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This research deals with the economic-environmental dispatch problem (EEDP) in multi-terminal high-voltage direct-current (MT-HVDC) systems by proposing a convex approximation based on semi-definite programming (SDP). The exact formulation of the EEDP corresponds to a non-convex, nonlinear programming problem due to the presence of a nonlinear quadratic constraint regarding the products between voltage variables. The thermal plants' economic and objective functions are modeled using typical quadratic functions. The SDP approach allows reaching a convex approximation that ensures the global optimal solution for each objective function independently or the construction of the optimal Pareto front via the weighting-factor optimization methodology. The proposed SDP approach also considers uncertainties in the demand and in the available power of renewable sources, which makes it robust. The main contribution of this research is a multi-period analysis that includes large-scale renewable generation sources and a robust analysis regarding demand and variations in renewable generation. Numerical results in two MT-HVDC systems demonstrate the effectiveness of the proposed SDP approach when compared to combinatorial optimization algorithms. All numerical simulations were carried out using the CVX convex disciplined tool, with the help of the SEDUMI and SDPT solvers, in the MATLAB programming environment.

This article presents a new control strategy for a DC-DC boost converter used to supply a constant power load. The designed controller allows regulates the converter output voltage to a required reference value. By designing the controller based on the reduced order averaged model of the converter and using a nonlinear control technique based on feedback linearization, a simple-to-implement and stable controller for both operating modes of the converter (continuous and discontinuous conduction modes respectively) is obtained. The designed control strategy is validated through simulation and experimental results, demonstrating good dynamic performance and stability in both conduction modes, under variations in reference values and significant load changes. In addition, a comparison of the performance and implementation costs between the control strategy proposed in this paper and two strategies proposed by other authors is included.

This research addresses the parametric estimation problem in three-phase induction motors by applying a recently developed metaheuristic method known as the generalized normal distribution optimizer (GNDO). A nonlinear programming model based on the steady-state circuit of the induction motor, which uses its Thevenin equivalent, is employed to model the estimation problem. Estimation is carried out by minimizing the mean square error between torque data (obtained from measurements or provided by the manufacturer) and the values calculated with the model. The main advantage of using the GNDO is its effective balance between the exploration and exploitation of the solution space via Gaussian distributions. Numerical tests in two three-phase induction machines confirm the effectiveness of this approach in comparison with the classical genetic algorithm, the particle swarm optimizer, and the sine cosine algorithm. The GNDO approach reports objective function values of about 9.7834×10−14 and 2.6500×10−14, while the sine cosine algorithm reaches solutions of about 4.6327×10−10 and 1.2400×10−6 in both tested motors. All numerical simulations were performed in the MATLAB software, version 2022b.

Today, the main challenge of miniaturization and integration of power converters is reducing the volume of parts, eliminating capacitors and inductors.This paper proposes a non-isolated synchronous buck converter (NSBC) under soft switching practice. This converter uses the zero voltage switching (ZVS) topology, which not only provides soft-switching conditions for the converter switches but also prevents the voltage kicking off the two ends of the main switches from being off-state due to self-resonance. To achieve the highest voltage gain in different working cycles, complementary switching approach and use of non-isolated Dc-Dc converter for simultaneous switching of two switches will be used.The non-isolated synchronous buck DC-DC converter with ZVS is to use in some parts of the converter for providing a purpose range of output voltage. In addition, the implementation of the proposed converter control circuit is conducted very simply and needs lower costs. To confirm relations in converter theory, the results of a laboratory sample in the discontinuous and continuous modes are examined. It should be noted that the proposed converter can operate under different levels of voltage, which requires a protection heat policy. The proposed converter implements under the nominal 30-V input and 40 KHz switching frequency, which touches on issues on both sides of discontinuous conduction mode (DCM) and the continuous conduction mode (CCM) conditions and experiment as an example two rates of power 30W and 20W respectively, and also has an acceptable interest rate, which will be evaluated and compared with three different types of similar modes.

This research addresses the problem of optimal load balancing in terminals of the three-phase substation by proposing three quadratic objective functions. These objective functions are formulated considering active, reactive, and apparent power consumptions aggregated at the terminals of the substation. The proposed formulation belongs to the mixed-integer quadratic models’ family, which can be solved globally with specialized mixed-integer convex tools. To evaluate the effect of load redistribution in the substation terminals, the 15- and 35-bus grids are tested using each of the proposed quadratic functions. In addition, Broyden's unbalanced power flow method is used to determine the extent of power loss reduction and enhancement of voltage profile. Numerical results confirm the effectiveness of the proposed mixed-integer quadratic model in enhancing electrical performance in three-phase asymmetric networks through load balancing at the substation terminals. After solving each quadratic function for the 15-bus grid, power losses were reduced between 12.9624% and 17.2550%, and these reductions were between 5.0771% and 7.7389% in the 35-bus grid.

This study focuses on optimizing the efficient operation of standalone direct-current (DC) microgrids with photovoltaic (PV) sources using semi-definite programming (SDP) optimization. The PV source operation model is formulated as a nonlinear programming (NLP) problem with the objective of minimizing daily energy losses and reducing CO2 emissions compared to diesel generators. Transforming the NLP model into convex optimization involves a linear matrix model that combines positive semi-definite matrices with an affine space. This approach enhances robustness by incorporating uncertainties in demand and PV source power. The robust SDP model employs a min–max strategy for worst-case scenario energy management dispatch (EMD). Evaluating a 27-bus standalone DC microgrid, the SDP model outperforms random-based algorithms by achieving global optima in both objectives. Under uncertainties, the energy loss objective increases by 21.6706% with demand uncertainty, 0.3997% with PV source uncertainty, and 22.2009% with both. Meanwhile, the CO2 emissions objective increases by 11.9184%, 1.8237%, and 14.0045%, respectively. Additional simulations on an 85-node DC network confirm the efficacy of SDP in worst-case scenario EMD. All simulations utilized MATLAB’s Yalmip tool with the Mosek solver.

This research presents a multi-objective dispatch (MOD) for energy storage systems (ESS) utilizing batteries and renewable energy resources (RES) in distribution network applications. This proposal employs a convex weighting-based approach. The exact MOD nonlinear programming model, which is non-convex due to the power balance constraint, is approximated using a second-order cone equivalent in order to ensure a global optimum, leveraging the convex properties of the objective functions and the conic equivalent in the complex-variable domain. The MOD, based on the linear combination of the objective functions via the weighting-based method, enables the construction of the optimal Pareto front, as each combination of the weighting factors generates a convex optimization sub-problem. The MOD analysis simultaneously considers the minimization of the expected grid operating costs with regard to energy purchasing at the substation terminals, the reduction of the operating costs associated with the RES and the ESS, and the minimization of the expected daily energy losses. This research makes two contributions: 1) the incorporation of the active and reactive power capabilities of the power electronic converters that interface with the ESS and the RES and 2) a robust analysis via convex optimization to address the uncertainties related to the expected daily generation and demand profiles. Numerical results obtained in the IEEE 33- and 85-bus test system confirm the effectiveness and robustness of the proposed MOD in comparison with the continuous genetic algorithm, the particle swarm optimizer, and the vortex search algorithm. In addition, the best metah-euristic technique was employed for constructing the Pareto front and comparing its performance against the proposed convex approach for the 33-bus grid. In the case of the 85-bus grid, battery power losses were included to demonstrate the effectiveness of this solution methodology in medium-scale distribution grids.INDEX TERMS Energy losses minimization, grid operating cost minimization, multi-objective dispatch, second-order conic equivalent, robust convex optimization, variable power factor operation, weighting-based multi-objective methodology.

Efficiency in logistics is often affected by the fair distribution of the customers along the routes and the available depots for goods delivery. From this perspective, in this study, the Multi-depot Vehicle Routing Problem (MDVRP), by considering two objectives, is addressed. The two objectives in conflict for MDVRP are the distance traveled by vehicles and the standard deviation of the routes’ length. A significant standard deviation value provides a small distance traveled by vehicles, translated into unbalanced routes. We have used a weighted average objective function involving the two objectives. A Variable Neighborhood Search algorithm within a Chu-Beasley Genetic Algorithm has been proposed to solve the problem. For decision-making purposes, several values are chosen for the weight factors multiplying the terms at the objective function to build up a non-dominated front of solutions. The methodology is tested in large-size instances for the MDVRP, reporting noticeable results for managerial insights.

The growing demand for electric power and the need for an energy transition that contributes to the reduction of global greenhouse gas emissions have driven the development of various energy generation, storage, and offset technologies. These technologies are known as distributed energy resources. Their integration into distribution power systems not only contributes to improving operating aspects, but also allows supplying electricity to areas that do not have access to large-scale power systems. Therefore, the integration and management of these resources has become a topic of interest, and several studies seek to optimize their impact on technical, economic, and environmental aspects. However, this optimization poses specific challenges related to the type and number of variables related to the operation of a distribution power system. This review article aims to describe the main challenges posed by three-phase AC three-phase distribution power systems under scenarios involving the integration of distributed energy resources. In addition, it presents some approaches proposed by different authors to improve the technical, economic, and environmental aspects of power grids. It can be stated that the strategies presented in the literature fail to consider scenarios that simultaneously integrate different types of technologies and optimize them while following a multi-objective approach and considering three-phase systems in a context of variable generation and demand. Therefore, future work in this field should address these aspects in a holistic manner, taking into account the computation efforts and processing times required by intelligent algorithms.

This research proposes an effective energy management dispatch (EMD) using a convex approximation for battery-based energy storage systems (ESS) and renewable sources in medium-voltage distribution networks. By means of the branch flow formulation, the exact nonlinear programming model representing the EMD is transformed into a convex model through a second-order cone programming relaxation. Two contributions are presented in this work. The first involves analyzing battery-based EES and renewable sources, specifically focusing on their operation with a variable power factor. This analysis considers the reactive power control capabilities of the power electronic converters that connect distributed energy resources to the grid. The second contribution involves evaluating a robust operation scenario by incorporating uncertainties in the availability of renewable generation and the expected daily demand profile. Numerical validations are conducted on two radial distribution networks composed of 27 and 33 nodes. These networks were chosen to represent the characteristics of rural and urban networks in the Colombian distribution system. The results are then compared against those obtained with a classical unitary power factor. The main goal of the proposed EMD is to minimize the expected annual grid energy losses and operating costs through a single-objective analysis. A comparison with metaheuristic optimization algorithms, such as the parallel version of the particle swarm optimizer, the vortex search algorithm, and a genetic algorithm, demonstrates the effectiveness of our convex EMD model for both objective functions under study. All numerical simulations, including the robust model, were run using MATLAB’s Yalmip tool.

Piezoelectric devices are transducers that convert pressure variations into electricity. Piezoelectric energy harvesting technology is based on harnessing pavement energy, using the pressure of vehicles due to gravity, where electricity is generated through the deformation of pavement materials. The main objective of this article is to show how bicycle traffic- on a bikeway- can be harnessed to obtain electrical energy with piezo-electric devices, analyzing an existing model of bicycle forces and gathering a model of traffic on the bikeway. This article implements three phases of analysis: the characterization of the bicycle according to the type of bicycle and traffic characteristics, the classification and selection of piezoelectric devices, and the amount of electrical energy produced by bicycle traffic that can be harnessed in signaling. The results are promising since with average daily traffic of 40,000 bicycles, 1.02 kWh is obtained in one linear meter with a piezoelectric device, which means that this same traffic in one kilometer of bikeway can generate approximately 1000 kWh per day.

AbstractThe uncertainty of microgrid resources in power systems has increased, which implies many challenges for their design and planning. The penetration coefficient of microgrids in power systems, as well as the high uncertainty of these sources, requires an analysis of probabilistic methods. These types of energy sources are inherently uncertain and bring many unknowns to the power system. One of the most important aspects to be analyzed is the distribution of the probabilistic optimal power flow (POPF). This research examines some methods for the distribution of possible loads in power systems, namely the Monte Carlo method (MCM), the two-point estimation method (2PEM), and the three-point estimation method (3PEM). Then, these methods are used to distribute the possible POPF. This work studies the adjusted probability density function (PDF), average, and deviation of losses for each method. Moreover, the appropriate selection of microgrid resources is determined while considering correlated variables. In order to compare the effectiveness of the proposed methods, a 30-bus IEEE standard test system is used in the MATLAB software, showing that 2PEM is more suitable than the others. The results related to microgrids are executed by running P_OPF in buses 5, 7, and 8.According to the results, for bus 8, the possible load distribution value is 52.897 MW, which requires the placement of a wind power plant. For buses 2 and 5, the possible load distribution is less than 10 MW, which is predicted in these buses considering the solar power plant.

This article presents a heuristic methodology to address the operation problem of PV-STATCOMs, focusing on the dynamic compensation of active and reactive power to minimize daily energy losses and costs associated with purchasing energy in distribution networks. The methodology is developed using a master-slave type optimization approach. In the master stage, potential solutions are generated using an optimization algorithm based on the salp swarm. In contrast, the slave stage evaluates these solutions using the power flow method by successive approximations. The methodology was evaluated in two test systems, with 33 and 69 nodes, varying the power factor of each PV-STATCOM. The results demonstrate a significant reduction in both energy losses in the network and the cost of purchasing energy at the substation. Furthermore, the efficiency of the salp swarm algorithm for effective resolution of the problem is confirmed. This approach contributes to optimizing the operation of PV-STATCOMs and highlights the proposed algorithm's applicability and effectiveness in practical distribution network environments.

This paper presents a stability analysis for a horizon-one continuous control set model predictive control (MPC) for DC/DC converters connected to a constant power loads (CPLs). Different MPC approaches have been previously presented, each tailored to a specific type of converter but without a formal stability proof, failing to ensure robust performance across various operating conditions. Unlike previous works in the scientific literature, our approach is general and applicable to any second-order DC/DC converter, including the Buck, Boost, Buck-Boost, and non-inverting Buck-Boost converters. A formal stability analysis was conducted using the proposed approach, initially for the open-loop system and subsequently for the closed-loop system. The trace-determinant or triangle criterion was employed for discrete-time systems in R2. Three analyses were conducted to evaluate the performance of the proposed MPC, showing the stability issues of DC/DC converters with CPLs in open loops. This work proposes solutions using the suggested technique. Numerical simulations utilizing the Boost and Buck-Boost converters demonstrate that, in open-loop operation, even minor perturbations in the CPL value can destabilize the behavior of electrical variables. In addition, simulations show that, for the Boost converter, the settling time was about 1.5ms. Meanwhile, in the Buck-Boost case, it was about 5ms. Standardized control indicators, such as the integral absolute error criterion (IAE), the integral of the time-multiplied absolute error criterion (ITAE), and the integral of the time-multiplied square error criterion (ITSE), confirm the effective performance of the proposed one-step MPC. The values range between 1.521×10−2 and 4.591×10−6 for the Boost converter, and between 6.607×10−3 and 3.366×10−6 for the Buck-Boost topology.

This article presents a solution methodology to minimize power losses in three-phase unbalanced distribution networks. This approach involved an efficient complex-domain model that is categorized under mixed-integer convex optimization. The methodology employed consisted of efficient load rotation at each constant power node via a three-phase rotation matrix that allows defining each load connection to minimize the expected power imbalance at the terminals of the substation, as well as the total grid power losses, and improve voltage profile performance at each system phase. The load imbalance, expressed as a percentage, can be defined as a function of the active, reactive, or apparent power. In addition, considering the complex-domain representation of three-phase electrical networks under steady-state conditions, a mixed-integer convex model was formulated to reduce the power imbalances. With the purpose of determining the initial and final power losses of these distribution systems, the successive approximations method was employed to address the three-phase power flow problem. As a result, numerical validations in the IEEE 25-bus system and a 35-node three-phase feeder showed that the final active power losses vary depending on the objective function analyzed. Therefore, for the test feeders studied, it is necessary to evaluate each objective function, with the aim of finding the one that yields the best numerical results. Power losses reductions of about 3.8056 % and 6.8652 % were obtained for both test feeders via the proposed optimization methodology. All numerical validations were performed in the Julia programming environment, using the JuMP optimization tool and the HiGHS solver.

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This document presents a tutorial regarding the use of PV-STATCOMs for dynamic active and reactive power compensation in AC distribution networks. The main idea is to guide the reader from the classical power flow formulation for AC distribution networks in the complex domain to a multi-period power flow problem using the optimization tools available in the Julia programming software. The use of PV-STATCOMs as dynamic active and reactive power compensation tools allows considering different power factors, i.e., they can work as a PV generation plant with a unitary power factor, as a purely reactive power compensator (capacitive or inductive), or as a dynamic active and reactive power compensator with a variable power factor. The Julia software was selected as a programming environment due to its excellent performance in terms of its programming interface and the use of specialized solvers for nonlinear programming problems in the complex domain, which helps introduce some concepts for the efficient operation of distribution networks while focusing on the mathematical model instead of on the solution technique. Numerical results confirm that the proposed approach, which uses the Ipopt solver, yields excellent numerical solutions in comparison with optimization methodologies recently reported in the scientific literature. A version of the IEEE 33-bus grid adapted for multi-period optimal power flow studies was employed as a test feeder. All numerical results were obtained using Julia, with the help of the JuMP optimization environment and the Ipopt solver.

This research addresses, from a perspective of metaheuristic optimization, the problem regarding parametric estimation in single-phase transformers while considering voltage and current measures at the terminals of the transformer and weighing linear loads. Transformer parametric estimation is modeled as a nonlinear problem in order to minimize the mean square error between the calculated voltage and current variables and the measurements taken. The nonlinearities are associated with Kirchhoff's first and second laws applied to the equivalent electrical circuit of the single-phase transformer. The nonlinear optimization problem is solved by applying a metaheuristic optimization algorithm known as the generalized normal distribution optimizer (GNDO), which uses evolution rules that allow exploring and exploiting the solution space via the classical probability function based on normal distributions. Numerical results in three test transformers of 20, 45, and 112.5 kVA demonstrate the effectiveness and robustness of the proposed GNDO approach when compared to other optimizers reported in the literature, such as the crow search algorithm, the coyote optimization algorithm, and the exact solution of the nonlinear optimization model using the fmincon solver of the MATLAB software. All numerical simulations confirm the potential of the GNDO approach to deal with complex optimization problems in engineering and science with promising results and low computational effort.

The RIBIERSE-CYTED network, i.e., the network for the large-scale integration of renewable energies in electrical systems (723RT0150) (2023-2026) is a hub for researchers and technologists belonging to Ibero-American universities, companies, and local administrations. This network promotes cross-training, mobility between centers, and the dissemination and implementation of technical and training activities aimed at analyzing and developing opportunities for the maximal integration of renewable resources, seeking a more sustainable energy model while allowing for the increased use of renewable resources and electric mobility (e-mobility).

Most of the concepts involved in Electrical Engineering have been developed from the 20th century to the present day. One of the most popular studies in this field is the power flow problem for electrical systems composed of multiple nonlinear loads (i.e., loads with constant power consumption) [1].

This research addresses the optimal phase-balancing problem by applying a master-slave optimization methodology. The master stage defines the load connections per node using a discrete codification, while the slave stage evaluates each load configuration provided by the master stage via a three-phase power flow. For the master stage, the salp swarm algorithm (SSA) was selected, which is an efficient bio-inspired technique to deal with continuous and discrete nonlinear optimization problems. The slave stage employed the matricial backward/forward power flow method for three-phase asymmetric networks. Numerical simulations in IEEE test feeders composed of 8, 25, and 37 nodes confirm the effectiveness of the SSA approach in finding efficient solutions regarding the expected grid power losses after optimal load phase-swapping. Numerical comparisons with the vortex search algorithm, the Chu & Beasley genetic algorithm, and the crow search algorithm demonstrate the effectiveness of the proposed methodology in dealing with the studied problem. All numerical validations were carried out in the MATLAB programming environment.

Operations with Electric Vehicles (EVs) on logistic companies and power utilities are increasingly related due to the charging stations representing the point of standard coupling between transportation and power networks. From this perspective, the Multi-depot Electric Vehicle Routing Problem (MDEVRP) is addressed in this research, considering a novel hybrid matheheuristic approach combining exact approaches and a Chu-Beasley Genetic Algorithm. An existing conflict is shown in three objectives handled through the experimentations: routing cost, cost of charging stations, and increased cost due to energy losses. EVs driving range is chosen as the parameter to perform the sensitivity analysis of the proposed MDEVRP. A 25-customer transportation network conforms to a newly designed test instance for methodology validation, spatially combined with a 33 nodes power distribution system.

This study utilizes inverse optimal control (IOC) theory to address the issue of output voltage regulation in a boost converter feeding an unknown direct current (DC) load. The proposed approach involves developing a general feedback control law through IOC to ensure asymptotic stability in closed-loop operation, with the added advantage of incorporating an integral gain without compromising stability. Two estimators are introduced to minimize the number of sensors required for implementing the IOC controller with integral action. The first estimator, based on the immersion and invariance (I&I) method, determines the current demand of the DC load by measuring the boost converter’s output voltage. While the second estimator, using the disturbance observer (DO) method, estimates the voltage input value by measuring the inductor’s current flow. Both methods guarantee exponential convergence to the precise value of the estimated variable, irrespective of the initial estimation points. Experimental validation using varying DC loads and estimation techniques confirms the proposed IOC approach’s effectiveness and robustness in regulating voltage for DC loads connected to a boost converter. Furthermore, the proposed controller is compared to the sliding mode control and presents a better performance with a more straightforward design, and the stability in closed-loop ensured.

This paper presents a generalized controller design to regulate the output voltage of second-order DC-DC converters feeding an unknown constant power load (CPL). Passivity-based control plus damping injection theory is employed to design a generalized action control to stabilize DC-DC converters. Furthermore, starting from the immersion and invariance (I&I) method, a generalized observer for second-order DC-DC converters is implemented to estimate the CPL value. By mixing the proposed controller with the I&I method, an adaptive generalized control approach is presented, which guarantees the locally asymptotic stability of the closed-loop for each converter. The main advantage of the nonlinear adaptive control design is its nonparametric dependence on the capacitance and inductance values, which makes it robust against parametric uncertainties. Phase portrait and sensitivity analyses are performed, and simulation and experimental results are examined to evaluate the performance of the proposed approach, which is also compared against feedback linearization and sliding mode control. Simulation and experimental results show the robustness and effectiveness of the adaptive proposed control approach.

This paper uses a convex reformulation to deal with the robust, optimal energy management of battery energy storage systems (BESS) and renewable energies in DC microgrids. This relaxed mathematical model guarantees the global optimum regarding energy management problems, even when including uncertainties in demand and renewable energy. The proposed robust model can reach the best scheduling for energy management in worst-case cost scenarios while satisfying all requirements. Furthermore, a model for power transfer losses in converter devices is added to the proposed model by using a binary polynomial representation, which is convexified. This convexification is performed by transforming the nonlinear non-convex equations of the optimal energy management model into second-order cone constraints. Four scenarios implemented in the modified IEEE 123-bus radial distribution feeder are proposed in order to analyze the robust optimal energy management strategy: the deterministic model, demand uncertainty, uncertainty in solar and wind generators, and uncertainty in demand and solar and wind generators. In all scenarios, the energy management model reduces the total energy costs. Simulation scenario 1 showed daily operating costs of about US $\$ $ 4376.82, while simulation scenarios 2, 3, and 4, including demand and generation uncertainties, increased the daily operating costs by about US $\$ $ 5243.23 (19.79%), US $\$ $ 5072.23 (15.88%), and US $\$ $ 5738.75 (31.12%), respectively. Scenario 4 showed the highest costs, as it involves more uncertainty. Hence, the robust optimal energy management strategy dispatches more energy from conventional generators, increasing the operating costs to satisfy those of the worst case.

The problem regarding the reconfiguration of electrical distribution grids is addressed in this research through the implementation of a practical solution using a constructive heuristic algorithm. The most important characteristic of the proposed heuristic approach is its low-computation effort, given that few power flow solutions are required in order to solve the reconfiguration problem. The constructive algorithm starts its exploration of the solution space by closing all the tie lines form a fully meshed distribution network. The distribution line with the minimum current is permanently opened. A new power flow evaluation is made for the new distribution system, and the distribution line with the minimum current is opened if and only if this action does not generate isolated nodes. This procedure is repeated until the number of closed lines is equal to the number of nodes minus one, which is a condition required to maintain a radial configuration. Numerical validations in test feeders composed of 16, 33, 69, 84, 136, and 415 nodes demonstrate that the proposed constructive algorithm finds adequate solutions with minimum processing times. The proposed approach is practical for distribution companies since its implementation only requires a power flow tool for distribution networks that can deal with radial and meshed configurations.

Objective: This article presents an analysis of different optimization methodologies, which aims to make an objective comparison between metaheuristic and convex optimization methods in distribution networks, focusing on the inclusion of distributed generation (DG). The MATLAB software is used as a tool for implementation and obtaining results. The objective was to determine the optimal size of the DGs to be integrated into the networks, with the purpose of reducing the active power losses (objective function). Methodology: Based on the specialized literature, the methodologies are selected, and the bases and conditions for the implementation of the optimization techniques are determined. In the case of second-order cone programming (SOCP), the relaxation of the nonlinear optimal power flow (OPF) problem is performed in order to use convex optimization. Then, the structures of each technique are established and applied in the MATLAB software. Due to the iterative nature of metaheuristic methods, the data corresponding to 100 compilations for each algorithm are collected. Finally, by means of a statistical analysis, the optimal solutions for the objective function in each methodology are determined, and, with these results, the different methods applied to the networks are compared. Results: By analyzing 33- and 69-node systems, it is demonstrated that metaheuristic methods are able to effectively size DGs in distribution systems and yield good results that are similar and comparable to SOCP regarding the OPF problem. Genetic algorithms (GA) showed the best results for the studied implementation, even surpassing the SOCP. Conclusions: Metaheuristic methods proved to be algorithms with a high computational efficiency and are suitable for real-time applications if implemented in distribution systems with well-defined conditions. These techniques provide innovative ideas because they are not rigid algorithms, which makes them very versatile methods that can be adapted to any combinatorial optimization problem and software, yielding results even at the convex optimization level.

Photovoltaic (PV) systems are a clean energy source that allows for power generation integration into electrical networks without destructive environmental effects. PV systems are usually integrated into electrical networks only to provide active power during the day, without taking full advantage of power electronics devices, which can compensate for the reactive power at any moment during their operation. These systems can also generate dynamic reactive power by means of voltage source converters, which are called PV-STATCOM devices. This paper presents a convex formulation for the optimal integration (placement and sizing) of PV-STATCOM devices in electrical distribution systems. The proposed model considers reducing the costs of the annual energy losses and installing PV-STATCOM devices. A convex formulation was obtained to transform the hyperbolic relation between the products of the voltage into a second-order constraint via relaxation. Two simulation cases in the two IEEE test systems (33- and 69-node) with radial and meshed topologies were implemented to demonstrate the effectiveness of the proposed mixed-integer convex model. The results show that PV-STATCOM devices reduce the annual cost of energy losses of electrical networks in a more significant proportion than PV systems alone.

This number of the Ingeniería journal explores the participation of the researchers from Bogotá City in Colombia that selects our journal to present their contributions in different research areas of Engineering, which includes Industrial, Electrical, Electronic, Civil, Environmental, Agricultural, and Mechanical Engineering, among others.

The integration of distributed generation (DG), energy storage systems (ESS), and controllable loads near the place of consumption has led to the creation of microgrids. However, the uncertain nature of renewable energy sources (wind and photovoltaic), market prices, and loads have caused issues with guaranteeing power quality and balancing generation and consumption. To solve these issues, microgrids should be managed with an energy management system (EMS), which facilitates the minimization of operating (performance) costs, the emission of pollutants, and peak loads while meeting technical constraints. To this effect, this research attempts to adjust parameters by defining indicators related to the best possible conditions of the microgrid. Generation planning, the storage of generated power, and exchange with the main grid are carried out by defining a dual-purpose objective function, which includes reducing the operating cost of power generation, as well as the pollution caused by it in the microgrid, by means of the SALP optimization algorithm. Moreover, in order to make the process more realistic and practical for microgrid planning, some parameters are considered as indefinite values, as they do not have exact values in their natural state. The results show the effect of using the introduced intelligent optimization method on reducing the objective function value (cost and pollution).

The load flow problem (LFP) in power distribution networks allows us to find the nodal voltage values within the electrical systems. These values, along with the system parameters, are useful to identify the (technical, economic, and environmental) operational indices and constraints that describe the system's behavior under an established load scenario. The solution of the LFP requires the implementation of numerical methods due to its mathematical model's nonlinear and non-convex nature. In the specialized literature, multiple classical and modern methods seek to improve the solutions achieved in terms of convergence and processing times. However, the most efficient method in both radial and meshed networks has not been determined. Consequently, this study identified the most widely used and efficient classical and modern methods reported in the literature: Newton–Raphson (NR), Gauss-Seidel (GS), Iterative Sweep (IS), Successive Approximations (SA), Taylor's Series (TS), and Triangular Method (TM). The analysis also identified and selected the most common test scenarios to validate the effectiveness of the proposed solution methods: 10-, 33-, and 69-node systems in radial and meshed topologies. The software employed to validate the processing times and convergence of the numerical methods was MATLAB. The results obtained by the different methods were compared, taking the NR methodology as the base case. Thanks to its convergence, this method is used in commercial software packages to solve the LFP, as is the case of DIgSILENT. After analyzing the results of this study, we can state that all the selected methods were suitable in terms of convergence. The greatest errors were 6.064×10−07 for power losses and 8.017×10−04 for nodal voltages, which are negligible values for practical purposes in radial and meshed networks. In this work, processing time was employed as the selection criterion, and TM was identified as the most efficient method for solving the AC power flow in radial and meshed topologies for all the scenarios analyzed.

The power flow solution is a classical problem in electrical engineering that has been studied for more than 60 years [1]. One of the most widely used methods corresponds to the Newton-Raphson approach, which is currently employed for analyzing power systems with meshed configurations and multiple generation sources, i.e., it is typically employed for power systems in high-voltage levels [2].

The main problem in the operation of micro-grids is controlling the voltage and frequency. The inertia of the whole grid is low, so the operation of the system is interrupted by sudden changes in load or incidence in the absence of a proper control system. In order to solve this issue, various control structures have been proposed. In this paper, an optimal distributed control strategy for coordinating multiple distributed generation instances is presented in an islanded microgrid. A secondary frequency control method is implemented in order to eliminate voltage deviation and reduce the small signal error. In this layer, an optimized PID controller is used. PID controller optimization is carried out via the Honey Badger Algorithm, and results are obtained using the MATLAB software. According to the results, inadequate adjustment of a secondary loop leads to poor and unacceptable outcomes, and the necessary power quality is not achieved. However, by using the proposed method, a proper performance of the microgrid in the face of disturbances is achieved.

Abstract Objective: This work involves a convex-based mathematical reformulation for the optimal power flow problem in DC networks. The objective of the proposed optimization model corresponds to the minimization of the power losses through all the network branches considering a convex conic model that warranties finding the global optimal. Methodology: This is split into three stages: The first stage presents the mathematical model of optimal power flow for DC networks and all its geometric features that make it non-convex; the second stage presents the convex reformulation from a second order conic relaxation; the third stage shows the main characteristics of the DC system under study; and the fourth stage presents the optimal solution of the power flow problem and its comparisons with some methods reported in the specialized literature. Results: The numerical validations demonstrate that the model of proposed convex optimal power flow obtains the same solution as the exact model of the problem with an efficiency of 100%, which is in contrast with the variability of the results that are presented by the metaheuristic techniques reported as comparison methodologies. Conclusions: The proposed second-order conic relaxation warrantied the convexity of the solution space and therefore, the finding of the optimal solution at each execution; besides of this, demonstrated that for optimal power flow problems in DC networks, the numerical performance is better than most of the comparative metaheuristic methods; and the provided solution by the proposed relaxation is equivalent to that provided by the exact model. Keywords: Direct current networks, second-order conic relaxation, non-linear programming model, convex optimization.

This research addresses the efficient integration and sizing of flexible alternating current transmission systems (FACTS) in electrical distribution networks via a convex optimization approach. The exact mixed-integer nonlinear programming (MINLP) model associated with FACTS siting and sizing aims for the minimization of the expected annual operating costs of the network (i.e., energy losses and FACTS purchasing costs). The constraints of this problem include power equilibrium equalities, voltage regulation bounds, and device capacities, among others. Due to the power equilibrium constraints per node and period, the MINLP model is a non-convex optimization problem. To transform the exact MINLP model into a mixed-integer convex one, the approximation of the product between two variables in the complex domain is relaxed through its hyperbolic equivalent, which generates a set of convex cones. The main advantage of the proposed mixed-integer convex model is that it ensures the global optimum of the problem, even when considering objective multiplexes. Numerical simulations in the IEEE 33-, 69-, and 85-bus grids demonstrate the effectiveness and robustness of FACTS integration via the proposed convex approach in comparison with the exact solution of the MINLP model in the GAMS software as well as with combinatorial optimization algorithms (i.e., the black widow optimizer and the vortex search algorithm). All simulations were carried out in MATLAB with Yalmip optimization and the Gurobi and Mosek solvers. The simulation results show that, for a fixed operation of the FACTS devices (i.e., a VAR compensator) during the day, the annual operating costs are reduced by 12.63%, 13.97%, and 26.53% for the IEEE 33-, 69-, and 85-bus test systems, respectively, while for the operation variable, the reductions are by 14.24%, 15.79%, and 30.31%, respectively.

This research addresses the problem regarding the efficient operation of photovoltaic (PV) plants in monopolar direct current (DC) distribution networks from a perspective of convex optimization. PV plant operation is formulated as a nonlinear programming (NLP) problem while considering two single-objective functions: the minimization of the expected daily energy losses and the reduction in the expected CO2 emissions at the terminals of conventional generation systems. The NLP model that represents the energy management system (EMS) design is transformed into a convex optimization problem via the second-order cone equivalent of the product between two positive variables. The main contribution of this research is that it considers the uncertain nature of solar generation and expected demand curves through robust convex optimization. Numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness and robustness of the proposed second-order cone programming model in defining an EMS for a monopolar DC distribution network. A comparative analysis with four different combinatorial optimizers is carried out, i.e., multiverse optimization (MVO), the salp swarm algorithm (SSA), the particle swarm optimizer (PSO), and the crow search algorithm (CSA). All this is achieved while including an iterative convex method (ICM). This analysis shows that the proposed robust model can find the global optimum for two single-objective functions. The daily energy losses are reduced by 44.0082% with respect to the benchmark case, while the CO2 emissions (kg) are reduced by 27.3771%. As for the inclusion of uncertainties, during daily operation, the energy losses increase by 22.8157%, 0.2023%, and 23.7893% with respect to the benchmark case when considering demand uncertainty, PV generation uncertainty, and both. Similarly, CO2 emissions increase by 11.1854%, 0.9102%, and 12.1198% with regard to the benchmark case. All simulations were carried out using the Mosek solver in the Yalmip tool of the MATLAB software.

This research addresses the power flow analysis in bipolar asymmetric direct current (DC) networks by applying Broyden’s numerical method. This general successive approximations method allows for a simple Newton-based recursive formula to reach the roots of multiple nonlinear equations. The main advantage of Broyden’s approach is its simple but efficient structure which can be applied to real complex nonlinear equations.The power flow problem in bipolar DC networks is still challenging, as multiple operating options must be considered, e.g., the possibility of having a solidly grounded or floating neutral wire. The main goal of this research is to contribute with a generalization of Broyden’s method for the power flow solution in bipolar DC networks, with the main advantage that, under well-defined conditions, this is a numerical method equivalent to the matricial backward/forward power flow, which is equivalent to the successive approximations power flow method. Numerical results in the 21-, 33-, and 85-bus grids while considering two connections for the neutral wire (i.e., solidly grounded at any node or floating) show the effectiveness of Broyden’s method in the power flow solution for bipolar asymmetric DC networks. All numerical simulations were carried out in the MATLAB programming environment.

This research analyzes electrical distribution networks using renewable generation sources based on photovoltaic (PV) sources and distribution static compensators (D-STATCOMs) in order to minimize the expected annual grid operating costs for a planning period of 20 years. The separate and simultaneous placement of PVs and D-STATCOMs is evaluated through a mixed-integer nonlinear programming model (MINLP), whose binary part pertains to selecting the nodes where these devices must be located, and whose continuous part is associated with the power flow equations and device constraints. This optimization model is solved using the vortex search algorithm for the sake of comparison. Numerical results in the IEEE 33- and 69-bus grids demonstrate that combining PV sources and D-STATCOM devices entails the maximum reduction in the expected annual grid operating costs when compared to the solutions reached separately by each device, with expected reductions of about 35.50% and 35.53% in the final objective function value with respect to the benchmark case. All computational validations were carried out in the MATLAB programming environment (version 2021b) with our own scripts.

This paper utilizes convex optimization to implement a day-ahead scheduling strategy for operating a photovoltaic distribution static compensator (PV-STATCOM) in medium-voltage distribution networks. The nonlinear non-convex programming model of the day-ahead scheduling strategy is transformed into a convex optimization model using the second-order cone programming approach in the complex domain. The main goal of efficiently operating PV-STATCOMs in distribution networks is to dynamically compensate for the active and reactive power generated by renewable energy resources such as photovoltaic plants. This is achieved by controlling power electronic converters, usually voltage source converters, to manage reactive power with lagging or leading power factors. Numerical simulations were conducted to analyze the effects of different power factors on the IEEE 33- and 69-bus systems. The simulations considered operations with a unity power factor (active power injection only), a zero power factor (reactive power injection only), and a variable power factor (active and reactive power injections). The results demonstrated the benefits of dynamic, active and reactive power compensation in reducing grid power losses, voltage profile deviations, and energy purchasing costs at the substation terminals. These simulations were conducted using the CVX tool and the Gurobi solver in the MATLAB programming environment.

This paper deals with the problem of the optimal placement and sizing of distribution static compensators (D-STATCOM) in electrical distribution networks to reduce the total annual operative costs associated with the total costs of energy losses added with the investment costs in D-STATCOM. The metaheuristic sine-cosine optimization algorithm determines nodes with the location and optimal sizes of the D-STATCOM. A discrete-continuous codification represents the decision variables, where the discrete part is entrusted with the best candidate nodes selection. The continuous part deals with the optimal sizes assigned to the D-STATCOM. Numerical results in the IEEE 33- and IEEE 69-bus systems demonstrate the effectiveness of this approach since it helps to minimize the total grid operation costs compared with the solution of the mixed-integer nonlinear programming model in GAMS. All the numerical validations are carried out in the MATLAB programming environment.

This paper proposes a mixed-integer convex model for optimal load-balancing in bipolar DC networks while considering multiple constant power terminals. The proposed convex model combines the Branch and Cut method with interior point optimization to solve the problem of optimal load balancing in bipolar DC networks. Additionally, the proposed convex model guarantees that global optimum of the problem is found, which ensures minimal power losses in the bipolar DC distribution grid branches, as the total monopolar load consumption has been balanced at the substation's terminals. In addition, an optimal load balancing improves the voltage profiles due to current redistribution between the positive and negative poles. Numerical results in the 21- and 85-bus test feeders and a comparison with three metaheuristic techniques show the effectiveness of the proposed convex model in reducing the total grid imbalance while minimizing the power losses and improving the voltage profiles.

Electricity retailers participate in electricity markets as intermediaries between wholesale and retail markets. They acquire energy on the wholesale side by participating in next-day markets and the pool of power. On the retail side, they make contracts with consumers in order to meet their energy demand at a fixed price for a set period of time –generally a year. To maximize profit in planning, a retailer must choose the best strategy, which should be able to reduce the cost of purchasing energy in the wholesale market while simultaneously determining the best selling price for consumers. Customers may choose a different retailer if the selling price is too high, and the retailer may take a loss if the price is too low. One of the issues that complicate retailers’ decision is the uncertain demand response parameters that affect profit. This paper contributes with a strategic bidding model for planning with short-term energy storage while considering the uncertainty of consumer demand response and load response programs simultaneously. GAMS and MATLAB are implemented in this research to analyze the data and review the results, which indicate that an increase in profit is expected to be greater than when the retailer uses only a load response program or a short-term energy storage system. As uncertainty grows, so does local price sensitivity, and, as a result, so does the predicted rate of profit. Profits from participatory reservation, energy, and regulation markets increase in the robust model, while profits from the common participatory market decrease, i.e., according to this study, which looked at both probabilistic and robust models of retail market participation. When a robust model is used, the overall profit is higher than that obtained from a probabilistic model.

This article presents a methodology to solve to the Optimal Power Flow (OPF) problem in Direct Current (DC) networks using the Arithmetic Optimization Algorithm (AOA) and Successive Approximation (SA). This master-slave methodology solves the OPF problem in two stages: the master stage estimates the solution to the OPF problem considering its constraints and variables, and the slave stage assesses the fitness of the solution proposed by the master stage. To validate the methodology suggested in this article, three test systems cited multiple times in the literature were used: the 10, 21 and the 69 nodes test systems. In addition, three scenarios varying the allowable power limits for the Distributed Generators (DGs) are presented; thus, the methodology explores solutions under different conditions. To prove its efficiency and robustness, the solution was compared with four other methods reported in the literature: Ant Lion Optimization (ALO), Black Hole Optimization (BHO), the Continuous Genetic Algorithm (CGA), and Particle Swarm Optimization (PSO). The results show that the methodology proposed here to reduce power losses presents the best solution in terms of standard deviation.

En este artículo se aborda el problema de ubicación y dimensionamiento óptimo de generadores fotovoltaicos (PV) en redes de corriente continua (CC) con el objetivo de minimizar los costos de inversión y operación de la red para la empresa de distribución de energía en un horizonte de operación de 20 años. Este problema es presentado mediante un modelo matemático de programación no lineal entera mixta (PNLEM), el cual se resuelve mediante la aplicación de una metodología de optimización del tipo maestro-esclava. La etapa maestra corresponde a una versión mejorada del algoritmo de optimización aritmética que incluye una etapa de exploración y explotación del espacio de solución que involucra la generación de nuevas soluciones a partir de la aplicación de funciones de distribución gaussiana alrededor de actual en cada iteración . En la etapa esclava se emplea el algoritmo de flujo de potencia especializado para redes de CC, el cual permite evaluar cada posible solución obtenida de la etapa maestra en relación con la ubicación (nodos) y el dimensionamiento de los generadores PV (tamaños), y verificar que todas las restricciones asociadas al modelo de PNLEM se cumplan. El resultado principal de esta investigación corresponde a una metodología mejorada basada en la combinación del algoritmo de optimización aritmética y las funciones de distribución gaussiana para mejorar las etapas de exploración y explotación del espacio de soluciones y encontrar soluciones de mejor calidad que las reportadas en la literatura especializada. En conclusión, los resultados numéricos en los sistemas de prueba IEEE 33 e IEEE 69 nodos demostraron que el algoritmo de optimización propuesto mejoró los resultados existentes en la literatura especializada para la ubicación y el dimensionamiento de fuentes PV en sistemas de distribución de CC, lo cual genera un nuevo punto de referencia para futuras investigaciones en esta temática.

This research focused on the development of a methodology for calculating the optimal power factor (OPF) in microgrids with the photovoltaic generation, in order to use solar inverters as reactive compensators, which will change their power factor according to the needs of the load. The developed methodology proposes a convex optimization model with multiple constraints to solve the OPF problem. Wirtinger's linearization in the power balance equation was implemented. The stochastic behavior of solar radiation was considered using the average sampling approach (ASA) to generate solar scenarios, which are used to calculate the magnitude of the generation of photovoltaic systems for specific hours of the day. Finally, the algorithm was run on CIGRE's 19-node test grid. The proposed methodology showed that as the radiation level increases during the day, more radiation scenarios can be tested, which increases the accuracy of the power factor value for each PV system. Although the general idea in power systems is to have a unity power factor, the algorithm resulted in power factors with values less than one in some inverters. This represents an injection of reactive power from the inverters to meet the reactive needs of the loads connected close to said PV generators, which is reflected in a variation in the magnitude of the power factor.

This article addresses the problem regarding power regulation in classical DC-DC second-order converters by means of a nonlinear control technique based on inverse optimal control theory. There are few papers that describe inverse optimal control for DC-DC converters in the literature. Therefore, this study constitutes a contribution to the state of the art on nonlinear control techniques for DC-DC converters. In this vein, the main objective of this research was to implement inverse optimal control theory with integral action to the typical DC-DC conversion topologies for power regulation, regardless of the load variations and the application. The converter topologies analyzed were: (i) Buck; (ii) Boost; (iii) Buck-Boost; and (iv) Non-Inverting Buck-Boost. A dynamical model was proposed as a function of the state variable error, which helped to demonstrate that the inverse optimal control law with proportional-integral action implemented in the different converters ensures stability in each closed-loop operation via Lyapunov’s theorem. Numerical validations were carried out by means of simulations in the PSIM software, comparing the established control law, the passivity-based PI control law, and an open-loop control. As a conclusion, it was possible to determine that the proposed model is easier to implement and has a better dynamical behavior than the PI-PBC, ensuring asymptotic stability from the closed-loop control design.

Este artículo presenta un análisis de la caracterización físico-mecánica de un concreto geopolimérico fibrorreforzado, cuya matriz HCV está conformada por 90 % ceniza volante (CV) y 10 % cemento portland ordinario (OPC); se utiliza como activador alcalino una solución compuesta por silicato e hidróxido de sodio (NaOH, Na2SiO3) y agua. El refuerzo utilizado fueron fibras de acero SikaFiber Xorex incorporadas a la matriz en proporciones de 50 y 75 kg/m3. La mezcla HCV-50 reportó una resistencia a la compresión de 26,77 MPa a los 28 días de curado, por lo que se clasifica como concreto estructural según la NSR-10; a la misma edad de curado se obtuvo resistencia a la tracción indirecta de 3,49 MPa, módulo de elasticidad de 29,32 GPa, resistencia a la flexión de 5,15 MPa y tenacidad hasta la deflexión de agrietamiento (δf) de 1.971,9 N.mm. Esta mezcla, considerada la óptima, fue empleada en la fabricación de losas de concreto, las cuales presentaron una deflexión de ruptura δf de 4,45 mm, deflexión última de 16,15 mm, carga máxima soportada 15,6 kN, tenacidad de 49.464,8 N.mm hasta δf y 14.5847,3 N.mm hasta 3 veces δf. El material geopolimérico fibrorreforzado se propone además para ser utilizado en la producción de concreto proyectado (shotcrete) y en la construcción de losetas aligeradas.

Objetivo: Este trabajo analiza el costo óptimo de expansión, el número de líneas a incluir y el tiempo de simulación computacional para dos sistemas transmisión, empleando programación no lineal entera mixta a través de los solver del software GAMS (General Algebraic Modeling System). El objetivo es determinar las diferencias en los costos de expansión cuando se emplea el modelo de transporte, DC, híbrido lineal y lineal disyuntivo. Metodología: Está dividida en cinco etapas: la primera identifica el sistema de transmisión, la segunda establece el problema de planeamiento del sistema de transmisión, la tercera realiza la formulación del sistema de potencia de acuerdo con cada uno de los modelos de optimización, la cuarta aplica la formulación del sistema de potencia en el software GAMS y la última selecciona el costo óptimo. Resultados: Desde el análisis de costo óptimo, el modelo DC y el modelo lineal disyuntivo presentaron el mayor costo óptimo respecto al modelo de transporte y al modelo hibrido lineal. Conclusiones: Los modelos implementados presentaron desempeños equivalentes en el sistema de prueba con el menor número de nodos; al aumentar el número de nodos, el desempeño de los modelos no presenta similitud. Por el contrario, se identifican diferencias importantes en los resultados obtenidos, lo que permite caracterizar solver específicos de acuerdo con el número de nodos. Metodología: Está dividida en 5 etapas, la primera identifica el sistema de transmisión, la segunda establece el problema de planeamiento del sistema de transmisión, la tercera realiza la formulación del sistema de potencia de acuerdo a cada uno de los modelos de optimización, la cuarta aplica la formulación del sistema de potencia en el software GAMS y la última seleccionar el costo óptimo. Resultados: Desde el análisis de costo optimo, el modelo DC y el modelo lineal disyuntivo presento el mayor costo optimo respecto al modelo de transporte y al modelo hibrido lineal. Conclusiones: Los modelos implementados presentaron desempeños equivalente en el sistema de prueba con el menor número de nodos, al aumentar el número de nodos el desempeño de los modelos no presenta similitud, por el contrario se identifican diferencias importantes en los resultados obtenidos, lo que permite caracterizar solver específicos de acuerdo al número de nodos.

The problem of the optimal placement and sizing of dynamic reactive power compensators in AC distribution networks is addressed in this paper from convex optimization. The exact mixed-integer nonlinear programming (MINLP) model is transformed into a mixed-integer second-order cone programming (MISOCP) model. The main advantage of the MISOCP formulation is the possibility of finding a global optimum with branch & cut combined with interior-point method due to the convex structure of the continuous part of the problem, i.e., the multi-period branch optimal power flow. The dynamic reactive power compensators are sized and dimensioned considering daily load curves and variable reactive power injections. Numerical validations are tested in the 33- and 69-bus test feeders using the CVX tool available for MATLAB with the MOSEK solver. These simulations demonstrate the effectiveness and robustness of the MISOCP approach when compared with the solution of the exact MINLP obtained in the GAMS software.

Contexto: El crecimiento acelerado de las ciudades y áreas rurales requiere de la adecuada expansión del sistema eléctrico de distribución para atender de manera eficiente, confiable y segura los requerimientos de energía eléctrica de usuarios comerciales, residenciales e industriales. Para atender a los diferentes usuarios de la red eléctrica, una metodología típica empleada por los operadores de red corresponde a la asignación óptima de los calibres de los conductores asociados a las rutas de distribución, teniendo en cuenta su costo de inversión y operación para un horizonte de planeación determinando. Método: Para resolver el problema de selección optima en redes de distribución trifásicas, se propone la aplicación de un algoritmo de optimización de la familia de las técnicas combinatoriales conocido como algoritmo metaheurístico de Newton (AMN). La principal ventaja del AMN es que emplea reglas de evolución basadas en la primera y segunda derivadas de la función objetivo, las cuales se aplican a cada individuo de la población. Además, las reglas de evolución del AMN hacen que este algoritmo tenga un balance adecuando entre la exploración y la explotación del espacio de soluciones a medida que avanza proceso iterativo. Resultados: Validaciones numéricas en dos sistemas de distribución trifásicos compuestos de 8 y 27 nodos con escenarios de operación balanceada y desbalanceada demuestran que el AMN alcanza la solución óptima reportada en la literatura para el sistema de 8 nodos y mejora los reportes científicos para el sistema de prueba de 27 nodos en ambos escenarios de prueba. Conclusiones: Los resultados obtenidos mediante la aplicación del AMN al problema de selección optima de conductores en sistemas de distribución demuestran la eficacia de la metodología de solución propuesta para problemas de programación no lineal entera mixta en ingeniería eléctrica con tamaños exponenciales del espacio de soluciones. Además, los resultados reportados para los sistemas de prueba de 8 y 27 nodos constituyen un punto de referencia para futuras investigaciones.