Abstract The integration of stochastic renewable energy sources, like wind turbines and photovoltaic systems, into electrical grids introduces challenges to grid stability and reliability, leading to voltage and frequency deviations. This study addresses the fluctuation issue by examining hybrid energy storage systems combining batteries and supercapacitors. A novel power smoothing approach is proposed, involving two strategies: employing LSTM neural networks for short-term prediction of RES power profiles and optimizing HESS through charge/discharge cycle control using a machine learning-based algorithm. This paper also introduces the synergy of vanadium redox flow batteries and supercapacitor for efficient energy storage. The proposed approach is validated through experimental testing in a controlled microgrid setting. The evaluation demonstrates significant improvements, including a 74.2% reduction in power fluctuations and an enhanced smoothing quality evaluation index by up to 40%, surpassing conventional methods like moving average, ramp rate, and low pass filter. The contributions of this research encompass an advanced energy smoothing methodology, streamlined storage integration, and an enhanced energy quality framework for hybrid renewable energy systems.

Abstract The integration of photovoltaic generation into distribution networks enhances energy sustainability but poses challenges for voltage stability. This study analyses voltage stability in the Manabí distribution system using a static model with different levels of photovoltaic penetration. The experiments highlight the importance of voltage stability indices, determined using artificial neural networks with a 10-neuron structure in each hidden layer of the multilayer perceptron architecture. The scaled conjugate gradient training algorithm exhibits superior learning performance, achieving a mean square error of 5.6231E-05. The results confirm that voltage stability indices effectively determine the most resilient nodes for photovoltaic integration, with voltage variations ranging from 0.05% to 0.12% in distributed installations and from 0.04% to 0.05% in centralized locations. These findings validate the usefulness of voltage stability indices for assessing system stability and optimizing the placement of photovoltaic generation in distribution networks.

The integration of simultaneous distributed generation (DG) and capacitor allocation strategies has shown considerable promise in reducing power losses, mitigating voltage deviations, and lowering operational expenses within power systems. Achieving these benefits hinges on the optimal placement of DG units and shunt capacitors, which in turn depends on accurate modeling of electricity demand. However, such modeling is inherently challenging due to the variable nature of electrical loads, which fluctuate with voltage levels and are influenced by consumer types. Electrical loads are typically represented by a combination of constant-power, constant-current, and constant-impedance components. Establishing a clear relationship between these components and consumer categories is essential for precise load modeling. Despite its critical role, this aspect has been largely overlooked in existing studies on DG and capacitor placement. This article presents a novel mathematical framework that links load components to consumer types, thereby enhancing the flexibility and effectiveness of DG and capacitor placement strategies. The proposed formulations aim to improve system performance and economic efficiency, offering a significant advancement in the optimization of power systems.

Power system optimization may involve reducing network losses, minimizing voltage deviations, decreasing line failures, and lowering maintenance costs. Achieving these objectives is possible through the optimal reconfiguration of distribution networks. Line switching is a key method for modifying the distribution network configuration, aiming to minimize losses, improve voltage stability, reduce fault currents, and implement cost-effective maintenance procedures. The power of load, a crucial factor in this process, requires more accurate modeling of the demand side to depict the behavior of electricity consumers. However, load fluctuates with changes in voltage magnitude, and this fluctuation is influenced by the type of consumer. Additionally, load is characterized by its impedance, current, and power. Consequently, identifying the relationship between these elements and the type of load is pivotal, especially when specific load components are accessible. This critical aspect has largely been unexplored in related studies on reconfiguration. Therefore, this article addresses this gap by mathematically formulating the correlation between demand components and load categories. The formulations proposed in this article contribute to more efficient and flexible reconfiguration frameworks, enhancing our understanding about the intricate dynamics of distribution system reconfiguration.

Abstract The interest in incorporating environmentally friendly and renewable sources of energy, like photovoltaic (PV) technology, into electricity grids has grown significantly. These sources offer benefits, such as reduced power losses and improved voltage stability. To optimize these advantages, it is essential to determine optimal placement and management of these energy resources. This paper proposes an Improved RUNge–Kutta optimizer (IRUN) for allocating PV-based distributed generations (DGs) and Battery Energy Storage (BES) in distribution networks. IRUN utilizes three strategies to avoid local optima and enhance exploration and exploitation phases: a non-linear operator for smoother transitions, a Chaotic Local Search for thorough exploration, and diverse solution updates for refinement. The efficacy of IRUN is evaluated using 10 benchmark functions from the CEC’20 test suite, followed by statistical analysis. Next, IRUN is used to optimize the allocation of PVDG and BES to minimize energy losses in two standard IEEE distribution networks. The optimization problem is divided into two stages. In the first stage, the optimal size and the location of PV systems are calculated to meet peak load demand. In the second stage, considering time-varying load demand and intermittent PV generation, effective energy management of BES is employed. The effectiveness of IRUN is compared against the original RUN and other well-known optimization algorithms through simulation results. The comprehensive analysis demonstrates that IRUN outperforms the compared algorithms, making it a leading solution for optimizing PV distributed generation and BES allocation in distribution networks and the results show that the energy loss reduction reaches 63.54% and 68.19% when using PVand BES in IEEE 33-bus and IEEE 69 bus respectively.

Abstract In this study, an improved bald eagle search optimization algorithm (IBES) is utilized to develop home energy management systems for smart homes. This research is crucial for energy field researchers who are interested in optimizing energy consumption. The primary objective is to optimally manage load demand, reduce the average peak ratio, lower electricity bills, and enhance user comfort. To accomplish this goal, the load conversion strategy is used to coordinate household appliances and manage the home power system effectively. This approach aims to minimize peak–average ratios and electricity costs while ensuring consumer convenience. To minimize electricity bills, the study schedules the consumer’s daily activities based on actual time and next day’s energy demand. Furthermore, a fitness criterion is used to balance the load between off-peak and on-peak hours. The scheduler is designed to achieve an optimal device on/off state that minimizes device waiting time by coordinating household appliances in real time. To address the background problem of real-time rescheduling, dynamic programming is employed. The study evaluates the modified algorithm’s performance using three pricing strategies: critical peak pricing, real-time pricing, and time of use. The modified IBES technique is utilized to achieve the specified objectives of minimizing the electricity bill, reducing the peak–average ratio, and enhancing user convenience.

Abstract The concept of interconnected multi-microgrids (MMGs) is presented as a promising solution for the improvement in the operation, control, and economic performance of the distribution networks. The energy management of the MMGs is a strenuous and challenging task, especially with the integration of renewable energy resources (RERs) and variation in the loading due to the intermittency of these resources and the stochastic nature of the load demand. In this regard, the energy management of the MMGs is optimized with optimal inclusion of a hybrid system consisting of a photovoltaic (PV) and a wind turbine (WT)-based distributed generation (DGs) under uncertainties of the generated powers and the load variation. A modified Capuchin Search Algorithm (MCapSA) is presented and applied for the energy management of the MMGs. The MCapSA is based on enhancing the searching abilities of the standard Capuchin Search Algorithm (CapSA) using three improvement strategies including the quasi-oppositional-based learning (QOBL), the random movement-based Levy flight distribution, and the exploitation mechanism of the prairie dogs in the prairie dog optimization (PDO). The optimized function is a multi-objective function that comprises of the cost and the voltage deviation reduction along with stability enhancement. The effectiveness of the proposed technique is verified on standard benchmark functions and the obtained results. Then, the proposed method is used for energy management of IEEE 33-bus and 69-bus MMGs at uncertainties conation. The results depict that the energy management with inclusion of WTs and PVs using the proposed technique can reduce the cost and summation of the VD by 46.41% and 62.54%, and the VSI is enhanced by 15.1406% for the first MMG. Likewise, for the second MMG, the cost and summation of the VD are reduced by 44.19% and 39.70%, and the VSI is enhanced by 4.49%.

Abstract Integrating distributed generations (DGs) into the radial distribution system (RDS) are becoming more crucial to capture the benefits of these DGs. However, the non-optimal integration of renewable DGs and shunt capacitors may lead to several operational challenges in distribution systems, including high energy losses, poor voltage quality, reverse power flow, and lower voltage stability. Therefore, in this paper, the multi-objective optimization problem is expressed with precisely selected three conflicting goals, incorporating the reduction in both power loss and voltage deviation and improvement of voltage stability. A new index for voltage deviation called root mean square voltage is suggested. The proposed multi-objective problems are addressed using two freshly metaheuristic techniques for optimal sitting and sizing multiple SCs and renewable DGs with unity and optimally power factors into RDS, presuming several voltage-dependent load models. These optimization techniques are the multi-objective thermal exchange optimization (MOTEO) and the multi-objective Lichtenberg algorithm (MOLA), which are regarded as being physics-inspired techniques. The MOLA is inspired by the physical phenomena of lightning storms and Lichtenberg figures (LF), while the MOTEO is developed based on the concept of Newtonian cooling law. The MOLA as a hybrid algorithm differs from many in the literature since it combines the population and trajectory-based search approaches. Further, the developed methodology is implemented on the IEEE 69-bus distribution network during several optimization scenarios, such as bi- and tri-objective problems. The fetched simulation outcomes confirmed the superiority of the MOTEO algorithm in achieving accurate non-dominated solutions with fewer outliers and standard deviation among all studied metrics.

Abstract In order to assure accurate modelling, this study presents a new technique for appropriately modelling and simulating a proton exchange membrane fuel cell (PEMFC) system. The PEMFC is a cleaner and more sustainable energy source as compared to fossil fuels. The fundamental idea is to minimize the sum of squared error (SSE) between the estimated and measured output voltage for the Ballard Mark V model in order to identify the model parameters of PEMFC stacks as efficiently as possible using a newly developed meta-heuristic called enhanced efficient optimization algorithm (EINFO). The proposed optimizer is considered an enhanced version of the original INFO algorithm. By balancing the exploration and exploitation phases better, the EINFO algorithm is intended to improve the performance of the original INFO approach and prevent local optima. The new method was tested on 23 benchmark functions and compared to the original INFO algorithm as well as other recently evolved optimizers. The algorithm is examined and compared with some literature meta-heuristics, including the particle swarm optimization, sine cosine algorithm, dragonfly algorithm, atom search optimization, Harris hawks optimization, and efficient optimization algorithm, using 50 independent runs, in terms of convergence speed and least SSE. When compared to other methods, the final findings show that, the suggested technique achieves the fastest convergence speed.

SSSC is an important member of Flexible AC transmission Systems (FACTS) device family. It is considered to be a superior series FACTS device, as it has the ability to control the flow of both active and reactive power in a transmission line. Therefore, it is important to have a model for this device to facilitate studies involving it. This paper presents a simple SSSC model in NEPLAN power system analysis software, where it mainly depends on power injection method. This model is developed so that it can be utilized in studies concerned with SSSC in NEPLAN software, as the software suffers a lack of such a model. This model is tested on the standard IEEE 30 bus and IEEE 14-bus systems in different conditions to ensure its performance, quality in various load flow calculations and effectivity in NEPLAN software.

This paper describes and evaluates an adaptive neuro-fuzzy inference system (ANFIS)-based energy management system (EMS) of a grid-connected hybrid system. It presents a wind turbine (WT) and photovoltaic (PV) solar panels as primary energy sources, and an energy storage system (ESS) based on hydrogen (fuel cell -FC-, hydrogen tank and electrolyzer) and battery. All of the energy sources use dc/dc power converters in order to connect them to a central DC bus. An ANFIS-based supervisory control system determines the power that must be generated by/stored in the hydrogen and battery, taking into account the power demanded by the grid, the available power, the hydrogen tank level and the state-of-charge (SOC) of the battery. Furthermore, an ANFIS-based control is applied to the three-phase inverter, which connects the hybrid system to grid. Otherwise, this new EMS is compared with a classical EMS composed of state-based supervisory control system based on states and inverter control system based on PI controllers. Dynamic simulations demonstrate the right performance of the ANFIS-based EMS for the hybrid system under study and the better performance with respect to the classical EMS.

This paper evaluates a hybrid powertrain based on fuel cell (FC), battery, and supercapacitor (SC) for the “Urbos 3” tramway, which currently operates powered by SC in the city of Zaragoza, Spain. Due to the dynamic limitations of the main energy source, a proton-exchange-membrane (PEM) FC, other energy secondary sources (ESSs), battery and SC, are needed to supply the vehicle power demand. Moreover, these energy sources allow the energy recovery during regenerative braking. The different sources are connected to a dc bus through dc-dc converters which adapt their variable voltages to the bus voltage and allow the control of energy flow between the sources and the load. The components of the hybrid tramway have been modeled in Matlab/Simulink and are based on commercially available devices. The energy management system used in this work to achieve a proper operation of the energy sources of the hybrid powertrain is based on predictive control. Simulations for the real cycle of the tramway show the suitability of the proposed powertrain and control strategy.

One of the main problems related with the design of e-learning is the current composition approaches do not support “personalized-learning”, that is, not take into account the difference in the prior knowledge of the learner and his learning ability. In order to provide solution for this problem, various e-course composition approaches have been proposed to use various techniques like Genetic Algorithm and Particle swarm optimization. This paper proposes an improved personalized e-course composition approach using shuffled frog-leaping algorithm (SFLA). The results of the simulations performed demonstrate that the proposed approach is a good solution to the problem raised. In addition, the method is compared with genetic algorithms and particle swarm optimization.

Six-phase matrix converters allow an all-silicon solution to the problem of changing AC power from one frequency to another. A simple model is purposed to simulate the power circuit, as well as the filters. The power semiconductors are modeled as ideal bidirectional switches and the matrix converter is controlled using a direct transfer function approach. This paper shows that it is possible to use the six-phase matrix converter to increase the maximum voltage gain.

In this paper, authors present a new design method for pulse width modulation inverters in microturbines by using a multiobjective genetic algorithm. The design problem is converted to an equivalent optimization problem, and then a multiobjective genetic algorithm is adopted to find a solution. The genetic algorithm is proposed to design a fuzzy controller. In this GA approach, an individual is constructed to represent the fuzzy controller. Multiobjective genetic algorithm confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This optimal fuzzy controller is suitable for the specific harmonic elimination PWM technology, as demonstrated by the examples given in this paper

To determine the potential impacts of micro-turbines on future distribution system, dynamic models of micro-turbines should be created, reduced in order, and scattered throughout test feeders. This paper presents the implementation of an efficient method for computing low order linear system models of micro-turbines from time domain simulations. The method is the Box-Jenkins algorithm for calculating the transfer function of a linear system from samples of its input and output.