCP approach’s effectiveness and robustness to achieve the global optimal solution. Two test feeders compose of 21 and 69 nodes that can locate three distributed generators are considered. All of the computational validations have been carried out in the MATLAB software and the CVX tool for convex optimization

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

The optimal power flow problem in direct current (DC) networks considering dispersal generation is addressed in this paper from the recursive programming point of view. The nonlinear programming model is transformed into two quadratic programming approximations that are convex since the power balance constraint is approximated between affine equivalents. These models are recursively (iteratively) solved from the initial point vt equal to 1.0 pu with t equal to 0, until that the error between both consecutive voltage iterations reaches the desired convergence criteria. The main advantage of the proposed quadratic programming models is that the global optimum finding is ensured due to the convexity of the solution space around vt. Numerical results in the DC version of the IEEE 69-bus system demonstrate the effectiveness and robustness of both proposals when compared with classical metaheuristic approaches such as particle swarm and antlion optimizers, among others. All the numerical validations are carried out in the MATLAB programming environment version 2021b with the software for disciplined convex programming known as CVX tool in conjuction with the Gurobi solver version 9.0; while the metaheuristic optimizers are directly implemented in the MATLAB scripts

Optimal economic-environmental dispatch in MT-HVDC systems via sine-cosine algorithm

This paper addresses the problem of optimal economic-environmental dispatch in Multi-Terminal High-Voltage Direct Current (MT-HVDC) networks using the Sine-Cosine Algorithm (SCA). This optimization methodology allows working with nonlinear non-convex large-scale optimization problems via sequential programming. The SCA works with an initial population and rules of advance based on the best current solution and sine and cosine functions that define the direction of the next solution. Three variants of the SCA are evaluated in a standard six-node MT-HVDC system considering a linear combination of the objective functions (i.e., greenhouse emissions and energy production costs). The main advantage of the proposed evolutionary approach lies in its pure algorithmic structure. Thus, it can be easily adapted to any continuous optimization problem. All numerical calculations are performed using MATLAB software. © 202

Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation

Due to the need to include renewable energy resources in electrical grids as well as the development and high implementation of PV generation and DC grids worldwide, it is necessary to propose effective optimization methodologies that guarantee that PV generators are located and sized on the DC electrical network. This will reduce the operation costs and cover the investment and maintenance cost related to the new technologies (PV distributed generators), thus satisfying all technical and operative constraints of the distribution grid. It is important to propose solution methodologies that require short processing times, with the aim of exploring a large number of scenarios while planning energy projects that are to be presented in public and private contracts, as well as offering solutions to technical problems of electrical distribution companies within short periods of time. Based on these needs, this paper proposes the implementation of a Discrete–Continuous Parallel version of the Particle Swarm Optimization algorithm (DCPPSO) to solve the problem regarding the integration of photovoltaic (PV) distributed generators (DGs) in Direct Current (DC) grids, with the purpose of reducing the annual costs related to energy purchasing as well as the investment and maintenance cost associated with PV sources in a scenario of variable power demand and generation. In order to evaluate the effectiveness, repeatability, and robustness of the proposed methodology, four comparison methods were employed, i.e., a commercial software and three discrete–continuous methodologies, as well as two test systems of 33 and 69 buses. In analyzing the results obtained in terms of solution quality, it was possible to identify that the DCPPSO proposed obtained the best performance in relation to the comparison methods used, with excellent results in relation to the processing times and standard deviation. The main contribution of the proposed methodology is the implementation of a discrete–continuous codification with a parallel processing tool for the evaluation of the fitness function. The results obtained and the reports in the literature for alternating current networks demonstrate that the DCPPSO is the optimization methodology with the best performance in solving the problem of the optimal integration of PV sources in economic terms and for any kind of electrical system and size. © 2022 by the authors

Black hole optimizer for the optimal power injection in distribution networks using DG

The optimal sizing of Distributed Generators (DG) in electric power distribution networks is carried out through a metaheuristic optimization strategy. To size DG it is proposed an optimal power flow model is formulated by considering that the location of these sources has been previously defined by the distribution company. The solution of the optimal power flow is reached with the Black Hole Optimizer (BHO). A methodology is used master-slave optimization methodology, where the BHO (i.e., master stage) defines the sizes of the DG and the slave stage evaluates the objective function with a load flow algorithm, this work using the triangular-based power flow method. Numerical results in the 33-node and the 69-node test system demonstrates the effectiveness and robustness of the proposed approach when compared with literature results. © 2021 Institute of Physics Publishing. All rights reserved

An Effective Power Dispatch of Photovoltaic Generators in DC Networks via the Antlion Optimizer

This paper studies the problem regarding the optimal power dispatch of photovoltaic (PV) distributed generators (DGs) in Direct Current (DC) grid-connected and standalone networks. The mathematical model employed considers the reduction of operating costs, energy losses, and CO2 emissions as objective functions, and it integrates all technical and operating constraints implied by DC grids in a scenario of variable PV generation and power demand. As a solution methodology, a master–slave strategy was proposed, whose master stage employs Antlion Optimizer (ALO) for identifying the values of power to be dispatched by each PV-DG installed in the grid, whereas the slave stage uses a matrix hourly power flow method based on successive approximations to evaluate the objective functions and constraints associated with each solution proposed within the iterative process of the ALO. Two test scenarios were considered: a grid-connected network that considers the operating characteristics of the city of Medellín, Antioquia, and a standalone network that uses data from the municipality of Capurganá, Chocó, both of them located in Colombia. As comparison methods, five continuous optimization methods were used which were proposed in the specialized literature to solve optimal power flow problems in DC grids: the crow search algorithm, the particle swarm optimization algorithm, the multiverse optimization algorithm, the salp swarm algorithm, and the vortex search algorithm. The effectiveness of the proposed method was evaluated in terms of the solution, its repeatability, and its processing times, and it obtained the best results with respect to the comparison methods for both grid types. The simulation results obtained for both test systems evidenced that the proposed methodology obtained the best results with regard to the solution, with short processing times for all of the objective functions analyzed

Parametric estimation in photovoltaic modules using the crow search algorithm

The problem of parametric estimation in photovoltaic (PV) modules considering manufacturer information is addressed in this research from the perspective of combinatorial optimization. With the data sheet provided by the PV manufacturer, a non-linear non-convex optimization problem is formulated that contains information regarding maximum power, open-circuit, and short-circuit points. To estimate the three parameters of the PV model (i.e., the ideality diode factor (a) and the parallel and series resistances (Rp and Rs)), the crow search algorithm (CSA) is employed, which is a metaheuristic optimization technique inspired by the behavior of the crows searching food deposits. The CSA allows the exploration and exploitation of the solution space through a simple evolution rule derived from the classical PSO method. Numerical simulations reveal the effectiveness and robustness of the CSA to estimate these parameters with objective function values lower than 1 × 10−28 and processing times less than 2 s. All the numerical simulations were developed in MATLAB 2020a and compared with the sine-cosine and vortex search algorithms recently reported in the literature