Precise modeling of induction motors is essential for high-performance industrial applications. Traditionally, equivalent circuit with constant parameters is derived from standardized locked-rotor and no-load tests or alternative methods. However, these models exhibit limitations when calculating the electrical and mechanical quantities developed by the motor at low speeds, leading to significant inaccuracies. This work presents an innovative methodology for estimating the equivalent circuit parameters of induction machines, considering the variation of rotor reactance and resistance with speed. Rather than aiming to identify the exact physical parameters, the method seeks to obtain parameter values that accurately reproduce the motor input and output variables over the entire speed range. The proposed approach is non-intrusive, relying exclusively on the manufacturer’s catalog data. The methodology consists of three steps: (i) application of an established method from the literature for traditional constant-parameter estimation; (ii) formulation of an optimization problem to address the full speed range; and (iii) a nonlinear combination of affine functions using switching functions, following the logic of Takagi-Sugeno fuzzy inference, to produce accessible mathematical expressions for the equivalent circuit under any speed/slip condition. To evaluate the effectiveness of the methodology, the torque and current curves obtained from the model are compared to data provided by manufacturers and established literature. Parameter estimation results for nine induction motors, obtained from different manufacturers, are reported. A mean error of 10^-2

This paper presents a straightforward approach for determining the total transfer capability (TTC) between two power system areas, incorporating security constraints. A multi-objective methodology is proposed to optimize the generator variables—active power generation and terminal voltage magnitude—to maximize the TTC and the minimum damping ratio, which is an index associated with small-signal stability, considering contingency scenarios and static constraints. The formulation is based on optimal power flow (OPF), and the multi-objective approach facilitates the identification of the conflicting relationship between the two objectives via the Pareto front. To solve the optimization problem, two metaheuristics are employed: NSGA-II and MOCS. Each individual, from the metaheuristic perspective, is evaluated using conventional power flow and modal analysis tools, implemented in MATLAB with the PSAT toolbox. The methodology is tested on the New England test system. A systematic comparison of Pareto fronts, computational time, and performance metrics is conducted to evaluate the methods. Nonlinear time-domain simulations confirm that some solutions obtained are feasible even under transient stability conditions.

Microgrids offer an innovative solution to modern energy challenges, providing enhanced reliability and resilience to distribution systems while accommodating the growing presence of distributed energy resources (DER). However, realizing the potential benefits of microgrids requires a comprehensive assessment of their complex technical and economic aspects. This paper introduces a dedicated techno-economic evaluation model for community microgrids (CMGs) while considering a consumer-centric internal market. Uncertainties arising from load variations and intermittent generation are addressed via Monte Carlo simulation (MCS). The proposed internal market model aligns with the Brazilian regulatory framework and enables currently unavailable innovations, such as a community internal market; however, the model can be adapted to the context of any system. We demonstrate the capabilities of the methodology by presenting a case study involving a real microgrid, where economic benefits are shared among all consumers. This study, derived from a research and development (R&D) project, underscores its practical relevance in advancing cleaner and more sustainable energy systems.

The integration of distributed energy resources (DERs) into power grids requires new methodologies to optimize and efficiently control distribution network parameters. This paper presents a multi-objective optimization approach to enhance the operation of distribution networks with high penetration of distributed generation (DG). The proposed method jointly optimizes the active power output of dispatchable DG units and the Volt-Var control parameters of photovoltaic (PV) inverters to minimize power losses and voltage unbalance, ensuring compliance with the 2% limit set by Brazilian regulations. The optimization problem is solved using NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOPSO (Multi-Objective Particle Swarm Optimization), with three-phase power flow analysis performed using OpenDSS integrated into a Python-based framework. Simulations on the IEEE 123-bus system demonstrate that coordinated control of intermittent and dispatchable sources improves voltage profiles, reduces system losses, and significantly mitigates voltage unbalance.

The electric distribution sector is facing significant challenges in integrating distributed energy resources (DERs), with most assessment methodologies being primarily tested on standard feeders. However, given the nature of electric distribution systems, each feeder is unique and constantly changing, emphasizing the need for customized and updated feeder models for local quantitative studies. These studies encompass network operation, expansion planning, and DER integration. In response to this context, this paper proposes a methodology for extracting and processing information from public data to model up-to-date electric feeders. As these feeder models represent real operating circuits, they can be utilized by utilities, investors, and consumers. The proposed approach involves processing data from the Brazilian utilities’ geographic database (BDGD, in Portuguese) to develop structured simulations of distribution feeders using OpenDSS software. The BDGD standards are established by the Brazilian National Electric Energy Agency (ANEEL). In order to validate the methodology, two case studies are presented evaluating the impacts of distributed generation on real feeders from the Brazilian utilities: Minas Gerais Electric Power Company (CEMIG) and Bahia State Electricity Company (Neoenergia COELBA).

Microgrids have emerged as a popular solution for electric energy distribution due to their reliability, sustainability, and growing accessibility. However, their implementation can be challenging, particularly due to regulatory and market issues. Building smaller-scale microgrids, also known as nanogrids, can present additional challenges, such as high investment costs that need to be justified by local demands. To address these challenges, this work proposes an economic feasibility assessment model that is applied to a real nanogrid under construction in the Brazilian electrical system, with electric vehicle charging stations as its main load. The model, which takes into account uncertainties, evaluates the economic viability of constructing a nanogrid using economic indicators estimated by the Monte Carlo simulation method, with the system operation represented by the OpenDSS software. The model also considers aspects of energy transactions within the net-metering paradigm, with energy compensation between the nanogrid and the main distribution network, and investigates how incentives can impact the viability of these microgrids.

This article addresses two main issues concerning the maximum loading point in three-phase distribution systems. Initially, a tool was developed using OpenDSS to generate PV curves tailored to the reality of unbalanced distribution systems. Subsequently, the impact of Volt/Var control implemented by smart inverters was investigated in photovoltaic systems connected to the network. The results demonstrate significant improvements in the voltage profile and an increase of about 17.6% in the maximum loading point for a 34 nodes network, highlighting the efficiency of reactive power injection controlled by voltage in photovoltaic systems.

Voltage imbalance can produce several negative impacts on the electric power distribution network, such as equipment deterioration and improper operation of the protection system. This work presents a multi-objective formulation for the phase reconfiguration problem in three-phase buses, aiming to minimize voltage imbalance and the number of interventions in the network. To solve the problem, we propose the Multi-Objective Heuristic Algorithm for Phase Switching (AHMTF), which leverages specific knowledge of the network along with an exhaustive search. For calculating unbalanced power flow, the OpenDSS software is used. The methodology is validated through simulations with phase switching in the modified and unbalanced IEEE 34-bus feeder. An analysis of the solutions obtained shows that the employed methodology can significantly reduce voltage imbalance and offer other operational benefits.

One of the challenges faced by Brazilian distribution utilities to enable the connection and operation of microgrids (MGs) is the absence of a solid set of technical standards in the country. An alternative has been to use and adapt existing standards applied to micro- and mini-distributed generation. In this context, this paper presents an analysis of the development status of norms, standards, and general requirements for the connection and operation of microgrids, as well as a proposal for the regulation and structuring of technical and operational requirements related to the implementation of microgrid projects. Some critical points highlighted in the paper include: the modes of operation, the minimum requirements for the different modes of operation, interoperability of systems, a conceptual model with attribution of responsible actors for the decentralized management of microgrids adapted to the institutional standards of the Brazilian sectorial model, a proposal for a standard connection structure considering the point of connection (PoC) implanted using multifunctional relay and recloser, procedures for technical feasibility assessment (operational studies) of MGs connection, and, finally, a discussion of operational issues of storage systems in a microgrid environment.

This paper proposes an Optimal Power Flow for defining the minimum load to be shed to maintain the steady-state frequency between allowable limits in islanded microgrids. The droop coefficients and setpoints (no-load voltage and frequency of each Distributed Generator) are optimized considering the limits of microgrid frequency, nodal voltages, and power generation (apparent and reactive powers). The approach is applied to a microgrid of 33 nodes, considering a 24 hours time-horizon. The found results point to the methodology effectiveness.

Distributed generation (DG), when renewable, is part of the necessary decarbonization of the electric energy sector. Although DG can provide benefits, it is known as well for its operational challenges. This work addresses the problem caused by DGs in distribution systems with meshed secondary circuits. In these kinds of networks, there is more than one medium-voltage (MV) primary feeder supplying a meshed low-voltage (LV) grid, and the distribution transformers in between are connected to network protectors (NPs) through their LV side. These NPs are adjusted to activate the circuit breaker when reverse power flow is detected, usually meaning there is a fault in the upstream feeder. Alternatively, DG penetration can lead to NP false tripping since it produces energy that can flow from LV to the MV side. Considering faults on MV and DG penetration, a set of simulations is carried out with two distribution networks to verify any patterns in the power flow reversion. A Brazilian 81-bus test system and the IEEE 390-bus are employed using the OpenDSS software. The found results indicate the high complexity of the problem, which does not have a simple solution. As DG grows, further work needs to be done to provide advances in this rising protection issue.

The costs of large storage systems have been decreasing consistently over the past decade. Thus, employing this technology to improve the operation and planning of electrical power systems is getting economically viable. In this work, having storage resources available, some possible benefits to the network are investigated considering photovoltaics distributed generation. Two battery storage management systems are presented and compared, aiming to reduce the substation's peak load and improve the voltage profile during 24 hours. The first method is based on defining threshold values of the substation imported power. Thus, those power limits trigger the charge or discharge mode on the battery asset. The second operation strategy relies on a multiobjective optimization formulation, to be solved by the Non-dominated Sorting Genetic Algorithm II (NSGA-II). All simulations are carried out on MATLAB and OpenDSS environments. Applying the methodology on the IEEE 34-bus test system point that benefits to the grid can be obtained by relatively simple management logic, like the charge/discharge trigger, based on substation power. However, improved gains are found when the problem is formulated and solved by an optimization approach.

This paper presents an optimization approach to determine the maximum hosting capacity of photovoltaic generation in unbalanced three-phase distribution systems considering two kinds of constraints: voltage magnitude and reverse power flow at the substation. The goal is to maximize the sum of the nominal power at the maximum power point of photovoltaic panels considering a set of operating points. The Particle Swarm Optimization method and the OpenDSS software are employed to solve the proposed approach. The IEEE 13 bus test system is used to evaluate the effectiveness of the proposed method.

This paper proposes an optimization approach to solve the Small-Signal Constrained Optimal Power Flow (SSOPF) in transmission systems. The main goal is to minimize the active power losses taking the minimum damping ratio in closed-loop operation as a small-signal stability constraint. The Particle Swarm Optimization (PSO) is used and its individuals are evaluated through a conventional power flow tool and a modal analysis tool for small-signal stability assessment. Results are obtained for the New England test system and validated through time-domain simulations.

Esse artigo soluciona o problema de alocação ótima de geradores distribuídos em microrredes ilhadas para minimização de perdas de potência ativa. Um Algoritmo Genético binário é empregado no qual cada indivíduo é avaliado por um Fluxo de Potência Ótimo (FPO). O FPO considera os limites de tensões nodais, gerações de potência e frequência, bem como o efeito de droop dos geradores distribuídos conectados à rede através de Inversores Fonte de Tensão (Voltage Source Inverters). A metodologia é avaliada em uma microrrede ilhada de 33 barras e os resultados são comparados com aqueles fornecidos por uma busca exaustiva.

This paper proposes a straightforward approach to determine the Total Transfer Capability (TTC) between two power system areas taking into account security constraints. The main goal is to adjust the generator's control variables (active generated power and terminal voltage) to maximize the TTC considering contingency scenarios and small-signal stability constraints (minimum damping ratio requirement). It is a challenging nonconvex and nonlinear optimization problem that is generally solved in the literature by methods based on approximations in which the computational complexity involved outweighs the accuracy. Here, conventional power flow and modal analysis tools are easily integrated into an optimization approach solved by six bioinspired optimization techniques. Simulations are conducted for the well-known New-England test system, and transient stability studies validate the obtained results.

O presente trabalho apresenta uma metodologia de otimização para a estimação de estados em sistemas de distribuição trifásicos desequilibrados em que as variáveis de estado são as demandas de potência e as gerações de potência das unidades de geração distribuída. São consideradas medidas fasoriais de tensão e corrente em pontos estratégicos do alimentador. Para a solução do problema, quatro metaheurísticas são empregadas e comparadas com um método numérico baseado em derivadas. Para avaliação das soluções das metaheurísticas, o software OpenDSS é adotado durante o processo de solução.

Este artigo apresenta o estudo comparativo entre duas metaheurísticas (Busca Harmônica Melhorada e Otimização Baseada em Enxame de Partículas) para o projeto ótimo e robusto de controladores do tipo Proporcional-Integral (PI) aplicados ao controle de frequência em sistemas interligados na presença de geradores eólicos. Considera-se vários cenários operativos para garantia de robustez e busca-se minimizar a Integral do Erro Quadrático com base em simulações no domínio do tempo. Um sistema interligado de três áreas é utilizado e é mostrado que a Busca Harmônica Melhorada é capaz de fornecer soluções de melhor qualidade e com baixa variabilidade em um tempo computacional reduzido.