Power distribution systems face continuous challenges from increased demand and lengthening of feeders, resulting in power loss augmentation and unacceptable voltage drops. Thus, to reduce technical ...losses and improve the voltage profile, common techniques such as reactive compensation, network reconfiguration, and placing of voltage regulators are employed. Distribution network reconfiguration (DNR) consists of modifying the system topology with the aim of minimizing power losses, enhancing voltage profile, and improving network reliability. Optimal placement of voltage regulators (OPVRs) improves the voltage profile and helps to reduce power losses. DNR and OPVRs are challenging optimization problems involving both integer and continuous decision variables. In this paper, a mixed-integer linear programming (MILP) model is presented to simultaneously solve the problems of DNR and OPVRs in radial distribution networks. The combined optimal DNR and OPVRs aim at both the minimization of power losses and the improvement of the voltage profile. This approach has not been reported in the specialized literature. The proposed MILP model may be solved through commercially available software, obtaining global optimal solutions with lower computational effort than metaheuristic techniques applied for the same purpose. Several tests were conducted on three benchmark distribution test systems to demonstrate the efficacy and applicability of the proposed approach.
This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm. ...Since the performance of a KELM depends on a proper parameter selection, the MVMO is used to optimize such task. In the proposed MVMO-KELM model the inputs and output are the magnitudes of voltage phasors and the VSM index, respectively. A Monte Carlo simulation was implemented to build a data base for the training and validation of the model. The data base considers different operative scenarios for three type of customers (residential commercial and industrial) as well as N-1 contingencies. The proposed MVMO-KELM model was validated with the IEEE 39 bus power system comparing its performance with a support vector machine (SVM) and an Artificial Neural Network (ANN) approach. Results evidenced a better performance of the proposed MVMO-KELM model when compared to such techniques. Furthermore, the higher robustness of the MVMO-KELM was also evidenced when considering noise in the input data.
The ever increasing presence of renewable distributed generation (DG) in microgrids is imposing new challenges in protection coordination. The high penetration of renewable DG enables microgrids to ...operate under different topologies, giving rise to bidirectional power flows and in consequence, rendering traditional coordination approaches inappropriate to guarantee network security. This paper proposes an approach for the optimal coordination of directional over-current relays (OCRs) in microgrids that integrate renewable DG and feature several operational modes. As a main contribution, the characteristic curves of directional OCRs are considered to be decision variables, instead of fixing a single type of curve for all relays as considered in previous works. The proposed approach allows for the selection of several IEC and IEEE curves which combination results in the best protection coordination. Several tests were carried out on an IEC benchmark microgrid in order to show the applicability of the proposed approach. Furthermore, a comparison with other coordination approaches evidenced that the proposed approach is able to find lower operation times and, at the same time, guarantee the suitable operation of protections under different condition faults and operational modes.
Amid growing concerns about climate change, electricity-powered transportation systems stand out as an opportunity to help in reducing fuel consumption. Electric vehicles (EVs) would connect to the ...grid using clean, renewable electricity; however, the interconnection between EVs and the grid brings about new challenges for traditional power systems. Plug-in hybrid EVs and plug-in EVs have started to become more prevalent in the system; therefore, their impacts and benefits are also of concern. Among these concerns is the detailed analysis of the impact that EVs may have on short-circuit levels in microgrid protection schemes. In this context, the main contribution of this paper is a detailed evaluation of the impact of EVs on the short-circuit levels and protection coordination schemes in microgrids. For this purpose, a methodology was proposed to measure the impact of EVs on the protection coordination schemes in microgrids using different evaluation indices. The proposed approach was validated on a benchmark IEC microgrid considering different operative scenarios that envisage several levels of EVs penetration. The results evidenced the applicability of the proposed approach and allows to conclude that the incorporation of EVs in microgids impacts the performance of the protection schemes, specifically with respect to short-circuit levels.
This paper presents a Multi-Period Optimal Power Flow (MOPF) modeling applied to the minimization of energy losses in Distribution Networks (DNs) considering the reactive power control of ...Photovoltaic Generation (PVG) that can be applied to both short-term and long-term operation planning. Depending on the PV Power Factor (PVpf) limitations, PVG may provide both active and reactive power. The optimal power factor control on the buses with PVG contributes to an economical and safe operation, minimizing losses and improving the voltage profile of the DN. The proposed MOPF was modeled in order to minimize active energy losses subject to grid constraints and PVpf limitations. The variations of loads and PVG were discretized hour by hour, composing a time horizon of 24 h for day-ahead planning; nonetheless, the methodology can be applied to any other time period, such as a month, year, etc., by simply having generation and load forecasts. To demonstrate the effectiveness and applicability of the proposed approach, various tests were carried out on 33-bus and 69-bus distribution test systems. The analyses considered the DN operating with PVG in four different cases: (a) PVpf fixed at 1.0; (b) PVpf fixed at 0.9 capacitive; (c) hourly PVpf optimization; and (d) optimization of PVpf for a single value. The results show that a single optimal adjustment of PVpf minimizes losses, improves voltage profile, and promotes safe operation, avoiding multiple PVpf adjustments during the operating time horizon. The algorithm is extremely fast, taking around 2 s to reach a solution.
Modern distribution systems and microgrids must deal with high levels of uncertainty in their planning and operation. These uncertainties are mainly due to variations in loads and distributed ...generation (DG) introduced by new technologies. This scenario brings new challenges to planners and system operators that need new tools to perform more assertive analyses of the grid state. This paper presents an optimization methodology capable of considering uncertainties in the optimal allocation and sizing problem of DG in distribution networks. The proposed methodology uses an interval power flow (IPF) that adds uncertainties to the combinatorial optimization problem in charge of sizing and allocating DG units in the network. Two metaheuristics were implemented for comparative purposes, namely, symbiotic organism search (SOS) and particle swarm optimization (PSO). The proposed methodology was implemented in Python® using as benchmark distribution systems the IEEE 33-bus and IEEE 69-bus test distribution networks. The objective function consists of minimizing technical losses and regulating network voltage levels. The results obtained from the proposed IPF on the tested networks are compatible with those obtained by the PPF, thus evidencing the robustness and applicability of the proposed method. For the solution of the optimization problem, the SOS metaheuristic proved to be robust, since it was able to find the best solutions (lowest losses) while keeping voltage levels within the predetermined range. On the other hand, the PSO metaheuristic showed less satisfactory results, since for all test systems, the solutions found were of lower quality than the ones found by the SOS.
Power system restoration must be accomplished as soon as possible after a blackout. In this process, available black-start (BS) units are used to provide cranking power to non-black-start (NBS) units ...so as to maximize the overall power system generation capacity. This procedure is known as the generation start-up problem, which is intrinsically combinatorial with complex non-linear constraints. This paper presents a new mixed integer linear programming (MILP) formulation for the generation start-up problem that integrates non-conventional renewable energy sources (NCRES) and battery energy storage systems (BESS). The main objective consists of determining an initial starting sequence for both BS and NBS units that would maximize the generation capacity of the system while meeting the non-served demand of the network. The nature of the proposed model leads to global optimal solutions, clearly outperforming heuristic and enumerative approaches, since the latter may take higher computational time while the former do not guarantee global optimal solutions. Several tests were carried out on the IEEE 39-bus test system considering BESS as well as wind and solar generation. The results showed the positive impact of NCRES in the restoration processes and evidenced the effectiveness and applicability of the proposed approach. It was found that including NCRES and BESS in the restoration process allows a reduction of 24.4% of the objective function compared to the classical restoration without these technologies.
The ever increasing active and reactive power demands, along with limited sources of generation and delays in transmission expansion projects, have led many power systems to operate near their ...voltage stability limits. In this context, voltage stability monitoring methodologies have become an important topic in power systems research. This paper presents a novel methodology for long-term voltage stability monitoring in power systems that exploits the feasibility of phasor-type information in order to estimate the long-term voltage stability status. The information regarding the current system condition is acquired through synchronized phasor measurements and the power system is divided in sub-areas for improving its supervision; then, an artificial intelligence approach based on kernel extreme learning machine is used for long-term voltage stability assessment. The proposed scheme allows foreseeing the voltage instability caused by limitations in reactive power transmission, and it also permits alerting when a system area experiences a deficit of reactive power from supply sources. The validation of the proposed method is performed on the 39-bus test system, obtaining feasible results. The tests confirmed that the proposed method works properly under different scenarios and system conditions, always ensuring proper voltage stability status results independently of its cause.
En este artículo se analiza la importancia de incluir la educación financiera en el proceso de formación de los profesionales de la ingeniería. La educación financiera se hace cada vez más relevante ...dado el número creciente de productos que ofrecen los mercados financieros y su efecto en el desarrollo económico de los estados y la calidad de vida de sus ciudadanos. Es evidente que una de las principales causas de las últimas crisis económicas mundiales ha sido la carencia de este tipo de educación. Se analiza el concepto de educación financiera, su relevancia en el mundo moderno y su relación con la ingeniería económica. Finalmente, se describen los principales aspectos que deben ser incluidos en los currículos de ingeniería y se recomiendan algunas medidas para la puesta en práctica de estos conceptos.
This paper presents a mathematical modeling approach by which to solve the power flow and state estimation problems in electric power systems through a mathematical programming language (AMPL). The ...main purpose of this work is to show the advantages of representing these problems through mathematical optimization models in AMPL, which is a modeling language extensively used in a wide range of research applications. The proposed mathematical optimization models allow for dealing with particular issues in that they are not usually considered in the classical approach for power flow and state estimation, such as solving the power flow problem considering reactive power limits in generation buses, as well as the treatment of errors in state estimation analysis. Furthermore, the linearized mathematical optimization models for both problems at hand are also presented and discussed. Several tests were carried out to validate the proposed optimization models, evidencing the applicability of the proposed approach.
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